258 lines
16 KiB
Python
258 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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from transformers.processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
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from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
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from ...image_processing_utils import BatchFeature
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from ...image_utils import ImageInput, make_flat_list_of_images
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class Llama4ImagesKwargs(ImagesKwargs, total=False):
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max_patches: Optional[int]
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resize_to_max_canvas: Optional[bool]
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class Llama4ProcessorKwargs(ProcessingKwargs, total=False):
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images_kwargs: Llama4ImagesKwargs
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_defaults = {
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"text_kwargs": {
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"padding_side": "left",
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},
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}
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chat_template = "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %} \n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- else %}\n {#- FIXME: The processor requires an array, always. #}\n {%- set system_message = messages[0]['content'][0]['text']|trim %}\n {%- endif %}\n {%- set messages = messages[1:] %}\n {%- set user_supplied_system_message = true %}\n{%- else %}\n {%- set system_message = \"\" %}\n {%- set user_supplied_system_message = false %}\n{%- endif %}\n\n{#- System message if the user supplied one #}\n{%- if user_supplied_system_message %}\n {{- \"<|header_start|>system<|header_end|>\n\n\" }}\n {%- if tools is not none %}\n {{- \"Environment: ipython\n\" }}\n {%- endif %}\n {%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {%- endif %}\n {{- system_message }}\n {{- \"<|eot|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|header_start|>user<|header_end|>\n\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\n\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|header_start|>' + message['role'] + '<|header_end|>\n\n' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- elif 'tool_calls' in message and message.tool_calls|length > 0 %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' -}}\n {{- '<|python_start|>' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- '<|python_end|>' }}\n {%- for tool_call in message.tool_calls %}\n {{- '{\"name\": \"' + tool_call.function.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.function.arguments | tojson }}\n {{- \"}\" }}\n {%- endfor %}\n {{- \"<|eot|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|header_start|>ipython<|header_end|>\n\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' }}\n{%- endif %}\n"
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class Llama4Processor(ProcessorMixin):
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r"""
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Constructs a Llama4 processor which wraps a [`AutoImageProcessor`] and
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[`PretrainedTokenizerFast`] tokenizer into a single processor that inherits both the image processor and
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tokenizer functionalities. See the [`~Llama4Processor.__call__`] and [`~Llama4Processor.decode`] for more information.
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Args:
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image_processor ([`AutoImageProcessor`], *optional*):
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The image processor is a required input.
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tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], *optional*):
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The tokenizer is a required input.
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patch_size (`int`, *optional*, defaults to 28):
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The size of image patches for tokenization.
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img_size (`int`, *optional*, defaults to 364):
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The size of the image to be tokenized. This should correspond to the size given to the image processor.
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image_token (`str`, *optional*, defaults to `"<|image|>"`):
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The token to be used to represent an image in the text.
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downsample_factor (`int`, *optional*, defaults to 1):
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The factor by which to scale the patch size.
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start_of_img_token (`str`, *optional*, defaults to `"<|START_OF_IMG|>"`):
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The token to be used to represent the start of an image in the text.
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end_of_img_token (`str`, *optional*, defaults to `"<|END_OF_IMG|>"`):
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The token to be used to represent the end of an image in the text.
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img_patch_token (`str`, *optional*, defaults to `"<|IMG_PATCH|>"`):
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The token to be used to represent an image patch in the text.
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img_line_break_token (`str`, *optional*, defaults to `"<|IMG_LINE_BREAK|>"`):
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The token to be used to represent a line break in the text.
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tile_token (`str`, *optional*, defaults to `"TILE"`):
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The token to be used to represent an image patch in the text.
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tile_global_token (`str`, *optional*, defaults to `"TILE_GLOBAL"`):
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The token to be used to represent the cover image in the text.
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chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
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in a chat into a tokenizable string.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "AutoImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(
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self,
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image_processor=None,
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tokenizer=None,
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patch_size: int = 14,
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pixel_shuffle_ratio: float = 0.5,
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fake_image_token="<|image|>",
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image_token="<|image|>",
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start_of_image_token="<|image_start|>",
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end_of_image_token="<|image_end|>",
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patch_token="<|patch|>",
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tile_x_separator_token="<|tile_x_separator|>",
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tile_y_separator_token="<|tile_y_separator|>",
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chat_template=chat_template,
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**kwargs,
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):
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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self.downsample_ratio = int(round(1.0 / (pixel_shuffle_ratio**2)))
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self.patch_size = patch_size
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self.fake_image_token = fake_image_token
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self.image_token = image_token
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self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
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self.start_of_img_token = start_of_image_token
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self.end_of_img_token = end_of_image_token
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self.img_patch_token = patch_token
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self.tile_token = tile_x_separator_token
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self.tile_global_token = tile_y_separator_token
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def _prompt_split_image(self, aspect_ratio, num_patches_per_chunk):
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"""
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Create a structured string representation of image tokens
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Args:
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num_patches: Number of patches in the image
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Returns:
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String with appropriate image tokens
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"""
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img_string = "<|image_start|>"
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ratio_h, ratio_w = aspect_ratio
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if ratio_h * ratio_w > 1:
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for yy in range(ratio_h):
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for xx in range(ratio_w):
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img_string += "<|patch|>" * num_patches_per_chunk
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if xx < ratio_w - 1:
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img_string += "<|tile_x_separator|>"
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img_string += "<|tile_y_separator|>"
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img_string += "<|image|>"
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img_string += "<|patch|>" * num_patches_per_chunk
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img_string += "<|image_end|>"
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return img_string
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def __call__(
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self,
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images: Optional[ImageInput] = None,
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text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
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audio=None,
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videos=None,
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**kwargs: Unpack[Llama4ProcessorKwargs],
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) -> BatchFeature:
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"""
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Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
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and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] to encode the text.
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To prepare the vision inputs, this method forwards the `images` and `kwargs` arguments to
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Llama4ImageProcessor's [`~Llama4ImageProcessor.__call__`] if `images` is not `None`.
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Args:
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images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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tensor. Both channels-first and channels-last formats are supported.
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text (`str`, `list[str]`, `list[list[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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return_tensors (`str` or [`~utils.TensorType`], *optional*):
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If set, will return tensors of a particular framework. Acceptable values are:
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- `'tf'`: Return TensorFlow `tf.constant` objects.
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- `'pt'`: Return PyTorch `torch.Tensor` objects.
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- `'np'`: Return NumPy `np.ndarray` objects.
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- `'jax'`: Return JAX `jnp.ndarray` objects.
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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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"""
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if text is None:
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raise ValueError("You have to specify text.")
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output_kwargs = self._merge_kwargs(
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Llama4ProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if not isinstance(text, (list, tuple)):
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text = [text]
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# Process images
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image_inputs = {}
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if images is not None:
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images = make_flat_list_of_images(images)
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image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
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image_height, image_width = image_inputs["pixel_values"][0].shape[-2:]
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num_patches_per_chunk = int(
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(image_height // self.patch_size) * (image_width // self.patch_size) // self.downsample_ratio
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)
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aspect_ratios = image_inputs.pop("aspect_ratios")
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total_placeholders = sum(prompt.count(self.fake_image_token) for prompt in text)
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if total_placeholders != len(images):
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raise ValueError(
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f"Found {total_placeholders} placeholders across the batch, "
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f"but have {len(images)} flattened images."
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)
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image_index = 0
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processed_text = []
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for prompt in text:
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placeholder_count = prompt.count(self.fake_image_token)
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if placeholder_count == 0:
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# do nothing if there is no image
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processed_text.append(prompt)
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continue
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prompt_splits = prompt.split(self.fake_image_token)
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new_prompt = []
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for local_image_index, split_part in enumerate(prompt_splits):
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new_prompt.append(split_part)
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if local_image_index < placeholder_count:
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tokens_for_this_image = self._prompt_split_image(
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aspect_ratios[image_index], num_patches_per_chunk
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)
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image_index += 1
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new_prompt.append(tokens_for_this_image)
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processed_text.append("".join(new_prompt))
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if image_index != len(images):
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raise ValueError("Number of image placeholders in the prompt does not match the number of images.")
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text = processed_text
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
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self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
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return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
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the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(tokenizer_input_names) + list(image_processor_input_names)
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__all__ = ["Llama4Processor"]
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