161 lines
6.6 KiB
Python
161 lines
6.6 KiB
Python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team.
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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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"""
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Processor class for Nougat.
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"""
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from typing import Dict, List, Optional, Union
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from transformers.tokenization_utils_base import PreTokenizedInput, TextInput, TruncationStrategy
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from ...processing_utils import ProcessorMixin
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from ...utils import PaddingStrategy, TensorType
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class NougatProcessor(ProcessorMixin):
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r"""
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Constructs a Nougat processor which wraps a Nougat image processor and a Nougat tokenizer into a single processor.
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[`NougatProcessor`] offers all the functionalities of [`NougatImageProcessor`] and [`NougatTokenizerFast`]. See the
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[`~NougatProcessor.__call__`] and [`~NougatProcessor.decode`] for more information.
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Args:
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image_processor ([`NougatImageProcessor`]):
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An instance of [`NougatImageProcessor`]. The image processor is a required input.
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tokenizer ([`NougatTokenizerFast`]):
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An instance of [`NougatTokenizerFast`]. The tokenizer is a required input.
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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__(self, image_processor, tokenizer):
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super().__init__(image_processor, tokenizer)
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self.current_processor = self.image_processor
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def __call__(
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self,
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images=None,
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text=None,
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do_crop_margin: bool = None,
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do_resize: bool = None,
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size: Dict[str, int] = None,
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resample: "PILImageResampling" = None, # noqa: F821
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do_thumbnail: bool = None,
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do_align_long_axis: bool = None,
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do_pad: bool = None,
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do_rescale: bool = None,
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rescale_factor: Union[int, float] = None,
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do_normalize: bool = None,
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image_mean: Optional[Union[float, List[float]]] = None,
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image_std: Optional[Union[float, List[float]]] = None,
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data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821
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input_data_format: Optional[Union[str, "ChannelDimension"]] = None, # noqa: F821
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text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
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text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
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text_pair_target: Optional[
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Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]
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] = None,
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add_special_tokens: bool = True,
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padding: Union[bool, str, PaddingStrategy] = False,
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truncation: Union[bool, str, TruncationStrategy] = None,
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max_length: Optional[int] = None,
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stride: int = 0,
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is_split_into_words: bool = False,
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pad_to_multiple_of: Optional[int] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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return_token_type_ids: Optional[bool] = None,
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return_attention_mask: Optional[bool] = None,
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return_overflowing_tokens: bool = False,
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return_special_tokens_mask: bool = False,
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return_offsets_mapping: bool = False,
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return_length: bool = False,
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verbose: bool = True,
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):
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if images is None and text is None:
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raise ValueError("You need to specify either an `images` or `text` input to process.")
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if images is not None:
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inputs = self.image_processor(
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images,
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do_crop_margin=do_crop_margin,
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do_resize=do_resize,
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size=size,
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resample=resample,
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do_thumbnail=do_thumbnail,
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do_align_long_axis=do_align_long_axis,
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do_pad=do_pad,
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do_rescale=do_rescale,
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rescale_factor=rescale_factor,
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do_normalize=do_normalize,
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image_mean=image_mean,
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image_std=image_std,
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return_tensors=return_tensors,
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data_format=data_format,
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input_data_format=input_data_format,
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)
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if text is not None:
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encodings = self.tokenizer(
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text,
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text_pair=text_pair,
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text_target=text_target,
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text_pair_target=text_pair_target,
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add_special_tokens=add_special_tokens,
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padding=padding,
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truncation=truncation,
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max_length=max_length,
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stride=stride,
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is_split_into_words=is_split_into_words,
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pad_to_multiple_of=pad_to_multiple_of,
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return_tensors=return_tensors,
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return_token_type_ids=return_token_type_ids,
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return_attention_mask=return_attention_mask,
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return_overflowing_tokens=return_overflowing_tokens,
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return_special_tokens_mask=return_special_tokens_mask,
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return_offsets_mapping=return_offsets_mapping,
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return_length=return_length,
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verbose=verbose,
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)
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if text is None:
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return inputs
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elif images is None:
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return encodings
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else:
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inputs["labels"] = encodings["input_ids"]
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return inputs
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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 NougatTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
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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 NougatTokenizer'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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def post_process_generation(self, *args, **kwargs):
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"""
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This method forwards all its arguments to NougatTokenizer's [`~PreTrainedTokenizer.post_process_generation`].
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Please refer to the docstring of this method for more information.
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"""
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return self.tokenizer.post_process_generation(*args, **kwargs)
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