

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase       ="""LayoutLMv2ImageProcessor"""
							__UpperCAmelCase       =("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")




							def __init__( self					,						_A=None					,						_A=None					,						**_A							):
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'					,						_A					,						)
																	__lowerCAmelCase					=							kwargs.pop('feature_extractor'							)

												__lowerCAmelCase					=							image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('You need to specify an `image_processor`.'							)
												if tokenizer is None:
																	raise ValueError('You need to specify a `tokenizer`.'							)

												super().__init__(_A					,						_A							)




							def __call__( self					,						_A					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						_A = True					,						_A = False					,						_A = None					,						_A = None					,						_A = 0					,						_A = None					,						_A = None					,						_A = None					,						_A = False					,						_A = False					,						_A = False					,						_A = False					,						_A = True					,						_A = None					,						**_A					,						):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																	raise ValueError(
																	    'You cannot provide bounding boxes '
																	    'if you initialized the image processor with apply_ocr set to True.'							)

												if self.image_processor.apply_ocr and (word_labels is not None):
																	raise ValueError(
																	    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'							)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																	raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'							)

												# first, apply the image processor
												__lowerCAmelCase					=							self.image_processor(images=_A					,						return_tensors=_A							)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																	if isinstance(_A					,						_A							):
																						__lowerCAmelCase					=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
																	__lowerCAmelCase					=							features['words']

												__lowerCAmelCase					=							self.tokenizer(
												    text=text if text is not None else features['words']					,						text_pair=text_pair if text_pair is not None else None					,						boxes=boxes if boxes is not None else features['boxes']					,						word_labels=_A					,						add_special_tokens=_A					,						padding=_A					,						truncation=_A					,						max_length=_A					,						stride=_A					,						pad_to_multiple_of=_A					,						return_token_type_ids=_A					,						return_attention_mask=_A					,						return_overflowing_tokens=_A					,						return_special_tokens_mask=_A					,						return_offsets_mapping=_A					,						return_length=_A					,						verbose=_A					,						return_tensors=_A					,						**_A					,						)

												# add pixel values
												__lowerCAmelCase					=							features.pop('pixel_values'							)
												if return_overflowing_tokens is True:
																	__lowerCAmelCase					=							self.get_overflowing_images(_A					,						encoded_inputs['overflow_to_sample_mapping']							)
												__lowerCAmelCase					=							images

												return encoded_inputs




							def        A__  ( self					,						_A					,						_A							):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												__lowerCAmelCase					=							[]
												for sample_idx in overflow_to_sample_mapping:
																	images_with_overflow.append(images[sample_idx]							)

												if len(_A							) != len(_A							):
																	raise ValueError(
																	    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
																	    F""" {len(_A							)} and {len(_A							)}"""							)

												return images_with_overflow




							def        A__  ( self					,						*_A					,						**_A							):
												return self.tokenizer.batch_decode(*_A					,						**_A							)




							def        A__  ( self					,						*_A					,						**_A							):
												return self.tokenizer.decode(*_A					,						**_A							)




							@property
							def        A__  ( self							):
												return ["input_ids", "bbox", "attention_mask", "image"]




							@property
							def        A__  ( self							):
												warnings.warn(
												    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,						_A					,						)
												return self.image_processor_class




							@property
							def        A__  ( self							):
												warnings.warn(
												    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,						_A					,						)
												return self.image_processor









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""facebook/bart-large-mnli"""
							__UpperCAmelCase       =(
							    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
							    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
							    """It returns the most likely label in the list of provided `labels` for the input text."""
							)
							__UpperCAmelCase       ="""text_classifier"""
							__UpperCAmelCase       =AutoTokenizer
							__UpperCAmelCase       =AutoModelForSequenceClassification

							__UpperCAmelCase       =["""text""", ["""text"""]]
							__UpperCAmelCase       =["""text"""]




							def        A__  ( self							):
												super().setup()
												__lowerCAmelCase					=							self.model.config
												__lowerCAmelCase					=							-1
												for idx, label in config.idalabel.items():
																	if label.lower().startswith('entail'							):
																						__lowerCAmelCase					=							int(_A							)
												if self.entailment_id == -1:
																	raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'							)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






