




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       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=["image_processor", "tokenizer"]
						lowerCAmelCase__ 			="LayoutLMv2ImageProcessor"
						lowerCAmelCase__ 			=("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")



						def __init__( self  :      Optional[int]				,       snake_case__  :      Optional[int]=None				,       snake_case__  :      List[str]=None				,       **snake_case__  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'				,       snake_case__				,       )
										SCREAMING_SNAKE_CASE_							=       kwargs.pop('feature_extractor'     )

								SCREAMING_SNAKE_CASE_							=       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__(snake_case__				,       snake_case__     )



						def __call__( self  :      Dict				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,       snake_case__  :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,       snake_case__  :      Union[List[List[int]], List[List[List[int]]]] = None				,       snake_case__  :      Optional[Union[List[int], List[List[int]]]] = None				,       snake_case__  :      bool = True				,       snake_case__  :      Union[bool, str, PaddingStrategy] = False				,       snake_case__  :      Union[bool, str, TruncationStrategy] = False				,       snake_case__  :      Optional[int] = None				,       snake_case__  :      int = 0				,       snake_case__  :      Optional[int] = None				,       snake_case__  :      Optional[bool] = None				,       snake_case__  :      Optional[bool] = None				,       snake_case__  :      bool = False				,       snake_case__  :      bool = False				,       snake_case__  :      bool = False				,       snake_case__  :      bool = False				,       snake_case__  :      bool = True				,       snake_case__  :      Optional[Union[str, TensorType]] = None				,       **snake_case__  :      List[Any]				,       ):

								"""simple docstring"""

								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
								SCREAMING_SNAKE_CASE_							=       self.image_processor(images=snake_case__				,       return_tensors=snake_case__     )

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

								SCREAMING_SNAKE_CASE_							=       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=snake_case__				,       add_special_tokens=snake_case__				,       padding=snake_case__				,       truncation=snake_case__				,       max_length=snake_case__				,       stride=snake_case__				,       pad_to_multiple_of=snake_case__				,       return_token_type_ids=snake_case__				,       return_attention_mask=snake_case__				,       return_overflowing_tokens=snake_case__				,       return_special_tokens_mask=snake_case__				,       return_offsets_mapping=snake_case__				,       return_length=snake_case__				,       verbose=snake_case__				,       return_tensors=snake_case__				,       **snake_case__				,       )

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

								return encoded_inputs



						def   __a				( self  :      List[str]				,       snake_case__  :      Any				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       []
								for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]     )

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

								return images_with_overflow



						def   __a				( self  :      str				,       *snake_case__  :      Optional[Any]				,       **snake_case__  :      List[Any]     ):

								"""simple docstring"""

								return self.tokenizer.batch_decode(*snake_case__				,       **snake_case__     )



						def   __a				( self  :      Union[str, Any]				,       *snake_case__  :      Union[str, Any]				,       **snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								return self.tokenizer.decode(*snake_case__				,       **snake_case__     )



						@property
						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



						@property
						def   __a				( self  :      Any     ):

								"""simple docstring"""

								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,       snake_case__				,       )
								return self.image_processor_class






						@property
						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,       snake_case__				,       )
								return self.image_processor





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE:    List[str]											=   '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''



class       lowercase_		(unittest.TestCase       ,  SCREAMING_SNAKE_CASE__   ):



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'     )
								self.tool.setup()
								SCREAMING_SNAKE_CASE_							=       load_tool('summarization'				,       remote=snake_case__     )



						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )






						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.remote_tool(text=snake_case__     )
								self.assertEqual(
								    snake_case__				,       'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'				,       )