

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       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				str     =    ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    '''LayoutLMv3ImageProcessor'''
				__SCREAMING_SNAKE_CASE      :				Tuple     =    ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')




				def __init__(      self   ,     snake_case=None   ,     snake_case=None   ,     **snake_case ):
											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   ,     )
																		snake_case_           =					kwargs.pop('feature_extractor' )

											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   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = True   ,     snake_case = False   ,     snake_case = None   ,     snake_case = None   ,     snake_case = 0   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = False   ,     snake_case = False   ,     snake_case = False   ,     snake_case = False   ,     snake_case = True   ,     snake_case = None   ,     **snake_case   ,     ):
											# 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.' )

											# first, apply the image processor
											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 ):
																									snake_case_           =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
																		snake_case_           =					features['words']

											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
											snake_case_           =					features.pop('pixel_values' )
											if return_overflowing_tokens is True:
																		snake_case_           =					self.get_overflowing_images(snake_case   ,     encoded_inputs['overflow_to_sample_mapping'] )
											snake_case_           =					images

											return encoded_inputs




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											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   ,     *snake_case   ,     **snake_case ):
											return self.tokenizer.batch_decode(*snake_case   ,     **snake_case )




				def 					a							(      self   ,     *snake_case   ,     **snake_case ):
											return self.tokenizer.decode(*snake_case   ,     **snake_case )




				@property
				def 					a							(      self ):
											return ["input_ids", "bbox", "attention_mask", "pixel_values"]




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


from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 				lowercase       ( lowercase_    ):




				def 					a							(      self ):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]




				def 					a							(      self ):
											snake_case_           =					{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
											return Dataset.from_dict(snake_case )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertListEqual(dset.column_names   ,     ['col_1', 'col_2'] )
											for i, r in enumerate(snake_case ):
																		self.assertDictEqual(snake_case   ,     example_records[i] )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											snake_case_           =					Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
											self.assertEqual(dset.info   ,     dset_from_dict.info )




				def 					a							(      self ):  # checks what happens with missing columns
											snake_case_           =					[{'col_1': 1}, {'col_2': 'x'}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertDictEqual(dset[0]   ,     {'col_1': 1} )
											self.assertDictEqual(dset[1]   ,     {'col_1': None} )  # NB: first record is used for columns




				def 					a							(      self ):  # checks if the type can be inferred from the second record
											snake_case_           =					[{'col_1': []}, {'col_1': [1, 2]}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertEqual(dset.info.features['col_1']   ,     Sequence(Value('int64' ) ) )




				def 					a							(      self ):
											snake_case_           =					Dataset.from_list([] )
											self.assertEqual(len(snake_case )   ,     0 )
											self.assertListEqual(dset.column_names   ,     [] )




