





import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_pytesseract_available():
				from PIL import Image

				from transformers import LayoutLMvaImageProcessor







class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def __init__(						self    :  Optional[int] ,					__lowercase    :  Optional[int] ,					__lowercase    :  Optional[Any]=7 ,					__lowercase    :  Any=3 ,					__lowercase    :  Dict=18 ,					__lowercase    :  List[Any]=30 ,					__lowercase    :  Union[str, Any]=4_00 ,					__lowercase    :  Tuple=True ,					__lowercase    :  Dict=None ,					__lowercase    :  Optional[int]=True ,					):






						'''simple docstring'''







						UpperCAmelCase_								=							size if size is not None else {"""height""": 18, """width""": 18}
						UpperCAmelCase_								=							parent
						UpperCAmelCase_								=							batch_size
						UpperCAmelCase_								=							num_channels
						UpperCAmelCase_								=							image_size
						UpperCAmelCase_								=							min_resolution
						UpperCAmelCase_								=							max_resolution
						UpperCAmelCase_								=							do_resize
						UpperCAmelCase_								=							size
						UpperCAmelCase_								=							apply_ocr





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class   _UpperCamelCase  (       A_						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     Optional[int]			    =       LayoutLMvaImageProcessor if is_pytesseract_available() else None





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=							LayoutLMvaImageProcessingTester(self		)





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






						'''simple docstring'''







						return self.image_processor_tester.prepare_image_processor_dict()





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=							self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(__A ,					"""do_resize"""		)		)
						self.assertTrue(hasattr(__A ,					"""size"""		)		)
						self.assertTrue(hasattr(__A ,					"""apply_ocr"""		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=							self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size ,					{"""height""": 18, """width""": 18}		)

						UpperCAmelCase_								=							self.image_processing_class.from_dict(self.image_processor_dict ,					size=42		)
						self.assertEqual(image_processor.size ,					{"""height""": 42, """width""": 42}		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						pass





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=							self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						UpperCAmelCase_								=							prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__A		)
						for image in image_inputs:
							self.assertIsInstance(__A ,					Image.Image		)

						# Test not batched input
						UpperCAmelCase_								=							image_processing(image_inputs[0] ,					return_tensors="""pt"""		)
						self.assertEqual(
						    encoding.pixel_values.shape ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ) ,					)

						self.assertIsInstance(encoding.words ,					__A		)
						self.assertIsInstance(encoding.boxes ,					__A		)

						# Test batched
						UpperCAmelCase_								=							image_processing(__A ,					return_tensors="""pt"""		).pixel_values
						self.assertEqual(
						    encoded_images.shape ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ) ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=							self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						UpperCAmelCase_								=							prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__A ,					numpify=__A		)
						for image in image_inputs:
							self.assertIsInstance(__A ,					np.ndarray		)

						# Test not batched input
						UpperCAmelCase_								=							image_processing(image_inputs[0] ,					return_tensors="""pt"""		).pixel_values
						self.assertEqual(
						    encoded_images.shape ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ) ,					)

						# Test batched
						UpperCAmelCase_								=							image_processing(__A ,					return_tensors="""pt"""		).pixel_values
						self.assertEqual(
						    encoded_images.shape ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ) ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=							self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						UpperCAmelCase_								=							prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__A ,					torchify=__A		)
						for image in image_inputs:
							self.assertIsInstance(__A ,					torch.Tensor		)

						# Test not batched input
						UpperCAmelCase_								=							image_processing(image_inputs[0] ,					return_tensors="""pt"""		).pixel_values
						self.assertEqual(
						    encoded_images.shape ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ) ,					)

						# Test batched
						UpperCAmelCase_								=							image_processing(__A ,					return_tensors="""pt"""		).pixel_values
						self.assertEqual(
						    encoded_images.shape ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size["""height"""],
						        self.image_processor_tester.size["""width"""],
						    ) ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=							LayoutLMvaImageProcessor()

						from datasets import load_dataset

						UpperCAmelCase_								=							load_dataset("""hf-internal-testing/fixtures_docvqa""" ,					split="""test"""		)

						UpperCAmelCase_								=							Image.open(ds[0]["""file"""]		).convert("""RGB"""		)

						UpperCAmelCase_								=							image_processing(__A ,					return_tensors="""pt"""		)

						self.assertEqual(encoding.pixel_values.shape ,					(1, 3, 2_24, 2_24)		)
						self.assertEqual(len(encoding.words		) ,					len(encoding.boxes		)		)

						# fmt: off
						# the words and boxes were obtained with Tesseract 4.1.1
						UpperCAmelCase_								=							[["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
						UpperCAmelCase_								=							[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
						# fmt: on

						self.assertListEqual(encoding.words ,					__A		)
						self.assertListEqual(encoding.boxes ,					__A		)

						# with apply_OCR = False
						UpperCAmelCase_								=							LayoutLMvaImageProcessor(apply_ocr=__A		)

						UpperCAmelCase_								=							image_processing(__A ,					return_tensors="""pt"""		)

						self.assertEqual(encoding.pixel_values.shape ,					(1, 3, 2_24, 2_24)		)









from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  int ,					__lowercase    :  int ,					__lowercase    :  float ,					**__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     feature_size
						UpperCAmelCase_								=     sampling_rate
						UpperCAmelCase_								=     padding_value

						UpperCAmelCase_								=     kwargs.pop("""padding_side""" ,					"""right"""		)
						UpperCAmelCase_								=     kwargs.pop("""return_attention_mask""" ,					__lowercase		)

						super().__init__(**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ] ,					__lowercase    :  Union[bool, str, PaddingStrategy] = True ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  bool = False ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[bool] = None ,					__lowercase    :  Optional[Union[str, TensorType]] = None ,					):






						'''simple docstring'''







						if isinstance(__lowercase ,					(list, tuple)		) and isinstance(processed_features[0] ,					(dict, BatchFeature)		):
							UpperCAmelCase_								=     {
							    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
							}

						# The model's main input name, usually `input_values`, has be passed for padding
						if self.model_input_names[0] not in processed_features:
							raise ValueError(
							    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
							    F""" to this method that includes {self.model_input_names[0]}, but you provided"""
							    F""" {list(processed_features.keys()		)}"""		)

						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]
						UpperCAmelCase_								=     (
						    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
						)

						if len(__lowercase		) == 0:
							if return_attention_mask:
								UpperCAmelCase_								=     []
							return processed_features

						# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
						# and rebuild them afterwards if no return_tensors is specified
						# Note that we lose the specific device the tensor may be on for PyTorch

						UpperCAmelCase_								=     required_input[0]
						if isinstance(__lowercase ,					(list, tuple)		):
							# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
							UpperCAmelCase_								=     0
							while len(required_input[index]		) == 0:
								index += 1
							if index < len(__lowercase		):
								UpperCAmelCase_								=     required_input[index][0]

						if return_tensors is None:
							if is_tf_tensor(__lowercase		):
								UpperCAmelCase_								=     """tf"""
							elif is_torch_tensor(__lowercase		):
								UpperCAmelCase_								=     """pt"""
							elif isinstance(__lowercase ,					(int, float, list, tuple, np.ndarray)		):
								UpperCAmelCase_								=     """np"""
							else:
								raise ValueError(
								    F"""type of {first_element} unknown: {type(__lowercase		)}. """
								    """Should be one of a python, numpy, pytorch or tensorflow object."""		)

						for key, value in processed_features.items():
							if isinstance(value[0] ,					(int, float)		):
								UpperCAmelCase_								=     to_numpy(__lowercase		)
							else:
								UpperCAmelCase_								=     [to_numpy(__lowercase		) for v in value]

        # Convert padding_strategy in PaddingStrategy
						UpperCAmelCase_								=     self._get_padding_strategies(padding=__lowercase ,					max_length=__lowercase		)

						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]

						UpperCAmelCase_								=     len(__lowercase		)
						if not all(len(__lowercase		) == batch_size for v in processed_features.values()		):
							raise ValueError("""Some items in the output dictionary have a different batch size than others."""		)

						UpperCAmelCase_								=     []
						for i in range(__lowercase		):
							UpperCAmelCase_								=     {k: v[i] for k, v in processed_features.items()}
							# truncation
							UpperCAmelCase_								=     self._truncate(
							    __lowercase ,					max_length=__lowercase ,					pad_to_multiple_of=__lowercase ,					truncation=__lowercase ,					)
							truncated_inputs.append(__lowercase		)

						if padding_strategy == PaddingStrategy.LONGEST:
							# make sure that `max_length` cannot be longer than the longest truncated length
							UpperCAmelCase_								=     max(len(input_slice[self.model_input_names[0]]		) for input_slice in truncated_inputs		)
							UpperCAmelCase_								=     PaddingStrategy.MAX_LENGTH

						UpperCAmelCase_								=     {}
						for i in range(__lowercase		):
							# padding
							UpperCAmelCase_								=     self._pad(
							    truncated_inputs[i] ,					max_length=__lowercase ,					padding_strategy=__lowercase ,					pad_to_multiple_of=__lowercase ,					return_attention_mask=__lowercase ,					)

							for key, value in outputs.items():
								if key not in batch_outputs:
									UpperCAmelCase_								=     []
								if value.dtype is np.dtype(np.floataa		):
									UpperCAmelCase_								=     value.astype(np.floataa		)
								batch_outputs[key].append(__lowercase		)

						return BatchFeature(__lowercase ,					tensor_type=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  Union[Dict[str, np.ndarray], BatchFeature] ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[bool] = None ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
							UpperCAmelCase_								=     len(__lowercase		)

						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
							UpperCAmelCase_								=     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						UpperCAmelCase_								=     padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowercase		) < max_length

						if return_attention_mask and "attention_mask" not in processed_features:
							UpperCAmelCase_								=     np.ones(len(__lowercase		) ,					dtype=np.intaa		)

						if needs_to_be_padded:
							UpperCAmelCase_								=     max_length - len(__lowercase		)
							if self.padding_side == "right":
								if return_attention_mask:
									UpperCAmelCase_								=     np.pad(
									    processed_features["""attention_mask"""] ,					(0, difference)		)
								UpperCAmelCase_								=     ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
								UpperCAmelCase_								=     np.pad(
								    __lowercase ,					__lowercase ,					"""constant""" ,					constant_values=self.padding_value		)
							elif self.padding_side == "left":
								if return_attention_mask:
									UpperCAmelCase_								=     np.pad(
									    processed_features["""attention_mask"""] ,					(difference, 0)		)
								UpperCAmelCase_								=     ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
								UpperCAmelCase_								=     np.pad(
								    __lowercase ,					__lowercase ,					"""constant""" ,					constant_values=self.padding_value		)
							else:
								raise ValueError("""Invalid padding strategy:""" + str(self.padding_side		)		)

						return processed_features





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Union[Dict[str, np.ndarray], BatchFeature] ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[bool] = None ,					):






						'''simple docstring'''







						if not truncation:
							return processed_features
						elif truncation and max_length is None:
							raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""		)

						UpperCAmelCase_								=     processed_features[self.model_input_names[0]]

						# find `max_length` that fits `pad_to_multiple_of`
						if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
							UpperCAmelCase_								=     ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						UpperCAmelCase_								=     len(__lowercase		) > max_length

						if needs_to_be_truncated:
							UpperCAmelCase_								=     processed_features[self.model_input_names[0]][:max_length]
							if "attention_mask" in processed_features:
								UpperCAmelCase_								=     processed_features["""attention_mask"""][:max_length]

						return processed_features





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  int=False ,					__lowercase    :  List[str]=None		):






						'''simple docstring'''







						if padding is not False:
							if padding is True:
								UpperCAmelCase_								=     PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
							elif not isinstance(__lowercase ,					__lowercase		):
								UpperCAmelCase_								=     PaddingStrategy(__lowercase		)
							elif isinstance(__lowercase ,					__lowercase		):
								UpperCAmelCase_								=     padding
						else:
							UpperCAmelCase_								=     PaddingStrategy.DO_NOT_PAD

						# Set max length if needed
						if max_length is None:
							if padding_strategy == PaddingStrategy.MAX_LENGTH:
								raise ValueError(
								    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""		)

        # Test if we have a padding value
						if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
							raise ValueError(
							    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
							    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""		)

						return padding_strategy


