
'''simple docstring'''




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  A				(							unittest.TestCase						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=7						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=18						,			SCREAMING_SNAKE_CASE=30						,			SCREAMING_SNAKE_CASE=400						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			) ->				Union[str, Any]:






         """simple docstring"""






         A       :   int					=						size if size is not None else {'''height''': 18, '''width''': 18}
         A       :   List[str]					=						parent
         A       :   str					=						batch_size
         A       :   List[Any]					=						num_channels
         A       :   Optional[int]					=						image_size
         A       :   Optional[int]					=						min_resolution
         A       :   Dict					=						max_resolution
         A       :   Optional[int]					=						do_resize
         A       :   List[Any]					=						size
         A       :   Union[str, Any]					=						apply_ocr





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






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





@require_torch
@require_pytesseract
class  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		LayoutLMvaImageProcessor if is_pytesseract_available() else None





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Tuple					=						LayoutLMvaImageProcessingTester(self	)





     @property
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return self.image_processor_tester.prepare_image_processor_dict()





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_resize'''	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''size'''	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''apply_ocr'''	)	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Dict					=						self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size						,			{'''height''': 18, '''width''': 18}	)

         A       :   List[str]					=						self.image_processing_class.from_dict(self.image_processor_dict						,			size=42	)
         self.assertEqual(image_processor.size						,			{'''height''': 42, '''width''': 42}	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         pass





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   List[str]					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         A       :   Tuple					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			Image.Image	)

         # Test not batched input
         A       :   Dict					=						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						,			SCREAMING_SNAKE_CASE	)
         self.assertIsInstance(encoding.boxes						,			SCREAMING_SNAKE_CASE	)

         # Test batched
         A       :   str					=						image_processing(SCREAMING_SNAKE_CASE						,			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 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   List[Any]					=						self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         A       :   int					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE						,			numpify=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			np.ndarray	)

         # Test not batched input
         A       :   Optional[Any]					=						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
         A       :   Union[str, Any]					=						image_processing(SCREAMING_SNAKE_CASE						,			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 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   List[str]					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         A       :   List[Any]					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE						,			torchify=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			torch.Tensor	)

         # Test not batched input
         A       :   Union[str, Any]					=						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
         A       :   int					=						image_processing(SCREAMING_SNAKE_CASE						,			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 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         A       :   List[Any]					=						LayoutLMvaImageProcessor()

         from datasets import load_dataset

         A       :   Any					=						load_dataset('''hf-internal-testing/fixtures_docvqa'''						,			split='''test'''	)

         A       :   str					=						Image.open(ds[0]['''file''']	).convert('''RGB'''	)

         A       :   Dict					=						image_processing(SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	)

         self.assertEqual(encoding.pixel_values.shape						,			(1, 3, 224, 224)	)
         self.assertEqual(len(encoding.words	)						,			len(encoding.boxes	)	)

         # fmt: off
         # the words and boxes were obtained with Tesseract 4.1.1
         A       :   List[Any]					=						[['''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
         A       :   Optional[Any]					=						[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
         # fmt: on

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

         # with apply_OCR = False
         A       :   Any					=						LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE	)

         A       :   int					=						image_processing(SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	)

         self.assertEqual(encoding.pixel_values.shape						,			(1, 3, 224, 224)	)






'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase			:				Optional[int]    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		['''input_features''', '''attention_mask''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE=80						,			SCREAMING_SNAKE_CASE=16000						,			SCREAMING_SNAKE_CASE=80						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         super().__init__(feature_size=SCREAMING_SNAKE_CASE						,			sampling_rate=SCREAMING_SNAKE_CASE						,			padding_value=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						num_mel_bins
         A       :   Tuple					=						do_ceptral_normalize
         A       :   Dict					=						normalize_means
         A       :   List[Any]					=						normalize_vars
         A       :   List[str]					=						True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         A       :   List[Any]					=						waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
         A       :   Any					=						torch.from_numpy(SCREAMING_SNAKE_CASE	).unsqueeze(0	)
         A       :   Any					=						ta_kaldi.fbank(SCREAMING_SNAKE_CASE						,			num_mel_bins=self.num_mel_bins						,			sample_frequency=self.sampling_rate	)
         return features.numpy()





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 0.0						,			) ->				np.ndarray:






         """simple docstring"""






         if normalize_means:
             A       :   Dict					=						x[:input_length].mean(axis=0	)
             A       :   Optional[Any]					=						np.subtract(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         if normalize_vars:
             A       :   str					=						x[:input_length].std(axis=0	)
             A       :   int					=						np.divide(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         if input_length < x.shape[0]:
             A       :   List[str]					=						padding_value

         # make sure array is in float32
         A       :   Tuple					=						x.astype(np.floataa	)

         return x





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[np.ndarray]:






         """simple docstring"""






         A       :   List[Any]					=						attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [
             self.utterance_cmvn(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			self.normalize_means						,			self.normalize_vars						,			self.padding_value	)
             for x, n in zip(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         ]





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				BatchFeature:






         """simple docstring"""






         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                     F' {self.sampling_rate} and not {sampling_rate}.'	)
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''	)

         A       :   List[Any]					=						isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	) and len(raw_speech.shape	) > 1
         if is_batched_numpy and len(raw_speech.shape	) > 2:
             raise ValueError(F'Only mono-channel audio is supported for input to {self}'	)
         A       :   Tuple					=						is_batched_numpy or (
             isinstance(SCREAMING_SNAKE_CASE						,			(list, tuple)	) and (isinstance(raw_speech[0]						,			(np.ndarray, tuple, list)	))
         )

         if is_batched:
             A       :   Union[str, Any]					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	) for speech in raw_speech]
         elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	):
             A       :   Union[str, Any]					=						np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	)
         elif isinstance(SCREAMING_SNAKE_CASE						,			np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
             A       :   Optional[int]					=						raw_speech.astype(np.floataa	)

         # always return batch
         if not is_batched:
             A       :   Any					=						[raw_speech]

         # extract fbank features
         A       :   List[str]					=						[self._extract_fbank_features(SCREAMING_SNAKE_CASE	) for waveform in raw_speech]

         # convert into correct format for padding
         A       :   str					=						BatchFeature({'''input_features''': features}	)

         A       :   Union[str, Any]					=						self.pad(
             SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			pad_to_multiple_of=SCREAMING_SNAKE_CASE						,			return_attention_mask=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         # make sure list is in array format
         A       :   List[str]					=						padded_inputs.get('''input_features'''	)
         if isinstance(input_features[0]						,			SCREAMING_SNAKE_CASE	):
             A       :   str					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.floataa	) for feature in input_features]

         A       :   Union[str, Any]					=						padded_inputs.get('''attention_mask'''	)
         if attention_mask is not None:
             A       :   Union[str, Any]					=						[np.asarray(SCREAMING_SNAKE_CASE						,			dtype=np.intaa	) for array in attention_mask]

         # Utterance-level cepstral mean and variance normalization
         if self.do_ceptral_normalize:
             A       :   Dict					=						(
                 np.array(SCREAMING_SNAKE_CASE						,			dtype=np.intaa	)
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE						,			max_length=SCREAMING_SNAKE_CASE	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             A       :   List[Any]					=						self.normalize(
                 padded_inputs['''input_features''']						,			attention_mask=SCREAMING_SNAKE_CASE	)

         if return_tensors is not None:
             A       :   int					=						padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE	)

         return padded_inputs




