"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset

SCREAMING_SNAKE_CASE__      						=  random.Random()





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Tuple=1.0 ,			_UpperCamelCase       :				Optional[int]=None ,			_UpperCamelCase       :				List[Any]=None					)					->				Optional[Any]:





						"""simple docstring"""






						if rng is None:
												snake_case								=			global_rng

						snake_case								=			[]
						for batch_idx in range(shape[0]					):
												values.append([]					)
												for _ in range(shape[1]					):
																		values[-1].append(rng.random() * scale					)

						return values



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=4_00		,    lowerCAmelCase=20_00		,    lowerCAmelCase=20_48		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=30		,    lowerCAmelCase=4_41_00		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			min_seq_length
										snake_case								=			max_seq_length
										snake_case								=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case								=			spectrogram_length
										snake_case								=			feature_size
										snake_case								=			num_audio_channels
										snake_case								=			hop_length
										snake_case								=			chunk_length
										snake_case								=			sampling_rate

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}

				def 		snake_case (				self		,    lowerCAmelCase=False		,    lowerCAmelCase=False    ):






										"""simple docstring"""

										def _flatten(lowerCAmelCase    ):
																return list(itertools.chain(*lowerCAmelCase    )    )

										if equal_length:
																snake_case								=			[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
										else:
																# make sure that inputs increase in size
																snake_case								=			[
																    floats_list((x, self.feature_size)    )
																    for x in range(self.min_seq_length		,    self.max_seq_length		,    self.seq_length_diff    )
																]
										if numpify:
																snake_case								=			[np.asarray(lowerCAmelCase    ) for x in speech_inputs]
										return speech_inputs





@require_torch
@require_torchaudio
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						TvltFeatureExtractor

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TvltFeatureExtractionTester(self    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'spectrogram_length'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'feature_size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'num_audio_channels'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'hop_length'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'chunk_length'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'sampling_rate'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )

										with tempfile.TemporaryDirectory() as tmpdirname:
																snake_case								=			feat_extract_first.save_pretrained(lowerCAmelCase    )[0]
																check_json_file_has_correct_format(lowerCAmelCase    )
																snake_case								=			self.feature_extraction_class.from_pretrained(lowerCAmelCase    )

										snake_case								=			feat_extract_first.to_dict()
										snake_case								=			feat_extract_second.to_dict()
										snake_case								=			dict_first.pop('mel_filters'    )
										snake_case								=			dict_second.pop('mel_filters'    )
										self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase    )    )
										self.assertEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )

										with tempfile.TemporaryDirectory() as tmpdirname:
																snake_case								=			os.path.join(lowerCAmelCase		,    'feat_extract.json'    )
																feat_extract_first.to_json_file(lowerCAmelCase    )
																snake_case								=			self.feature_extraction_class.from_json_file(lowerCAmelCase    )

										snake_case								=			feat_extract_first.to_dict()
										snake_case								=			feat_extract_second.to_dict()
										snake_case								=			dict_first.pop('mel_filters'    )
										snake_case								=			dict_second.pop('mel_filters'    )
										self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase    )    )
										self.assertEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )

										# create three inputs of length 800, 1000, and 1200
										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
										snake_case								=			[np.asarray(lowerCAmelCase    ) for speech_input in speech_inputs]

										# Test not batched input
										snake_case								=			feature_extractor(np_speech_inputs[0]		,    return_tensors='np'		,    sampling_rate=4_41_00    ).audio_values

										self.assertTrue(encoded_audios.ndim == 4    )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

										# Test batched
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'		,    sampling_rate=4_41_00    ).audio_values

										self.assertTrue(encoded_audios.ndim == 4    )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

										# Test audio masking
										snake_case								=			feature_extractor(
										    lowerCAmelCase		,    return_tensors='np'		,    sampling_rate=4_41_00		,    mask_audio=lowerCAmelCase    ).audio_values

										self.assertTrue(encoded_audios.ndim == 4    )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

										# Test 2-D numpy arrays are batched.
										snake_case								=			[floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
										snake_case								=			np.asarray(lowerCAmelCase    )
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'		,    sampling_rate=4_41_00    ).audio_values
										self.assertTrue(encoded_audios.ndim == 4    )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			load_dataset('hf-internal-testing/librispeech_asr_dummy'		,    'clean'		,    split='validation'    )
										# automatic decoding with librispeech
										snake_case								=			ds.sort('id'    ).select(range(lowerCAmelCase    )    )[:num_samples]['audio']

										return [x["array"] for x in speech_samples]

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self._load_datasamples(1    )
										snake_case								=			TvltFeatureExtractor()
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='pt'    ).audio_values

										self.assertEquals(audio_values.shape		,    (1, 1, 1_92, 1_28)    )

										snake_case								=			torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]    )
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,    lowerCAmelCase		,    atol=1E-4    )    )



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=False		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			min_resolution
										snake_case								=			max_resolution
										snake_case								=			do_resize
										snake_case								=			size if size is not None else {'height': 18, 'width': 20}
										snake_case								=			do_thumbnail
										snake_case								=			do_align_axis
										snake_case								=			do_pad
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_thumbnail": self.do_thumbnail,
										    "do_align_long_axis": self.do_align_axis,
										    "do_pad": self.do_pad,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}





@require_torch
@require_vision
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						DonutImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DonutImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_resize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_thumbnail'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_align_long_axis'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_pad'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_normalize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_mean'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 18, 'width': 20}    )

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

										# Previous config had dimensions in (width, height) order
										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=(42, 84)    )
										self.assertEqual(image_processor.size		,    {'height': 84, 'width': 42}    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

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

										# Test not batched input
										snake_case								=			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
										snake_case								=			image_processing(lowerCAmelCase		,    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'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

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

										# Test not batched input
										snake_case								=			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
										snake_case								=			image_processing(lowerCAmelCase		,    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'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

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

										# Test not batched input
										snake_case								=			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
										snake_case								=			image_processing(lowerCAmelCase		,    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'],
										    )		,    )


