
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch


_SCREAMING_SNAKE_CASE     :   Any							 =      random.Random()

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    Tuple=1.0				,    __UpperCamelCase			:    Union[str, Any]=None				,    __UpperCamelCase			:    Tuple=None							)			->							int:

				"""simple docstring"""







				if rng is None:
								A__	:						Optional[int]								=				global_rng

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

				return values







@require_torch
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__=7					,							UpperCamelCase__=400					,							UpperCamelCase__=2000					,							UpperCamelCase__=1					,							UpperCamelCase__=0.0					,							UpperCamelCase__=2_4000					,							UpperCamelCase__=True					,							):
					A__	:						List[str]								=				parent
					A__	:						List[Any]								=				batch_size
					A__	:						Optional[int]								=				min_seq_length
					A__	:						Any								=				max_seq_length
					A__	:						Tuple								=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					A__	:						Any								=				feature_size
					A__	:						Any								=				padding_value
					A__	:						List[Any]								=				sampling_rate
					A__	:						str								=				return_attention_mask





	def 	__snake_case					(      self		):
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					}





	def 	__snake_case					(      self					,							UpperCamelCase__=False					,							UpperCamelCase__=False		):


					def _flatten(UpperCamelCase__		):
									return list(itertools.chain(*_A		)		)

					if equal_length:
									A__	:						List[str]								=				floats_list((self.batch_size, self.max_seq_length)		)
					else:
									# make sure that inputs increase in size
									A__	:						str								=				[
									    _flatten(floats_list((x, self.feature_size)		)		)
									    for x in range(self.min_seq_length					,							self.max_seq_length					,							self.seq_length_diff		)
									]

					if numpify:
									A__	:						Tuple								=				[np.asarray(_A		) for x in audio_inputs]

					return audio_inputs







@require_torch
class 			UpperCamelCase__		(     snake_case_,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					EncodecFeatureExtractor





	def 	__snake_case					(      self		):
					A__	:						Any								=				EnCodecFeatureExtractionTester(self		)





	def 	__snake_case					(      self		):
					A__	:						Optional[Any]								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
					# create three inputs of length 800, 1000, and 1200
					A__	:						Optional[Any]								=				[floats_list((1, x)		)[0] for x in range(800					,							1400					,							200		)]
					A__	:						Any								=				[np.asarray(_A		) for audio_input in audio_inputs]

					# Test not batched input
					A__	:						str								=				feat_extract(audio_inputs[0]					,							return_tensors='''np'''		).input_values
					A__	:						Tuple								=				feat_extract(np_audio_inputs[0]					,							return_tensors='''np'''		).input_values
					self.assertTrue(np.allclose(_A					,							_A					,							atol=1e-3		)		)

					# Test batched
					A__	:						Dict								=				feat_extract(_A					,							padding=_A					,							return_tensors='''np'''		).input_values
					A__	:						Optional[int]								=				feat_extract(_A					,							padding=_A					,							return_tensors='''np'''		).input_values
					for enc_seq_a, enc_seq_a in zip(_A					,							_A		):
									self.assertTrue(np.allclose(_A					,							_A					,							atol=1e-3		)		)





	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
					A__	:						str								=				np.random.rand(100		).astype(np.floataa		)
					A__	:						Optional[Any]								=				np_audio_inputs.tolist()

					for inputs in [py_audio_inputs, np_audio_inputs]:
									A__	:						Optional[Any]								=				feature_extractor.pad([{'''input_values''': inputs}]					,							return_tensors='''np'''		)
									self.assertTrue(np_processed.input_values.dtype == np.floataa		)
									A__	:						List[str]								=				feature_extractor.pad([{'''input_values''': inputs}]					,							return_tensors='''pt'''		)
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					from datasets import load_dataset

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

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





	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				torch.tensor(
					    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
					     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
					     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
					     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
					     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
					     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]		)
					# fmt: on
					A__	:						List[Any]								=				self._load_datasamples(1		)
					A__	:						Dict								=				EncodecFeatureExtractor()
					A__	:						str								=				feature_extractor(_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(input_values.shape					,							(1, 1, 9_3680)		)
					self.assertTrue(torch.allclose(input_values[0, 0, :30]					,							_A					,							atol=1e-6		)		)





	def 	__snake_case					(      self		):
					A__	:						Optional[Any]								=				torch.tensor(
					    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
					     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
					     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
					     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
					     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
					     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]		)
					# fmt: on
					A__	:						int								=				self._load_datasamples(1		)
					A__	:						List[str]								=				[np.tile(input_audio[0][None]					,							reps=(2, 1)		)]
					input_audio[0][1] *= 0.5
					A__	:						List[str]								=				EncodecFeatureExtractor(feature_size=2		)
					A__	:						Dict								=				feature_extractor(_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(input_values.shape					,							(1, 2, 9_3680)		)
					self.assertTrue(torch.allclose(input_values[0, 0, :30]					,							_A					,							atol=1e-6		)		)
					self.assertTrue(torch.allclose(input_values[0, 1, :30]					,							EXPECTED_INPUT_VALUES * 0.5					,							atol=1e-6		)		)





	def 	__snake_case					(      self		):
					A__	:						Optional[int]								=				self._load_datasamples(2		)
					# would be easier if the stride was like
					A__	:						Dict								=				EncodecFeatureExtractor(feature_size=1					,							chunk_length_s=1					,							overlap=0.0_1		)

					# pad and trunc raise an error ?
					with self.assertRaisesRegex(
					    _A					,							'''^Both padding and truncation were set. Make sure you only set one.$'''					,							):
									A__	:						Dict								=				feature_extractor(
									    _A					,							padding='''max_length'''					,							truncation=_A					,							return_tensors='''pt'''		).input_values

					# truncate to chunk
					A__	:						Optional[int]								=				feature_extractor(_A					,							truncation=_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(truncated_outputs.shape					,							(2, 1, 7_1520)		)  # 2 chunks

					# force truncate to max_length
					A__	:						Optional[int]								=				feature_extractor(
					    _A					,							truncation=_A					,							max_length=4_8000					,							return_tensors='''pt'''		).input_values
					self.assertEquals(truncated_outputs.shape					,							(2, 1, 4_8000)		)

					# pad to chunk
					A__	:						str								=				feature_extractor(_A					,							padding=_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(padded_outputs.shape					,							(2, 1, 9_5280)		)

					# pad to chunk
					A__	:						Dict								=				feature_extractor(_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(truncated_outputs.shape					,							(2, 1, 9_5280)		)

					# force pad to max length
					A__	:						Tuple								=				feature_extractor(
					    _A					,							padding='''max_length'''					,							max_length=10_0000					,							return_tensors='''pt'''		).input_values
					self.assertEquals(truncated_outputs.shape					,							(2, 1, 10_0000)		)

					# force no pad
					with self.assertRaisesRegex(
					    _A					,							'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''					,							):
									A__	:						Optional[Any]								=				feature_extractor(_A					,							padding=_A					,							return_tensors='''pt'''		).input_values

					A__	:						Optional[int]								=				feature_extractor(input_audio[0]					,							padding=_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(truncated_outputs.shape					,							(1, 1, 9_3680)		)

					# no pad if no chunk_length_s
					A__	:						List[Any]								=				None
					with self.assertRaisesRegex(
					    _A					,							'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''					,							):
									A__	:						Optional[int]								=				feature_extractor(_A					,							padding=_A					,							return_tensors='''pt'''		).input_values

					A__	:						Optional[Any]								=				feature_extractor(input_audio[0]					,							padding=_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(truncated_outputs.shape					,							(1, 1, 9_3680)		)

					# no pad if no overlap
					A__	:						Optional[int]								=				2
					A__	:						List[Any]								=				None
					with self.assertRaisesRegex(
					    _A					,							'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''					,							):
									A__	:						List[str]								=				feature_extractor(_A					,							padding=_A					,							return_tensors='''pt'''		).input_values

					A__	:						Any								=				feature_extractor(input_audio[0]					,							padding=_A					,							return_tensors='''pt'''		).input_values
					self.assertEquals(truncated_outputs.shape					,							(1, 1, 9_3680)		)






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@patch('diffusers.utils.hub_utils.get_full_repo_name'						)
						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   Mock						)				->     None:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						'full_repo_name'
							with TemporaryDirectory() as tmpdir:
								# Dummy args values
								snake_case_		:      Optional[int]           =						Mock()
								snake_case_		:      Union[str, Any]           =						tmpdir
								snake_case_		:      Optional[Any]           =						0
								snake_case_		:      Union[str, Any]           =						'hub_token'
								snake_case_		:      Any           =						'dataset_name'
								snake_case_		:      Tuple           =						0.0_1
								snake_case_		:      Dict           =						100000
								snake_case_		:      Any           =						10000
								snake_case_		:      str           =						0.0_1
								snake_case_		:      Any           =						0.0_2
								snake_case_		:      Optional[Any]           =						0.0_3
								snake_case_		:      Tuple           =						0.0_0_0_5
								snake_case_		:      Tuple           =						0.0_0_0_0_0_1
								snake_case_		:      str           =						1
								snake_case_		:      Optional[int]           =						10
								snake_case_		:      List[str]           =						0.0_0_1
								snake_case_		:      Any           =						0.1
								snake_case_		:      List[str]           =						0.2
								snake_case_		:      Dict           =						True

								# Model card mush be rendered and saved
								diffusers.utils.hub_utils.create_model_card(_A		, model_name='model_name'						)
								self.assertTrue((Path(_A						) / 'README.md').is_file()						)

