

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


A									=  random.Random()





def a(lowercase__      ,			lowercase__=1.0      ,			lowercase__=None      ,			lowercase__=None						):





	'''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


@require_torch
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=7			,					__UpperCamelCase=4_00			,					__UpperCamelCase=20_00			,					__UpperCamelCase=1			,					__UpperCamelCase=0.0			,					__UpperCamelCase=1_60_00			,					__UpperCamelCase=True			,					__UpperCamelCase=80			,					__UpperCamelCase=16			,					__UpperCamelCase=64			,					__UpperCamelCase="hann_window"			,					__UpperCamelCase=80			,					__UpperCamelCase=76_00			,					__UpperCamelCase=1E-10			,					__UpperCamelCase=True			,					):

				"""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_   		=			feature_size
				snake_case_   		=			padding_value
				snake_case_   		=			sampling_rate
				snake_case_   		=			do_normalize
				snake_case_   		=			num_mel_bins
				snake_case_   		=			hop_length
				snake_case_   		=			win_length
				snake_case_   		=			win_function
				snake_case_   		=			fmin
				snake_case_   		=			fmax
				snake_case_   		=			mel_floor
				snake_case_   		=			return_attention_mask


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return {
				    "feature_size": self.feature_size,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "do_normalize": self.do_normalize,
				    "num_mel_bins": self.num_mel_bins,
				    "hop_length": self.hop_length,
				    "win_length": self.win_length,
				    "win_function": self.win_function,
				    "fmin": self.fmin,
				    "fmax": self.fmax,
				    "mel_floor": self.mel_floor,
				    "return_attention_mask": self.return_attention_mask,
				}


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=False			,					__UpperCamelCase=False				):

				"""simple docstring"""


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

				if equal_length:
					snake_case_   		=			floats_list((self.batch_size, self.max_seq_length)				)
				else:
					# make sure that inputs increase in size
					snake_case_   		=			[
					    _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:
					snake_case_   		=			[np.asarray(__UpperCamelCase				) for x in speech_inputs]

				return speech_inputs







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=False			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if equal_length:
					snake_case_   		=			[floats_list((self.max_seq_length, self.num_mel_bins)				) for _ in range(self.batch_size				)]
				else:
					# make sure that inputs increase in size
					snake_case_   		=			[
					    floats_list((x, self.num_mel_bins)				)
					    for x in range(self.min_seq_length			,					self.max_seq_length			,					self.seq_length_diff				)
					]

				if numpify:
					snake_case_   		=			[np.asarray(__UpperCamelCase				) for x in speech_inputs]

				return speech_inputs







@require_torch
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	SpeechTaFeatureExtractor


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			SpeechTaFeatureExtractionTester(self				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				self.assertTrue(np.all(np.mean(__UpperCamelCase			,					axis=0				) < 1E-3				)				)
				self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase			,					axis=0				) - 1				) < 1E-3				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_tester.prepare_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(__UpperCamelCase				) for speech_input in speech_inputs]

				# Test not batched input
				snake_case_   		=			feat_extract(speech_inputs[0]			,					return_tensors='np'				).input_values
				snake_case_   		=			feat_extract(np_speech_inputs[0]			,					return_tensors='np'				).input_values
				self.assertTrue(np.allclose(__UpperCamelCase			,					__UpperCamelCase			,					atol=1E-3				)				)

				# Test batched
				snake_case_   		=			feat_extract(__UpperCamelCase			,					return_tensors='np'				).input_values
				snake_case_   		=			feat_extract(__UpperCamelCase			,					return_tensors='np'				).input_values
				for enc_seq_a, enc_seq_a in zip(__UpperCamelCase			,					__UpperCamelCase				):
					self.assertTrue(np.allclose(__UpperCamelCase			,					__UpperCamelCase			,					atol=1E-3				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
				snake_case_   		=			[floats_list((1, x)				)[0] for x in range(8_00			,					14_00			,					2_00				)]

				snake_case_   		=			['longest', 'max_length', 'do_not_pad']
				snake_case_   		=			[None, 16_00, None]
				for max_length, padding in zip(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			feat_extract(__UpperCamelCase			,					padding=__UpperCamelCase			,					max_length=__UpperCamelCase			,					return_tensors='np'				)
					snake_case_   		=			processed.input_values

					self._check_zero_mean_unit_variance(input_values[0][:8_00]				)
					self.assertTrue(input_values[0][8_00:].sum() < 1E-6				)
					self._check_zero_mean_unit_variance(input_values[1][:10_00]				)
					self.assertTrue(input_values[0][10_00:].sum() < 1E-6				)
					self._check_zero_mean_unit_variance(input_values[2][:12_00]				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
				snake_case_   		=			range(8_00			,					14_00			,					2_00				)
				snake_case_   		=			[floats_list((1, x)				)[0] for x in lengths]

				snake_case_   		=			['longest', 'max_length', 'do_not_pad']
				snake_case_   		=			[None, 16_00, None]

				for max_length, padding in zip(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			feat_extract(__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding=__UpperCamelCase				)
					snake_case_   		=			processed.input_values

					self._check_zero_mean_unit_variance(input_values[0][:8_00]				)
					self._check_zero_mean_unit_variance(input_values[1][:10_00]				)
					self._check_zero_mean_unit_variance(input_values[2][:12_00]				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
				snake_case_   		=			[floats_list((1, x)				)[0] for x in range(8_00			,					14_00			,					2_00				)]
				snake_case_   		=			feat_extract(
				    __UpperCamelCase			,					truncation=__UpperCamelCase			,					max_length=10_00			,					padding='max_length'			,					return_tensors='np'				)
				snake_case_   		=			processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :8_00]				)
				self._check_zero_mean_unit_variance(input_values[1]				)
				self._check_zero_mean_unit_variance(input_values[2]				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
				snake_case_   		=			[floats_list((1, x)				)[0] for x in range(8_00			,					14_00			,					2_00				)]
				snake_case_   		=			feat_extract(
				    __UpperCamelCase			,					truncation=__UpperCamelCase			,					max_length=10_00			,					padding='longest'			,					return_tensors='np'				)
				snake_case_   		=			processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :8_00]				)
				self._check_zero_mean_unit_variance(input_values[1, :10_00]				)
				self._check_zero_mean_unit_variance(input_values[2]				)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertTrue(input_values.shape == (3, 10_00)				)

				snake_case_   		=			[floats_list((1, x)				)[0] for x in range(8_00			,					14_00			,					2_00				)]
				snake_case_   		=			feat_extract(
				    __UpperCamelCase			,					truncation=__UpperCamelCase			,					max_length=20_00			,					padding='longest'			,					return_tensors='np'				)
				snake_case_   		=			processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :8_00]				)
				self._check_zero_mean_unit_variance(input_values[1, :10_00]				)
				self._check_zero_mean_unit_variance(input_values[2]				)

				# make sure that if max_length > longest -> then pad to longest
				self.assertTrue(input_values.shape == (3, 12_00)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
				snake_case_   		=			np.random.rand(1_00				).astype(np.floataa				)
				snake_case_   		=			np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
					snake_case_   		=			feature_extractor.pad([{'input_values': inputs}]			,					return_tensors='np'				)
					self.assertTrue(np_processed.input_values.dtype == np.floataa				)
					snake_case_   		=			feature_extractor.pad([{'input_values': inputs}]			,					return_tensors='pt'				)
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_tester.prepare_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(__UpperCamelCase				) for speech_input in speech_inputs]

				# Test feature size
				snake_case_   		=			feature_extractor(audio_target=__UpperCamelCase			,					padding=__UpperCamelCase			,					return_tensors='np'				).input_values
				self.assertTrue(input_values.ndim == 3				)
				self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins				)

				# Test not batched input
				snake_case_   		=			feature_extractor(speech_inputs[0]			,					return_tensors='np'				).input_values
				snake_case_   		=			feature_extractor(np_speech_inputs[0]			,					return_tensors='np'				).input_values
				self.assertTrue(np.allclose(__UpperCamelCase			,					__UpperCamelCase			,					atol=1E-3				)				)

				# Test batched
				snake_case_   		=			feature_extractor(__UpperCamelCase			,					return_tensors='np'				).input_values
				snake_case_   		=			feature_extractor(__UpperCamelCase			,					return_tensors='np'				).input_values
				for enc_seq_a, enc_seq_a in zip(__UpperCamelCase			,					__UpperCamelCase				):
					self.assertTrue(np.allclose(__UpperCamelCase			,					__UpperCamelCase			,					atol=1E-3				)				)

				# 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(__UpperCamelCase				)
				snake_case_   		=			feature_extractor(__UpperCamelCase			,					return_tensors='np'				).input_values
				snake_case_   		=			feature_extractor(__UpperCamelCase			,					return_tensors='np'				).input_values
				for enc_seq_a, enc_seq_a in zip(__UpperCamelCase			,					__UpperCamelCase				):
					self.assertTrue(np.allclose(__UpperCamelCase			,					__UpperCamelCase			,					atol=1E-3				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feat_extract_tester.prepare_inputs_for_target()
				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_dict				)
				snake_case_   		=			feat_extract.model_input_names[0]

				snake_case_   		=			BatchFeature({input_name: speech_inputs}				)

				self.assertTrue(all(len(__UpperCamelCase				) == len(__UpperCamelCase				) for x, y in zip(__UpperCamelCase			,					processed_features[input_name]				)				)				)

				snake_case_   		=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase				)
				snake_case_   		=			BatchFeature({input_name: speech_inputs}			,					tensor_type='np'				)

				snake_case_   		=			processed_features[input_name]

				if len(batch_features_input.shape				) < 3:
					snake_case_   		=			batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)


			@require_torch
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase				)
				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_dict				)
				snake_case_   		=			feat_extract.model_input_names[0]

				snake_case_   		=			BatchFeature({input_name: speech_inputs}			,					tensor_type='pt'				)

				snake_case_   		=			processed_features[input_name]

				if len(batch_features_input.shape				) < 3:
					snake_case_   		=			batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.num_mel_bins)				)


			@require_torch
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feature_extraction_class(**self.feat_extract_dict				)
				snake_case_   		=			self.feat_extract_tester.prepare_inputs_for_target()
				snake_case_   		=			feat_extract.model_input_names[0]

				snake_case_   		=			BatchFeature({input_name: speech_inputs}				)

				snake_case_   		=			feat_extract.num_mel_bins  # hack!

				snake_case_   		=			feat_extract.pad(__UpperCamelCase			,					padding='longest'			,					return_tensors='np'				)[input_name]
				snake_case_   		=			feat_extract.pad(__UpperCamelCase			,					padding='longest'			,					return_tensors='pt'				)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1E-2				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feat_extract_dict
				snake_case_   		=			True
				snake_case_   		=			self.feature_extraction_class(**__UpperCamelCase				)
				snake_case_   		=			self.feat_extract_tester.prepare_inputs_for_target()
				snake_case_   		=			[len(__UpperCamelCase				) for x in speech_inputs]
				snake_case_   		=			feat_extract.model_input_names[0]

				snake_case_   		=			BatchFeature({input_name: speech_inputs}				)

				snake_case_   		=			feat_extract.num_mel_bins  # hack!

				snake_case_   		=			feat_extract.pad(__UpperCamelCase			,					padding='longest'			,					return_tensors='np'				)
				self.assertIn('attention_mask'			,					__UpperCamelCase				)
				self.assertListEqual(list(processed.attention_mask.shape				)			,					list(processed[input_name].shape[:2]				)				)
				self.assertListEqual(processed.attention_mask.sum(-1				).tolist()			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.feat_extract_dict
				snake_case_   		=			True
				snake_case_   		=			self.feature_extraction_class(**__UpperCamelCase				)
				snake_case_   		=			self.feat_extract_tester.prepare_inputs_for_target()
				snake_case_   		=			[len(__UpperCamelCase				) for x in speech_inputs]
				snake_case_   		=			feat_extract.model_input_names[0]

				snake_case_   		=			BatchFeature({input_name: speech_inputs}				)
				snake_case_   		=			min(__UpperCamelCase				)

				snake_case_   		=			feat_extract.num_mel_bins  # hack!

				snake_case_   		=			feat_extract.pad(
				    __UpperCamelCase			,					padding='max_length'			,					max_length=__UpperCamelCase			,					truncation=__UpperCamelCase			,					return_tensors='np'				)
				self.assertIn('attention_mask'			,					__UpperCamelCase				)
				self.assertListEqual(
				    list(processed_pad.attention_mask.shape				)			,					[processed_pad[input_name].shape[0], max_length]				)
				self.assertListEqual(
				    processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()			,					[max_length for x in speech_inputs]				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				from datasets import load_dataset

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

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			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

				snake_case_   		=			self._load_datasamples(1				)
				snake_case_   		=			SpeechTaFeatureExtractor()
				snake_case_   		=			feature_extractor(__UpperCamelCase			,					return_tensors='pt'				).input_values
				self.assertEquals(input_values.shape			,					(1, 9_36_80)				)
				self.assertTrue(torch.allclose(input_values[0, :30]			,					__UpperCamelCase			,					atol=1E-6				)				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.tensor(
				    [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
				     -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
				     -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
				     -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]				)
				# fmt: on

				snake_case_   		=			self._load_datasamples(1				)
				snake_case_   		=			SpeechTaFeatureExtractor()
				snake_case_   		=			feature_extractor(audio_target=__UpperCamelCase			,					return_tensors='pt'				).input_values
				self.assertEquals(input_values.shape			,					(1, 3_66, 80)				)
				self.assertTrue(torch.allclose(input_values[0, 0, :30]			,					__UpperCamelCase			,					atol=1E-4				)				)



import functools





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''

	# Validation
	if not isinstance(lowercase__      ,			lowercase__						) or not all(isinstance(lowercase__      ,			lowercase__						) for day in days						):
		raise ValueError('The parameter days should be a list of integers'						)

	if len(lowercase__						) != 3 or not all(isinstance(lowercase__      ,			lowercase__						) for cost in costs						):
		raise ValueError('The parameter costs should be a list of three integers'						)

	if len(lowercase__						) == 0:
		return 0

	if min(lowercase__						) <= 0:
		raise ValueError('All days elements should be greater than 0'						)

	if max(lowercase__						) >= 366:
		raise ValueError('All days elements should be less than 366'						)

	snake_case_   		=			set(lowercase__						)

	@functools.cache
	def dynamic_programming(lowercase__						) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1						)

		return min(
		    costs[0] + dynamic_programming(index + 1						)      ,			costs[1] + dynamic_programming(index + 7						)      ,			costs[2] + dynamic_programming(index + 30						)      ,			)

	return dynamic_programming(1						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
