






import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase : Optional[Any]						   =       random.Random()
def       snake_case_		(     lowerCAmelCase_       :						Optional[Any]       ,       lowerCAmelCase_       :						Tuple=1.0       ,       lowerCAmelCase_       :						Union[str, Any]=None       ,       lowerCAmelCase_       :						List[Any]=None      ):
							if rng is None:
														__lowercase      :							Tuple				=     global_rng

							__lowercase      :							Tuple				=     []
							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				:	Any		,     __a				:	Union[str, Any]		,     __a				:	Union[str, Any]=7		,     __a				:	Optional[Any]=400		,     __a				:	Union[str, Any]=2000		,     __a				:	Optional[Any]=1		,     __a				:	Optional[int]=0.0		,     __a				:	List[Any]=16000		,     __a				:	int=True		,     __a				:	Optional[Any]=True		,     )			->  int:


												"""simple docstring"""

												__lowercase      :							int				=     parent
												__lowercase      :							List[Any]				=     batch_size
												__lowercase      :							Dict				=     min_seq_length
												__lowercase      :							List[Any]				=     max_seq_length
												__lowercase      :							Union[str, Any]				=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												__lowercase      :							List[str]				=     feature_size
												__lowercase      :							List[Any]				=     padding_value
												__lowercase      :							int				=     sampling_rate
												__lowercase      :							Tuple				=     return_attention_mask
												__lowercase      :							Optional[Any]				=     do_normalize



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return {
												    "feature_size": self.feature_size,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												    "do_normalize": self.do_normalize,
												}



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Union[str, Any]=False		,     __a				:	Optional[int]=False							)			->  Tuple:


												"""simple docstring"""

												def _flatten(__a				:	Union[str, Any]							):
																			return list(itertools.chain(*__a							)							)

												if equal_length:
																			__lowercase      :							Tuple				=     floats_list((self.batch_size, self.max_seq_length)							)
												else:
																			# make sure that inputs increase in size
																			__lowercase      :							Union[str, Any]				=     [
																			    _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:
																			__lowercase      :							str				=     [np.asarray(__a							) for x in speech_inputs]

												return speech_inputs







class 		lowerCAmelCase     ( __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Optional[Any]									=      WavaVecaFeatureExtractor



					def       lowerCAmelCase				(  self				:	Tuple							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     WavaVecaFeatureExtractionTester(self							)



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	List[Any]							)			->  List[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
												# create three inputs of length 800, 1000, and 1200
												__lowercase      :							Optional[int]				=     [floats_list((1, x)							)[0] for x in range(800		,     1400		,     200							)]
												__lowercase      :							str				=     [np.asarray(__a							) for speech_input in speech_inputs]

												# Test not batched input
												__lowercase      :							Union[str, Any]				=     feat_extract(speech_inputs[0]		,     return_tensors="""np"""							).input_values
												__lowercase      :							List[Any]				=     feat_extract(np_speech_inputs[0]		,     return_tensors="""np"""							).input_values
												self.assertTrue(np.allclose(__a		,     __a		,     atol=1E-3							)							)

												# Test batched
												__lowercase      :							Tuple				=     feat_extract(__a		,     return_tensors="""np"""							).input_values
												__lowercase      :							Dict				=     feat_extract(__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							)							)

												# Test 2-D numpy arrays are batched.
												__lowercase      :							str				=     [floats_list((1, x)							)[0] for x in (800, 800, 800)]
												__lowercase      :							Optional[Any]				=     np.asarray(__a							)
												__lowercase      :							List[Any]				=     feat_extract(__a		,     return_tensors="""np"""							).input_values
												__lowercase      :							int				=     feat_extract(__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       lowerCAmelCase				(  self				:	str							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
												__lowercase      :							Optional[Any]				=     [floats_list((1, x)							)[0] for x in range(800		,     1400		,     200							)]

												__lowercase      :							int				=     ["""longest""", """max_length""", """do_not_pad"""]
												__lowercase      :							Dict				=     [None, 1600, None]
												for max_length, padding in zip(__a		,     __a							):
																			__lowercase      :							List[Any]				=     feat_extract(__a		,     padding=__a		,     max_length=__a		,     return_tensors="""np"""							)
																			__lowercase      :							Optional[int]				=     processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:800]							)
																			self.assertTrue(input_values[0][800:].sum() < 1E-6							)
																			self._check_zero_mean_unit_variance(input_values[1][:1000]							)
																			self.assertTrue(input_values[0][1000:].sum() < 1E-6							)
																			self._check_zero_mean_unit_variance(input_values[2][:1200]							)



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							int				=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
												__lowercase      :							Optional[int]				=     range(800		,     1400		,     200							)
												__lowercase      :							Optional[Any]				=     [floats_list((1, x)							)[0] for x in lengths]

												__lowercase      :							str				=     ["""longest""", """max_length""", """do_not_pad"""]
												__lowercase      :							Union[str, Any]				=     [None, 1600, None]

												for max_length, padding in zip(__a		,     __a							):
																			__lowercase      :							Optional[int]				=     feat_extract(__a		,     max_length=__a		,     padding=__a							)
																			__lowercase      :							Any				=     processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:800]							)
																			self._check_zero_mean_unit_variance(input_values[1][:1000]							)
																			self._check_zero_mean_unit_variance(input_values[2][:1200]							)



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


												"""simple docstring"""

												__lowercase      :							str				=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
												__lowercase      :							Any				=     [floats_list((1, x)							)[0] for x in range(800		,     1400		,     200							)]
												__lowercase      :							List[str]				=     feat_extract(
												    __a		,     truncation=__a		,     max_length=1000		,     padding="""max_length"""		,     return_tensors="""np"""							)
												__lowercase      :							int				=     processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]							)
												self._check_zero_mean_unit_variance(input_values[1]							)
												self._check_zero_mean_unit_variance(input_values[2]							)



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
												__lowercase      :							List[Any]				=     [floats_list((1, x)							)[0] for x in range(800		,     1400		,     200							)]
												__lowercase      :							List[str]				=     feat_extract(
												    __a		,     truncation=__a		,     max_length=1000		,     padding="""longest"""		,     return_tensors="""np"""							)
												__lowercase      :							int				=     processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]							)
												self._check_zero_mean_unit_variance(input_values[1, :1000]							)
												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, 1000)							)

												__lowercase      :							Dict				=     [floats_list((1, x)							)[0] for x in range(800		,     1400		,     200							)]
												__lowercase      :							Optional[int]				=     feat_extract(
												    __a		,     truncation=__a		,     max_length=2000		,     padding="""longest"""		,     return_tensors="""np"""							)
												__lowercase      :							Optional[int]				=     processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]							)
												self._check_zero_mean_unit_variance(input_values[1, :1000]							)
												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, 1200)							)



					@require_torch
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												import torch

												__lowercase      :							List[Any]				=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
												__lowercase      :							Optional[int]				=     np.random.rand(100							).astype(np.floataa							)
												__lowercase      :							List[str]				=     np_speech_inputs.tolist()

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



					@slow
					@require_torch
					def       lowerCAmelCase				(  self				:	List[Any]							)			->  List[Any]:


												"""simple docstring"""

												for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																			__lowercase      :							Optional[int]				=     WavaVecaConfig.from_pretrained(__a							)
																			__lowercase      :							List[str]				=     WavaVecaFeatureExtractor.from_pretrained(__a							)

																			# only "layer" feature extraction norm should make use of
																			# attention_mask
																			self.assertEqual(feat_extract.return_attention_mask		,     config.feat_extract_norm == """layer"""							)







from torch import nn

class 		lowerCAmelCase     ( nn.Module			):

					'''simple docstring'''

					def __init__(  self				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__()
												__lowercase      :							int				=     class_size
												__lowercase      :							int				=     embed_size
												# self.mlp1 = nn.Linear(embed_size, embed_size)
												# self.mlp2 = (nn.Linear(embed_size, class_size))
												__lowercase      :							str				=     nn.Linear(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	int							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							str				=     self.mlp(__a							)
												return logits