
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


lowercase        =  random.Random()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      Dict=1.0			,				UpperCAmelCase__				:      Any=None			,				UpperCAmelCase__				:      Optional[int]=None       )  ->    Any:
							if rng is None:
														lowerCamelCase_							  =					global_rng

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

							return values





class       __A(				unittest.TestCase       ):

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]=7   ,					__UpperCamelCase					:     Dict=4_0_0   ,					__UpperCamelCase					:     List[Any]=2_0_0_0   ,					__UpperCamelCase					:     Union[str, Any]=1   ,					__UpperCamelCase					:     Union[str, Any]=0.0   ,					__UpperCamelCase					:     Optional[int]=1_6_0_0_0   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     Union[str, Any]=True   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					min_seq_length
												lowerCamelCase_							  =					max_seq_length
												lowerCamelCase_							  =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCamelCase_							  =					feature_size
												lowerCamelCase_							  =					padding_value
												lowerCamelCase_							  =					sampling_rate
												lowerCamelCase_							  =					return_attention_mask
												lowerCamelCase_							  =					do_normalize

					def    lowercase__      (	self					:     Tuple   ):
												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    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     List[str]=False   ):
												def _flatten(__UpperCamelCase					:     List[Any]   ):
																			return list(itertools.chain(*__UpperCamelCase   )   )

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

												return speech_inputs





class       __A(				UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =       WavaVecaFeatureExtractor

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					WavaVecaFeatureExtractionTester(self   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[str]   ):
												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    lowercase__      (	self					:     Any   ):
												# Tests that all call wrap to encode_plus and batch_encode_plus
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												# create three inputs of length 800, 1000, and 1200
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ) for speech_input in speech_inputs]

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

												# Test batched
												lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					return_tensors="""np"""   ).input_values
												lowerCamelCase_							  =					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   )   )

												# Test 2-D numpy arrays are batched.
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
												lowerCamelCase_							  =					np.asarray(__UpperCamelCase   )
												lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					return_tensors="""np"""   ).input_values
												lowerCamelCase_							  =					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    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]

												lowerCamelCase_							  =					["""longest""", """max_length""", """do_not_pad"""]
												lowerCamelCase_							  =					[None, 1_6_0_0, None]
												for max_length, padding in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					feat_extract(__UpperCamelCase   ,					padding=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					return_tensors="""np"""   )
																			lowerCamelCase_							  =					processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
																			self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6   )
																			self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
																			self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6   )
																			self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					range(8_0_0   ,					1_4_0_0   ,					2_0_0   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in lengths]

												lowerCamelCase_							  =					["""longest""", """max_length""", """do_not_pad"""]
												lowerCamelCase_							  =					[None, 1_6_0_0, None]

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

																			self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
																			self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
																			self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					feat_extract(
												    __UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=1_0_0_0   ,					padding="""max_length"""   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
												self._check_zero_mean_unit_variance(input_values[1]   )
												self._check_zero_mean_unit_variance(input_values[2]   )

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					feat_extract(
												    __UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=1_0_0_0   ,					padding="""longest"""   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
												self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
												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, 1_0_0_0)   )

												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					feat_extract(
												    __UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=2_0_0_0   ,					padding="""longest"""   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
												self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
												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, 1_2_0_0)   )

					@require_torch
					def    lowercase__      (	self					:     Union[str, Any]   ):
												import torch

												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
												lowerCamelCase_							  =					np.random.rand(1_0_0   ).astype(np.floataa   )
												lowerCamelCase_							  =					np_speech_inputs.tolist()

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



					@slow
					@require_torch
					def    lowercase__      (	self					:     Any   ):
												# this test makes sure that models that are using
												# group norm don't have their feature extractor return the
												# attention_mask
												for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																			lowerCamelCase_							  =					WavaVecaConfig.from_pretrained(__UpperCamelCase   )
																			lowerCamelCase_							  =					WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase   )

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







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.generation import DisjunctiveConstraint





@require_torch
class       __A(				unittest.TestCase       ):

					def    lowercase__      (	self					:     Optional[Any]   ):
												# For consistency across different places the DisjunctiveConstraint is called,
												# dc.token_ids is a list of integers. It is also initialized only by integers.

												lowerCamelCase_							  =					[[1, 2, 4], [1, 2, 3, 4]]
												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )
												self.assertTrue(isinstance(dc.token_ids   ,					__UpperCamelCase   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

					def    lowercase__      (	self					:     List[Any]   ):
												# We can't have constraints that are complete subsets of another. This leads to a preverse
												# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
												# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
												# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
												# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
												lowerCamelCase_							  =					[[1, 2], [1, 2, 3, 4]]

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(__UpperCamelCase   )  # fails here

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(3   )
												lowerCamelCase_							  =					stepped is True and completed is True and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 3]   )



					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(4   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2, 4]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

												dc.reset()

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 3   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 2   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.remaining() == 0   )
												self.assertTrue(dc.current_seq == [1, 2, 5]   )





