





'''simple docstring'''


import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor

_SCREAMING_SNAKE_CASE									=				random.Random()


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       List[Any]=1.0		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]=None		,							SCREAMING_SNAKE_CASE_	:       List[Any]=None     ):



    '''simple docstring'''





    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

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




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=400      ,  _lowerCAmelCase=2000      ,  _lowerCAmelCase=24      ,  _lowerCAmelCase=24      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=16000      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  )     ->				Optional[int]:
     _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						      =   num_mel_bins
     _lowerCAmelCase						      =   padding_value
     _lowerCAmelCase						      =   sampling_rate
     _lowerCAmelCase						      =   return_attention_mask
     _lowerCAmelCase						      =   do_normalize




 def 				_snake_case (						self		)     ->				str:
     return {
         "feature_size": self.feature_size,
         "num_mel_bins": self.num_mel_bins,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }





 def 				_snake_case (						self      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False		)     ->				List[Any]:
     def _flatten(_lowerCAmelCase		):
         return list(itertools.chain(*_lowerCAmelCase		)		)

     if equal_length:
         _lowerCAmelCase						      =   [floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
     else:
         # make sure that inputs increase in size
         _lowerCAmelCase						      =   [
             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(_lowerCAmelCase		) for x in speech_inputs]
     return speech_inputs







@require_torch
@require_torchaudio
class  lowerCAmelCase_		(		__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Union[str, Any]								=       SpeechaTextFeatureExtractor if is_speech_available() else None




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   SpeechaTextFeatureExtractionTester(self		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[int]:
     self.assertTrue(np.all(np.mean(_lowerCAmelCase      ,  axis=0		) < 1E-3		)		)
     self.assertTrue(np.all(np.abs(np.var(_lowerCAmelCase      ,  axis=0		) - 1		) < 1E-3		)		)




 def 				_snake_case (						self		)     ->				List[str]:
     # 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(800      ,  1400      ,  200		)]
     _lowerCAmelCase						      =   [np.asarray(_lowerCAmelCase		) for speech_input in speech_inputs]

     # Test feature size
     _lowerCAmelCase						      =   feature_extractor(_lowerCAmelCase      ,  padding=_lowerCAmelCase      ,  return_tensors="np"		).input_features
     self.assertTrue(input_features.ndim == 3		)
     self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

     # Test not batched input
     _lowerCAmelCase						      =   feature_extractor(speech_inputs[0]      ,  return_tensors="np"		).input_features
     _lowerCAmelCase						      =   feature_extractor(np_speech_inputs[0]      ,  return_tensors="np"		).input_features
     self.assertTrue(np.allclose(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-3		)		)

     # Test batched
     _lowerCAmelCase						      =   feature_extractor(_lowerCAmelCase      ,  return_tensors="np"		).input_features
     _lowerCAmelCase						      =   feature_extractor(_lowerCAmelCase      ,  return_tensors="np"		).input_features
     for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase      ,  _lowerCAmelCase		):
         self.assertTrue(np.allclose(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-3		)		)

     # Test 2-D numpy arrays are batched.
     _lowerCAmelCase						      =   [floats_list((1, x)		)[0] for x in (800, 800, 800)]
     _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
     _lowerCAmelCase						      =   feature_extractor(_lowerCAmelCase      ,  return_tensors="np"		).input_features
     _lowerCAmelCase						      =   feature_extractor(_lowerCAmelCase      ,  return_tensors="np"		).input_features
     for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase      ,  _lowerCAmelCase		):
         self.assertTrue(np.allclose(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-3		)		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
     _lowerCAmelCase						      =   [floats_list((1, x)		)[0] for x in range(800      ,  1400      ,  200		)]

     _lowerCAmelCase						      =   ["longest", "max_length", "do_not_pad"]
     _lowerCAmelCase						      =   [None, 16, None]
     for max_length, padding in zip(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   feature_extractor(
             _lowerCAmelCase      ,  padding=_lowerCAmelCase      ,  max_length=_lowerCAmelCase      ,  return_attention_mask=_lowerCAmelCase		)
         _lowerCAmelCase						      =   inputs.input_features
         _lowerCAmelCase						      =   inputs.attention_mask
         _lowerCAmelCase						      =   [np.sum(_lowerCAmelCase		) for x in attention_mask]

         self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
         self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
         self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
     _lowerCAmelCase						      =   [floats_list((1, x)		)[0] for x in range(800      ,  1400      ,  200		)]

     _lowerCAmelCase						      =   ["longest", "max_length", "do_not_pad"]
     _lowerCAmelCase						      =   [None, 16, None]
     for max_length, padding in zip(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   feature_extractor(
             _lowerCAmelCase      ,  max_length=_lowerCAmelCase      ,  padding=_lowerCAmelCase      ,  return_tensors="np"      ,  return_attention_mask=_lowerCAmelCase		)
         _lowerCAmelCase						      =   inputs.input_features
         _lowerCAmelCase						      =   inputs.attention_mask
         _lowerCAmelCase						      =   [np.sum(_lowerCAmelCase		) for x in attention_mask]

         self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
         self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6		)
         self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
         self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6		)
         self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
     _lowerCAmelCase						      =   [floats_list((1, x)		)[0] for x in range(800      ,  1400      ,  200		)]
     _lowerCAmelCase						      =   feature_extractor(
         _lowerCAmelCase      ,  padding="max_length"      ,  max_length=4      ,  truncation=_lowerCAmelCase      ,  return_tensors="np"      ,  return_attention_mask=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   inputs.input_features
     _lowerCAmelCase						      =   inputs.attention_mask
     _lowerCAmelCase						      =   np.sum(attention_mask == 1      ,  axis=1		)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
     self._check_zero_mean_unit_variance(input_features[1]		)
     self._check_zero_mean_unit_variance(input_features[2]		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
     _lowerCAmelCase						      =   [floats_list((1, x)		)[0] for x in range(800      ,  1400      ,  200		)]
     _lowerCAmelCase						      =   feature_extractor(
         _lowerCAmelCase      ,  padding="longest"      ,  max_length=4      ,  truncation=_lowerCAmelCase      ,  return_tensors="np"      ,  return_attention_mask=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   inputs.input_features
     _lowerCAmelCase						      =   inputs.attention_mask
     _lowerCAmelCase						      =   np.sum(attention_mask == 1      ,  axis=1		)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
     self._check_zero_mean_unit_variance(input_features[2]		)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape      ,  (3, 4, 24)		)

     _lowerCAmelCase						      =   [floats_list((1, x)		)[0] for x in range(800      ,  1400      ,  200		)]
     _lowerCAmelCase						      =   feature_extractor(
         _lowerCAmelCase      ,  padding="longest"      ,  max_length=16      ,  truncation=_lowerCAmelCase      ,  return_tensors="np"      ,  return_attention_mask=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   inputs.input_features
     _lowerCAmelCase						      =   inputs.attention_mask
     _lowerCAmelCase						      =   np.sum(attention_mask == 1      ,  axis=1		)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
     self._check_zero_mean_unit_variance(input_features[2]		)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape      ,  (3, 6, 24)		)




 def 				_snake_case (						self		)     ->				List[str]:
     import torch

     _lowerCAmelCase						      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
     _lowerCAmelCase						      =   np.random.rand(100      ,  32		).astype(np.floataa		)
     _lowerCAmelCase						      =   np_speech_inputs.tolist()

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




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     from datasets import load_dataset

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

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





 def 				_snake_case (						self		)     ->				Any:
     # fmt: off
     _lowerCAmelCase						      =   np.array([
         -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
         -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
         -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
     ]		)
     # fmt: on

     _lowerCAmelCase						      =   self._load_datasamples(1		)
     _lowerCAmelCase						      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
     _lowerCAmelCase						      =   feature_extractor(_lowerCAmelCase      ,  return_tensors="pt"		).input_features
     self.assertEquals(input_features.shape      ,  (1, 584, 24)		)
     self.assertTrue(np.allclose(input_features[0, 0, :30]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)






import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )




@require_torch
class A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	int					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =3
             _lowerCamelCase			:						Dict            =250

             _lowerCamelCase			:						Tuple            =ids_tensor((batch_size, length)     ,  lowercase_					)
             _lowerCamelCase			:						str            =torch.ones((batch_size, length)     ,  device=lowercase_     ,  dtype=torch.float					) / length
             return input_ids, scores






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Dict            =StoppingCriteriaList(
                 [
                     MaxLengthCriteria(max_length=10					),
                     MaxTimeCriteria(max_time=0.1					),
                 ]					)

             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Any            =MaxLengthCriteria(max_length=10					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =MaxNewTokensCriteria(start_length=5     ,  max_new_tokens=5					)

             _lowerCamelCase      ,	_lowerCamelCase			:						str            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Dict            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						Optional[Any]            =StoppingCriteriaList([criteria]					)
             self.assertEqual(criteria_list.max_length     ,  10					)






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Tuple            =MaxTimeCriteria(max_time=0.1					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						int            =MaxTimeCriteria(max_time=0.1     ,  initial_timestamp=time.time() - 0.2					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  10					)

             with self.assertWarns(lowercase_					):
                    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  11					)

             _lowerCamelCase			:						Optional[Any]            =validate_stopping_criteria(StoppingCriteriaList()     ,  11					)

             self.assertEqual(len(lowercase_					)     ,  1					)


