


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

UpperCAmelCase      =   random.Random()






def 	__lowerCAmelCase      (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None )->  Dict:




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

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=7		,				_UpperCAmelCase=4_00		,				_UpperCAmelCase=20_00		,				_UpperCAmelCase=24		,				_UpperCAmelCase=24		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=1_60_00		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				):
       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_	=				num_mel_bins
       snake_case_	=				padding_value
       snake_case_	=				sampling_rate
       snake_case_	=				return_attention_mask
       snake_case_	=				do_normalize



 def 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False ):
       def _flatten(_UpperCAmelCase ):
             return list(itertools.chain(*_UpperCAmelCase ) )

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







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

 '''simple docstring'''

 __snake_case												=  SpeechaTextFeatureExtractor if is_speech_available() else None



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				SpeechaTextFeatureExtractionTester(self )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       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 						UpperCamelCase__					(     self ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       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(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				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
       snake_case_	=				feature_extractor(speech_inputs[0]		,				return_tensors='''np''' ).input_features
       snake_case_	=				feature_extractor(np_speech_inputs[0]		,				return_tensors='''np''' ).input_features
       self.assertTrue(np.allclose(_UpperCAmelCase		,				_UpperCAmelCase		,				atol=1E-3 ) )

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



 def 						UpperCamelCase__					(     self ):
       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, None]
       for max_length, padding in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				feature_extractor(
                 _UpperCAmelCase		,				padding=_UpperCAmelCase		,				max_length=_UpperCAmelCase		,				return_attention_mask=_UpperCAmelCase )
             snake_case_	=				inputs.input_features
             snake_case_	=				inputs.attention_mask
             snake_case_	=				[np.sum(_UpperCAmelCase ) 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 						UpperCamelCase__					(     self ):
       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, None]
       for max_length, padding in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				feature_extractor(
                 _UpperCAmelCase		,				max_length=_UpperCAmelCase		,				padding=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase )
             snake_case_	=				inputs.input_features
             snake_case_	=				inputs.attention_mask
             snake_case_	=				[np.sum(_UpperCAmelCase ) 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 						UpperCamelCase__					(     self ):
       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_	=				feature_extractor(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=4		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				inputs.input_features
       snake_case_	=				inputs.attention_mask
       snake_case_	=				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 						UpperCamelCase__					(     self ):
       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_	=				feature_extractor(
           _UpperCAmelCase		,				padding='''longest'''		,				max_length=4		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				inputs.input_features
       snake_case_	=				inputs.attention_mask
       snake_case_	=				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) )

       snake_case_	=				[floats_list((1, x) )[0] for x in range(8_00		,				14_00		,				2_00 )]
       snake_case_	=				feature_extractor(
           _UpperCAmelCase		,				padding='''longest'''		,				max_length=16		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				return_attention_mask=_UpperCAmelCase		,				)
       snake_case_	=				inputs.input_features
       snake_case_	=				inputs.attention_mask
       snake_case_	=				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 						UpperCamelCase__					(     self ):
       import torch

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				np.random.rand(1_00		,				32 ).astype(np.floataa )
       snake_case_	=				np_speech_inputs.tolist()

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       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 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				np.array([
           -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
           -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
           -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
       ] )
       # fmt: on

       snake_case_	=				self._load_datasamples(1 )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       snake_case_	=				feature_extractor(_UpperCAmelCase		,				return_tensors='''pt''' ).input_features
       self.assertEquals(input_features.shape		,				(1, 5_84, 24) )
       self.assertTrue(np.allclose(input_features[0, 0, :30]		,				_UpperCAmelCase		,				atol=1E-4 ) )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase      =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ViTMSNModel""",
       """ViTMSNForImageClassification""",
       """ViTMSNPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase      =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)