

import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase      =   random.Random()

if is_torch_available():
   import torch






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None )->  List[Any]:




      """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







class lowerCAmelCase_						(			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=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_	=				padding_value
       snake_case_	=				sampling_rate
       snake_case_	=				return_attention_mask
       snake_case_	=				do_normalize



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

       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(_A ) for x in speech_inputs]

       return speech_inputs







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

 '''simple docstring'''

 __snake_case												=  ASTFeatureExtractor



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



 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(_A ) 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(_A		,				_A		,				atol=1E-3 ) )

       # Test batched
       snake_case_	=				feat_extract(_A		,				padding=_A		,				return_tensors='''np''' ).input_values
       snake_case_	=				feat_extract(_A		,				padding=_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.
       snake_case_	=				[floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
       snake_case_	=				np.asarray(_A )
       snake_case_	=				feat_extract(_A		,				return_tensors='''np''' ).input_values
       snake_case_	=				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 ) )



 @require_torch
 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 ).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 						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(_A ) )[:num_samples]['audio']

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





 @require_torch
 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				torch.tensor(
           [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
            -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
            -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
            -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869] )
       # fmt: on

       snake_case_	=				self._load_datasamples(1 )
       snake_case_	=				ASTFeatureExtractor()
       snake_case_	=				feature_extractor(_A		,				return_tensors='''pt''' ).input_values
       self.assertEquals(input_values.shape		,				(1, 10_24, 1_28) )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]		,				_A		,				atol=1E-4 ) )



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "convnextv2"



 def __init__(     self		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=4		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-12		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=2_24		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase )

       snake_case_	=				num_channels
       snake_case_	=				patch_size
       snake_case_	=				num_stages
       snake_case_	=				[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
       snake_case_	=				[3, 3, 9, 3] if depths is None else depths
       snake_case_	=				hidden_act
       snake_case_	=				initializer_range
       snake_case_	=				layer_norm_eps
       snake_case_	=				drop_path_rate
       snake_case_	=				image_size
       snake_case_	=				['''stem'''] + [F'''stage{idx}''' for idx in range(1		,				len(self.depths ) + 1 )]
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(
           out_features=_UpperCAmelCase		,				out_indices=_UpperCAmelCase		,				stage_names=self.stage_names )