



'''simple docstring'''





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


lowercase_									=    random.Random()

if is_torch_available():
  import torch




def lowerCAmelCase    (__A    ,      __A=1.0    ,      __A=None    ,      __A=None):



     """simple docstring"""



     if rng is None:
          _a           =    global_rng

     _a           =    []
     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		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=400					,  A=2_000					,  A=1					,  A=0.0					,  A=16_000					,  A=True					,  A=True					,  )       ->	Optional[Any]:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    min_seq_length
      _a           =    max_seq_length
      _a           =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      _a           =    feature_size
      _a           =    padding_value
      _a           =    sampling_rate
      _a           =    return_attention_mask
      _a           =    do_normalize



 def    a__    (self			)       ->	List[str]:


      """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    a__    (self					,  A=False					,  A=False			)       ->	Optional[Any]:


      """simple docstring"""



      def _flatten(A			):
           return list(itertools.chain(*A			)			)

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

      return speech_inputs






@require_torch
@require_torchaudio
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			int		=			ASTFeatureExtractor



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    ASTFeatureExtractionTester(self			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      _a           =    [floats_list((1, x)			)[0] for x in range(800					,  1_400					,  200			)]
      _a           =    [np.asarray(A			) for speech_input in speech_inputs]

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

      # Test batched
      _a           =    feat_extract(A					,  padding=A					,  return_tensors='''np'''			).input_values
      _a           =    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.
      _a           =    [floats_list((1, x)			)[0] for x in (800, 800, 800)]
      _a           =    np.asarray(A			)
      _a           =    feat_extract(A					,  return_tensors='''np'''			).input_values
      _a           =    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    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      import torch

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

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



 def    a__    (self					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      from datasets import load_dataset

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

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



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


      """simple docstring"""



      _a           =    torch.tensor(
          [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
           -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
           -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
           -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]			)
      # fmt: on

      _a           =    self._load_datasamples(1			)
      _a           =    ASTFeatureExtractor()
      _a           =    feature_extractor(A					,  return_tensors='''pt'''			).input_values
      self.assertEquals(input_values.shape					,  (1, 1_024, 128)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :30]					,  A					,  atol=1E-4			)			)




'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_      	=	logging.get_logger(__name__)

UpperCamelCase_      	=	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= 'gpt_neox'




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       int=5_04_32							,							UpperCamelCase_ :       Dict=61_44							,							UpperCamelCase_ :       int=44							,							UpperCamelCase_ :       Any=64							,							UpperCamelCase_ :       Optional[int]=2_45_76							,							UpperCamelCase_ :       str="gelu"							,							UpperCamelCase_ :       Tuple=0.25							,							UpperCamelCase_ :       Tuple=1_00_00							,							UpperCamelCase_ :       Tuple=0.0							,							UpperCamelCase_ :       Optional[int]=0.0							,							UpperCamelCase_ :       str=0.1							,							UpperCamelCase_ :       List[Any]=20_48							,							UpperCamelCase_ :       List[str]=0.02							,							UpperCamelCase_ :       Optional[int]=1e-5							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       Optional[Any]=0							,							UpperCamelCase_ :       str=2							,							UpperCamelCase_ :       str=False							,							UpperCamelCase_ :       Union[str, Any]=True							,							UpperCamelCase_ :       Tuple=None							,							**UpperCamelCase_ :       Any							,							)     ->				int:
													super().__init__(bos_token_id=UpperCamelCase_							,							eos_token_id=UpperCamelCase_							,							**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Dict						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Dict						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rotary_pct
													SCREAMING_SNAKE_CASE__     :Any						    = rotary_emb_base
													SCREAMING_SNAKE_CASE__     :Dict						    = attention_dropout
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_dropout
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = classifier_dropout
													SCREAMING_SNAKE_CASE__     :Dict						    = initializer_range
													SCREAMING_SNAKE_CASE__     :List[str]						    = layer_norm_eps
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_cache
													SCREAMING_SNAKE_CASE__     :Any						    = tie_word_embeddings
													SCREAMING_SNAKE_CASE__     :List[str]						    = use_parallel_residual
													SCREAMING_SNAKE_CASE__     :int						    = rope_scaling
													self._rope_scaling_validation()

													if self.hidden_size % self.num_attention_heads != 0:
																				raise ValueError(
																				    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Union[str, Any]:
													if self.rope_scaling is None:
																				return

													if not isinstance(self.rope_scaling							,							UpperCamelCase_      ) or len(self.rope_scaling      ) != 2:
																				raise ValueError(
																				    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
																				    f'''got {self.rope_scaling}'''      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.rope_scaling.get('type'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.rope_scaling.get('factor'							,							UpperCamelCase_      )
													if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																				raise ValueError(
																				    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''      )
													if rope_scaling_factor is None or not isinstance(UpperCamelCase_							,							UpperCamelCase_      ) or rope_scaling_factor <= 1.0:
																				raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )






