





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


_UpperCAmelCase        =				random.Random()




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=1.0					,		UpperCamelCase_=None					,		UpperCamelCase_=None  )     ->		Union[str, Any]:
       if rng is None:
              UpperCamelCase_          =  global_rng

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

       return values



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def __init__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int=7					,   _SCREAMING_SNAKE_CASE: List[str]=400					,   _SCREAMING_SNAKE_CASE: Dict=2000					,   _SCREAMING_SNAKE_CASE: Optional[int]=1					,   _SCREAMING_SNAKE_CASE: Tuple=0.0					,   _SCREAMING_SNAKE_CASE: List[str]=16000					,   _SCREAMING_SNAKE_CASE: int=True					,   _SCREAMING_SNAKE_CASE: Optional[Any]=True					,   )       -> List[Any]:




              """simple docstring"""


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

       def lowercase (		self: Tuple		)       -> Union[str, Any]:




              """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 lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False		)       -> Any:




              """simple docstring"""


              def _flatten(_SCREAMING_SNAKE_CASE: List[str]		):
                     return list(itertools.chain(*_SCREAMING_SNAKE_CASE		)		)

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

              return speech_inputs



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					List[str]         =   WavaVecaFeatureExtractor

       def lowercase (		self: Any		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  WavaVecaFeatureExtractionTester(self		)

       def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: Dict		)       -> Tuple:




              """simple docstring"""


              self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE					,   axis=0		) < 1e-3		)		)
              self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE					,   axis=0		) - 1		) < 1e-3		)		)

       def lowercase (		self: Optional[Any]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
              # create three inputs of length 800, 1000, and 1200
              UpperCamelCase_          =  [floats_list((1, x)		)[0] for x in range(800					,   1400					,   200		)]
              UpperCamelCase_          =  [np.asarray(_SCREAMING_SNAKE_CASE		) for speech_input in speech_inputs]

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

              # Test batched
              UpperCamelCase_          =  feat_extract(_SCREAMING_SNAKE_CASE					,   return_tensors="np"		).input_values
              UpperCamelCase_          =  feat_extract(_SCREAMING_SNAKE_CASE					,   return_tensors="np"		).input_values
              for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   atol=1e-3		)		)

              # Test 2-D numpy arrays are batched.
              UpperCamelCase_          =  [floats_list((1, x)		)[0] for x in (800, 800, 800)]
              UpperCamelCase_          =  np.asarray(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  feat_extract(_SCREAMING_SNAKE_CASE					,   return_tensors="np"		).input_values
              UpperCamelCase_          =  feat_extract(_SCREAMING_SNAKE_CASE					,   return_tensors="np"		).input_values
              for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   atol=1e-3		)		)

       def lowercase (		self: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
              UpperCamelCase_          =  [floats_list((1, x)		)[0] for x in range(800					,   1400					,   200		)]

              UpperCamelCase_          =  ["longest", "max_length", "do_not_pad"]
              UpperCamelCase_          =  [None, 1600, None]
              for max_length, padding in zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  feat_extract(_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE					,   max_length=_SCREAMING_SNAKE_CASE					,   return_tensors="np"		)
                     UpperCamelCase_          =  processed.input_values

                     self._check_zero_mean_unit_variance(input_values[0][:800]		)
                     self.assertTrue(input_values[0][800:].sum() < 1e-6		)
                     self._check_zero_mean_unit_variance(input_values[1][:1000]		)
                     self.assertTrue(input_values[0][1000:].sum() < 1e-6		)
                     self._check_zero_mean_unit_variance(input_values[2][:1200]		)

       def lowercase (		self: int		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
              UpperCamelCase_          =  range(800					,   1400					,   200		)
              UpperCamelCase_          =  [floats_list((1, x)		)[0] for x in lengths]

              UpperCamelCase_          =  ["longest", "max_length", "do_not_pad"]
              UpperCamelCase_          =  [None, 1600, None]

              for max_length, padding in zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  feat_extract(_SCREAMING_SNAKE_CASE					,   max_length=_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  processed.input_values

                     self._check_zero_mean_unit_variance(input_values[0][:800]		)
                     self._check_zero_mean_unit_variance(input_values[1][:1000]		)
                     self._check_zero_mean_unit_variance(input_values[2][:1200]		)

       def lowercase (		self: List[Any]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
              UpperCamelCase_          =  [floats_list((1, x)		)[0] for x in range(800					,   1400					,   200		)]
              UpperCamelCase_          =  feat_extract(
                  _SCREAMING_SNAKE_CASE					,   truncation=_SCREAMING_SNAKE_CASE					,   max_length=1000					,   padding="max_length"					,   return_tensors="np"		)
              UpperCamelCase_          =  processed.input_values

              self._check_zero_mean_unit_variance(input_values[0, :800]		)
              self._check_zero_mean_unit_variance(input_values[1]		)
              self._check_zero_mean_unit_variance(input_values[2]		)

       def lowercase (		self: Tuple		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
              UpperCamelCase_          =  [floats_list((1, x)		)[0] for x in range(800					,   1400					,   200		)]
              UpperCamelCase_          =  feat_extract(
                  _SCREAMING_SNAKE_CASE					,   truncation=_SCREAMING_SNAKE_CASE					,   max_length=1000					,   padding="longest"					,   return_tensors="np"		)
              UpperCamelCase_          =  processed.input_values

              self._check_zero_mean_unit_variance(input_values[0, :800]		)
              self._check_zero_mean_unit_variance(input_values[1, :1000]		)
              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, 1000)		)

              UpperCamelCase_          =  [floats_list((1, x)		)[0] for x in range(800					,   1400					,   200		)]
              UpperCamelCase_          =  feat_extract(
                  _SCREAMING_SNAKE_CASE					,   truncation=_SCREAMING_SNAKE_CASE					,   max_length=2000					,   padding="longest"					,   return_tensors="np"		)
              UpperCamelCase_          =  processed.input_values

              self._check_zero_mean_unit_variance(input_values[0, :800]		)
              self._check_zero_mean_unit_variance(input_values[1, :1000]		)
              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, 1200)		)

       @require_torch
       def lowercase (		self: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              import torch

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

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



       @slow
       @require_torch
       def lowercase (		self: List[Any]		)       -> Optional[Any]:




              """simple docstring"""


              for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                     UpperCamelCase_          =  WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE		)

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








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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[str]         =   '''openai-gpt'''
       _UpperCamelCase				:					List[str]         =   {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }

       def __init__(		self: int					,   _SCREAMING_SNAKE_CASE: int=40478					,   _SCREAMING_SNAKE_CASE: Optional[Any]=512					,   _SCREAMING_SNAKE_CASE: List[Any]=768					,   _SCREAMING_SNAKE_CASE: str=12					,   _SCREAMING_SNAKE_CASE: Any=12					,   _SCREAMING_SNAKE_CASE: Optional[int]="gelu"					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: Optional[int]=0.1					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0.1					,   _SCREAMING_SNAKE_CASE: Any=1e-5					,   _SCREAMING_SNAKE_CASE: Tuple=0.02					,   _SCREAMING_SNAKE_CASE: str="cls_index"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=0.1					,   **_SCREAMING_SNAKE_CASE: List[Any]					,   )       -> int:




              """simple docstring"""


              UpperCamelCase_          =  vocab_size
              UpperCamelCase_          =  n_positions
              UpperCamelCase_          =  n_embd
              UpperCamelCase_          =  n_layer
              UpperCamelCase_          =  n_head
              UpperCamelCase_          =  afn
              UpperCamelCase_          =  resid_pdrop
              UpperCamelCase_          =  embd_pdrop
              UpperCamelCase_          =  attn_pdrop
              UpperCamelCase_          =  layer_norm_epsilon
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  summary_type
              UpperCamelCase_          =  summary_use_proj
              UpperCamelCase_          =  summary_activation
              UpperCamelCase_          =  summary_first_dropout
              UpperCamelCase_          =  summary_proj_to_labels
              super().__init__(**_SCREAMING_SNAKE_CASE		)

