


from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE						:Any			=  {
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'ernie_m'
  _SCREAMING_SNAKE_CASE		     =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}



  def __init__(  self							:							int     ,					_lowerCAmelCase							:							int = 2_5_0_0_0_2     ,					_lowerCAmelCase							:							int = 7_6_8     ,					_lowerCAmelCase							:							int = 1_2     ,					_lowerCAmelCase							:							int = 1_2     ,					_lowerCAmelCase							:							int = 3_0_7_2     ,					_lowerCAmelCase							:							str = "gelu"     ,					_lowerCAmelCase							:							float = 0.1     ,					_lowerCAmelCase							:							float = 0.1     ,					_lowerCAmelCase							:							int = 5_1_4     ,					_lowerCAmelCase							:							float = 0.02     ,					_lowerCAmelCase							:							int = 1     ,					_lowerCAmelCase							:							float = 1e-05     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Optional[Any]=0.0     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Optional[int]:




     """simple docstring"""
     super().__init__(pad_token_id=_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       vocab_size
     snake_case_            =       hidden_size
     snake_case_            =       num_hidden_layers
     snake_case_            =       num_attention_heads
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       hidden_dropout_prob
     snake_case_            =       attention_probs_dropout_prob
     snake_case_            =       max_position_embeddings
     snake_case_            =       initializer_range
     snake_case_            =       layer_norm_eps
     snake_case_            =       classifier_dropout
     snake_case_            =       is_decoder
     snake_case_            =       act_dropout








import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

SCREAMING_SNAKE_CASE						:Optional[int]			=  random.Random()



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Tuple=1.0		,    lowerCAmelCase_				:Optional[int]=None		,    lowerCAmelCase_				:List[Any]=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							:							Optional[int]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=7     ,					_lowerCAmelCase							:							List[Any]=4_0_0     ,					_lowerCAmelCase							:							Optional[Any]=2_0_0_0     ,					_lowerCAmelCase							:							Tuple=1_0     ,					_lowerCAmelCase							:							Optional[int]=1_6_0     ,					_lowerCAmelCase							:							List[Any]=8     ,					_lowerCAmelCase							:							List[Any]=0.0     ,					_lowerCAmelCase							:							Dict=4_0_0_0     ,					_lowerCAmelCase							:							str=False     ,					_lowerCAmelCase							:							List[str]=True     ,					) ->			str:




     """simple docstring"""
     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_            =       padding_value
     snake_case_            =       sampling_rate
     snake_case_            =       return_attention_mask
     snake_case_            =       do_normalize
     snake_case_            =       feature_size
     snake_case_            =       chunk_length
     snake_case_            =       hop_length



  def        lowerCAmelCase__	(  self							:							int	) ->			str:




     """simple docstring"""
     return {
         "feature_size": self.feature_size,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							Optional[Any]=False     ,					_lowerCAmelCase							:							List[Any]=False	) ->			int:




     """simple docstring"""
     def _flatten(_lowerCAmelCase							:							Union[str, Any]	):
        return list(itertools.chain(*_lowerCAmelCase	)	)

     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(_lowerCAmelCase	) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							WhisperFeatureExtractor if is_speech_available() else None



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




     """simple docstring"""
     snake_case_            =       WhisperFeatureExtractionTester(self	)



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




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       feat_extract_first.save_pretrained(_lowerCAmelCase	)[0]
        check_json_file_has_correct_format(_lowerCAmelCase	)
        snake_case_            =       self.feature_extraction_class.from_pretrained(_lowerCAmelCase	)

     snake_case_            =       feat_extract_first.to_dict()
     snake_case_            =       feat_extract_second.to_dict()
     snake_case_            =       feat_extract_first.mel_filters
     snake_case_            =       feat_extract_second.mel_filters
     self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase	)	)
     self.assertEqual(_lowerCAmelCase     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			str:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       os.path.join(_lowerCAmelCase     ,					"feat_extract.json"	)
        feat_extract_first.to_json_file(_lowerCAmelCase	)
        snake_case_            =       self.feature_extraction_class.from_json_file(_lowerCAmelCase	)

     snake_case_            =       feat_extract_first.to_dict()
     snake_case_            =       feat_extract_second.to_dict()
     snake_case_            =       feat_extract_first.mel_filters
     snake_case_            =       feat_extract_second.mel_filters
     self.assertTrue(np.allclose(_lowerCAmelCase     ,					_lowerCAmelCase	)	)
     self.assertEqual(_lowerCAmelCase     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			Optional[int]:




     """simple docstring"""
     # 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_0_0     ,					1_4_0_0     ,					2_0_0	)]
     snake_case_            =       [np.asarray(_lowerCAmelCase	) for speech_input in speech_inputs]

     # Test feature size
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					padding="max_length"     ,					return_tensors="np"	).input_features
     self.assertTrue(input_features.ndim == 3	)
     self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames	)
     self.assertTrue(input_features.shape[-2] == 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(_lowerCAmelCase     ,					_lowerCAmelCase     ,					atol=1e-3	)	)

     # Test batched
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       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.
     snake_case_            =       [floats_list((1, x)	)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     snake_case_            =       np.asarray(_lowerCAmelCase	)
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       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 truncation required
     snake_case_            =       [floats_list((1, x)	)[0] for x in range(2_0_0     ,					(feature_extractor.n_samples + 5_0_0)     ,					2_0_0	)]
     snake_case_            =       [np.asarray(_lowerCAmelCase	) for speech_input in speech_inputs]

     snake_case_            =       [x[: feature_extractor.n_samples] for x in speech_inputs]
     snake_case_            =       [np.asarray(_lowerCAmelCase	) for speech_input in speech_inputs_truncated]

     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="np"	).input_features
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




     """simple docstring"""
     import torch

     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       np.random.rand(1_0_0     ,					3_2	).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        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Dict	) ->			int:




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

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



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			Optional[Any]:




     """simple docstring"""
     # fmt: off
     snake_case_            =       torch.tensor(
         [
             0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
             0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
             0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
             -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
         ]	)
     # fmt: on

     snake_case_            =       self._load_datasamples(1	)
     snake_case_            =       WhisperFeatureExtractor()
     snake_case_            =       feature_extractor(_lowerCAmelCase     ,					return_tensors="pt"	).input_features
     self.assertEqual(input_features.shape     ,					(1, 8_0, 3_0_0_0)	)
     self.assertTrue(torch.allclose(input_features[0, 0, :3_0]     ,					_lowerCAmelCase     ,					atol=1e-4	)	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     snake_case_            =       self._load_datasamples(1	)[0]
     snake_case_            =       ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
     snake_case_            =       feat_extract.zero_mean_unit_var_norm([audio]     ,					attention_mask=_lowerCAmelCase	)[0]

     self.assertTrue(np.all(np.mean(_lowerCAmelCase	) < 1e-3	)	)
     self.assertTrue(np.all(np.abs(np.var(_lowerCAmelCase	) - 1	) < 1e-3	)	)




