


'''simple docstring'''






import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     BioGptForCausalLM,
     BioGptForSequenceClassification,
     BioGptForTokenClassification,
     BioGptModel,
     BioGptTokenizer,
 )
 from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=7 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=False ,			__magic_name__=True ,			__magic_name__=99 ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=5_12 ,			__magic_name__=16 ,			__magic_name__=2 ,			__magic_name__=0.0_2 ,			__magic_name__=3 ,			__magic_name__=4 ,			__magic_name__=None ,			)   ->							List[Any]:
      _a      =							parent
      _a      =							batch_size
      _a      =							seq_length
      _a      =							is_training
      _a      =							use_input_mask
      _a      =							use_token_type_ids
      _a      =							use_labels
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							hidden_act
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							type_vocab_size
      _a      =							type_sequence_label_size
      _a      =							initializer_range
      _a      =							num_labels
      _a      =							num_choices
      _a      =							scope
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

      _a      =							None
      if self.use_input_mask:
           _a      =							random_attention_mask([self.batch_size, self.seq_length]  )

      _a      =							None
      if self.use_token_type_ids:
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size  )

      _a      =							None
      _a      =							None
      _a      =							None
      if self.use_labels:
           _a      =							ids_tensor([self.batch_size] ,			self.type_sequence_label_size  )
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.num_labels  )
           _a      =							ids_tensor([self.batch_size] ,			self.num_choices  )

      _a      =							self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      return BioGptConfig(
          vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			is_decoder=SCREAMING_SNAKE_CASE_ ,			initializer_range=self.initializer_range ,			)
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							BioGptModel(config=SCREAMING_SNAKE_CASE_  )
      model.to(SCREAMING_SNAKE_CASE_  )
      model.eval()
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_  )
      _a      =							model(SCREAMING_SNAKE_CASE_  )
      self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			)   ->							List[str]:
      _a      =							BioGptForCausalLM(config=SCREAMING_SNAKE_CASE_  )
      model.to(SCREAMING_SNAKE_CASE_  )
      model.eval()
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_ ,			token_type_ids=SCREAMING_SNAKE_CASE_ ,			labels=SCREAMING_SNAKE_CASE_  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			*__magic_name__  )   ->							List[str]:
      _a      =							BioGptModel(config=SCREAMING_SNAKE_CASE_  )
      model.to(SCREAMING_SNAKE_CASE_  )
      model.eval()

      # create attention mask
      _a      =							torch.ones(input_ids.shape ,			dtype=torch.long ,			device=SCREAMING_SNAKE_CASE_  )
      _a      =							self.seq_length // 2
      _a      =							0

      # first forward pass
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_  ).to_tuple()

      # create hypothetical next token and extent to next_input_ids
      _a      =							ids_tensor((self.batch_size, 1) ,			config.vocab_size  )

      # change a random masked slice from input_ids
      _a      =							ids_tensor((1,) ,			SCREAMING_SNAKE_CASE_  ).item() + 1
      _a      =							ids_tensor((self.batch_size, 1) ,			config.vocab_size  ).squeeze(-1  )
      _a      =							random_other_next_tokens

      # append to next input_ids and attn_mask
      _a      =							torch.cat([input_ids, next_tokens] ,			dim=-1  )
      _a      =							torch.cat(
          [attn_mask, torch.ones((attn_mask.shape[0], 1) ,			dtype=torch.long ,			device=SCREAMING_SNAKE_CASE_  )] ,			dim=1 ,			)

      # get two different outputs
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_  )["""last_hidden_state"""]
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			past_key_values=SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_  )["""last_hidden_state"""]

      # select random slice
      _a      =							ids_tensor((1,) ,			output_from_past.shape[-1]  ).item()
      _a      =							output_from_no_past[:, -1, random_slice_idx].detach()
      _a      =							output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			atol=1e-3  )  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			*__magic_name__  )   ->							Optional[int]:
      _a      =							BioGptModel(config=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  ).eval()

      _a      =							torch.ones(input_ids.shape ,			dtype=torch.long ,			device=SCREAMING_SNAKE_CASE_  )

      # first forward pass
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_ ,			use_cache=SCREAMING_SNAKE_CASE_  )

      _a      =							outputs.to_tuple()

      # create hypothetical multiple next token and extent to next_input_ids
      _a      =							ids_tensor((self.batch_size, 3) ,			config.vocab_size  )
      _a      =							ids_tensor((self.batch_size, 3) ,			2  )

      # append to next input_ids and
      _a      =							torch.cat([input_ids, next_tokens] ,			dim=-1  )
      _a      =							torch.cat([attention_mask, next_attn_mask] ,			dim=-1  )

      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_  )["""last_hidden_state"""]
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_ ,			past_key_values=SCREAMING_SNAKE_CASE_  )[
          """last_hidden_state"""
      ]

      # select random slice
      _a      =							ids_tensor((1,) ,			output_from_past.shape[-1]  ).item()
      _a      =							output_from_no_past[:, -3:, random_slice_idx].detach()
      _a      =							output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			atol=1e-3  )  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			*__magic_name__ ,			__magic_name__=False  )   ->							Dict:
      _a      =							BioGptForCausalLM(SCREAMING_SNAKE_CASE_  )
      model.to(SCREAMING_SNAKE_CASE_  )
      if gradient_checkpointing:
           model.gradient_checkpointing_enable()

      _a      =							model(SCREAMING_SNAKE_CASE_ ,			labels=SCREAMING_SNAKE_CASE_  )
      self.parent.assertEqual(result.loss.shape ,			()  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )
      result.loss.backward()
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			*__magic_name__  )   ->							List[Any]:
      _a      =							BioGptModel(SCREAMING_SNAKE_CASE_  )
      _a      =							model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers  )
      for key in model.state_dict().keys():
           if "c_proj" in key and "weight" in key:
                self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]  ) - model_std  ) ,			0.0_0_1  )
                self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]  ) - 0.0  ) ,			0.0_1  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			*__magic_name__  )   ->							Tuple:
      _a      =							self.num_labels
      _a      =							BioGptForTokenClassification(SCREAMING_SNAKE_CASE_  )
      model.to(SCREAMING_SNAKE_CASE_  )
      model.eval()
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_ ,			token_type_ids=SCREAMING_SNAKE_CASE_  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)  )




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.prepare_config_and_inputs()
      (
          _a							
      )      =							config_and_inputs
      _a      =							{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict





@require_torch
class 	a       (       _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase	   =		(BioGptForCausalLM,) if is_torch_available() else ()
 _lowerCAmelCase	   =		(
     {
         'feature-extraction': BioGptModel,
         'text-classification': BioGptForSequenceClassification,
         'text-generation': BioGptForCausalLM,
         'token-classification': BioGptForTokenClassification,
         'zero-shot': BioGptForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							BioGptModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=SCREAMING_SNAKE_CASE_ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _a      =							type
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_attention_mask_past(*SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_forward_and_backwards(*SCREAMING_SNAKE_CASE_ ,			gradient_checkpointing=SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_model_past_large_inputs(*SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_weight_initialization(*SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_biogpt_for_token_classification(*SCREAMING_SNAKE_CASE_  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
      model.to(SCREAMING_SNAKE_CASE_  )
      _a      =							BioGptTokenizer.from_pretrained('microsoft/biogpt'  )

      _a      =							"""left"""

      # Define PAD Token = EOS Token = 50256
      _a      =							tokenizer.eos_token
      _a      =							model.config.eos_token_id

      # use different length sentences to test batching
      _a      =							[
          """Hello, my dog is a little""",
          """Today, I""",
      ]

      _a      =							tokenizer(SCREAMING_SNAKE_CASE_ ,			return_tensors='pt' ,			padding=SCREAMING_SNAKE_CASE_  )
      _a      =							inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_  )

      _a      =							model.generate(
          input_ids=SCREAMING_SNAKE_CASE_ ,			attention_mask=inputs['attention_mask'].to(SCREAMING_SNAKE_CASE_  ) ,			)

      _a      =							tokenizer(sentences[0] ,			return_tensors='pt'  ).input_ids.to(SCREAMING_SNAKE_CASE_  )
      _a      =							model.generate(input_ids=SCREAMING_SNAKE_CASE_  )

      _a      =							inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
      _a      =							tokenizer(sentences[1] ,			return_tensors='pt'  ).input_ids.to(SCREAMING_SNAKE_CASE_  )
      _a      =							model.generate(input_ids=SCREAMING_SNAKE_CASE_ ,			max_length=model.config.max_length - num_paddings  )

      _a      =							tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ ,			skip_special_tokens=SCREAMING_SNAKE_CASE_  )
      _a      =							tokenizer.decode(output_non_padded[0] ,			skip_special_tokens=SCREAMING_SNAKE_CASE_  )
      _a      =							tokenizer.decode(output_padded[0] ,			skip_special_tokens=SCREAMING_SNAKE_CASE_  )

      _a      =							[
          """Hello, my dog is a little bit bigger than a little bit.""",
          """Today, I have a good idea of how to use the information""",
      ]
      self.assertListEqual(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
      self.assertListEqual(SCREAMING_SNAKE_CASE_ ,			[non_padded_sentence, padded_sentence]  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							BioGptModel.from_pretrained(SCREAMING_SNAKE_CASE_  )
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a      =							3
      _a      =							input_dict["""input_ids"""]
      _a      =							input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE_  )
      _a      =							ids_tensor([self.model_tester.batch_size] ,			self.model_tester.type_sequence_label_size  )
      _a      =							BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_  )
      model.to(SCREAMING_SNAKE_CASE_  )
      model.eval()
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_ ,			labels=SCREAMING_SNAKE_CASE_  )
      self.assertEqual(result.logits.shape ,			(self.model_tester.batch_size, self.model_tester.num_labels)  )




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a      =							3
      _a      =							"""multi_label_classification"""
      _a      =							input_dict["""input_ids"""]
      _a      =							input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE_  )
      _a      =							ids_tensor(
          [self.model_tester.batch_size, config.num_labels] ,			self.model_tester.type_sequence_label_size  ).to(torch.float  )
      _a      =							BioGptForSequenceClassification(SCREAMING_SNAKE_CASE_  )
      model.to(SCREAMING_SNAKE_CASE_  )
      model.eval()
      _a      =							model(SCREAMING_SNAKE_CASE_ ,			attention_mask=SCREAMING_SNAKE_CASE_ ,			labels=SCREAMING_SNAKE_CASE_  )
      self.assertEqual(result.logits.shape ,			(self.model_tester.batch_size, self.model_tester.num_labels)  )





@require_torch
class 	a       (       unittest.TestCase    ):
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
      _a      =							torch.tensor([[2, 48_05, 9, 6_56, 21]]  )
      _a      =							model(SCREAMING_SNAKE_CASE_  )[0]

      _a      =							4_23_84

      _a      =							torch.Size((1, 5, vocab_size)  )
      self.assertEqual(output.shape ,			SCREAMING_SNAKE_CASE_  )

      _a      =							torch.tensor(
          [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]]  )

      self.assertTrue(torch.allclose(output[:, :3, :3] ,			SCREAMING_SNAKE_CASE_ ,			atol=1e-4  )  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							BioGptTokenizer.from_pretrained('microsoft/biogpt'  )
      _a      =							BioGptForCausalLM.from_pretrained('microsoft/biogpt'  )
      model.to(SCREAMING_SNAKE_CASE_  )

      torch.manual_seed(0  )
      _a      =							tokenizer('COVID-19 is' ,			return_tensors='pt'  ).to(SCREAMING_SNAKE_CASE_  )
      _a      =							model.generate(
          **SCREAMING_SNAKE_CASE_ ,			min_length=1_00 ,			max_length=10_24 ,			num_beams=5 ,			early_stopping=SCREAMING_SNAKE_CASE_ ,			)
      _a      =							tokenizer.decode(output_ids[0] ,			skip_special_tokens=SCREAMING_SNAKE_CASE_  )

      _a      =							(
          """COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
          """ causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
          """ territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
          """ and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
          """ more than 800,000 deaths."""
      )
      self.assertEqual(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )





'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a_   :      Tuple				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		["""input_features"""]
 def __init__(	self ,			__magic_name__=80 ,			__magic_name__=1_60_00 ,			__magic_name__=1_60 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=0.0 ,			__magic_name__=False ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(
          feature_size=__magic_name__ ,			sampling_rate=__magic_name__ ,			padding_value=__magic_name__ ,			return_attention_mask=__magic_name__ ,			**__magic_name__ ,			)
      _a      =							n_fft
      _a      =							hop_length
      _a      =							chunk_length
      _a      =							chunk_length * sampling_rate
      _a      =							self.n_samples // hop_length
      _a      =							sampling_rate
      _a      =							mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2 ,			num_mel_filters=__magic_name__ ,			min_frequency=0.0 ,			max_frequency=8_0_0_0.0 ,			sampling_rate=__magic_name__ ,			norm='slaney' ,			mel_scale='slaney' ,			)
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							np.ndarray:
      _a      =							spectrogram(
          __magic_name__ ,			window_function(self.n_fft ,			'hann'  ) ,			frame_length=self.n_fft ,			hop_length=self.hop_length ,			power=2.0 ,			mel_filters=self.mel_filters ,			log_mel='log10' ,			)
      _a      =							log_spec[:, :-1]
      _a      =							np.maximum(__magic_name__ ,			log_spec.max() - 8.0  )
      _a      =							(log_spec + 4.0) / 4.0
      return log_spec
 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__ = 0.0  )   ->							List[np.ndarray]:
      if attention_mask is not None:
           _a      =							np.array(__magic_name__ ,			np.intaa  )
           _a      =							[]

           for vector, length in zip(__magic_name__ ,			attention_mask.sum(-1  )  ):
                _a      =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7  )
                if length < normed_slice.shape[0]:
                     _a      =							padding_value

                normed_input_values.append(__magic_name__  )
      else:
           _a      =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7  ) for x in input_values]

      return normed_input_values
 def __call__(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = "max_length" ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							BatchFeature:

      if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                    f' was sampled with {self.sampling_rate} and not {sampling_rate}.'  )
      else:
           logger.warning(
               'It is strongly recommended to pass the `sampling_rate` argument to this function. '
               'Failing to do so can result in silent errors that might be hard to debug.'  )

      _a      =							isinstance(__magic_name__ ,			np.ndarray  ) and len(raw_speech.shape  ) > 1
      if is_batched_numpy and len(raw_speech.shape  ) > 2:
           raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
      _a      =							is_batched_numpy or (
          isinstance(__magic_name__ ,			(list, tuple)  ) and (isinstance(raw_speech[0] ,			(np.ndarray, tuple, list)  ))
      )

      if is_batched:
           _a      =							[np.asarray([speech] ,			dtype=np.floataa  ).T for speech in raw_speech]
      elif not is_batched and not isinstance(__magic_name__ ,			np.ndarray  ):
           _a      =							np.asarray(__magic_name__ ,			dtype=np.floataa  )
      elif isinstance(__magic_name__ ,			np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
           _a      =							raw_speech.astype(np.floataa  )

      # always return batch
      if not is_batched:
           _a      =							[np.asarray([raw_speech]  ).T]

      _a      =							BatchFeature({'input_features': raw_speech}  )

      # convert into correct format for padding

      _a      =							self.pad(
          __magic_name__ ,			padding=__magic_name__ ,			max_length=max_length if max_length else self.n_samples ,			truncation=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=return_attention_mask or do_normalize ,			)

      # zero-mean and unit-variance normalization
      if do_normalize:
           _a      =							self.zero_mean_unit_var_norm(
               padded_inputs['input_features'] ,			attention_mask=padded_inputs['attention_mask'] ,			padding_value=self.padding_value ,			)
           _a      =							np.stack(padded_inputs['input_features'] ,			axis=0  )

      # make sure list is in array format
      _a      =							padded_inputs.get('input_features'  ).transpose(2 ,			0 ,			1  )

      _a      =							[self._np_extract_fbank_features(__magic_name__  ) for waveform in input_features[0]]

      if isinstance(input_features[0] ,			__magic_name__  ):
           _a      =							[np.asarray(__magic_name__ ,			dtype=np.floataa  ) for feature in input_features]
      else:
           _a      =							input_features

      if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           _a      =							padded_inputs['attention_mask'][:, :: self.hop_length]

      if return_tensors is not None:
           _a      =							padded_inputs.convert_to_tensors(__magic_name__  )

      return padded_inputs




 def 							__UpperCAmelCase						(	self  )   ->							Dict[str, Any]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.__class__.__name__
      if "mel_filters" in output:
           del output["mel_filters"]
      return output

