






'''simple docstring'''






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A     						= logging.get_logger(__name__)




class a_     (     UpperCamelCase_				):
   _snake_case	   =				["""input_values""", """attention_mask"""]







   def __init__(self       , __a=1       , __a=1_6_0_0_0       , __a=0.0       , __a=False       , __a=True       , **__a       , )      ->   Dict:
      """simple docstring"""






      super().__init__(feature_size=__a       , sampling_rate=__a       , padding_value=__a       , **__a)
      __snake_case					:   Optional[Any]  				=			return_attention_mask
      __snake_case					:   Tuple  				=			do_normalize







   @staticmethod
   def      SCREAMING_SNAKE_CASE__       (__a       , __a       , __a = 0.0)      ->   List[np.ndarray]:
      """simple docstring"""






      if attention_mask is not None:
         __snake_case					:   Union[str, Any]  				=			np.array(__a       , np.intaa)
         __snake_case					:   Optional[Any]  				=			[]

         for vector, length in zip(__a       , attention_mask.sum(-1)):
            __snake_case					:   List[Any]  				=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
            if length < normed_slice.shape[0]:
               __snake_case					:   Union[str, Any]  				=			padding_value

            normed_input_values.append(__a)
      else:
         __snake_case					:   int  				=			[(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

      return normed_input_values







   def __call__(self       , __a       , __a = False       , __a = None       , __a = False       , __a = None       , __a = None       , __a = None       , __a = None       , **__a       , )      ->   BatchFeature:
      """simple docstring"""






      if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
            raise ValueError(
                F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
                F""" {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.')

      __snake_case					:   int  				=			isinstance(__a       , 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}""")
      __snake_case					:   Optional[int]  				=			is_batched_numpy or (
          isinstance(__a       , (list, tuple)) and (isinstance(raw_speech[0]       , (np.ndarray, tuple, list)))
      )

      # always return batch
      if not is_batched:
         __snake_case					:   Optional[int]  				=			[raw_speech]

      # convert into correct format for padding
      __snake_case					:   Optional[int]  				=			BatchFeature({'input_values': raw_speech})

      __snake_case					:   str  				=			self.pad(
          __a       , padding=__a       , max_length=__a       , truncation=__a       , pad_to_multiple_of=__a       , return_attention_mask=__a       , )

      # convert input values to correct format
      __snake_case					:   Any  				=			padded_inputs['input_values']
      if not isinstance(input_values[0]       , np.ndarray):
         __snake_case					:   Optional[int]  				=			[np.asarray(__a       , dtype=np.floataa) for array in input_values]
      elif (
          not isinstance(__a       , np.ndarray)
          and isinstance(input_values[0]       , np.ndarray)
          and input_values[0].dtype is np.dtype(np.floataa)
      ):
         __snake_case					:   Optional[int]  				=			[array.astype(np.floataa) for array in input_values]
      elif isinstance(__a       , np.ndarray) and input_values.dtype is np.dtype(np.floataa):
         __snake_case					:   int  				=			input_values.astype(np.floataa)

      # convert attention_mask to correct format
      __snake_case					:   List[Any]  				=			padded_inputs.get('attention_mask')
      if attention_mask is not None:
         __snake_case					:   Union[str, Any]  				=			[np.asarray(__a       , dtype=np.intaa) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if self.do_normalize:
         __snake_case					:   Dict  				=			(
             attention_mask
             if self._get_padding_strategies(__a       , max_length=__a) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         __snake_case					:   Dict  				=			self.zero_mean_unit_var_norm(
             padded_inputs['input_values']       , attention_mask=__a       , padding_value=self.padding_value)

      if return_tensors is not None:
         __snake_case					:   Dict  				=			padded_inputs.convert_to_tensors(__a)

      return padded_inputs







'''simple docstring'''






import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoModel,
      )
      from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class a_     :







   def __init__(self       , __a       , __a=1_3       , __a=7       , __a=True       , __a=True       , __a=True       , __a=True       , __a=9_9       , __a=3_2       , __a=5       , __a=4       , __a=3_7       , __a="gelu"       , __a=0.1       , __a=0.1       , __a=5_1_2       , __a=1_6       , __a=2       , __a=0.02       , __a=3       , __a=4       , __a=None       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			parent
      __snake_case					:   Dict  				=			batch_size
      __snake_case					:   Union[str, Any]  				=			seq_length
      __snake_case					:   str  				=			is_training
      __snake_case					:   Optional[Any]  				=			use_input_mask
      __snake_case					:   Dict  				=			use_token_type_ids
      __snake_case					:   Union[str, Any]  				=			use_labels
      __snake_case					:   Optional[Any]  				=			vocab_size
      __snake_case					:   Optional[Any]  				=			hidden_size
      __snake_case					:   int  				=			num_hidden_layers
      __snake_case					:   Any  				=			num_attention_heads
      __snake_case					:   Optional[Any]  				=			intermediate_size
      __snake_case					:   List[str]  				=			hidden_act
      __snake_case					:   List[Any]  				=			hidden_dropout_prob
      __snake_case					:   Optional[Any]  				=			attention_probs_dropout_prob
      __snake_case					:   str  				=			max_position_embeddings
      __snake_case					:   str  				=			type_vocab_size
      __snake_case					:   Dict  				=			type_sequence_label_size
      __snake_case					:   int  				=			initializer_range
      __snake_case					:   Dict  				=			num_labels
      __snake_case					:   Optional[int]  				=			num_choices
      __snake_case					:   Dict  				=			scope







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size)

      __snake_case					:   Optional[Any]  				=			None
      if self.use_input_mask:
         __snake_case					:   Union[str, Any]  				=			random_attention_mask([self.batch_size, self.seq_length])

      __snake_case					:   Tuple  				=			None
      if self.use_token_type_ids:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size)

      __snake_case					:   Dict  				=			None
      __snake_case					:   List[str]  				=			None
      __snake_case					:   List[str]  				=			None
      if self.use_labels:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size]       , self.type_sequence_label_size)
         __snake_case					:   Tuple  				=			ids_tensor([self.batch_size, self.seq_length]       , self.num_labels)
         __snake_case					:   str  				=			ids_tensor([self.batch_size]       , self.num_choices)

      __snake_case					:   List[str]  				=			self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      return YosoConfig(
          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=__a       , initializer_range=self.initializer_range       , )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.get_config()
      __snake_case					:   Tuple  				=			3_0_0
      return config







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Optional[Any]  				=			self.prepare_config_and_inputs()

      __snake_case					:   Optional[int]  				=			True
      __snake_case					:   Optional[int]  				=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      __snake_case					:   Dict  				=			ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      __snake_case					:   int  				=			model(__a       , token_type_ids=__a)
      __snake_case					:   Optional[Any]  				=			model(__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			True
      __snake_case					:   Dict  				=			YosoModel(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   List[str]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , encoder_attention_mask=__a       , )
      __snake_case					:   List[Any]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , )
      __snake_case					:   List[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			YosoForMaskedLM(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Union[str, Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			YosoForQuestionAnswering(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   str  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , start_positions=__a       , end_positions=__a       , )
      self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length))
      self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForSequenceClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[int]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForTokenClassification(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Tuple  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   str:
      """simple docstring"""






      __snake_case					:   int  				=			self.num_choices
      __snake_case					:   Tuple  				=			YosoForMultipleChoice(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Any  				=			input_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Tuple  				=			token_type_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   List[str]  				=			input_mask.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Optional[int]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , labels=__a       , )
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   int  				=			self.prepare_config_and_inputs()
      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Union[str, Any]  				=			config_and_inputs
      __snake_case					:   Optional[int]  				=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict




@require_torch
class a_     (     UpperCamelCase_		,   UpperCamelCase_		,   unittest.TestCase				):
   _snake_case	   =				(
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False

   _snake_case	   =				()
   _snake_case	   =				(
       {
           """feature-extraction""": YosoModel,
           """fill-mask""": YosoForMaskedLM,
           """question-answering""": YosoForQuestionAnswering,
           """text-classification""": YosoForSequenceClassification,
           """token-classification""": YosoForTokenClassification,
           """zero-shot""": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModelTester(self)
      __snake_case					:   List[Any]  				=			ConfigTester(self       , config_class=__a       , hidden_size=3_7)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      self.config_tester.run_common_tests()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __snake_case					:   List[Any]  				=			type
         self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   str  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__a)







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case					:   Tuple  				=			YosoModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      return




@require_torch
class a_     (     unittest.TestCase				):







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			YosoModel.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   str  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   str  				=			model(__a)[0]

      __snake_case					:   Union[str, Any]  				=			torch.Size((1, 6, 7_6_8))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Dict  				=			torch.tensor(
          [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]])

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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Tuple  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   Any  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[str]  				=			torch.Size((1, 6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   str  				=			torch.tensor(
          [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]])

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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Dict  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Optional[Any]  				=			torch.arange(4_0_9_6).unsqueeze(0)

      with torch.no_grad():
         __snake_case					:   Dict  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[Any]  				=			torch.Size((1, 4_0_9_6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Tuple  				=			torch.tensor(
          [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]])

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