


"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.distilbert.modeling_tf_distilbert import (
       TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFDistilBertForMaskedLM,
       TFDistilBertForMultipleChoice,
       TFDistilBertForQuestionAnswering,
       TFDistilBertForSequenceClassification,
       TFDistilBertForTokenClassification,
       TFDistilBertModel,
   )


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				)       ->							List[Any]:
          _a    	=				parent
          _a    	=				13
          _a    	=				7
          _a    	=				True
          _a    	=				True
          _a    	=				False
          _a    	=				True
          _a    	=				99
          _a    	=				32
          _a    	=				2
          _a    	=				4
          _a    	=				37
          _a    	=				'''gelu'''
          _a    	=				0.1
          _a    	=				0.1
          _a    	=				512
          _a    	=				16
          _a    	=				2
          _a    	=				0.02
          _a    	=				3
          _a    	=				4
          _a    	=				None


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _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
          _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    	=				DistilBertConfig(
              vocab_size=self.vocab_size    ,				dim=self.hidden_size    ,				n_layers=self.num_hidden_layers    ,				n_heads=self.num_attention_heads    ,				hidden_dim=self.intermediate_size    ,				hidden_act=self.hidden_act    ,				dropout=self.hidden_dropout_prob    ,				attention_dropout=self.attention_probs_dropout_prob    ,				max_position_embeddings=self.max_position_embeddings    ,				initializer_range=self.initializer_range    ,				)

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _a    	=				TFDistilBertModel(config=__UpperCAmelCase							)
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}

          _a    	=				model(__UpperCAmelCase							)

          _a    	=				[input_ids, input_mask]

          _a    	=				model(__UpperCAmelCase							)

          self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				TFDistilBertForMaskedLM(config=__UpperCAmelCase							)
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							int:
          _a    	=				TFDistilBertForQuestionAnswering(config=__UpperCAmelCase							)
          _a    	=				{
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
          }
          _a    	=				model(__UpperCAmelCase							)
          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _a    	=				self.num_labels
          _a    	=				TFDistilBertForSequenceClassification(__UpperCAmelCase							)
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				self.num_choices
          _a    	=				TFDistilBertForMultipleChoice(__UpperCAmelCase							)
          _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
          _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
          _a    	=				{
              '''input_ids''': multiple_choice_inputs_ids,
              '''attention_mask''': multiple_choice_input_mask,
          }
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_choices)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				self.num_labels
          _a    	=				TFDistilBertForTokenClassification(__UpperCAmelCase							)
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.num_labels)							)





     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.prepare_config_and_inputs()
          ((_a)	,      (_a)	,      (_a)	,      (_a)	,      (_a)	,      (_a))    	=				config_and_inputs
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict







@require_tf
class     __lowerCamelCase  (					a__  ,					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Optional[Any]    =		(
         (
             TFDistilBertModel,
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
             TFDistilBertForTokenClassification,
             TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
     )
     A_       :	Any    =		(
         {
             'feature-extraction': TFDistilBertModel,
             'fill-mask': TFDistilBertForMaskedLM,
             'question-answering': TFDistilBertForQuestionAnswering,
             'text-classification': TFDistilBertForSequenceClassification,
             'token-classification': TFDistilBertForTokenClassification,
             'zero-shot': TFDistilBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     A_       :	Optional[Any]    =		False
     A_       :	str    =		False


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				TFDistilBertModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				dim=37							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_model(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_masked_lm(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_question_answering(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_sequence_classification(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_multiple_choice(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_token_classification(*__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
               _a    	=				TFDistilBertModel.from_pretrained(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)







@require_tf
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''							)
          _a    	=				tf.constant([[0, 1, 2, 3, 4, 5]]							)
          _a    	=				model(__UpperCAmelCase							)[0]

          _a    	=				[1, 6, 768]
          self.assertEqual(output.shape    ,				__UpperCAmelCase							)

          _a    	=				tf.constant(
              [
                  [
                      [0.19261885, -0.13732955, 0.4119799],
                      [0.22150156, -0.07422661, 0.39037204],
                      [0.22756018, -0.0896414, 0.3701467],
                  ]
              ]							)
          tf.debugging.assert_near(output[:, :3, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)



"""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


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		['input_values', 'padding_mask']


     def __init__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 24000    ,				__UpperCAmelCase = 0.0    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(feature_size=__UpperCAmelCase    ,				sampling_rate=__UpperCAmelCase    ,				padding_value=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				chunk_length_s
          _a    	=				overlap


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1    ,				int((1.0 - self.overlap) * self.chunk_length							)							)





     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				)       ->							BatchFeature:
          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 audio 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.'''							)

          if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
          elif padding is None:
               # by default let's pad the inputs
               _a    	=				True

          _a    	=				bool(
              isinstance(__UpperCAmelCase    ,				(list, tuple)							) and (isinstance(raw_audio[0]    ,				(np.ndarray, tuple, list)							))							)

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

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

          # verify inputs are valid
          for idx, example in enumerate(__UpperCAmelCase							):
               if example.ndim > 2:
                    raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'							)
               if self.feature_size == 1 and example.ndim != 1:
                    raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'							)
               if self.feature_size == 2 and example.shape[-1] != 2:
                    raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'							)

          _a    	=				None
          _a    	=				BatchFeature({'''input_values''': raw_audio}							)
          if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                    _a    	=				min(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.floor(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _a    	=				max(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.ceil(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                    _a    	=				'''max_length'''
               else:
                    _a    	=				input_values

        # normal padding on batch
          if padded_inputs is None:
               _a    	=				self.pad(
                   __UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)
               if padding:
                    _a    	=				padded_inputs.pop('''attention_mask'''							)

          _a    	=				[]
          for example in padded_inputs.pop('''input_values'''							):
               if self.feature_size == 1:
                    _a    	=				example[..., None]
               input_values.append(example.T							)

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

          return padded_inputs