import unittest

from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
      )
      from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST






class 		_lowerCamelCase		:
    def __init__(  self , lowerCAmelCase , lowerCAmelCase=13 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=99 , lowerCAmelCase=24 , lowerCAmelCase=2 , lowerCAmelCase=6 , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=512 , lowerCAmelCase=16 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=3 , lowerCAmelCase=None , lowerCAmelCase=1000 , )       ->					str:
     SCREAMING_SNAKE_CASE__:   str=      parent
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      batch_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      seq_length
     SCREAMING_SNAKE_CASE__:   Any=      is_training
     SCREAMING_SNAKE_CASE__:   Dict=      use_input_mask
     SCREAMING_SNAKE_CASE__:   str=      use_token_type_ids
     SCREAMING_SNAKE_CASE__:   Any=      use_labels
     SCREAMING_SNAKE_CASE__:   Dict=      vocab_size
     SCREAMING_SNAKE_CASE__:   Tuple=      hidden_size
     SCREAMING_SNAKE_CASE__:   Optional[int]=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   List[str]=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   Optional[int]=      intermediate_size
     SCREAMING_SNAKE_CASE__:   str=      hidden_act
     SCREAMING_SNAKE_CASE__:   List[Any]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   Tuple=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   str=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   str=      type_sequence_label_size
     SCREAMING_SNAKE_CASE__:   Optional[int]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      num_labels
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      scope
     SCREAMING_SNAKE_CASE__:   Any=      range_bbox
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Any=      ids_tensor([self.batch_size, self.seq_length] , self.vocab_size   )

     SCREAMING_SNAKE_CASE__:   Tuple=      ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox   )
     # Ensure that bbox is legal
     for i in range(bbox.shape[0]   ):
      for j in range(bbox.shape[1]   ):
       if bbox[i, j, 3] < bbox[i, j, 1]:
        SCREAMING_SNAKE_CASE__:   str=      bbox[i, j, 3]
        SCREAMING_SNAKE_CASE__:   str=      bbox[i, j, 1]
        SCREAMING_SNAKE_CASE__:   Union[str, Any]=      t
       if bbox[i, j, 2] < bbox[i, j, 0]:
        SCREAMING_SNAKE_CASE__:   Optional[Any]=      bbox[i, j, 2]
        SCREAMING_SNAKE_CASE__:   Any=      bbox[i, j, 0]
        SCREAMING_SNAKE_CASE__:   Union[str, Any]=      t

     SCREAMING_SNAKE_CASE__:   List[Any]=      None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE__:   int=      ids_tensor([self.batch_size, self.seq_length] , vocab_size=2   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE__:   Dict=      ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size   )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      None
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None
     if self.use_labels:
      SCREAMING_SNAKE_CASE__:   str=      ids_tensor([self.batch_size] , self.type_sequence_label_size   )
      SCREAMING_SNAKE_CASE__:   str=      ids_tensor([self.batch_size, self.seq_length] , self.num_labels   )

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_config()

     return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     return LiltConfig(
         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 , initializer_range=self.initializer_range , )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   int=      LiltModel(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__:   List[Any]=      model(lowerCAmelCase , bbox=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      model(lowerCAmelCase , bbox=lowerCAmelCase , token_type_ids=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      model(lowerCAmelCase , bbox=lowerCAmelCase   )

     self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)   )
     self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size)   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   str=      self.num_labels
     SCREAMING_SNAKE_CASE__:   Dict=      LiltForTokenClassification(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__:   List[Any]=      model(
         lowerCAmelCase , bbox=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels)   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   int=      LiltForQuestionAnswering(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__:   Optional[int]=      model(
         lowerCAmelCase , bbox=lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , start_positions=lowerCAmelCase , end_positions=lowerCAmelCase , )
     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   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Dict=      self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	(
         SCREAMING_SNAKE_CASE__
     ),	
     ):   Optional[int]=      config_and_inputs
     SCREAMING_SNAKE_CASE__:   str=      {
         '''input_ids''': input_ids,
         '''bbox''': bbox,
         '''token_type_ids''': token_type_ids,
         '''attention_mask''': input_mask,
     }
     return config, inputs_dict






@require_torch
class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      (
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    __a  =      (
        {
            "feature-extraction": LiltModel,
            "question-answering": LiltForQuestionAnswering,
            "text-classification": LiltForSequenceClassification,
            "token-classification": LiltForTokenClassification,
            "zero-shot": LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __a  =      False
    __a  =      False
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[str]:
     return True
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   str=      LiltModelTester(self   )
     SCREAMING_SNAKE_CASE__:   int=      ConfigTester(self , config_class=lowerCAmelCase , hidden_size=37   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     self.config_tester.run_common_tests()
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Any=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Any=      self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      SCREAMING_SNAKE_CASE__:   str=      type
      self.model_tester.create_and_check_model(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   str=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase   )



    @slow
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      LiltModel.from_pretrained(lowerCAmelCase   )
      self.assertIsNotNone(lowerCAmelCase   )






@require_torch
@slow
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   str=      LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''   ).to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      torch.tensor([[1, 2]] , device=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=lowerCAmelCase   )

     # forward pass
     with torch.no_grad():
      SCREAMING_SNAKE_CASE__:   Tuple=      model(input_ids=lowerCAmelCase , bbox=lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      torch.Size([1, 2, 768]   )
     SCREAMING_SNAKE_CASE__:   str=      torch.tensor(
         [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=lowerCAmelCase , )

     self.assertTrue(outputs.last_hidden_state.shape , lowerCAmelCase   )
     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , lowerCAmelCase , atol=1e-3   )   )


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

lowercase_     :   Any 				=				random.Random()







def 			A__      (	snake_case_ :		int       ,				snake_case_ :		Tuple=1.0       ,				snake_case_ :		Optional[Any]=None       ,				snake_case_ :		List[Any]=None      ):
 if rng is None:
  SCREAMING_SNAKE_CASE__:   Tuple=      global_rng

 SCREAMING_SNAKE_CASE__:   List[str]=      []
 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    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase=7 , lowerCAmelCase=400 , lowerCAmelCase=2000 , lowerCAmelCase=10 , lowerCAmelCase=160 , lowerCAmelCase=8 , lowerCAmelCase=0.0 , lowerCAmelCase=4000 , lowerCAmelCase=False , lowerCAmelCase=True , )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Any=      parent
     SCREAMING_SNAKE_CASE__:   Dict=      batch_size
     SCREAMING_SNAKE_CASE__:   List[Any]=      min_seq_length
     SCREAMING_SNAKE_CASE__:   Tuple=      max_seq_length
     SCREAMING_SNAKE_CASE__:   Optional[int]=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     SCREAMING_SNAKE_CASE__:   str=      padding_value
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      sampling_rate
     SCREAMING_SNAKE_CASE__:   List[str]=      return_attention_mask
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      do_normalize
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      chunk_length
     SCREAMING_SNAKE_CASE__:   List[Any]=      hop_length
    def 						UpperCamelCase_				(  self   )       ->					str:
     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 						UpperCamelCase_				(  self , lowerCAmelCase=False , lowerCAmelCase=False   )       ->					Optional[Any]:
     def _flatten(lowerCAmelCase   ):
      return list(itertools.chain(*lowerCAmelCase   )   )

     if equal_length:
      SCREAMING_SNAKE_CASE__:   Dict=      [floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
     else:
      # make sure that inputs increase in size
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [
          floats_list((x, self.feature_size)   )
          for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff   )
      ]
     if numpify:
      SCREAMING_SNAKE_CASE__:   str=      [np.asarray(lowerCAmelCase   ) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      WhisperFeatureExtractor if is_speech_available() else None
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Any=      WhisperFeatureExtractionTester(self   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.save_pretrained(lowerCAmelCase   )[0]
      check_json_file_has_correct_format(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   str=      self.feature_extraction_class.from_pretrained(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Any=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      os.path.join(lowerCAmelCase , '''feat_extract.json'''   )
      feat_extract_first.to_json_file(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Tuple=      self.feature_extraction_class.from_json_file(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     # create three inputs of length 800, 1000, and 1200
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(800 , 1400 , 200   )]
     SCREAMING_SNAKE_CASE__:   Tuple=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     # Test feature size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      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
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(speech_inputs[0] , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feature_extractor(np_speech_inputs[0] , return_tensors='''np'''   ).input_features
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      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.
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [floats_list((1, x)   )[0] for x in (800, 800, 800)]
     SCREAMING_SNAKE_CASE__:   Any=      np.asarray(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      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
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200   )]
     SCREAMING_SNAKE_CASE__:   str=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     SCREAMING_SNAKE_CASE__:   str=      [x[: feature_extractor.n_samples] for x in speech_inputs]
     SCREAMING_SNAKE_CASE__:   int=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs_truncated]

     SCREAMING_SNAKE_CASE__:   Dict=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[str]=      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 						UpperCamelCase_				(  self   )       ->					Dict:
     import torch

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      np.random.rand(100 , 32   ).astype(np.floataa   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
      SCREAMING_SNAKE_CASE__:   Tuple=      feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np'''   )
      self.assertTrue(np_processed.input_features.dtype == np.floataa   )
      SCREAMING_SNAKE_CASE__:   Any=      feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt'''   )
      self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation'''   )
     # automatic decoding with librispeech
     SCREAMING_SNAKE_CASE__:   List[str]=      ds.sort('''id'''   ).select(range(lowerCAmelCase   )   )[:num_samples]['''audio''']

     return [x["array"] for x in speech_samples]
    def 						UpperCamelCase_				(  self   )       ->					int:
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Dict=      torch.tensor(
         [
             0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
             0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
             0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
             -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
         ]   )
     # fmt: on

     SCREAMING_SNAKE_CASE__:   str=      self._load_datasamples(1   )
     SCREAMING_SNAKE_CASE__:   Dict=      WhisperFeatureExtractor()
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''pt'''   ).input_features
     self.assertEqual(input_features.shape , (1, 80, 3000)   )
     self.assertTrue(torch.allclose(input_features[0, 0, :30] , lowerCAmelCase , atol=1e-4   )   )



    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      self._load_datasamples(1   )[0]
     SCREAMING_SNAKE_CASE__:   str=      ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
     SCREAMING_SNAKE_CASE__:   Optional[int]=      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   )   )

