


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE ) as metadata_file:
            snake_case_	=				json.load(SCREAMING_SNAKE_CASE )
      snake_case_	=				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE , **metadata['''model_config'''] )

      # Load in the weights from the checkpoint_path
      snake_case_	=				torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''module''']

      # Load the entity vocab file
      snake_case_	=				load_original_entity_vocab(SCREAMING_SNAKE_CASE )
      # add an entry for [MASK2]
      snake_case_	=				max(entity_vocab.values() ) + 1
      config.entity_vocab_size += 1

      snake_case_	=				XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )

      # Add special tokens to the token vocabulary for downstream tasks
      snake_case_	=				AddedToken('''<ent>''' , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE )
      snake_case_	=				AddedToken('''<ent2>''' , lstrip=SCREAMING_SNAKE_CASE , rstrip=SCREAMING_SNAKE_CASE )
      tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
      config.vocab_size += 2

      print(f'''Saving tokenizer to {pytorch_dump_folder_path}''' )
      tokenizer.save_pretrained(SCREAMING_SNAKE_CASE )
      with open(os.path.join(SCREAMING_SNAKE_CASE , '''tokenizer_config.json''' ) , '''r''' ) as f:
            snake_case_	=				json.load(SCREAMING_SNAKE_CASE )
      snake_case_	=				'''MLukeTokenizer'''
      with open(os.path.join(SCREAMING_SNAKE_CASE , '''tokenizer_config.json''' ) , '''w''' ) as f:
            json.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

      with open(os.path.join(SCREAMING_SNAKE_CASE , MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ) , '''w''' ) as f:
            json.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

      snake_case_	=				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )

      # Initialize the embeddings of the special tokens
      snake_case_	=				tokenizer.convert_tokens_to_ids(['''@'''] )[0]
      snake_case_	=				tokenizer.convert_tokens_to_ids(['''#'''] )[0]

      snake_case_	=				state_dict['''embeddings.word_embeddings.weight''']
      snake_case_	=				word_emb[ent_init_index].unsqueeze(0 )
      snake_case_	=				word_emb[enta_init_index].unsqueeze(0 )
      snake_case_	=				torch.cat([word_emb, ent_emb, enta_emb] )
      # add special tokens for 'entity_predictions.bias'
      for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
            snake_case_	=				state_dict[bias_name]
            snake_case_	=				decoder_bias[ent_init_index].unsqueeze(0 )
            snake_case_	=				decoder_bias[enta_init_index].unsqueeze(0 )
            snake_case_	=				torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers ):
            for matrix_name in ["query.weight", "query.bias"]:
                  snake_case_	=				f'''encoder.layer.{layer_index}.attention.self.'''
                  snake_case_	=				state_dict[prefix + matrix_name]
                  snake_case_	=				state_dict[prefix + matrix_name]
                  snake_case_	=				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      snake_case_	=				state_dict['''entity_embeddings.entity_embeddings.weight''']
      snake_case_	=				entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
      snake_case_	=				torch.cat([entity_emb, entity_mask_emb] )
      # add [MASK2] for 'entity_predictions.bias'
      snake_case_	=				state_dict['''entity_predictions.bias''']
      snake_case_	=				entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
      snake_case_	=				torch.cat([entity_prediction_bias, entity_mask_bias] )

      snake_case_	=				LukeForMaskedLM(config=SCREAMING_SNAKE_CASE ).eval()

      state_dict.pop('''entity_predictions.decoder.weight''' )
      state_dict.pop('''lm_head.decoder.weight''' )
      state_dict.pop('''lm_head.decoder.bias''' )
      snake_case_	=				OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
                  snake_case_	=				state_dict[key]
            else:
                  snake_case_	=				state_dict[key]

      snake_case_	,   snake_case_	=				model.load_state_dict(SCREAMING_SNAKE_CASE , strict=SCREAMING_SNAKE_CASE )

      if set(SCREAMING_SNAKE_CASE ) != {"luke.embeddings.position_ids"}:
            raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}''' )
      if set(SCREAMING_SNAKE_CASE ) != {
          "lm_head.decoder.weight",
          "lm_head.decoder.bias",
          "entity_predictions.decoder.weight",
      }:
            raise ValueError(f'''Unexpected missing_keys: {missing_keys}''' )

      model.tie_weights()
      assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
      assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

      # Check outputs
      snake_case_	=				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE , task='''entity_classification''' )

      snake_case_	=				'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
      snake_case_	=				(0, 9)
      snake_case_	=				tokenizer(SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors='''pt''' )

      snake_case_	=				model(**SCREAMING_SNAKE_CASE )

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            snake_case_	=				torch.Size((1, 33, 768) )
            snake_case_	=				torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]] )

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''' )
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            snake_case_	=				torch.Size((1, 1, 768) )
            snake_case_	=				torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]] )

      if not (outputs.entity_last_hidden_state.shape == expected_shape):
            raise ValueError(
                f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
                f''' {expected_shape}''' )
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ):
            raise ValueError

      # Verify masked word/entity prediction
      snake_case_	=				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
      snake_case_	=				'''Tokyo is the capital of <mask>.'''
      snake_case_	=				(24, 30)
      snake_case_	=				tokenizer(SCREAMING_SNAKE_CASE , entity_spans=[span] , return_tensors='''pt''' )

      snake_case_	=				model(**SCREAMING_SNAKE_CASE )

      snake_case_	=				encoding['''input_ids'''][0].tolist()
      snake_case_	=				input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
      snake_case_	=				outputs.logits[0][mask_position_id].argmax(dim=-1 )
      assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE )

      snake_case_	=				outputs.entity_logits[0][0].argmax().item()
      snake_case_	=				[
          entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
      ]
      assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"

      # Finally, save our PyTorch model and tokenizer
      print('''Saving PyTorch model to {}'''.format(SCREAMING_SNAKE_CASE ) )
      model.save_pretrained(SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      snake_case_	=				['''[MASK]''', '''[PAD]''', '''[UNK]''']

      snake_case_	=				[json.loads(SCREAMING_SNAKE_CASE ) for line in open(SCREAMING_SNAKE_CASE )]

      snake_case_	=				{}
      for entry in data:
            snake_case_	=				entry['''id''']
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        snake_case_	=				entity_id
                        break
                  snake_case_	=				f'''{language}:{entity_name}'''
                  snake_case_	=				entity_id
      return new_mapping


if __name__ == "__main__":
   UpperCAmelCase      =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
   parser.add_argument(
       """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
   )
   parser.add_argument(
       """--entity_vocab_path""",
       default=None,
       type=str,
       help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
   )
   parser.add_argument(
       """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
   )
   UpperCAmelCase      =   parser.parse_args()
   convert_luke_checkpoint(
       args.checkpoint_path,
       args.metadata_path,
       args.entity_vocab_path,
       args.pytorch_dump_folder_path,
       args.model_size,
   )



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  None
 __snake_case												=  None



 @property
 def 						UpperCamelCase__					(     self ):
       return self.feat_extract_tester.prepare_feat_extract_dict()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''feature_size''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''sampling_rate''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''padding_value''' ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       self.assertTrue(all(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) for x, y in zip(_UpperCAmelCase		,				processed_features[input_name] ) ) )

       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''np''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''pt''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )



 @require_tf
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''tf''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

             for input_slice_a, input_slice_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
                   if not np.allclose(np.asarray(_UpperCAmelCase )		,				np.asarray(_UpperCAmelCase )		,				atol=1E-3 ):
                         return False
             return True

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				self.feat_extract_tester.seq_length_diff
       snake_case_	=				self.feat_extract_tester.max_seq_length + pad_diff
       snake_case_	=				self.feat_extract_tester.min_seq_length
       snake_case_	=				self.feat_extract_tester.batch_size
       snake_case_	=				self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest''' )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[-1] ) )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''max_length''' )[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )
       self.assertTrue(len(input_a[0] ) == pad_min_length )
       self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

       if feature_size > 1:
             self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

       self.assertTrue(all(len(_UpperCAmelCase ) % 10 == 0 for x in input_a ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )

       snake_case_	=				pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(_UpperCAmelCase ) == expected_mult_pad_length for x in input_a ) )
       self.assertEqual(input_a.shape[:2]		,				(batch_size, expected_mult_pad_length) )

       if feature_size > 1:
             self.assertTrue(input_a.shape[2] == feature_size )

       # Check padding value is correct
       snake_case_	=				(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
           < 1E-3 )
       self.assertTrue(
           abs(
               np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
           < 1E-3 )
       self.assertTrue(
           abs(
               np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
           < 1E-3 )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
           < 1E-3 )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

             for input_slice_a, input_slice_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
                   if not np.allclose(np.asarray(_UpperCAmelCase )		,				np.asarray(_UpperCAmelCase )		,				atol=1E-3 ):
                         return False
             return True

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       # truncate to smallest
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] ) )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )

       # truncate to smallest with np
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				return_tensors='''np'''		,				truncation=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )

       # truncate to middle
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				truncation=_UpperCAmelCase )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				truncation=_UpperCAmelCase )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				truncation=_UpperCAmelCase )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				truncation=_UpperCAmelCase )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       snake_case_	=				12
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				pad_to_multiple_of=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				pad_to_multiple_of=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       snake_case_	=				len(speech_inputs[0] )
       if expected_length % pad_to_multiple_of != 0:
             snake_case_	=				((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0] ) == expected_length )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''pt''' )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )



 @require_tf
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''tf''' )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )
       self.assertIn('''attention_mask'''		,				_UpperCAmelCase )
       self.assertListEqual(list(processed.attention_mask.shape )		,				list(processed[input_name].shape[:2] ) )
       self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()		,				_UpperCAmelCase )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )
       snake_case_	=				min(_UpperCAmelCase )

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''np''' )
       self.assertIn('''attention_mask'''		,				_UpperCAmelCase )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape )		,				[processed_pad[input_name].shape[0], max_length] )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()		,				[max_length for x in speech_inputs] )