


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}







class lowerCAmelCase_						(			__UpperCamelCase	):

 '''simple docstring'''

 __snake_case												=  """marian"""
 __snake_case												=  ["""past_key_values"""]
 __snake_case												=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



 def __init__(     self		,				_UpperCAmelCase=5_81_01		,				_UpperCAmelCase=None		,				_UpperCAmelCase=10_24		,				_UpperCAmelCase=12		,				_UpperCAmelCase=40_96		,				_UpperCAmelCase=16		,				_UpperCAmelCase=12		,				_UpperCAmelCase=40_96		,				_UpperCAmelCase=16		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=10_24		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=5_81_00		,				_UpperCAmelCase=False		,				_UpperCAmelCase=5_81_00		,				_UpperCAmelCase=0		,				_UpperCAmelCase=0		,				_UpperCAmelCase=True		,				**_UpperCAmelCase		,				):
       snake_case_	=				vocab_size
       snake_case_	=				decoder_vocab_size or vocab_size
       snake_case_	=				max_position_embeddings
       snake_case_	=				d_model
       snake_case_	=				encoder_ffn_dim
       snake_case_	=				encoder_layers
       snake_case_	=				encoder_attention_heads
       snake_case_	=				decoder_ffn_dim
       snake_case_	=				decoder_layers
       snake_case_	=				decoder_attention_heads
       snake_case_	=				dropout
       snake_case_	=				attention_dropout
       snake_case_	=				activation_dropout
       snake_case_	=				activation_function
       snake_case_	=				init_std
       snake_case_	=				encoder_layerdrop
       snake_case_	=				decoder_layerdrop
       snake_case_	=				use_cache
       snake_case_	=				encoder_layers
       snake_case_	=				scale_embedding  # scale factor will be sqrt(d_model) if True
       snake_case_	=				share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase		,				is_encoder_decoder=_UpperCAmelCase		,				decoder_start_token_id=_UpperCAmelCase		,				forced_eos_token_id=_UpperCAmelCase		,				**_UpperCAmelCase		,				)







class lowerCAmelCase_						(			__UpperCamelCase	):

 '''simple docstring'''

 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def 						UpperCamelCase__					(     self ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_	=				OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ] )

             if self.use_past:
                   snake_case_	=				{0: """batch"""}
                   snake_case_	=				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                   snake_case_	=				{0: """batch""", 1: """decoder_sequence"""}
                   snake_case_	=				{0: """batch""", 1: """decoder_sequence"""}

             if self.use_past:
                   self.fill_with_past_key_values_(_UpperCAmelCase		,				direction='''inputs''' )
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             snake_case_	=				OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ] )
             if self.use_past:
                   snake_case_	=				self.num_layers
                   for i in range(_UpperCAmelCase ):
                         snake_case_	=				{0: """batch""", 2: """past_sequence + sequence"""}
                         snake_case_	=				{0: """batch""", 2: """past_sequence + sequence"""}
       else:
             snake_case_	=				OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                     ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ] )

       return common_inputs



 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 						UpperCamelCase__					(     self ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_	=				super().outputs
       else:
             snake_case_	=				super(_UpperCAmelCase		,				self ).outputs
             if self.use_past:
                   snake_case_	=				self.num_layers
                   for i in range(_UpperCAmelCase ):
                         snake_case_	=				{0: """batch""", 2: """past_sequence + sequence"""}
                         snake_case_	=				{0: """batch""", 2: """past_sequence + sequence"""}
       return common_outputs



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = False		,				_UpperCAmelCase = None		,				):
       snake_case_	=				self._generate_dummy_inputs_for_encoder_and_decoder(
           _UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       # Generate decoder inputs
       snake_case_	=				seq_length if not self.use_past else 1
       snake_case_	=				self._generate_dummy_inputs_for_encoder_and_decoder(
           _UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       snake_case_	=				dict(**_UpperCAmelCase		,				**_UpperCAmelCase )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
             else:
                   import torch
             snake_case_	=				common_inputs["""input_ids"""].shape
             snake_case_	=				common_inputs["""decoder_input_ids"""].shape[1]
             snake_case_	=				self.num_attention_heads
             snake_case_	=				(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             snake_case_	=				decoder_seq_length + 3
             snake_case_	=				(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             snake_case_	=				torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase		,				_UpperCAmelCase )]		,				dim=1 )

             snake_case_	=				[]
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             snake_case_	=				self.num_layers
             snake_case_	=				min(_UpperCAmelCase		,				_UpperCAmelCase )
             snake_case_	=				max(_UpperCAmelCase		,				_UpperCAmelCase ) - min_num_layers
             snake_case_	=				"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

             for _ in range(_UpperCAmelCase ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_UpperCAmelCase ),
                           torch.zeros(_UpperCAmelCase ),
                           torch.zeros(_UpperCAmelCase ),
                           torch.zeros(_UpperCAmelCase ),
                       ) )
             # TODO: test this.
             snake_case_	=				encoder_shape if remaining_side_name == """encoder""" else decoder_shape
             for _ in range(_UpperCAmelCase		,				_UpperCAmelCase ):
                   common_inputs["past_key_values"].append((torch.zeros(_UpperCAmelCase ), torch.zeros(_UpperCAmelCase )) )
       return common_inputs



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = False		,				_UpperCAmelCase = None		,				):
       snake_case_	=				self._generate_dummy_inputs_for_encoder_and_decoder(
           _UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
             else:
                   import torch
             snake_case_	=				common_inputs["""input_ids"""].shape
             # Not using the same length for past_key_values
             snake_case_	=				seqlen + 2
             snake_case_	=				self.num_layers
             snake_case_	=				self.num_attention_heads
             snake_case_	=				(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             snake_case_	=				common_inputs["""attention_mask"""].dtype
             snake_case_	=				torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase		,				_UpperCAmelCase		,				dtype=_UpperCAmelCase )]		,				dim=1 )
             snake_case_	=				[
                 (torch.zeros(_UpperCAmelCase ), torch.zeros(_UpperCAmelCase )) for _ in range(_UpperCAmelCase )
             ]
       return common_inputs



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = False		,				_UpperCAmelCase = None		,				):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       snake_case_	=				compute_effective_axis_dimension(
           _UpperCAmelCase		,				fixed_dimension=OnnxConfig.default_fixed_batch		,				num_token_to_add=0 )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       snake_case_	=				tokenizer.num_special_tokens_to_add(_UpperCAmelCase )
       snake_case_	=				compute_effective_axis_dimension(
           _UpperCAmelCase		,				fixed_dimension=OnnxConfig.default_fixed_sequence		,				num_token_to_add=_UpperCAmelCase )

       # Generate dummy inputs according to compute batch and sequence
       snake_case_	=				[""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
       snake_case_	=				dict(tokenizer(_UpperCAmelCase		,				return_tensors=_UpperCAmelCase ) )
       return common_inputs



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = -1		,				_UpperCAmelCase = False		,				_UpperCAmelCase = None		,				):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_	=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _UpperCAmelCase		,				batch_size=_UpperCAmelCase		,				seq_length=_UpperCAmelCase		,				is_pair=_UpperCAmelCase		,				framework=_UpperCAmelCase )

       else:
             snake_case_	=				self._generate_dummy_inputs_for_causal_lm(
                 _UpperCAmelCase		,				batch_size=_UpperCAmelCase		,				seq_length=_UpperCAmelCase		,				is_pair=_UpperCAmelCase		,				framework=_UpperCAmelCase )

       return common_inputs



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_	=				super()._flatten_past_key_values_(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       else:
             snake_case_	=				super(_UpperCAmelCase		,				self )._flatten_past_key_values_(
                 _UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )





 @property
 def 						UpperCamelCase__					(     self ):
       return 1E-4



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] )