
import unittest

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

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


if is_torch_available():
							import torch

							from transformers import (
							    MraForMaskedLM,
							    MraForMultipleChoice,
							    MraForQuestionAnswering,
							    MraForSequenceClassification,
							    MraForTokenClassification,
							    MraModel,
							)
							from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class 						lowercase    :




							def __init__(						self   :					str	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Dict=2	,							_UpperCamelCase   :					int=8	,							_UpperCamelCase   :					Optional[Any]=True	,							_UpperCamelCase   :					Union[str, Any]=True	,							_UpperCamelCase   :					List[Any]=True	,							_UpperCamelCase   :					Union[str, Any]=True	,							_UpperCamelCase   :					Dict=99	,							_UpperCamelCase   :					str=16	,							_UpperCamelCase   :					Union[str, Any]=5	,							_UpperCamelCase   :					Optional[Any]=2	,							_UpperCamelCase   :					List[str]=36	,							_UpperCamelCase   :					List[str]="gelu"	,							_UpperCamelCase   :					List[Any]=0.0	,							_UpperCamelCase   :					int=0.0	,							_UpperCamelCase   :					Optional[Any]=512	,							_UpperCamelCase   :					Optional[Any]=16	,							_UpperCamelCase   :					Dict=2	,							_UpperCamelCase   :					List[str]=0.0_2	,							_UpperCamelCase   :					Union[str, Any]=3	,							_UpperCamelCase   :					List[str]=4	,							_UpperCamelCase   :					str=None	,							)					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       seq_length
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       use_input_mask
												SCREAMING_SNAKE_CASE    			=       use_token_type_ids
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       type_vocab_size
												SCREAMING_SNAKE_CASE    			=       type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       num_labels
												SCREAMING_SNAKE_CASE    			=       num_choices
												SCREAMING_SNAKE_CASE    			=       scope




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size      )

												SCREAMING_SNAKE_CASE    			=       None
												if self.use_input_mask:
																	SCREAMING_SNAKE_CASE    			=       random_attention_mask([self.batch_size, self.seq_length]      )

												SCREAMING_SNAKE_CASE    			=       None
												if self.use_token_type_ids:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size      )

												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							self.num_labels      )
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.num_choices      )

												SCREAMING_SNAKE_CASE    			=       self.get_config()

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




							def 	__snake_case(						self   :					Optional[Any]      )					->				List[str]:



												'''simple docstring'''

												return MraConfig(
												    vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							is_decoder=lowercase_	,							initializer_range=self.initializer_range	,							)




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_config()
												SCREAMING_SNAKE_CASE    			=       300
												return config




							def 	__snake_case(						self   :					List[str]      )					->				int:



												'''simple docstring'''

												(
												    (
												    SCREAMING_SNAKE_CASE	
												)			,   (
												    SCREAMING_SNAKE_CASE	
												)			,   (
												    SCREAMING_SNAKE_CASE	
												)			,   (
												    SCREAMING_SNAKE_CASE	
												)			,   (
												    SCREAMING_SNAKE_CASE	
												)			,   (
												    SCREAMING_SNAKE_CASE	
												)			,   (
												    SCREAMING_SNAKE_CASE	
												)			,   	
												)    			=       self.prepare_config_and_inputs()

												SCREAMING_SNAKE_CASE    			=       True
												SCREAMING_SNAKE_CASE    			=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size, self.seq_length]	,							vocab_size=2      )

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




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Dict      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MraModel(config=lowercase_      )
												model.to(lowercase_      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_      )
												SCREAMING_SNAKE_CASE    			=       model(lowercase_	,							token_type_ids=lowercase_      )
												SCREAMING_SNAKE_CASE    			=       model(lowercase_      )
												self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Tuple	,							)					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       True
												SCREAMING_SNAKE_CASE    			=       MraModel(lowercase_      )
												model.to(lowercase_      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(
												    lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_	,							encoder_hidden_states=lowercase_	,							encoder_attention_mask=lowercase_	,							)
												SCREAMING_SNAKE_CASE    			=       model(
												    lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_	,							encoder_hidden_states=lowercase_	,							)
												SCREAMING_SNAKE_CASE    			=       model(lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_      )
												self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Optional[int]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MraForMaskedLM(config=lowercase_      )
												model.to(lowercase_      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_	,							labels=lowercase_      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					int      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MraForQuestionAnswering(config=lowercase_      )
												model.to(lowercase_      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(
												    lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_	,							start_positions=lowercase_	,							end_positions=lowercase_	,							)
												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 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					int      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_labels
												SCREAMING_SNAKE_CASE    			=       MraForSequenceClassification(lowercase_      )
												model.to(lowercase_      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_	,							labels=lowercase_      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)      )




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					Dict      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_labels
												SCREAMING_SNAKE_CASE    			=       MraForTokenClassification(config=lowercase_      )
												model.to(lowercase_      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_	,							labels=lowercase_      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.num_labels)      )




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					int      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_choices
												SCREAMING_SNAKE_CASE    			=       MraForMultipleChoice(config=lowercase_      )
												model.to(lowercase_      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       input_ids.unsqueeze(1      ).expand(-1	,							self.num_choices	,							-1      ).contiguous()
												SCREAMING_SNAKE_CASE    			=       token_type_ids.unsqueeze(1      ).expand(-1	,							self.num_choices	,							-1      ).contiguous()
												SCREAMING_SNAKE_CASE    			=       input_mask.unsqueeze(1      ).expand(-1	,							self.num_choices	,							-1      ).contiguous()
												SCREAMING_SNAKE_CASE    			=       model(
												    lowercase_	,							attention_mask=lowercase_	,							token_type_ids=lowercase_	,							labels=lowercase_	,							)
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_choices)      )





							def 	__snake_case(						self   :					List[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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	
												)			,   	
												)    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       str								=							(
							    (
							        MraModel,
							        MraForMaskedLM,
							        MraForMultipleChoice,
							        MraForQuestionAnswering,
							        MraForSequenceClassification,
							        MraForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase__						:       Optional[int]								=							False
							lowercase__						:       Union[str, Any]								=							False
							lowercase__						:       Any								=							False
							lowercase__						:       Union[str, Any]								=							False

							lowercase__						:       int								=							()




							def 	__snake_case(						self   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MraModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=lowercase_	,							hidden_size=37      )




							def 	__snake_case(						self   :					Dict      )					->				Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_      )




							def 	__snake_case(						self   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	SCREAMING_SNAKE_CASE    			=       type
																	self.model_tester.create_and_check_model(*lowercase_      )




							def 	__snake_case(						self   :					List[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*lowercase_      )




							def 	__snake_case(						self   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*lowercase_      )




							def 	__snake_case(						self   :					Optional[int]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*lowercase_      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*lowercase_      )




							def 	__snake_case(						self   :					Optional[int]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*lowercase_      )




							@slow
							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       MraModel.from_pretrained(lowercase_      )
																	self.assertIsNotNone(lowercase_      )





							@unittest.skip(reason="MRA does not output attentions"      )
							def 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												return



@require_torch
class 						lowercase    (     unittest.TestCase     ):




							@slow
							def 	__snake_case(						self   :					Dict      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MraModel.from_pretrained("uw-madison/mra-base-512-4"      )
												SCREAMING_SNAKE_CASE    			=       torch.arange(256      ).unsqueeze(0      )

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(lowercase_      )[0]

												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 256, 768)      )
												self.assertEqual(output.shape	,							lowercase_      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor(
												    [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]      )

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




							@slow
							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"      )
												SCREAMING_SNAKE_CASE    			=       torch.arange(256      ).unsqueeze(0      )

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(lowercase_      )[0]

												SCREAMING_SNAKE_CASE    			=       50_265

												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 256, vocab_size)      )
												self.assertEqual(output.shape	,							lowercase_      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor(
												    [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]      )

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





							@slow
							def 	__snake_case(						self   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"      )
												SCREAMING_SNAKE_CASE    			=       torch.arange(4_096      ).unsqueeze(0      )

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(lowercase_      )[0]

												SCREAMING_SNAKE_CASE    			=       50_265

												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 4_096, vocab_size)      )
												self.assertEqual(output.shape	,							lowercase_      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor(
												    [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]      )

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





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					float	,							**_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       feature_size
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       padding_value

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("padding_side"	,							"right"      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("return_attention_mask"	,							_UpperCamelCase      )

												super().__init__(**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = True	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if isinstance(_UpperCamelCase	,							(list, tuple)      ) and isinstance(processed_features[0]	,							(dict, BatchFeature)      ):
																	SCREAMING_SNAKE_CASE    			=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

												# The model's main input name, usually `input_values`, has be passed for padding
												if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
																	    F" to this method that includes {self.model_input_names[0]}, but you provided"
																	    F" {list(processed_features.keys()      )}"      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]
												SCREAMING_SNAKE_CASE    			=       (
												    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
												)

												if len(_UpperCamelCase      ) == 0:
																	if return_attention_mask:
																						SCREAMING_SNAKE_CASE    			=       []
																	return processed_features

												# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
												# and rebuild them afterwards if no return_tensors is specified
												# Note that we lose the specific device the tensor may be on for PyTorch

												SCREAMING_SNAKE_CASE    			=       required_input[0]
												if isinstance(_UpperCamelCase	,							(list, tuple)      ):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	SCREAMING_SNAKE_CASE    			=       0
																	while len(required_input[index]      ) == 0:
																						index += 1
																	if index < len(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       required_input[index][0]

												if return_tensors is None:
																	if is_tf_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "tf"
																	elif is_torch_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "pt"
																	elif isinstance(_UpperCamelCase	,							(int, float, list, tuple, np.ndarray)      ):
																						SCREAMING_SNAKE_CASE    			=       "np"
																	else:
																						raise ValueError(
																						    F"type of {first_element} unknown: {type(_UpperCamelCase      )}. "
																						    "Should be one of a python, numpy, pytorch or tensorflow object."      )

												for key, value in processed_features.items():
																	if isinstance(value[0]	,							(int, float)      ):
																						SCREAMING_SNAKE_CASE    			=       to_numpy(_UpperCamelCase      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       [to_numpy(_UpperCamelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
												SCREAMING_SNAKE_CASE    			=       self._get_padding_strategies(padding=_UpperCamelCase	,							max_length=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )
												if not all(len(_UpperCamelCase      ) == batch_size for v in processed_features.values()      ):
																	raise ValueError("Some items in the output dictionary have a different batch size than others."      )

												SCREAMING_SNAKE_CASE    			=       []
												for i in range(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	SCREAMING_SNAKE_CASE    			=       self._truncate(
																	    _UpperCamelCase	,							max_length=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							truncation=_UpperCamelCase	,							)
																	truncated_inputs.append(_UpperCamelCase      )

												if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	SCREAMING_SNAKE_CASE    			=       max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.MAX_LENGTH

												SCREAMING_SNAKE_CASE    			=       {}
												for i in range(_UpperCamelCase      ):
																	# padding
																	SCREAMING_SNAKE_CASE    			=       self._pad(
																	    truncated_inputs[i]	,							max_length=_UpperCamelCase	,							padding_strategy=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							)

																	for key, value in outputs.items():
																						if key not in batch_outputs:
																											SCREAMING_SNAKE_CASE    			=       []
																						if value.dtype is np.dtype(np.floataa      ):
																											SCREAMING_SNAKE_CASE    			=       value.astype(np.floataa      )
																						batch_outputs[key].append(_UpperCamelCase      )

												return BatchFeature(_UpperCamelCase	,							tensor_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )

												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCamelCase      ) < max_length

												if return_attention_mask and "attention_mask" not in processed_features:
																	SCREAMING_SNAKE_CASE    			=       np.ones(len(_UpperCamelCase      )	,							dtype=np.intaa      )

												if needs_to_be_padded:
																	SCREAMING_SNAKE_CASE    			=       max_length - len(_UpperCamelCase      )
																	if self.padding_side == "right":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(0, difference)      )
																						SCREAMING_SNAKE_CASE    			=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	elif self.padding_side == "left":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(difference, 0)      )
																						SCREAMING_SNAKE_CASE    			=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	else:
																						raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return processed_features




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				Optional[int]:



												'''simple docstring'''

												if not truncation:
																	return processed_features
												elif truncation and max_length is None:
																	raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												# find `max_length` that fits `pad_to_multiple_of`
												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) > max_length

												if needs_to_be_truncated:
																	SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																						SCREAMING_SNAKE_CASE    			=       processed_features["attention_mask"][:max_length]

												return processed_features





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					Tuple=None      )					->				Tuple:



												'''simple docstring'''

												if padding is not False:
																	if padding is True:
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy(_UpperCamelCase      )
																	elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       padding
												else:
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.DO_NOT_PAD

												# Set max length if needed
												if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																						raise ValueError(
																						    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
												if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
																	    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."      )

												return padding_strategy




