





'''simple docstring'''

import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_PRETRAINING_MAPPING,
			    NezhaForMaskedLM,
			    NezhaForMultipleChoice,
			    NezhaForNextSentencePrediction,
			    NezhaForPreTraining,
			    NezhaForQuestionAnswering,
			    NezhaForSequenceClassification,
			    NezhaForTokenClassification,
			    NezhaModel,
			)
			from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST

class       UpperCAmelCase :




		def __init__(self      :	str							,			A__      :	int							,			A__      :	Optional[int]=1_3							,			A__      :	int=7							,			A__      :	Optional[int]=True							,			A__      :	int=True							,			A__      :	Union[str, Any]=True							,			A__      :	List[str]=True							,			A__      :	int=9_9							,			A__      :	Dict=3_2							,			A__      :	Any=5							,			A__      :	Optional[Any]=4							,			A__      :	Tuple=3_7							,			A__      :	Tuple="gelu"							,			A__      :	Optional[Any]=0.1							,			A__      :	List[Any]=0.1							,			A__      :	Tuple=1_2_8							,			A__      :	Union[str, Any]=3_2							,			A__      :	Any=1_6							,			A__      :	Any=2							,			A__      :	Dict=0.0_2							,			A__      :	str=3							,			A__      :	Any=4							,			A__      :	int=None							,			)					-> str:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = seq_length
						lowercase         = is_training
						lowercase         = use_input_mask
						lowercase         = use_token_type_ids
						lowercase         = use_labels
						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = type_vocab_size
						lowercase         = type_sequence_label_size
						lowercase         = initializer_range
						lowercase         = num_labels
						lowercase         = num_choices
						lowercase         = scope




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Dict:
						lowercase         = ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size				)

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

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

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

						lowercase         = self.get_config()

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




		def 				UpperCAmelCase__   (self      :	Any				)					-> Optional[int]:
						return NezhaConfig(
						    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=A__							,			initializer_range=self.initializer_range							,			)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Union[str, Any]:
						(
						    (
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							
						)         = self.prepare_config_and_inputs()

						lowercase         = True
						lowercase         = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
						lowercase         = 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 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	Dict							,			A__      :	int							,			A__      :	Tuple							,			A__      :	Tuple							,			A__      :	List[Any]							,			A__      :	Union[str, Any]							,			A__      :	Dict				)					-> Tuple:
						lowercase         = NezhaModel(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			attention_mask=A__							,			token_type_ids=A__				)
						lowercase         = model(A__							,			token_type_ids=A__				)
						lowercase         = model(A__				)
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)				)
						self.parent.assertEqual(result.pooler_output.shape							,			(self.batch_size, self.hidden_size)				)




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	int							,			A__      :	int							,			A__      :	Optional[int]							,			A__      :	Any							,			A__      :	Any							,			A__      :	Optional[int]							,			A__      :	str							,			A__      :	Dict							,			A__      :	Tuple							,			)					-> Any:
						lowercase         = True
						lowercase         = NezhaModel(A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(
						    A__							,			attention_mask=A__							,			token_type_ids=A__							,			encoder_hidden_states=A__							,			encoder_attention_mask=A__							,			)
						lowercase         = model(
						    A__							,			attention_mask=A__							,			token_type_ids=A__							,			encoder_hidden_states=A__							,			)
						lowercase         = model(A__							,			attention_mask=A__							,			token_type_ids=A__				)
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)				)
						self.parent.assertEqual(result.pooler_output.shape							,			(self.batch_size, self.hidden_size)				)




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Union[str, Any]							,			A__      :	str							,			A__      :	Optional[int]							,			A__      :	Any							,			A__      :	Any							,			A__      :	Any							,			A__      :	List[str]				)					-> Optional[int]:
						lowercase         = NezhaForMaskedLM(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			attention_mask=A__							,			token_type_ids=A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Any							,			A__      :	Union[str, Any]							,			A__      :	Optional[int]							,			A__      :	Optional[int]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	List[Any]				)					-> Optional[int]:
						lowercase         = NezhaForNextSentencePrediction(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(
						    A__							,			attention_mask=A__							,			token_type_ids=A__							,			labels=A__							,			)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, 2)				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Dict							,			A__      :	Dict							,			A__      :	Any							,			A__      :	str							,			A__      :	int							,			A__      :	Optional[Any]							,			A__      :	str				)					-> Optional[int]:
						lowercase         = NezhaForPreTraining(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(
						    A__							,			attention_mask=A__							,			token_type_ids=A__							,			labels=A__							,			next_sentence_label=A__							,			)
						self.parent.assertEqual(result.prediction_logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)				)
						self.parent.assertEqual(result.seq_relationship_logits.shape							,			(self.batch_size, 2)				)




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	List[str]							,			A__      :	Optional[Any]							,			A__      :	Union[str, Any]							,			A__      :	Tuple							,			A__      :	Tuple							,			A__      :	Union[str, Any]							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase         = NezhaForQuestionAnswering(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(
						    A__							,			attention_mask=A__							,			token_type_ids=A__							,			start_positions=A__							,			end_positions=A__							,			)
						self.parent.assertEqual(result.start_logits.shape							,			(self.batch_size, self.seq_length)				)
						self.parent.assertEqual(result.end_logits.shape							,			(self.batch_size, self.seq_length)				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Union[str, Any]							,			A__      :	List[str]							,			A__      :	Any							,			A__      :	Dict							,			A__      :	Optional[int]							,			A__      :	str							,			A__      :	List[str]				)					-> Union[str, Any]:
						lowercase         = self.num_labels
						lowercase         = NezhaForSequenceClassification(A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			attention_mask=A__							,			token_type_ids=A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)				)




		def 				UpperCAmelCase__   (self      :	List[Any]							,			A__      :	Tuple							,			A__      :	str							,			A__      :	Optional[int]							,			A__      :	Optional[int]							,			A__      :	int							,			A__      :	Any							,			A__      :	Optional[Any]				)					-> Optional[Any]:
						lowercase         = self.num_labels
						lowercase         = NezhaForTokenClassification(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			attention_mask=A__							,			token_type_ids=A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)				)




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	List[Any]							,			A__      :	int							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	List[Any]							,			A__      :	Optional[Any]							,			A__      :	List[str]				)					-> str:
						lowercase         = self.num_choices
						lowercase         = NezhaForMultipleChoice(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = input_ids.unsqueeze(1				).expand(-1							,			self.num_choices							,			-1				).contiguous()
						lowercase         = token_type_ids.unsqueeze(1				).expand(-1							,			self.num_choices							,			-1				).contiguous()
						lowercase         = input_mask.unsqueeze(1				).expand(-1							,			self.num_choices							,			-1				).contiguous()
						lowercase         = model(
						    A__							,			attention_mask=A__							,			token_type_ids=A__							,			labels=A__							,			)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_choices)				)





		def 				UpperCAmelCase__   (self      :	Any				)					-> int:
						lowercase         = self.prepare_config_and_inputs()
						(
						    (
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							
						)         = config_and_inputs
						lowercase         = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict




@require_torch
class       UpperCAmelCase (						_lowercase			,       _lowercase			,       _lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		(
		    (
		        NezhaModel,
		        NezhaForMaskedLM,
		        NezhaForMultipleChoice,
		        NezhaForNextSentencePrediction,
		        NezhaForPreTraining,
		        NezhaForQuestionAnswering,
		        NezhaForSequenceClassification,
		        NezhaForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase					:	Dict 			=		(
		    {
		        '''feature-extraction''': NezhaModel,
		        '''fill-mask''': NezhaForMaskedLM,
		        '''question-answering''': NezhaForQuestionAnswering,
		        '''text-classification''': NezhaForSequenceClassification,
		        '''token-classification''': NezhaForTokenClassification,
		        '''zero-shot''': NezhaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		UpperCAmelCase					:	int 			=		True




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	Tuple							,			A__      :	Union[str, Any]							,			A__      :	Union[str, Any]=False				)					-> Any:
						lowercase         = super()._prepare_for_class(A__							,			A__							,			return_labels=A__				)

						if return_labels:
										if model_class in get_values(A__				):
														lowercase         = torch.zeros(
														    (self.model_tester.batch_size, self.model_tester.seq_length)							,			dtype=torch.long							,			device=A__				)
														lowercase         = torch.zeros(
														    self.model_tester.batch_size							,			dtype=torch.long							,			device=A__				)
						return inputs_dict




		def 				UpperCAmelCase__   (self      :	int				)					-> List[str]:
						lowercase         = NezhaModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=A__							,			hidden_size=3_7				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> List[str]:
						self.config_tester.run_common_tests()




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Any:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*A__				)




		def 				UpperCAmelCase__   (self      :	int				)					-> List[Any]:
						# This regression test was failing with PyTorch < 1.3
						(
						    (
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							(
						    lowercase
						)   ,							
						)         = self.model_tester.prepare_config_and_inputs_for_decoder()

						lowercase         = None

						self.model_tester.create_and_check_model_as_decoder(
						    A__							,			A__							,			A__							,			A__							,			A__							,			A__							,			A__							,			A__							,			A__							,			)




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[int]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A__				)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Any:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*A__				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> str:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_next_sequence_prediction(*A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> List[Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*A__				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Tuple:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Tuple:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*A__				)




		@slow
		def 				UpperCAmelCase__   (self      :	Dict				)					-> Tuple:
						for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase         = NezhaModel.from_pretrained(A__				)
										self.assertIsNotNone(A__				)





		@slow
		@require_torch_gpu
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> List[Any]:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
										# NezhaForMultipleChoice behaves incorrectly in JIT environments.
										if model_class == NezhaForMultipleChoice:
														return

										lowercase         = True
										lowercase         = model_class(config=A__				)

										lowercase         = self._prepare_for_class(A__							,			A__				)
										lowercase         = torch.jit.trace(
										    A__							,			(inputs_dict["input_ids"].to("cpu"				), inputs_dict["attention_mask"].to("cpu"				))				)

										with tempfile.TemporaryDirectory() as tmp:
														torch.jit.save(A__							,			os.path.join(A__							,			"bert.pt"				)				)
														lowercase         = torch.jit.load(os.path.join(A__							,			"bert.pt"				)							,			map_location=A__				)
														loaded(inputs_dict["input_ids"].to(A__				)							,			inputs_dict["attention_mask"].to(A__				)				)




@require_torch
class       UpperCAmelCase (						unittest.TestCase  ):




		@slow
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> List[Any]:
						lowercase         = NezhaModel.from_pretrained("sijunhe/nezha-cn-base"				)
						lowercase         = torch.tensor([[0, 1, 2, 3, 4, 5]]				)
						lowercase         = torch.tensor([[0, 1, 1, 1, 1, 1]]				)
						with torch.no_grad():
										lowercase         = model(A__							,			attention_mask=A__				)[0]
						lowercase         = torch.Size((1, 6, 7_6_8)				)
						self.assertEqual(output.shape							,			A__				)
						lowercase         = torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]]				)

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





		@slow
		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> int:
						lowercase         = NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"				)
						lowercase         = torch.tensor([[0, 1, 2, 3, 4, 5]]				)
						lowercase         = torch.tensor([[1, 1, 1, 1, 1, 1]]				)
						with torch.no_grad():
										lowercase         = model(A__							,			attention_mask=A__				)[0]
						lowercase         = torch.Size((1, 6, 2_1_1_2_8)				)
						self.assertEqual(output.shape							,			A__				)
						lowercase         = torch.tensor(
						    [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]]				)

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











'''simple docstring'''

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 : List[str]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Any							,			A__      :	int							,			A__      :	int							,			A__      :	float							,			**A__      :	int				)					-> List[str]:
						lowercase         = feature_size
						lowercase         = sampling_rate
						lowercase         = padding_value

						lowercase         = kwargs.pop("padding_side"							,			"right"				)
						lowercase         = kwargs.pop("return_attention_mask"							,			A__				)

						super().__init__(**A__				)




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Union[
		        BatchFeature,
		        List[BatchFeature],
		        Dict[str, BatchFeature],
		        Dict[str, List[BatchFeature]],
		        List[Dict[str, BatchFeature]],
		    ]							,			A__      :	Union[bool, str, PaddingStrategy] = True							,			A__      :	Optional[int] = None							,			A__      :	bool = False							,			A__      :	Optional[int] = None							,			A__      :	Optional[bool] = None							,			A__      :	Optional[Union[str, TensorType]] = None							,			)					-> BatchFeature:
						# If we have a list of dicts, let's convert it in a dict of lists
						# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
						if isinstance(A__							,			(list, tuple)				) and isinstance(processed_features[0]							,			(dict, BatchFeature)				):
										lowercase         = {
										    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()				)}'				)

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

						if len(A__				) == 0:
										if return_attention_mask:
														lowercase         = []
										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

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

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

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

        # Convert padding_strategy in PaddingStrategy
						lowercase         = self._get_padding_strategies(padding=A__							,			max_length=A__				)

						lowercase         = processed_features[self.model_input_names[0]]

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

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

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

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

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

						return BatchFeature(A__							,			tensor_type=A__				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	Union[Dict[str, np.ndarray], BatchFeature]							,			A__      :	Optional[int] = None							,			A__      :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,			A__      :	Optional[int] = None							,			A__      :	Optional[bool] = None							,			)					-> dict:
						lowercase         = processed_features[self.model_input_names[0]]

						if padding_strategy == PaddingStrategy.LONGEST:
										lowercase         = len(A__				)

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

						lowercase         = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__				) < max_length

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

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

						return processed_features




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

						lowercase         = 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):
										lowercase         = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

						lowercase         = len(A__				) > max_length

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

						return processed_features





		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[Any]=False							,			A__      :	Dict=None				)					-> Optional[int]:

						# Get padding strategy
						if padding is not False:
										if padding is True:
														lowercase         = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
										elif not isinstance(A__							,			A__				):
														lowercase         = PaddingStrategy(A__				)
										elif isinstance(A__							,			A__				):
														lowercase         = padding
						else:
										lowercase         = 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




