import unittest

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

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


if is_torch_available():
							import torch

							from transformers import (
							    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    OpenAIGPTConfig,
							    OpenAIGPTDoubleHeadsModel,
							    OpenAIGPTForSequenceClassification,
							    OpenAIGPTLMHeadModel,
							    OpenAIGPTModel,
							)


class 						lowercase    :




							def __init__(						self   :					int	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[Any]=13	,							_UpperCamelCase   :					Optional[Any]=7	,							_UpperCamelCase   :					Union[str, Any]=True	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Any=True	,							_UpperCamelCase   :					int=99	,							_UpperCamelCase   :					List[str]=32	,							_UpperCamelCase   :					Optional[int]=5	,							_UpperCamelCase   :					Optional[Any]=4	,							_UpperCamelCase   :					Dict=37	,							_UpperCamelCase   :					int="gelu"	,							_UpperCamelCase   :					Optional[int]=0.1	,							_UpperCamelCase   :					Optional[Any]=0.1	,							_UpperCamelCase   :					Any=512	,							_UpperCamelCase   :					Dict=16	,							_UpperCamelCase   :					Dict=2	,							_UpperCamelCase   :					Any=0.0_2	,							_UpperCamelCase   :					List[str]=3	,							_UpperCamelCase   :					Optional[int]=4	,							_UpperCamelCase   :					Union[str, Any]=None	,							)					->				Tuple:



												'''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_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
												SCREAMING_SNAKE_CASE    			=       self.vocab_size - 1




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



												'''simple docstring'''

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

												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    			=       OpenAIGPTConfig(
												    vocab_size=self.vocab_size	,							n_embd=self.hidden_size	,							n_layer=self.num_hidden_layers	,							n_head=self.num_attention_heads	,							n_positions=self.max_position_embeddings	,							pad_token_id=self.pad_token_id	,							)

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,							2      )

												return (
												    config,
												    input_ids,
												    head_mask,
												    token_type_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							head_mask=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )

												self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTLMHeadModel(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.loss.shape	,							()      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTDoubleHeadsModel(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.loss.shape	,							()      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.num_labels
												SCREAMING_SNAKE_CASE    			=       OpenAIGPTForSequenceClassification(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()

												SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							token_type_ids=_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)      )





							def 	__snake_case(						self   :					int      )					->				Optional[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,
												    "head_mask": head_mask,
												}

												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       a				,       unittest.TestCase     ):
							lowercase__						:       List[str]								=							(
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							lowercase__						:       int								=							(
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							lowercase__						:       Any								=							(
							    {
							        """feature-extraction""": OpenAIGPTModel,
							        """text-classification""": OpenAIGPTForSequenceClassification,
							        """text-generation""": OpenAIGPTLMHeadModel,
							        """zero-shot""": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)




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



												'''simple docstring'''

												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
																	# tiny config could not be created.
																	return True

												return False




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       super()._prepare_for_class(_UpperCamelCase	,							_UpperCamelCase	,							return_labels=_UpperCamelCase      )

												if return_labels:
																	if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																						SCREAMING_SNAKE_CASE    			=       torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)	,							dtype=torch.long	,							device=_UpperCamelCase	,							)
																						SCREAMING_SNAKE_CASE    			=       inputs_dict["labels"]
																						SCREAMING_SNAKE_CASE    			=       inputs_dict["labels"]
																						SCREAMING_SNAKE_CASE    			=       torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices)	,							dtype=torch.long	,							device=_UpperCamelCase	,							)
																						SCREAMING_SNAKE_CASE    			=       torch.zeros(
																						    self.model_tester.batch_size	,							dtype=torch.long	,							device=_UpperCamelCase      )
												return inputs_dict




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							n_embd=37      )




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



												'''simple docstring'''

												self.config_tester.run_common_tests()




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_model(*_UpperCamelCase      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*_UpperCamelCase      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_double_lm_head_model(*_UpperCamelCase      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_UpperCamelCase      )





							@slow
							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       OpenAIGPTModel.from_pretrained(_UpperCamelCase      )
																	self.assertIsNotNone(_UpperCamelCase      )



@require_torch
class 						lowercase    (     unittest.TestCase     ):




							@slow
							def 	__snake_case(						self   :					Any      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"      )
												model.to(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       torch.tensor([[481, 4_735, 544]]	,							dtype=torch.long	,							device=_UpperCamelCase      )  # the president is
												SCREAMING_SNAKE_CASE    			=       [
												    481,
												    4_735,
												    544,
												    246,
												    963,
												    870,
												    762,
												    239,
												    244,
												    40_477,
												    244,
												    249,
												    719,
												    881,
												    487,
												    544,
												    240,
												    244,
												    603,
												    481,
												]  # the president is a very good man. " \n " i\'m sure he is, " said the

												SCREAMING_SNAKE_CASE    			=       model.generate(_UpperCamelCase	,							do_sample=_UpperCamelCase      )
												self.assertListEqual(output_ids[0].tolist()	,							_UpperCamelCase      )





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




