code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  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
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  	import torch
  
  	from transformers import (
  	    MODEL_FOR_PRETRAINING_MAPPING,
  	    ElectraForCausalLM,
  	    ElectraForMaskedLM,
  	    ElectraForMultipleChoice,
  	    ElectraForPreTraining,
  	    ElectraForQuestionAnswering,
  	    ElectraForSequenceClassification,
  	    ElectraForTokenClassification,
  	    ElectraModel,
  	)
  	from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
  						def __init__(  self  :			Any  ,			A  :			List[Any]  ,			A  :			List[str]=1_3  ,			A  :			Any=7  ,			A  :			Union[str, Any]=True  ,			A  :			Optional[int]=True  ,			A  :			Optional[Any]=True  ,			A  :			Tuple=True  ,			A  :			str=9_9  ,			A  :			int=3_2  ,			A  :			Any=5  ,			A  :			Dict=4  ,			A  :			Union[str, Any]=3_7  ,			A  :			Optional[int]="gelu"  ,			A  :			str=0.1  ,			A  :			Any=0.1  ,			A  :			Dict=5_1_2  ,			A  :			Union[str, Any]=1_6  ,			A  :			Union[str, Any]=2  ,			A  :			Optional[Any]=0.02  ,			A  :			Any=3  ,			A  :			Optional[Any]=4  ,			A  :			Tuple=None  ,			):
  											_UpperCAmelCase    :			List[Any]              =					parent
  											_UpperCAmelCase    :			str              =					batch_size
  											_UpperCAmelCase    :			Optional[int]              =					seq_length
  											_UpperCAmelCase    :			Optional[int]              =					is_training
  											_UpperCAmelCase    :			List[Any]              =					use_input_mask
  											_UpperCAmelCase    :			Any              =					use_token_type_ids
  											_UpperCAmelCase    :			Dict              =					use_labels
  											_UpperCAmelCase    :			Any              =					vocab_size
  											_UpperCAmelCase    :			Union[str, Any]              =					hidden_size
  											_UpperCAmelCase    :			Optional[Any]              =					num_hidden_layers
  											_UpperCAmelCase    :			Optional[Any]              =					num_attention_heads
  											_UpperCAmelCase    :			List[Any]              =					intermediate_size
  											_UpperCAmelCase    :			Optional[Any]              =					hidden_act
  											_UpperCAmelCase    :			int              =					hidden_dropout_prob
  											_UpperCAmelCase    :			int              =					attention_probs_dropout_prob
  											_UpperCAmelCase    :			str              =					max_position_embeddings
  											_UpperCAmelCase    :			List[str]              =					type_vocab_size
  											_UpperCAmelCase    :			int              =					type_sequence_label_size
  											_UpperCAmelCase    :			str              =					initializer_range
  											_UpperCAmelCase    :			Optional[int]              =					num_labels
  											_UpperCAmelCase    :			Any              =					num_choices
  											_UpperCAmelCase    :			List[Any]              =					scope
  
  
  
  						def 		snake_case_      (  self  :			Union[str, Any]		):
  											_UpperCAmelCase    :			int              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size		)
  
  											_UpperCAmelCase    :			List[Any]              =					None
  											if self.use_input_mask:
  																_UpperCAmelCase    :			List[Any]              =					random_attention_mask([self.batch_size, self.seq_length]		)
  
  											_UpperCAmelCase    :			List[str]              =					None
  											if self.use_token_type_ids:
  																_UpperCAmelCase    :			Union[str, Any]              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.type_vocab_size		)
  
  											_UpperCAmelCase    :			Tuple              =					None
  											_UpperCAmelCase    :			Tuple              =					None
  											_UpperCAmelCase    :			int              =					None
  											if self.use_labels:
  																_UpperCAmelCase    :			Union[str, Any]              =					ids_tensor([self.batch_size]  ,			self.type_sequence_label_size		)
  																_UpperCAmelCase    :			List[Any]              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.num_labels		)
  																_UpperCAmelCase    :			Dict              =					ids_tensor([self.batch_size]  ,			self.num_choices		)
  																_UpperCAmelCase    :			Optional[int]              =					ids_tensor([self.batch_size, self.seq_length]  ,			1		)
  
  											_UpperCAmelCase    :			List[str]              =					self.get_config()
  
  											return (
  											    config,
  											    input_ids,
  											    token_type_ids,
  											    input_mask,
  											    sequence_labels,
  											    token_labels,
  											    choice_labels,
  											    fake_token_labels,
  											)
  
  
  
  						def 		snake_case_      (  self  :			Dict		):
  											return ElectraConfig(
  											    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 		snake_case_      (  self  :			Optional[Any]		):
  											(
  											    _UpperCAmelCase					
  											)    :			str              =					self.prepare_config_and_inputs()
  
  											_UpperCAmelCase    :			Optional[int]              =					True
  											_UpperCAmelCase    :			Tuple              =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
  											_UpperCAmelCase    :			Optional[Any]              =					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  :			Optional[Any]  ,			A  :			int  ,			A  :			List[str]  ,			A  :			int  ,			A  :			int  ,			A  :			Any  ,			A  :			Tuple  ,			A  :			Optional[int]  ,			A  :			List[str]  ,			):
  											_UpperCAmelCase    :			int              =					ElectraModel(config=__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			Dict              =					model(__A  ,			attention_mask=__A  ,			token_type_ids=__A		)
  											_UpperCAmelCase    :			int              =					model(__A  ,			token_type_ids=__A		)
  											_UpperCAmelCase    :			Optional[int]              =					model(__A		)
  											self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.hidden_size)		)
  
  
  
  						def 		snake_case_      (  self  :			List[str]  ,			A  :			int  ,			A  :			List[Any]  ,			A  :			Optional[int]  ,			A  :			Tuple  ,			A  :			Any  ,			A  :			List[str]  ,			A  :			List[Any]  ,			A  :			int  ,			A  :			Any  ,			):
  											_UpperCAmelCase    :			Union[str, Any]              =					True
  											_UpperCAmelCase    :			Optional[Any]              =					ElectraModel(__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			Union[str, Any]              =					model(
  											    __A  ,			attention_mask=__A  ,			token_type_ids=__A  ,			encoder_hidden_states=__A  ,			encoder_attention_mask=__A  ,			)
  											_UpperCAmelCase    :			Dict              =					model(
  											    __A  ,			attention_mask=__A  ,			token_type_ids=__A  ,			encoder_hidden_states=__A  ,			)
  											_UpperCAmelCase    :			Tuple              =					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)		)
  
  
  
  						def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			str  ,			A  :			List[str]  ,			A  :			int  ,			A  :			Union[str, Any]  ,			A  :			Any  ,			A  :			Union[str, Any]  ,			A  :			Optional[Any]  ,			):
  											_UpperCAmelCase    :			Optional[int]              =					ElectraForMaskedLM(config=__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			Dict              =					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 		snake_case_      (  self  :			Optional[int]  ,			A  :			Tuple  ,			A  :			Union[str, Any]  ,			A  :			Union[str, Any]  ,			A  :			List[Any]  ,			A  :			Dict  ,			A  :			List[str]  ,			A  :			str  ,			A  :			Optional[int]  ,			A  :			List[Any]  ,			):
  											_UpperCAmelCase    :			List[Any]              =					ElectraForCausalLM(config=__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			Optional[int]              =					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 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Optional[int]  ,			A  :			Dict  ,			A  :			List[Any]  ,			A  :			Union[str, Any]  ,			A  :			Dict  ,			A  :			Any  ,			A  :			Dict  ,			A  :			str  ,			):
  											_UpperCAmelCase    :			Union[str, Any]              =					self.num_labels
  											_UpperCAmelCase    :			List[str]              =					ElectraForTokenClassification(config=__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			Union[str, Any]              =					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 		snake_case_      (  self  :			Optional[int]  ,			A  :			Optional[int]  ,			A  :			Tuple  ,			A  :			Optional[Any]  ,			A  :			List[Any]  ,			A  :			Optional[Any]  ,			A  :			Union[str, Any]  ,			A  :			List[str]  ,			A  :			Union[str, Any]  ,			):
  											_UpperCAmelCase    :			Union[str, Any]              =					self.num_labels
  											_UpperCAmelCase    :			Tuple              =					ElectraForPreTraining(config=__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			Dict              =					model(__A  ,			attention_mask=__A  ,			token_type_ids=__A  ,			labels=__A		)
  											self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length)		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Union[str, Any]  ,			A  :			Union[str, Any]  ,			A  :			Optional[int]  ,			A  :			Optional[int]  ,			A  :			Any  ,			A  :			Optional[int]  ,			A  :			str  ,			A  :			Optional[int]  ,			):
  											_UpperCAmelCase    :			int              =					self.num_labels
  											_UpperCAmelCase    :			Tuple              =					ElectraForSequenceClassification(__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			int              =					model(__A  ,			attention_mask=__A  ,			token_type_ids=__A  ,			labels=__A		)
  											self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_labels)		)
  
  
  
  						def 		snake_case_      (  self  :			str  ,			A  :			List[str]  ,			A  :			List[Any]  ,			A  :			List[Any]  ,			A  :			Optional[Any]  ,			A  :			int  ,			A  :			List[Any]  ,			A  :			Any  ,			A  :			List[Any]  ,			):
  											_UpperCAmelCase    :			int              =					ElectraForQuestionAnswering(config=__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			List[Any]              =					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 		snake_case_      (  self  :			Optional[int]  ,			A  :			List[Any]  ,			A  :			str  ,			A  :			str  ,			A  :			int  ,			A  :			Tuple  ,			A  :			Any  ,			A  :			Optional[Any]  ,			A  :			str  ,			):
  											_UpperCAmelCase    :			Any              =					self.num_choices
  											_UpperCAmelCase    :			Tuple              =					ElectraForMultipleChoice(config=__A		)
  											model.to(__A		)
  											model.eval()
  											_UpperCAmelCase    :			int              =					input_ids.unsqueeze(1		).expand(-1  ,			self.num_choices  ,			-1		).contiguous()
  											_UpperCAmelCase    :			str              =					token_type_ids.unsqueeze(1		).expand(-1  ,			self.num_choices  ,			-1		).contiguous()
  											_UpperCAmelCase    :			List[str]              =					input_mask.unsqueeze(1		).expand(-1  ,			self.num_choices  ,			-1		).contiguous()
  											_UpperCAmelCase    :			Optional[int]              =					model(
  											    __A  ,			attention_mask=__A  ,			token_type_ids=__A  ,			labels=__A  ,			)
  											self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_choices)		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[int]		):
  											_UpperCAmelCase    :			Optional[int]              =					self.prepare_config_and_inputs()
  											(
  											    _UpperCAmelCase					
  											)    :			str              =					config_and_inputs
  											_UpperCAmelCase    :			Dict              =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
  											return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 			UpperCAmelCase_						(       a__ ,    a__ ,    unittest.TestCase		):
  						__SCREAMING_SNAKE_CASE		:     Any				  =				(
  						    (
  						        ElectraModel,
  						        ElectraForPreTraining,
  						        ElectraForMaskedLM,
  						        ElectraForCausalLM,
  						        ElectraForMultipleChoice,
  						        ElectraForTokenClassification,
  						        ElectraForSequenceClassification,
  						        ElectraForQuestionAnswering,
  						    )
  						    if is_torch_available()
  						    else ()
  						)
  						__SCREAMING_SNAKE_CASE		:     List[Any]				  =				(
  						    {
  						        'feature-extraction': ElectraModel,
  						        'fill-mask': ElectraForMaskedLM,
  						        'question-answering': ElectraForQuestionAnswering,
  						        'text-classification': ElectraForSequenceClassification,
  						        'text-generation': ElectraForCausalLM,
  						        'token-classification': ElectraForTokenClassification,
  						        'zero-shot': ElectraForSequenceClassification,
  						    }
  						    if is_torch_available()
  						    else {}
  						)
  						__SCREAMING_SNAKE_CASE		:     List[str]				  =				True
  
  
  
  						def 		snake_case_      (  self  :			str  ,			A  :			Any  ,			A  :			Tuple  ,			A  :			Dict=False		):
  											_UpperCAmelCase    :			Optional[Any]              =					super()._prepare_for_class(__A  ,			__A  ,			return_labels=__A		)
  
  											if return_labels:
  																if model_class in get_values(__A		):
  																					_UpperCAmelCase    :			int              =					torch.zeros(
  																					    (self.model_tester.batch_size, self.model_tester.seq_length)  ,			dtype=torch.long  ,			device=__A		)
  											return inputs_dict
  
  
  
  						def 		snake_case_      (  self  :			List[Any]		):
  											_UpperCAmelCase    :			Union[str, Any]              =					ElectraModelTester(self		)
  											_UpperCAmelCase    :			Tuple              =					ConfigTester(self  ,			config_class=__A  ,			hidden_size=3_7		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[Any]		):
  											self.config_tester.run_common_tests()
  
  
  
  						def 		snake_case_      (  self  :			List[Any]		):
  											_UpperCAmelCase    :			Tuple              =					self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_electra_model(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			Any		):
  											_UpperCAmelCase    :			Optional[int]              =					self.model_tester.prepare_config_and_inputs_for_decoder()
  											self.model_tester.create_and_check_electra_model_as_decoder(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[int]		):
  											_UpperCAmelCase    :			Tuple              =					self.model_tester.prepare_config_and_inputs()
  											for type in ["absolute", "relative_key", "relative_key_query"]:
  																_UpperCAmelCase    :			str              =					type
  																self.model_tester.create_and_check_electra_model(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			Tuple		):
  											_UpperCAmelCase    :			Any              =					self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_electra_for_masked_lm(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			List[str]		):
  											_UpperCAmelCase    :			Tuple              =					self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_electra_for_token_classification(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[Any]		):
  											_UpperCAmelCase    :			str              =					self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_electra_for_pretraining(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[int]		):
  											_UpperCAmelCase    :			Union[str, Any]              =					self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_electra_for_sequence_classification(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			Tuple		):
  											_UpperCAmelCase    :			Optional[Any]              =					self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_electra_for_question_answering(*__A		)
  
  
  
  						def 		snake_case_      (  self  :			Optional[Any]		):
  											_UpperCAmelCase    :			Union[str, Any]              =					self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_electra_for_multiple_choice(*__A		)
  
  
  
  						@slow
  						def 		snake_case_      (  self  :			Tuple		):
  
  
  
  
  
  
  
  											for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  																_UpperCAmelCase    :			Optional[int]              =					ElectraModel.from_pretrained(__A		)
  																self.assertIsNotNone(__A		)
  
  
  
  						def 		snake_case_      (  self  :			List[Any]		):
  											_UpperCAmelCase    :			int              =					self.model_tester.prepare_config_and_inputs_for_decoder()
  											self.model_tester.create_and_check_electra_for_causal_lm(*__A		)
  
  
  
  
  
  
  
  @require_torch
  class 			UpperCAmelCase_						(       unittest.TestCase		):
  
  
  
  						@slow
  						def 		snake_case_      (  self  :			int		):
  											_UpperCAmelCase    :			str              =					ElectraModel.from_pretrained("google/electra-small-discriminator"		)
  											_UpperCAmelCase    :			int              =					torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]		)
  											_UpperCAmelCase    :			Optional[int]              =					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
  											_UpperCAmelCase    :			Optional[Any]              =					model(__A  ,			attention_mask=__A		)[0]
  											_UpperCAmelCase    :			Optional[Any]              =					torch.Size((1, 1_1, 2_5_6)		)
  											self.assertEqual(output.shape  ,			__A		)
  											_UpperCAmelCase    :			List[Any]              =					torch.tensor(
  											    [[[0.4_471, 0.6_821, -0.3_265], [0.4_627, 0.5_255, -0.3_668], [0.4_532, 0.3_313, -0.4_344]]]		)
  
  											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,			__A  ,			atol=1e-4		)		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
  
  
  logging.set_verbosity_info()
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
       _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
       _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )
  
       # Load weights from numpy
       load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       # Save pytorch-model
       _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       _UpperCAmelCase    :			int              =					pytorch_dump_folder_path + "/" + CONFIG_NAME
       print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
       torch.save(model.state_dict()		,						SCREAMING_SNAKE_CASE__       )
       print(f'Save configuration file to {pytorch_config_dump_path}'       )
       with open(SCREAMING_SNAKE_CASE__		,						"w"		,						encoding="utf-8"       ) as f:
            f.write(config.to_json_string()       )
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      List[str]							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--imagegpt_checkpoint_path",
       default=None,
       type=str,
       required=True,
       help="Path to the TensorFlow checkpoint path.",
   )
   parser.add_argument(
       "--model_size",
       default=None,
       type=str,
       required=True,
       help="Size of the model (can be either 'small', 'medium' or 'large').",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )
  