


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 _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=99, lowerCamelCase=32, lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=37, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=1_28, lowerCamelCase=32, lowerCamelCase=16, lowerCamelCase=2, lowerCamelCase=0.0_2, lowerCamelCase=3, lowerCamelCase=4, lowerCamelCase=None, )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[str] 						=						parent
           _lowercase			:				Tuple 						=						batch_size
           _lowercase			:				List[str] 						=						seq_length
           _lowercase			:				Optional[int] 						=						is_training
           _lowercase			:				Dict 						=						use_input_mask
           _lowercase			:				Optional[int] 						=						use_token_type_ids
           _lowercase			:				List[str] 						=						use_labels
           _lowercase			:				List[str] 						=						vocab_size
           _lowercase			:				Optional[Any] 						=						hidden_size
           _lowercase			:				Optional[Any] 						=						num_hidden_layers
           _lowercase			:				Dict 						=						num_attention_heads
           _lowercase			:				List[str] 						=						intermediate_size
           _lowercase			:				Optional[Any] 						=						hidden_act
           _lowercase			:				Optional[Any] 						=						hidden_dropout_prob
           _lowercase			:				Tuple 						=						attention_probs_dropout_prob
           _lowercase			:				Optional[int] 						=						max_position_embeddings
           _lowercase			:				List[str] 						=						type_vocab_size
           _lowercase			:				Optional[int] 						=						type_sequence_label_size
           _lowercase			:				Any 						=						initializer_range
           _lowercase			:				Optional[int] 						=						num_labels
           _lowercase			:				List[str] 						=						num_choices
           _lowercase			:				Tuple 						=						scope
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

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

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

           _lowercase			:				Union[str, Any] 						=						None
           _lowercase			:				List[Any] 						=						None
           _lowercase			:				str 						=						None
           if self.use_labels:
               _lowercase			:				int 						=						ids_tensor([self.batch_size], self.type_sequence_label_size)
               _lowercase			:				Any 						=						ids_tensor([self.batch_size, self.seq_length], self.num_labels)
               _lowercase			:				List[str] 						=						ids_tensor([self.batch_size], self.num_choices)

           _lowercase			:				List[Any] 						=						self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           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=lowerCamelCase, initializer_range=self.initializer_range, )
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           (
               (
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							
           )			:				Any 						=						self.prepare_config_and_inputs()

           _lowercase			:				Dict 						=						True
           _lowercase			:				List[str] 						=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _lowercase			:				List[str] 						=						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, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Any 						=						NezhaModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				List[Any] 						=						model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						model(lowerCamelCase, token_type_ids=lowerCamelCase)
           _lowercase			:				Tuple 						=						model(lowerCamelCase)
           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, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, )			->  Any:




           """simple docstring"""

           _lowercase			:				Dict 						=						True
           _lowercase			:				Union[str, Any] 						=						NezhaModel(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				Any 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, encoder_hidden_states=lowerCamelCase, encoder_attention_mask=lowerCamelCase, )
           _lowercase			:				Dict 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, encoder_hidden_states=lowerCamelCase, )
           _lowercase			:				Dict 						=						model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase)
           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, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						NezhaForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				str 						=						model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Dict 						=						NezhaForNextSentencePrediction(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				str 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase, )
           self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Any 						=						NezhaForPreTraining(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				Optional[Any] 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase, next_sentence_label=lowerCamelCase, )
           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, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						NezhaForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				Union[str, Any] 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, start_positions=lowerCamelCase, end_positions=lowerCamelCase, )
           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, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				int 						=						self.num_labels
           _lowercase			:				Tuple 						=						NezhaForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				int 						=						model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.num_labels
           _lowercase			:				Optional[Any] 						=						NezhaForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				Dict 						=						model(lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.num_choices
           _lowercase			:				Any 						=						NezhaForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				str 						=						input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
           _lowercase			:				Tuple 						=						token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
           _lowercase			:				str 						=						input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
           _lowercase			:				Tuple 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, token_type_ids=lowerCamelCase, labels=lowerCamelCase, )
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))




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




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							
           )			:				Optional[Any] 						=						config_and_inputs
           _lowercase			:				List[str] 						=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,					_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Union[str, Any]       					=     (
           (
               NezhaModel,
               NezhaForMaskedLM,
               NezhaForMultipleChoice,
               NezhaForNextSentencePrediction,
               NezhaForPreTraining,
               NezhaForQuestionAnswering,
               NezhaForSequenceClassification,
               NezhaForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       lowercase_					:      str       					=     (
           {
               """feature-extraction""": NezhaModel,
               """fill-mask""": NezhaForMaskedLM,
               """question-answering""": NezhaForQuestionAnswering,
               """text-classification""": NezhaForSequenceClassification,
               """token-classification""": NezhaForTokenClassification,
               """zero-shot""": NezhaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowercase_					:      Dict       					=     True
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase=False)			->  Dict:




           """simple docstring"""

           _lowercase			:				Dict 						=						super()._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase)

           if return_labels:
               if model_class in get_values(lowerCamelCase):
                   _lowercase			:				str 						=						torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=lowerCamelCase)
                   _lowercase			:				Optional[int] 						=						torch.zeros(
                       self.model_tester.batch_size, dtype=torch.long, device=lowerCamelCase)
           return inputs_dict
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						NezhaModelTester(self)
           _lowercase			:				Any 						=						ConfigTester(self, config_class=lowerCamelCase, hidden_size=37)
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           (
               (
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							
           )			:				List[str] 						=						self.model_tester.prepare_config_and_inputs_for_decoder()

           _lowercase			:				List[str] 						=						None

           self.model_tester.create_and_check_model_as_decoder(
               lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, )
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_next_sequence_prediction(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				str 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase)
       @slow
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase			:				int 						=						NezhaModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)




       @slow
       @require_torch_gpu
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Any 						=						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			:				Tuple 						=						True
               _lowercase			:				List[Any] 						=						model_class(config=lowerCamelCase)

               _lowercase			:				Dict 						=						self._prepare_for_class(lowerCamelCase, lowerCamelCase)
               _lowercase			:				List[str] 						=						torch.jit.trace(
                   lowerCamelCase, (inputs_dict['input_ids'].to('cpu'), inputs_dict['attention_mask'].to('cpu')))

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






@require_torch
class _lowerCamelCase(		unittest.TestCase  ):
       @slow
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						NezhaModel.from_pretrained('sijunhe/nezha-cn-base')
           _lowercase			:				Tuple 						=						torch.tensor([[0, 1, 2, 3, 4, 5]])
           _lowercase			:				Optional[Any] 						=						torch.tensor([[0, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               _lowercase			:				int 						=						model(lowerCamelCase, attention_mask=lowerCamelCase)[0]
           _lowercase			:				Any 						=						torch.Size((1, 6, 7_68))
           self.assertEqual(output.shape, lowerCamelCase)
           _lowercase			:				List[Any] 						=						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], lowerCamelCase, atol=1E-4))




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




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base')
           _lowercase			:				int 						=						torch.tensor([[0, 1, 2, 3, 4, 5]])
           _lowercase			:				Optional[int] 						=						torch.tensor([[1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               _lowercase			:				Dict 						=						model(lowerCamelCase, attention_mask=lowerCamelCase)[0]
           _lowercase			:				Optional[Any] 						=						torch.Size((1, 6, 2_11_28))
           self.assertEqual(output.shape, lowerCamelCase)
           _lowercase			:				Optional[int] 						=						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], lowerCamelCase, atol=1E-4))






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    # Initialise PyTorch model
    _lowercase			:				Optional[int] 						=						TaConfig.from_json_file(lowerCamelCase_					)
    print(F'''Building PyTorch model from configuration: {config}'''					)
    _lowercase			:				Union[str, Any] 						=						TaForConditionalGeneration(lowerCamelCase_					)

    # Load weights from tf checkpoint
    load_tf_weights_in_ta(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # Save pytorch-model
    print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
    model.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[int]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 SCREAMING_SNAKE_CASE			: Any				   =					parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


