


"""simple docstring"""







import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    # Initialise PyTorch model
    a					=FunnelConfig.from_json_file(lowercase		)
    print(f'''Building PyTorch model from configuration: {config}'''		)
    a					=FunnelBaseModel(lowercase		) if base_model else FunnelModel(lowercase		)

    # Load weights from tf checkpoint
    load_tf_weights_in_funnel(lowercase	,   lowercase	,   lowercase		)

    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}'''		)
    torch.save(model.state_dict()	,   lowercase		)


if __name__ == "__main__":
       lowerCamelCase_       :       Union[str, Any]      						=  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 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."""
       )
       parser.add_argument(
           """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
       )
       lowerCamelCase_       :       str      						=  parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
       )



"""simple docstring"""







import os
import sys
import unittest


lowerCamelCase_       :       Tuple      						=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
lowerCamelCase_       :       Dict      						=  os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")






class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=get_test_to_tester_mapping(__A						)
     a					=get_test_to_tester_mapping(__A						)

     a					={'''BertModelTest''': '''BertModelTester'''}

     a					={
         '''BlipModelTest''': '''BlipModelTester''',
         '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
         '''BlipTextModelTest''': '''BlipTextModelTester''',
         '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
         '''BlipVQAModelTest''': '''BlipVQAModelTester''',
         '''BlipVisionModelTest''': '''BlipVisionModelTester''',
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=get_model_to_test_mapping(__A						)
     a					=get_model_to_test_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTest'''],
         '''BertForMultipleChoice''': ['''BertModelTest'''],
         '''BertForNextSentencePrediction''': ['''BertModelTest'''],
         '''BertForPreTraining''': ['''BertModelTest'''],
         '''BertForQuestionAnswering''': ['''BertModelTest'''],
         '''BertForSequenceClassification''': ['''BertModelTest'''],
         '''BertForTokenClassification''': ['''BertModelTest'''],
         '''BertLMHeadModel''': ['''BertModelTest'''],
         '''BertModel''': ['''BertModelTest'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
         '''BlipModel''': ['''BlipModelTest'''],
         '''BlipTextModel''': ['''BlipTextModelTest'''],
         '''BlipVisionModel''': ['''BlipVisionModelTest'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=get_model_to_tester_mapping(__A						)
     a					=get_model_to_tester_mapping(__A						)

     a					={
         '''BertForMaskedLM''': ['''BertModelTester'''],
         '''BertForMultipleChoice''': ['''BertModelTester'''],
         '''BertForNextSentencePrediction''': ['''BertModelTester'''],
         '''BertForPreTraining''': ['''BertModelTester'''],
         '''BertForQuestionAnswering''': ['''BertModelTester'''],
         '''BertForSequenceClassification''': ['''BertModelTester'''],
         '''BertForTokenClassification''': ['''BertModelTester'''],
         '''BertLMHeadModel''': ['''BertModelTester'''],
         '''BertModel''': ['''BertModelTester'''],
     }

     a					={
         '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
         '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
         '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
         '''BlipModel''': ['''BlipModelTester'''],
         '''BlipTextModel''': ['''BlipTextModelTester'''],
         '''BlipVisionModel''': ['''BlipVisionModelTester'''],
     }

     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)
     self.assertEqual(get_test_info.to_json(__A						)			,   __A						)