
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[Any]       ,      __lowerCamelCase  :				Any       ,      __lowerCamelCase  :				Union[str, Any]       ,      __lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				Dict					):
       # Initialise PyTorch model
       snake_case :     List[Any]           =   BertConfig.from_pretrained(
           "bert-large-cased"       ,      vocab_size=__lowerCamelCase       ,      max_position_embeddings=512       ,      is_decoder=__lowerCamelCase       ,      add_cross_attention=__lowerCamelCase       ,      )
       snake_case :     int           =   bert_config.to_dict()
       del bert_config_dict["type_vocab_size"]
       snake_case :     Dict           =   BertGenerationConfig(**__lowerCamelCase					)
       if is_encoder:
              snake_case :     List[Any]           =   BertGenerationEncoder(__lowerCamelCase					)
       else:
              snake_case :     Optional[int]           =   BertGenerationDecoder(__lowerCamelCase					)
       print(f"""Building PyTorch model from configuration: {config}"""					)

       # Load weights from tf checkpoint
       load_tf_weights_in_bert_generation(
           __lowerCamelCase       ,      __lowerCamelCase       ,      model_class="bert"       ,      is_encoder_named_decoder=__lowerCamelCase       ,      is_encoder=__lowerCamelCase       ,      )

       # Save pytorch-model
       print(f"""Save PyTorch model and config to {pytorch_dump_path}"""					)
       model.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
   __lowerCamelCase							= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--is_encoder_named_decoder""",
       action="""store_true""",
       help="""If decoder has to be renamed to encoder in PyTorch model.""",
   )
   parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
   parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
   __lowerCamelCase							= parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils






class 						UpperCAmelCase	(		unittest.TestCase				):



   @patch("diffusers.utils.hub_utils.get_full_repo_name"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      Mock				)	->			None:


          '''simple docstring'''


          snake_case :     str           =   "full_repo_name"
          with TemporaryDirectory() as tmpdir:
                 # Dummy args values
                 snake_case :     List[str]           =   Mock()
                 snake_case :     List[str]           =   tmpdir
                 snake_case :     Optional[int]           =   0
                 snake_case :     int           =   "hub_token"
                 snake_case :     Union[str, Any]           =   "dataset_name"
                 snake_case :     Tuple           =   0.01
                 snake_case :     int           =   10_00_00
                 snake_case :     Optional[Any]           =   1_00_00
                 snake_case :     Optional[Any]           =   0.01
                 snake_case :     Dict           =   0.02
                 snake_case :     List[Any]           =   0.03
                 snake_case :     str           =   0.0005
                 snake_case :     List[str]           =   0.000001
                 snake_case :     Tuple           =   1
                 snake_case :     Optional[int]           =   10
                 snake_case :     List[str]           =   0.001
                 snake_case :     List[Any]           =   0.1
                 snake_case :     List[str]           =   0.2
                 snake_case :     Tuple           =   True

                 # Model card mush be rendered and saved
                 diffusers.utils.hub_utils.create_model_card(snake_case__					,		model_name="model_name"				)
                 self.assertTrue((Path(snake_case__				) / "README.md").is_file()				)





