




'''simple docstring'''




import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase_       				=      abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Any       ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(SCREAMING_SNAKE_CASE__       )







def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[Any]       ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_terminal_summary_main

 UpperCAmelCase__										=    terminalreporter.config.getoption("""--make-reports"""       )
 if make_reports:
  pytest_terminal_summary_main(SCREAMING_SNAKE_CASE__       ,       id=SCREAMING_SNAKE_CASE__       )






'''simple docstring'''




import argparse

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


logging.set_verbosity_info()

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 print(F'''Building PyTorch model from configuration: {config}'''       )
 UpperCAmelCase__										=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE__       )

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

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


if __name__ == "__main__":
    UpperCAmelCase_       				=      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.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
