




'''simple docstring'''




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	Union[str, Any] ):






   """simple docstring"""






   return cls



  @slow
  @require_torch_gpu
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    self.get_auto_remove_tmp_dir()
   UpperCAmelCase__										=    f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

   # we want this test to run even if there is only one GPU, but if there are more we use them all
   UpperCAmelCase__										=    get_gpu_count()
   UpperCAmelCase__										=    f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
   UpperCAmelCase__										=    [sys.executable] + distributed_args + args
   execute_subprocess_async(_UpperCAmelCase						,		env=self.get_env() )

   UpperCAmelCase__										=    os.path.join(_UpperCAmelCase						,		"""test_bleu.json""" )
   UpperCAmelCase__										=    load_json(_UpperCAmelCase )
   # print(metrics)
   self.assertGreaterEqual(metrics["""bleu"""]						,		25 )






'''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)
