


"""simple docstring"""



import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
__snake_case             =   logging.getLogger()







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Tuple			)     ->						List[Any]:


    """simple docstring"""





    snake_case   :				Union[str, Any]				 =   "\n".join(lowercase__			)
    Path(lowercase__			).open("w"			).writelines(lowercase__			)


__snake_case             =   'patrickvonplaten/t5-tiny-random'
__snake_case             =   'sshleifer/bart-tiny-random'
__snake_case             =   'sshleifer/tiny-mbart'

__snake_case             =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class _lowerCAmelCase    (				_A     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   Path(self.get_auto_remove_tmp_dir()						) / "utest_input.source"
       snake_case   :				Any				 =   input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()
       snake_case   :				int				 =   [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
       _dump_articles(__SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						)

       snake_case   :				Union[str, Any]				 =   str(Path(self.get_auto_remove_tmp_dir()						) / "scores.json"						)
       snake_case   :				List[str]				 =   "translation_en_to_de" if model == T5_TINY else "summarization"
       snake_case   :				Optional[int]				 =   F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

       with patch.object(__SCREAMING_SNAKE_CASE						,     "argv"						,     __SCREAMING_SNAKE_CASE						):
           run_generate()
           assert Path(__SCREAMING_SNAKE_CASE						).exists()
           # os.remove(Path(output_file_name))







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       self.run_eval_tester(__SCREAMING_SNAKE_CASE						)







   @parameterized.expand([BART_TINY, MBART_TINY]						)
   @slow
   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       self.run_eval_tester(__SCREAMING_SNAKE_CASE						)







   @parameterized.expand([T5_TINY, MBART_TINY]						)
   @slow
   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Dict				 =   Path(self.get_auto_remove_tmp_dir()						) / "utest_input.source"
       snake_case   :				List[str]				 =   input_file_name.parent / "utest_output.txt"
       assert not output_file_name.exists()

       snake_case   :				Any				 =   {
           "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
           "de": [
               "Maschinelles Lernen ist großartig, oder?",
               "Ich esse gerne Bananen",
               "Morgen ist wieder ein toller Tag!",
           ],
       }

       snake_case   :				List[Any]				 =   Path(self.get_auto_remove_tmp_dir()						)
       snake_case   :				Dict				 =   str(tmp_dir / "scores.json"						)
       snake_case   :				Dict				 =   str(tmp_dir / "val.target"						)
       _dump_articles(__SCREAMING_SNAKE_CASE						,     text["en"]						)
       _dump_articles(__SCREAMING_SNAKE_CASE						,     text["de"]						)
       snake_case   :				List[Any]				 =   "translation_en_to_de" if model == T5_TINY else "summarization"
       snake_case   :				List[str]				 =   F'\n            run_eval_search.py\n            {model}\n            {str(__SCREAMING_SNAKE_CASE						)}\n            {str(__SCREAMING_SNAKE_CASE						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
       testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]						)

       with patch.object(__SCREAMING_SNAKE_CASE						,     "argv"						,     __SCREAMING_SNAKE_CASE						):
           with CaptureStdout() as cs:
               run_search()
           snake_case   :				Optional[int]				 =   [" num_beams | length_penalty", model, "Best score args"]
           snake_case   :				Tuple				 =   ["Info"]
           if "translation" in task:
               expected_strings.append("bleu"						)
           else:
               expected_strings.extend(__SCREAMING_SNAKE_CASE						)
           for w in expected_strings:
               assert w in cs.out
           for w in un_expected_strings:
               assert w not in cs.out
           assert Path(__SCREAMING_SNAKE_CASE						).exists()
           os.remove(Path(__SCREAMING_SNAKE_CASE						)						)









"""simple docstring"""



import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__snake_case             =   data_utils.TransfoXLTokenizer
__snake_case             =   data_utils.TransfoXLCorpus
__snake_case             =   data_utils
__snake_case             =   data_utils







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  int						,							lowercase  :  List[Any]						,							lowercase  :  Union[str, Any]			)     ->						List[Any]:


    """simple docstring"""





    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(lowercase						,							"rb"			) as fp:
            snake_case   :				int				 =   pickle.load(lowercase						,							encoding="latin1"			)
        # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
        snake_case   :				int				 =   pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
        print(F'Save vocabulary to {pytorch_vocab_dump_path}'			)
        snake_case   :				str				 =   corpus.vocab.__dict__
        torch.save(lowercase						,							lowercase			)

        snake_case   :				str				 =   corpus.__dict__
        corpus_dict_no_vocab.pop("vocab"						,							lowercase			)
        snake_case   :				Dict				 =   pytorch_dump_folder_path + "/" + CORPUS_NAME
        print(F'Save dataset to {pytorch_dataset_dump_path}'			)
        torch.save(lowercase						,							lowercase			)

    if tf_checkpoint_path:
        # Convert a pre-trained TensorFlow model
        snake_case   :				Union[str, Any]				 =   os.path.abspath(lowercase			)
        snake_case   :				str				 =   os.path.abspath(lowercase			)

        print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'			)
        # Initialise PyTorch model
        if transfo_xl_config_file == "":
            snake_case   :				int				 =   TransfoXLConfig()
        else:
            snake_case   :				Optional[int]				 =   TransfoXLConfig.from_json_file(lowercase			)
        print(F'Building PyTorch model from configuration: {config}'			)
        snake_case   :				str				 =   TransfoXLLMHeadModel(lowercase			)

        snake_case   :				str				 =   load_tf_weights_in_transfo_xl(lowercase						,							lowercase						,							lowercase			)
        # Save pytorch-model
        snake_case   :				Union[str, Any]				 =   os.path.join(lowercase						,							lowercase			)
        snake_case   :				Optional[Any]				 =   os.path.join(lowercase						,							lowercase			)
        print(F'Save PyTorch model to {os.path.abspath(lowercase			)}'			)
        torch.save(model.state_dict()						,							lowercase			)
        print(F'Save configuration file to {os.path.abspath(lowercase			)}'			)
        with open(lowercase						,							"w"						,							encoding="utf-8"			) as f:
            f.write(config.to_json_string()			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
  )
  parser.add_argument(
      """--tf_checkpoint_path""",
      default="""""",
      type=str,
      help="""An optional path to a TensorFlow checkpoint path to be converted.""",
  )
  parser.add_argument(
      """--transfo_xl_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--transfo_xl_dataset_file""",
      default="""""",
      type=str,
      help="""An optional dataset file to be converted in a vocabulary.""",
  )
  __snake_case             =   parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )





