

"""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)
UpperCamelCase_					:						str				= logging.getLogger()
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						"\n".join(__a				)
					Path(__a				).open("w"				).writelines(__a				)


UpperCamelCase_					:						Dict				= '''patrickvonplaten/t5-tiny-random'''
UpperCamelCase_					:						Any				= '''sshleifer/bart-tiny-random'''
UpperCamelCase_					:						List[str]				= '''sshleifer/tiny-mbart'''

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




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
								A_				 =						input_file_name.parent / "utest_output.txt"
								assert not output_file_name.exists()
								A_				 =						[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
								_dump_articles(_snake_case       ,   _snake_case	)

								A_				 =						str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
								A_				 =						"translation_en_to_de" if model == T5_TINY else "summarization"
								A_				 =						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(_snake_case       ,   "argv"       ,   _snake_case	):
													run_generate()
													assert Path(_snake_case	).exists()
													# os.remove(Path(output_file_name))




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								self.run_eval_tester(_snake_case	)




			@parameterized.expand([BART_TINY, MBART_TINY]	)
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								self.run_eval_tester(_snake_case	)






			@parameterized.expand([T5_TINY, MBART_TINY]	)
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Union[str, Any]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
								A_				 =						input_file_name.parent / "utest_output.txt"
								assert not output_file_name.exists()

								A_				 =						{
								    "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!",
								    ],
								}

								A_				 =						Path(self.get_auto_remove_tmp_dir()	)
								A_				 =						str(tmp_dir / "scores.json"	)
								A_				 =						str(tmp_dir / "val.target"	)
								_dump_articles(_snake_case       ,   text["en"]	)
								_dump_articles(_snake_case       ,   text["de"]	)
								A_				 =						"translation_en_to_de" if model == T5_TINY else "summarization"
								A_				 =						F'\n            run_eval_search.py\n            {model}\n            {str(_snake_case	)}\n            {str(_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(_snake_case       ,   "argv"       ,   _snake_case	):
													with CaptureStdout() as cs:
																		run_search()
													A_				 =						[" num_beams | length_penalty", model, "Best score args"]
													A_				 =						["Info"]
													if "translation" in task:
																		expected_strings.append("bleu"	)
													else:
																		expected_strings.extend(_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(_snake_case	).exists()
													os.remove(Path(_snake_case	)	)






"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							A_     (__a				):

					'''simple docstring'''





					return ConvertCommand(
					    args.model_type				,   args.tf_checkpoint				,   args.pytorch_dump_output				,   args.config				,   args.finetuning_task_name				)


UpperCamelCase_					:						Tuple				= '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					ArgumentParser	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						parser.add_parser(
								    "convert"       ,   help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."       ,   )
								train_parser.add_argument("--model_type"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Model's type."	)
								train_parser.add_argument(
								    "--tf_checkpoint"       ,   type=_snake_case       ,   required=_snake_case       ,   help="TensorFlow checkpoint path or folder."	)
								train_parser.add_argument(
								    "--pytorch_dump_output"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Path to the PyTorch saved model output."	)
								train_parser.add_argument("--config"       ,   type=_snake_case       ,   default=""       ,   help="Configuration file path or folder."	)
								train_parser.add_argument(
								    "--finetuning_task_name"       ,   type=_snake_case       ,   default=_snake_case       ,   help="Optional fine-tuning task name if the TF model was a finetuned model."       ,   )
								train_parser.set_defaults(func=_snake_case	)




			def __init__(				self						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   *_snake_case						:					Optional[int]       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						logging.get_logger("transformers-cli/converting"	)

								self._logger.info(F'Loading model {model_type}'	)
								A_				 =						model_type
								A_				 =						tf_checkpoint
								A_				 =						pytorch_dump_output
								A_				 =						config
								A_				 =						finetuning_task_name






			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								if self._model_type == "albert":
													try:
																		from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "bert":
													try:
																		from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "funnel":
													try:
																		from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "t5":
													try:
																		from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "gpt":
													from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
													    convert_openai_checkpoint_to_pytorch,
													)

													convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "transfo_xl":
													try:
																		from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																		    convert_transfo_xl_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													if "ckpt" in self._tf_checkpoint.lower():
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													else:
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													convert_transfo_xl_checkpoint_to_pytorch(
													    _snake_case       ,   self._config       ,   self._pytorch_dump_output       ,   _snake_case	)
								elif self._model_type == "gpt2":
													try:
																		from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																		    convert_gpta_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "xlnet":
													try:
																		from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																		    convert_xlnet_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_xlnet_checkpoint_to_pytorch(
													    self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output       ,   self._finetuning_task_name	)
								elif self._model_type == "xlm":
													from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
													    convert_xlm_checkpoint_to_pytorch,
													)

													convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "lxmert":
													from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
													    convert_lxmert_checkpoint_to_pytorch,
													)

													convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "rembert":
													from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
													    convert_rembert_tf_checkpoint_to_pytorch,
													)

													convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								else:
													raise ValueError(
													    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"	)




