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

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCamelCase__	=							"\\n    Text data.\n    Second line of data."

lowerCamelCase__	=							"file"


@pytest.fixture(scope="""session""" )
def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
		_UpperCamelCase						    =	bytes(lowerCAmelCase						,  """utf-8""" )
		with zstd.open(lowerCAmelCase						,  """wb""" ) as f:
				f.write(lowerCAmelCase )
		return path


@pytest.fixture
def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		with open(os.path.join(tmpfs.local_root_dir						,  lowerCAmelCase )						,  """w""" ) as f:
				f.write(lowerCAmelCase )
		return FILE_PATH


@pytest.mark.parametrize("""compression_format"""						,  ["""gzip""", """xz""", """zstd"""] )
def 							SCREAMING_SNAKE_CASE(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
		_UpperCamelCase						    =	input_paths[compression_format]
		_UpperCamelCase						    =	tmp_path / """cache"""
		_UpperCamelCase						    =	DownloadConfig(cache_dir=lowerCAmelCase						,  extract_compressed_file=lowerCAmelCase )
		_UpperCamelCase						    =	cached_path(lowerCAmelCase						,  download_config=lowerCAmelCase )
		with open(lowerCAmelCase ) as f:
				_UpperCamelCase						    =	f.read()
		with open(lowerCAmelCase ) as f:
				_UpperCamelCase						    =	f.read()
		assert extracted_file_content == expected_file_content


@pytest.mark.parametrize("""default_extracted"""						,  [True, False] )
@pytest.mark.parametrize("""default_cache_dir"""						,  [True, False] )
def 							SCREAMING_SNAKE_CASE(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	"""custom_cache"""
		_UpperCamelCase						    =	"""custom_extracted_dir"""
		_UpperCamelCase						    =	tmp_path / """custom_extracted_path"""
		if default_extracted:
				_UpperCamelCase						    =	("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
		else:
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""						,  lowerCAmelCase )
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""						,  str(lowerCAmelCase ) )
				_UpperCamelCase						    =	custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

		_UpperCamelCase						    =	xz_file
		_UpperCamelCase						    =	(
		    DownloadConfig(extract_compressed_file=lowerCAmelCase )
		    if default_cache_dir
		    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir						,  extract_compressed_file=lowerCAmelCase )
		)
		_UpperCamelCase						    =	cached_path(lowerCAmelCase						,  download_config=lowerCAmelCase )
		assert Path(lowerCAmelCase ).parent.parts[-2:] == expected


def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	str(Path(lowerCAmelCase ).resolve() )
		assert cached_path(lowerCAmelCase ) == text_file
		# relative path
		_UpperCamelCase						    =	str(Path(lowerCAmelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
		assert cached_path(lowerCAmelCase ) == text_file


def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	str(tmp_path.resolve() / """__missing_file__.txt""" )
		with pytest.raises(lowerCAmelCase ):
				cached_path(lowerCAmelCase )
		# relative path
		_UpperCamelCase						    =	"""./__missing_file__.txt"""
		with pytest.raises(lowerCAmelCase ):
				cached_path(lowerCAmelCase )


def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	get_from_cache(F'tmp://{tmpfs_file}' )
		with open(lowerCAmelCase ) as f:
				_UpperCamelCase						    =	f.read()
		assert output_file_content == FILE_CONTENT


@patch("""datasets.config.HF_DATASETS_OFFLINE"""						,  lowerCAmelCase )
def 							SCREAMING_SNAKE_CASE()		->				Union[str, Any]:

		"""simple docstring"""






		with pytest.raises(lowerCAmelCase ):
				cached_path("""https://huggingface.co""" )


@patch("""datasets.config.HF_DATASETS_OFFLINE"""						,  lowerCAmelCase )
def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path_factory.mktemp("""data""" ) / """file.html"""
		with pytest.raises(lowerCAmelCase ):
				http_get("""https://huggingface.co"""						,  temp_file=lowerCAmelCase )
		with pytest.raises(lowerCAmelCase ):
				http_head("""https://huggingface.co""" )


@patch("""datasets.config.HF_DATASETS_OFFLINE"""						,  lowerCAmelCase )
def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path_factory.mktemp("""data""" ) / """file.html"""
		with pytest.raises(lowerCAmelCase ):
				ftp_get("""ftp://huggingface.co"""						,  temp_file=lowerCAmelCase )
		with pytest.raises(lowerCAmelCase ):
				ftp_head("""ftp://huggingface.co""" )







@patch("""datasets.config.HF_DATASETS_OFFLINE"""						,  lowerCAmelCase )
def 							SCREAMING_SNAKE_CASE(lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	tmp_path_factory.mktemp("""data""" ) / """file.html"""
		with pytest.raises(lowerCAmelCase ):
				fsspec_get("""s3://huggingface.co"""						,  temp_file=lowerCAmelCase )
		with pytest.raises(lowerCAmelCase ):
				fsspec_head("""s3://huggingface.co""" )





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






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


lowerCamelCase__	=							"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	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=a       ,							required=a       ,							help="""Model's type."""				)
								train_parser.add_argument(
								    """--tf_checkpoint"""       ,							type=a       ,							required=a       ,							help="""TensorFlow checkpoint path or folder."""				)
								train_parser.add_argument(
								    """--pytorch_dump_output"""       ,							type=a       ,							required=a       ,							help="""Path to the PyTorch saved model output."""				)
								train_parser.add_argument("""--config"""       ,							type=a       ,							default=""""""       ,							help="""Configuration file path or folder."""				)
								train_parser.add_argument(
								    """--finetuning_task_name"""       ,							type=a       ,							default=a       ,							help="""Optional fine-tuning task name if the TF model was a finetuned model."""       ,							)
								train_parser.set_defaults(func=a				)

						def __init__(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							*a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	logging.get_logger("""transformers-cli/converting"""				)

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







						def  A_				(			self				)					->      Tuple:


								'''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(a				)

										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(a				)

										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(a				)

										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(a				)

										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(a				)

										if "ckpt" in self._tf_checkpoint.lower():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										convert_transfo_xl_checkpoint_to_pytorch(
										    a       ,							self._config       ,							self._pytorch_dump_output       ,							a				)
								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(a				)

										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(a				)

										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]"""				)



