





import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__A     =    get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     enum.Enum   ):







 '''simple docstring'''
 lowercase_		=      "all_checks"
 lowercase_		=      "basic_checks"
 lowercase_		=      "no_checks"



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		Optional[int]:







  """simple docstring"""





  if expected_checksums is None:
    logger.info("Unable to verify checksums."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedDownloadedFile(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				List[Any]        =[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  lowerCamelCase__:				Union[str, Any]        =" for " + verification_name if verification_name is not None else ""
  if len(__a		) > 0:
    raise NonMatchingChecksumError(
        F"""Checksums didn't match{for_verification_name}:\n"""
        F"""{bad_urls}\n"""
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"		)
  logger.info("All the checksums matched successfully" + for_verification_name		)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  if expected_splits is None:
    logger.info("Unable to verify splits sizes."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreSplits(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedSplits(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				Optional[int]        =[
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(__a		) > 0:
    raise NonMatchingSplitsSizesError(str(__a		)		)
  logger.info("All the splits matched successfully."		)





def 					lowerCAmelCase_			(	__a     ,   __a = True		)					->		dict:







  """simple docstring"""





  if record_checksum:
    lowerCamelCase__:				str        =shaaaa()
    with open(__a     ,   "rb"		) as f:
      for chunk in iter(lambda: f.read(1 << 20		)     ,   b""		):
        m.update(__a		)
      lowerCamelCase__:				Dict        =m.hexdigest()
  else:
    lowerCamelCase__:				List[str]        =None
  return {"num_bytes": os.path.getsize(__a		), "checksum": checksum}






def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False








from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





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


__A     =    "\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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       ArgumentParser)	->str:



   '''simple docstring'''



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

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



   '''simple docstring'''



   lowerCamelCase__:				Dict        =logging.get_logger("transformers-cli/converting")

   self._logger.info(F"""Loading model {model_type}""")
   lowerCamelCase__:				Any        =model_type
   lowerCamelCase__:				Optional[int]        =tf_checkpoint
   lowerCamelCase__:				Any        =pytorch_dump_output
   lowerCamelCase__:				Union[str, Any]        =config
   lowerCamelCase__:				str        =finetuning_task_name







 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     if "ckpt" in self._tf_checkpoint.lower():
       lowerCamelCase__:				Tuple        =self._tf_checkpoint
       lowerCamelCase__:				List[str]        =""
     else:
       lowerCamelCase__:				Any        =self._tf_checkpoint
       lowerCamelCase__:				Dict        =""
     convert_transfo_xl_checkpoint_to_pytorch(
         UpperCAmelCase_	,			self._config	,			self._pytorch_dump_output	,			UpperCAmelCase_)
   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(UpperCAmelCase_)

     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(UpperCAmelCase_)

     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]")

