

'''simple docstring'''





import argparse
import hashlib  # hashlib is only used inside the Test class
import struct

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						int:
							'''simple docstring'''






							A__        =			data
							A__        =			[0X6_7_4_5_2_3_0_1, 0XE_F_C_D_A_B_8_9, 0X9_8_B_A_D_C_F_E, 0X1_0_3_2_5_4_7_6, 0XC_3_D_2_E_1_F_0]


			@staticmethod
			def      UpperCamelCase		(    lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							return ((n << b) | (n >> (32 - b))) & 0XF_F_F_F_F_F_F_F


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			b"""\x80""" + b"""\x00""" * (63 - (len(self.data       ) + 8) % 64)
							A__        =			self.data + padding + struct.pack(">Q"      ,      8 * len(self.data       )       )
							return padded_data


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							return [
							    self.padded_data[i : i + 64] for i in range(0      ,      len(self.padded_data       )      ,      64       )
							]


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			list(struct.unpack(">16L"      ,      lowerCamelCase_       )       ) + [0] * 64
							for i in range(16      ,      80       ):
											A__        =			self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])      ,      1       )
							return w


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			self.padding()
							A__        =			self.split_blocks()
							for block in self.blocks:
											A__        =			self.expand_block(lowerCamelCase_       )
											A__        =			self.h
											for i in range(0      ,      80       ):
															if 0 <= i < 20:
																			A__        =			(b & c) | ((~b) & d)
																			A__        =			0X5_A_8_2_7_9_9_9
															elif 20 <= i < 40:
																			A__        =			b ^ c ^ d
																			A__        =			0X6_E_D_9_E_B_A_1
															elif 40 <= i < 60:
																			A__        =			(b & c) | (b & d) | (c & d)
																			A__        =			0X8_F_1_B_B_C_D_C
															elif 60 <= i < 80:
																			A__        =			b ^ c ^ d
																			A__        =			0XC_A_6_2_C_1_D_6
															A__        =			(
															    self.rotate(lowerCamelCase_      ,      5       ) + f + e + k + expanded_block[i] & 0XF_F_F_F_F_F_F_F,
															    a,
															    self.rotate(lowerCamelCase_      ,      30       ),
															    c,
															    d,
															)
											A__        =			(
											    self.h[0] + a & 0XF_F_F_F_F_F_F_F,
											    self.h[1] + b & 0XF_F_F_F_F_F_F_F,
											    self.h[2] + c & 0XF_F_F_F_F_F_F_F,
											    self.h[3] + d & 0XF_F_F_F_F_F_F_F,
											    self.h[4] + e & 0XF_F_F_F_F_F_F_F,
											)
							return ("{:08x}" * 5).format(*self.h       )



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			B"""Test String"""
				assert SHAaHash(lowerCamelCase_      ).final_hash() == hashlib.shaa(lowerCamelCase_      ).hexdigest()  # noqa: S324



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser(description="Process some strings or files"      )
				parser.add_argument(
				    "--string"	,       dest="input_string"	,       default="Hello World!! Welcome to Cryptography"	,       help="Hash the string"	,       )
				parser.add_argument("--file"	,       dest="input_file"	,       help="Hash contents of a file"      )
				A__        =			parser.parse_args()
				A__        =			args.input_string
				# In any case hash input should be a bytestring
				if args.input_file:
								with open(args.input_file	,       "rb"      ) as f:
												A__        =			f.read()
				else:
								A__        =			bytes(lowerCamelCase_	,       "utf-8"      )
				print(SHAaHash(lowerCamelCase_      ).final_hash()      )


if __name__ == "__main__":
	main()
	import doctest

	doctest.testmod()




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Namespace      )			->      Tuple:




				'''simple docstring'''





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


lowerCAmelCase__	   = """
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 					a__						(					snake_case			):
			"""simple docstring"""







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


			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      *lowercase      ,      )  ->						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      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''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(lowercase       )

											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(lowercase       )

											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(lowercase       )

											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(lowercase       )

											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(lowercase       )

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

											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(lowercase       )

											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]"       )

