






'''simple docstring'''
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def  _a(  UpperCamelCase__				:						Dict							):




      '''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      (			lowerCamelCase				):




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




 def __init__(      self	:							Optional[Any]					,      __lowercase	:							Dict					,      __lowercase	:							str					,      __lowercase	:							Optional[int]					,      __lowercase	:							Tuple					,      __lowercase	:							List[str]					,      *__lowercase	:							List[Any]					,      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				Any									=logging.get_logger('''transformers-cli/converting'''      )

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




 def      __magic_name__	(      self	:							List[str]      )	->						Dict:






       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(_SCREAMING_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(_SCREAMING_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(_SCREAMING_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(_SCREAMING_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(_SCREAMING_SNAKE_CASE      )

             if "ckpt" in self._tf_checkpoint.lower():
                   SCREAMING_SNAKE_CASE__     :				Tuple									=self._tf_checkpoint
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=''''''
             else:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=self._tf_checkpoint
                   SCREAMING_SNAKE_CASE__     :				Tuple									=''''''
             convert_transfo_xl_checkpoint_to_pytorch(
                 _SCREAMING_SNAKE_CASE					,      self._config					,      self._pytorch_dump_output					,      _SCREAMING_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(_SCREAMING_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(_SCREAMING_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]'''      )







'''simple docstring'''
from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def  _a(  UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						list[int],	UpperCamelCase__				:						int,	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=coefficient_matrix.shape
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=constant_matrix.shape

      if rowsa != colsa:
            SCREAMING_SNAKE_CASE__     :				Any									=f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if colsa != 1:
            SCREAMING_SNAKE_CASE__     :				str									=f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if rowsa != rowsa:
            SCREAMING_SNAKE_CASE__     :				str									=(
                '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
                f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
            )
            raise ValueError(UpperCamelCase__							)

      if len(UpperCamelCase__							) != rowsa:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                '''Number of initial values must be equal to number of rows in coefficient '''
                f"matrix but received {len(UpperCamelCase__							)} and {rowsa}"
            )
            raise ValueError(UpperCamelCase__							)

      if iterations <= 0:
            raise ValueError('''Iterations must be at least 1'''							)

      SCREAMING_SNAKE_CASE__     :				NDArray[floataa]									=np.concatenate(
          (coefficient_matrix, constant_matrix),	axis=1							)

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Any									=table.shape

      strictly_diagonally_dominant(UpperCamelCase__							)

      # Iterates the whole matrix for given number of times
      for _ in range(UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				List[str]									=[]
            for row in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=0
                  for col in range(UpperCamelCase__							):
                        if col == row:
                              SCREAMING_SNAKE_CASE__     :				int									=table[row][col]
                        elif col == cols - 1:
                              SCREAMING_SNAKE_CASE__     :				Any									=table[row][col]
                        else:
                              temp += (-1) * table[row][col] * init_val[col]
                  SCREAMING_SNAKE_CASE__     :				int									=(temp + val) / denom
                  new_val.append(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=new_val

      return [float(UpperCamelCase__							) for i in new_val]





def  _a(  UpperCamelCase__				:						NDArray[floataa]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[int]									=table.shape

      SCREAMING_SNAKE_CASE__     :				Any									=True

      for i in range(0,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				int									=0
            for j in range(0,	cols - 1							):
                  if i == j:
                        continue
                  else:
                        total += table[i][j]

            if table[i][i] <= total:
                  raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''							)

      return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()