


from __future__ import annotations

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


def    __lowerCamelCase   (      A__						: NDArray[floataa]						,						A__						: NDArray[floataa]						,						A__						: list[int]						,						A__						: int						,						)     ->    list[float]:
   lowerCamelCase_,	lowerCamelCase_						:   Any		= coefficient_matrix.shape
   lowerCamelCase_,	lowerCamelCase_						:   List[Any]		= constant_matrix.shape

   if rowsa != colsa:
      lowerCamelCase_						:   str		= f'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
      raise ValueError(A__					)

   if colsa != 1:
      lowerCamelCase_						:   int		= f'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
      raise ValueError(A__					)

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

   if len(A__					) != rowsa:
      lowerCamelCase_						:   str		= (
          """Number of initial values must be equal to number of rows in coefficient """
          f'''matrix but received {len(A__					)} and {rowsa}'''
      )
      raise ValueError(A__					)

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

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

   lowerCamelCase_,	lowerCamelCase_						:   Optional[Any]		= table.shape

   strictly_diagonally_dominant(A__					)

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

   return [float(A__					) for i in new_val]


def    __lowerCamelCase   (      A__						: NDArray[floataa]					)     ->    bool:
   lowerCamelCase_,	lowerCamelCase_						:   List[str]		= table.shape

   lowerCamelCase_						:   List[str]		= True

   for i in range(0						,						A__					):
      lowerCamelCase_						:   Dict		= 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()









from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(
           metadata={"help": "The output directory where the model will be written."}						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The encoder model checkpoint for weights initialization."
                   "Don't set if you want to train an encoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The decoder model checkpoint for weights initialization."
                   "Don't set if you want to train a decoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}						)
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}						)







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.encoder_config_name					)
   # Use pretrained encoder model's config
   else:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path					)

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.decoder_config_name					)
   # Use pretrained decoder model's config
   else:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path					)

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   lowerCamelCase_						:   Optional[Any]		= True
   lowerCamelCase_						:   List[Any]		= True

   lowerCamelCase_						:   List[Any]		= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,						decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,						encoder_config=A__						,						decoder_config=A__						,						)

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   lowerCamelCase_						:   Tuple		= decoder_config.decoder_start_token_id
   lowerCamelCase_						:   int		= decoder_config.pad_token_id
   if decoder_start_token_id is None:
      lowerCamelCase_						:   Any		= decoder_config.bos_token_id
   if pad_token_id is None:
      lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id
   lowerCamelCase_						:   Tuple		= decoder_start_token_id
   lowerCamelCase_						:   Optional[Any]		= pad_token_id

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= tokenizer.convert_ids_to_tokens(model.config.pad_token_id					)

   model.save_pretrained(model_args.output_dir					)
   image_processor.save_pretrained(model_args.output_dir					)
   tokenizer.save_pretrained(model_args.output_dir					)


if __name__ == "__main__":
      main()





