





from dataclasses import dataclass, field
from typing import Optional

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







@dataclass
class    __snake_case	:





							'''simple docstring'''







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







def       A__							(   )       ->   List[str]:
		__snake_case:						Dict			  =						HfArgumentParser((ModelArguments,))
		(__snake_case      ):						List[str]			  =						parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				__snake_case:						List[str]			  =						AutoConfig.from_pretrained(model_args.encoder_config_name)
		# Use pretrained encoder model's config
		else:
				__snake_case:						Optional[Any]			  =						AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)

		# Use explicit specified decoder config
		if model_args.decoder_config_name:
				__snake_case:						List[str]			  =						AutoConfig.from_pretrained(model_args.decoder_config_name)
		# Use pretrained decoder model's config
		else:
				__snake_case:						Tuple			  =						AutoConfig.from_pretrained(model_args.decoder_model_name_or_path)

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

		__snake_case:						int			  =						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=_UpperCAmelCase , decoder_config=_UpperCAmelCase , )

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

		# This is necessary to make Flax's generate() work
		__snake_case:						int			  =						decoder_config.eos_token_id
		__snake_case:						Tuple			  =						decoder_start_token_id
		__snake_case:						int			  =						pad_token_id

		__snake_case:						Optional[Any]			  =						AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

		__snake_case:						Any			  =						AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
		__snake_case:						Tuple			  =						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()













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						float(
        d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creates a copy of the matrix with swapped positions of the elements
    __snake_case:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 3
      and len(matrix[0]) == 3
      and len(matrix[1]) == 3
      and len(matrix[2]) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    __snake_case:						Any			  =						float(
        (
            (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
            + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
            + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
        )
        - (
            (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
            + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
            + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
        ))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creating cofactor matrix
    __snake_case:						Tuple			  =						[
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
    ]
    __snake_case:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__)

        # Calculate the inverse of the matrix
    return [[float(d(SCREAMING_SNAKE_CASE__)) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")






