




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    def update_area_of_max_square(lowercase_					,     lowercase_      ) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        __UpperCamelCase  		=						update_area_of_max_square(_lowerCamelCase					,     col + 1      )
        __UpperCamelCase  		=						update_area_of_max_square(row + 1					,     col + 1      )
        __UpperCamelCase  		=						update_area_of_max_square(row + 1					,     _lowerCamelCase      )

        if mat[row][col]:
            __UpperCamelCase  		=						1 + min([right, diagonal, down]      )
            __UpperCamelCase  		=						max(largest_square_area[0]					,     _lowerCamelCase      )
            return sub_problem_sol
        else:
            return 0

    __UpperCamelCase  		=						[0]
    update_area_of_max_square(0					,     0      )
    return largest_square_area[0]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	str:

    """simple docstring"""


    def update_area_of_max_square_using_dp_array(
        lowercase_					,     lowercase_					,     lowercase_      ) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        __UpperCamelCase  		=						update_area_of_max_square_using_dp_array(_lowerCamelCase					,     col + 1					,     _lowerCamelCase      )
        __UpperCamelCase  		=						update_area_of_max_square_using_dp_array(row + 1					,     col + 1					,     _lowerCamelCase      )
        __UpperCamelCase  		=						update_area_of_max_square_using_dp_array(row + 1					,     _lowerCamelCase					,     _lowerCamelCase      )

        if mat[row][col]:
            __UpperCamelCase  		=						1 + min([right, diagonal, down]      )
            __UpperCamelCase  		=						max(largest_square_area[0]					,     _lowerCamelCase      )
            __UpperCamelCase  		=						sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    __UpperCamelCase  		=						[0]
    __UpperCamelCase  		=						[[-1] * cols for _ in range(_lowerCamelCase      )]
    update_area_of_max_square_using_dp_array(0					,     0					,     _lowerCamelCase      )

    return largest_square_area[0]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						[[0] * (cols + 1) for _ in range(rows + 1      )]
    __UpperCamelCase  		=						0
    for row in range(rows - 1					,     -1					,     -1      ):
        for col in range(cols - 1					,     -1					,     -1      ):
            __UpperCamelCase  		=						dp_array[row][col + 1]
            __UpperCamelCase  		=						dp_array[row + 1][col + 1]
            __UpperCamelCase  		=						dp_array[row + 1][col]

            if mat[row][col] == 1:
                __UpperCamelCase  		=						1 + min(_lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase      )
                __UpperCamelCase  		=						max(dp_array[row][col]					,     _lowerCamelCase      )
            else:
                __UpperCamelCase  		=						0

    return largest_square_area




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						[0] * (cols + 1)
    __UpperCamelCase  		=						[0] * (cols + 1)
    __UpperCamelCase  		=						0
    for row in range(rows - 1					,     -1					,     -1      ):
        for col in range(cols - 1					,     -1					,     -1      ):
            __UpperCamelCase  		=						current_row[col + 1]
            __UpperCamelCase  		=						next_row[col + 1]
            __UpperCamelCase  		=						next_row[col]

            if mat[row][col] == 1:
                __UpperCamelCase  		=						1 + min(_lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase      )
                __UpperCamelCase  		=						max(current_row[col]					,     _lowerCamelCase      )
            else:
                __UpperCamelCase  		=						0
        __UpperCamelCase  		=						current_row

    return largest_square_area


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )['''model''']

    # pop unnecessary weights
    __UpperCamelCase  		=						[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(lowercase_      )

    __UpperCamelCase  		=						{
        '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
        '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
        '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
        '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            __UpperCamelCase  		=						sd.pop(lowercase_      )

    __UpperCamelCase  		=						list(sd.keys()      )
    for key in keys:
        if ".qkv_proj." in key:
            __UpperCamelCase  		=						sd[key]
            # We split QKV in separate Q,K,V

            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.q_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.k_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.v_proj.'''      )

            __UpperCamelCase  		=						value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						torch.split(lowercase_					,     depth // 3					,     dim=0      )

            __UpperCamelCase  		=						q
            __UpperCamelCase  		=						k
            __UpperCamelCase  		=						v
            del sd[key]

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						OPTModel(lowercase_      ).half().eval()
    model.load_state_dict(lowercase_      )

    # Check results
    Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
    model.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--fairseq_path",
     type=str,
     help=(
         "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
         " https://huggingface.co/models?other=opt_metasq"
     ),
 )
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
 a_   		=     parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






