




from __future__ import annotations

import math




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    if len(lowerCAmelCase__		) != 2 or len(a[0]		) != 2 or len(lowerCAmelCase__		) != 2 or len(b[0]		) != 2:
        raise Exception("""Matrices are not 2x2"""		)
    A			:	Optional[Any]										=       [
        [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
        [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
    ]
    return new_matrix




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    return [
        [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]		)		)]
        for row in range(len(lowerCAmelCase__		)		)
    ]




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    return [
        [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]		)		)]
        for row in range(len(lowerCAmelCase__		)		)
    ]




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    if len(lowerCAmelCase__		) % 2 != 0 or len(a[0]		) % 2 != 0:
        raise Exception("""Odd matrices are not supported!"""		)

    A			:	List[Any]										=       len(lowerCAmelCase__		)
    A			:	str										=       matrix_length // 2

    A			:	int										=       [[a[i][j] for j in range(lowerCAmelCase__				,					lowerCAmelCase__		)] for i in range(lowerCAmelCase__		)]
    A			:	Any										=       [
        [a[i][j] for j in range(lowerCAmelCase__				,					lowerCAmelCase__		)] for i in range(lowerCAmelCase__				,					lowerCAmelCase__		)
    ]

    A			:	Tuple										=       [[a[i][j] for j in range(lowerCAmelCase__		)] for i in range(lowerCAmelCase__		)]
    A			:	Optional[Any]										=       [[a[i][j] for j in range(lowerCAmelCase__		)] for i in range(lowerCAmelCase__				,					lowerCAmelCase__		)]

    return top_left, top_right, bot_left, bot_right




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    return len(lowerCAmelCase__		), len(matrix[0]		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    print("""\n""".join(str(lowerCAmelCase__		) for line in matrix		)		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    if matrix_dimensions(lowerCAmelCase__		) == (2, 2):
        return default_matrix_multiplication(lowerCAmelCase__				,					lowerCAmelCase__		)

    A			:	Tuple										=       split_matrix(lowerCAmelCase__		)
    A			:	List[Any]										=       split_matrix(lowerCAmelCase__		)

    A			:	List[str]										=       actual_strassen(lowerCAmelCase__				,					matrix_subtraction(lowerCAmelCase__				,					lowerCAmelCase__		)		)
    A			:	Dict										=       actual_strassen(matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)				,					lowerCAmelCase__		)
    A			:	Tuple										=       actual_strassen(matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)				,					lowerCAmelCase__		)
    A			:	Dict										=       actual_strassen(lowerCAmelCase__				,					matrix_subtraction(lowerCAmelCase__				,					lowerCAmelCase__		)		)
    A			:	Optional[Any]										=       actual_strassen(matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)				,					matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)		)
    A			:	List[str]										=       actual_strassen(matrix_subtraction(lowerCAmelCase__				,					lowerCAmelCase__		)				,					matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)		)
    A			:	Optional[int]										=       actual_strassen(matrix_subtraction(lowerCAmelCase__				,					lowerCAmelCase__		)				,					matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)		)

    A			:	int										=       matrix_addition(matrix_subtraction(matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)				,					lowerCAmelCase__		)				,					lowerCAmelCase__		)
    A			:	Optional[Any]										=       matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)
    A			:	str										=       matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)
    A			:	Tuple										=       matrix_subtraction(matrix_subtraction(matrix_addition(lowerCAmelCase__				,					lowerCAmelCase__		)				,					lowerCAmelCase__		)				,					lowerCAmelCase__		)

    # construct the new matrix from our 4 quadrants
    A			:	str										=       []
    for i in range(len(lowerCAmelCase__		)		):
        new_matrix.append(top_left[i] + top_right[i]		)
    for i in range(len(lowerCAmelCase__		)		):
        new_matrix.append(bot_left[i] + bot_right[i]		)
    return new_matrix




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    if matrix_dimensions(lowerCAmelCase__		)[1] != matrix_dimensions(lowerCAmelCase__		)[0]:
        A			:	Optional[Any]										=       (
            '''Unable to multiply these matrices, please check the dimensions.\n'''
            f'''Matrix A: {matrixa}\n'''
            f'''Matrix B: {matrixa}'''
        )
        raise Exception(lowerCAmelCase__		)
    A			:	Tuple										=       matrix_dimensions(lowerCAmelCase__		)
    A			:	Union[str, Any]										=       matrix_dimensions(lowerCAmelCase__		)

    if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
        return [matrixa, matrixa]

    A			:	Any										=       max(*lowerCAmelCase__				,					*lowerCAmelCase__		)
    A			:	List[Any]										=       int(math.pow(2				,					math.ceil(math.loga(lowerCAmelCase__		)		)		)		)
    A			:	Tuple										=       matrixa
    A			:	Any										=       matrixa

    # Adding zeros to the matrices so that the arrays dimensions are the same and also
    # power of 2
    for i in range(0				,					lowerCAmelCase__		):
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]				,					lowerCAmelCase__		):
                new_matrixa[i].append(0		)
        else:
            new_matrixa.append([0] * maxim		)
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]				,					lowerCAmelCase__		):
                new_matrixa[i].append(0		)
        else:
            new_matrixa.append([0] * maxim		)

    A			:	str										=       actual_strassen(lowerCAmelCase__				,					lowerCAmelCase__		)

    # Removing the additional zeros
    for i in range(0				,					lowerCAmelCase__		):
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]				,					lowerCAmelCase__		):
                final_matrix[i].pop()
        else:
            final_matrix.pop()
    return final_matrix


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:List[str]					=						[
         [2, 3, 4, 5],
         [6, 4, 3, 1],
         [2, 3, 6, 7],
         [3, 1, 2, 4],
         [2, 3, 4, 5],
         [6, 4, 3, 1],
         [2, 3, 6, 7],
         [3, 1, 2, 4],
         [2, 3, 4, 5],
         [6, 2, 3, 1],
     ]
     SCREAMING_SNAKE_CASE_:str					=						[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
     print(strassen(matrixa, matrixa))







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[str] =				"xlm"
    __lowerCamelCase      :			Tuple =				{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }






    def __init__(	self,		lowerCamelCase__=3_0145,		lowerCamelCase__=2048,		lowerCamelCase__=12,		lowerCamelCase__=16,		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=False,		lowerCamelCase__=1,		lowerCamelCase__=True,		lowerCamelCase__=512,		lowerCamelCase__=2048**-0.5,		lowerCamelCase__=1e-12,		lowerCamelCase__=0.02,		lowerCamelCase__=0,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=5,		lowerCamelCase__=True,		lowerCamelCase__="first",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=True,		lowerCamelCase__=0.1,		lowerCamelCase__=5,		lowerCamelCase__=5,		lowerCamelCase__=0,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=0,		**lowerCamelCase__,		):
        A			:	Dict										=       vocab_size
        A			:	int										=       emb_dim
        A			:	str										=       n_layers
        A			:	Union[str, Any]										=       n_heads
        A			:	Optional[int]										=       dropout
        A			:	Union[str, Any]										=       attention_dropout
        A			:	Optional[Any]										=       gelu_activation
        A			:	Dict										=       sinusoidal_embeddings
        A			:	int										=       causal
        A			:	Optional[Any]										=       asm
        A			:	Any										=       n_langs
        A			:	List[str]										=       use_lang_emb
        A			:	Union[str, Any]										=       layer_norm_eps
        A			:	str										=       bos_index
        A			:	int										=       eos_index
        A			:	Tuple										=       pad_index
        A			:	str										=       unk_index
        A			:	Optional[Any]										=       mask_index
        A			:	Union[str, Any]										=       is_encoder
        A			:	Tuple										=       max_position_embeddings
        A			:	List[str]										=       embed_init_std
        A			:	Tuple										=       init_std
        A			:	Tuple										=       summary_type
        A			:	int										=       summary_use_proj
        A			:	List[Any]										=       summary_activation
        A			:	Optional[Any]										=       summary_proj_to_labels
        A			:	Optional[Any]										=       summary_first_dropout
        A			:	Optional[int]										=       start_n_top
        A			:	Optional[Any]										=       end_n_top
        A			:	List[str]										=       mask_token_id
        A			:	Tuple										=       lang_id

        if "n_words" in kwargs:
            A			:	List[str]										=       kwargs["""n_words"""]

        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		**lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Any										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	Union[str, Any]										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]		)


