






from __future__ import annotations

import math


def 					snake_case__				(				UpperCAmelCase				:	List[Any]     ,      UpperCAmelCase				:	Optional[Any]							):
    if len(__snake_case							) != 2 or len(a[0]							) != 2 or len(__snake_case							) != 2 or len(b[0]							) != 2:
        raise Exception("Matrices are not 2x2"							)
    lowerCAmelCase__ :str   				=					[
        [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 					snake_case__				(				UpperCAmelCase				:	Optional[int]     ,      UpperCAmelCase				:	Optional[int]							):
    return [
        [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
        for row in range(len(__snake_case							)							)
    ]


def 					snake_case__				(				UpperCAmelCase				:	Tuple     ,      UpperCAmelCase				:	List[str]							):
    return [
        [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
        for row in range(len(__snake_case							)							)
    ]


def 					snake_case__				(				UpperCAmelCase				:	Optional[Any]							):
    if len(__snake_case							) % 2 != 0 or len(a[0]							) % 2 != 0:
        raise Exception("Odd matrices are not supported!"							)

    lowerCAmelCase__ :str   				=					len(__snake_case							)
    lowerCAmelCase__ :Optional[Any]   				=					matrix_length // 2

    lowerCAmelCase__ :List[str]   				=					[[a[i][j] for j in range(__snake_case     ,      __snake_case							)] for i in range(__snake_case							)]
    lowerCAmelCase__ :List[Any]   				=					[
        [a[i][j] for j in range(__snake_case     ,      __snake_case							)] for i in range(__snake_case     ,      __snake_case							)
    ]

    lowerCAmelCase__ :Any   				=					[[a[i][j] for j in range(__snake_case							)] for i in range(__snake_case							)]
    lowerCAmelCase__ :Union[str, Any]   				=					[[a[i][j] for j in range(__snake_case							)] for i in range(__snake_case     ,      __snake_case							)]

    return top_left, top_right, bot_left, bot_right


def 					snake_case__				(				UpperCAmelCase				:	Dict							):
    return len(__snake_case							), len(matrix[0]							)


def 					snake_case__				(				UpperCAmelCase				:	int							):
    print("\n".join(str(__snake_case							) for line in matrix							)							)


def 					snake_case__				(				UpperCAmelCase				:	Any     ,      UpperCAmelCase				:	Optional[Any]							):
    if matrix_dimensions(__snake_case							) == (2, 2):
        return default_matrix_multiplication(__snake_case     ,      __snake_case							)

    lowerCAmelCase__ :Optional[int]   				=					split_matrix(__snake_case							)
    lowerCAmelCase__ :int   				=					split_matrix(__snake_case							)

    lowerCAmelCase__ :Any   				=					actual_strassen(__snake_case     ,      matrix_subtraction(__snake_case     ,      __snake_case							)							)
    lowerCAmelCase__ :int   				=					actual_strassen(matrix_addition(__snake_case     ,      __snake_case							)     ,      __snake_case							)
    lowerCAmelCase__ :Dict   				=					actual_strassen(matrix_addition(__snake_case     ,      __snake_case							)     ,      __snake_case							)
    lowerCAmelCase__ :str   				=					actual_strassen(__snake_case     ,      matrix_subtraction(__snake_case     ,      __snake_case							)							)
    lowerCAmelCase__ :int   				=					actual_strassen(matrix_addition(__snake_case     ,      __snake_case							)     ,      matrix_addition(__snake_case     ,      __snake_case							)							)
    lowerCAmelCase__ :int   				=					actual_strassen(matrix_subtraction(__snake_case     ,      __snake_case							)     ,      matrix_addition(__snake_case     ,      __snake_case							)							)
    lowerCAmelCase__ :Dict   				=					actual_strassen(matrix_subtraction(__snake_case     ,      __snake_case							)     ,      matrix_addition(__snake_case     ,      __snake_case							)							)

    lowerCAmelCase__ :Optional[Any]   				=					matrix_addition(matrix_subtraction(matrix_addition(__snake_case     ,      __snake_case							)     ,      __snake_case							)     ,      __snake_case							)
    lowerCAmelCase__ :Union[str, Any]   				=					matrix_addition(__snake_case     ,      __snake_case							)
    lowerCAmelCase__ :List[str]   				=					matrix_addition(__snake_case     ,      __snake_case							)
    lowerCAmelCase__ :List[Any]   				=					matrix_subtraction(matrix_subtraction(matrix_addition(__snake_case     ,      __snake_case							)     ,      __snake_case							)     ,      __snake_case							)

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


def 					snake_case__				(				UpperCAmelCase				:	int     ,      UpperCAmelCase				:	Optional[int]							):
    if matrix_dimensions(__snake_case							)[1] != matrix_dimensions(__snake_case							)[0]:
        lowerCAmelCase__ :Tuple   				=					(
            "Unable to multiply these matrices, please check the dimensions.\n"
            F'''Matrix A: {matrixa}\n'''
            F'''Matrix B: {matrixa}'''
        )
        raise Exception(__snake_case							)
    lowerCAmelCase__ :Tuple   				=					matrix_dimensions(__snake_case							)
    lowerCAmelCase__ :Optional[Any]   				=					matrix_dimensions(__snake_case							)

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

    lowerCAmelCase__ :List[str]   				=					max(*__snake_case     ,      *__snake_case							)
    lowerCAmelCase__ :Dict   				=					int(math.pow(2     ,      math.ceil(math.loga(__snake_case							)							)							)							)
    lowerCAmelCase__ :str   				=					matrixa
    lowerCAmelCase__ :Optional[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     ,      __snake_case							):
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]     ,      __snake_case							):
                new_matrixa[i].append(0							)
        else:
            new_matrixa.append([0] * maxim							)
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]     ,      __snake_case							):
                new_matrixa[i].append(0							)
        else:
            new_matrixa.append([0] * maxim							)

    lowerCAmelCase__ :Dict   				=					actual_strassen(__snake_case     ,      __snake_case							)

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


if __name__ == "__main__":
 _a     : Optional[Any]   	=      [
     [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],
 ]
 _a     : Optional[Any]   	=      [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
 print(strassen(matrixa, matrixa))








"""simple docstring"""






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	__snake_case				:  Optional[Any] =       argparse.ArgumentParser(
	    description=(
	        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
	        ' Distillation'
	    )
	)
	parser.add_argument('--model_type', default='bert', choices=['bert'])
	parser.add_argument('--model_name', default='bert-base-uncased', type=str)
	parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
	parser.add_argument('--vocab_transform', action='store_true')
	__snake_case				:  List[Any] =       parser.parse_args()

	if args.model_type == "bert":
		__snake_case				:  int =       BertForMaskedLM.from_pretrained(args.model_name)
		__snake_case				:  Tuple =       'bert'
	else:
		raise ValueError('args.model_type should be "bert".')

	__snake_case				:  List[Any] =       model.state_dict()
	__snake_case				:  Any =       {}

	for w in ["word_embeddings", "position_embeddings"]:
		__snake_case				:  List[str] =       state_dict[F"""{prefix}.embeddings.{w}.weight"""]
	for w in ["weight", "bias"]:
		__snake_case				:  Optional[int] =       state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

	__snake_case				:  List[str] =       0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		for w in ["weight", "bias"]:
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
			]
			__snake_case				:  Tuple =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
			]
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
			]

			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
			]
			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
			]

			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
			]
			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
			]
			__snake_case				:  Optional[int] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
			]
		std_idx += 1

	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.decoder.weight']
	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.bias']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			__snake_case				:  Union[str, Any] =       state_dict[F"""cls.predictions.transform.dense.{w}"""]
			__snake_case				:  List[str] =       state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

	print(F"""N layers selected for distillation: {std_idx}""")
	print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

	print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
	torch.save(compressed_sd, args.dump_checkpoint)