






'''simple docstring'''







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowercase					:							Any   =      logging.get_logger(__name__)

lowercase					:							Union[str, Any]   =      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
lowercase					:							Tuple   =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			List[str]:
	for attribute in key.split('.'			):
		_snake_case     		=	getattr(__A    ,       __A			)

	if weight_type is not None:
		_snake_case     		=	getattr(__A    ,       __A			).shape
	else:
		_snake_case     		=	hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
	    F' {value.shape} for {full_name}'
	)

	if weight_type == "weight":
		_snake_case     		=	value
	elif weight_type == "weight_g":
		_snake_case     		=	value
	elif weight_type == "weight_v":
		_snake_case     		=	value
	elif weight_type == "bias":
		_snake_case     		=	value
	else:
		_snake_case     		=	value

	logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Dict:
	_snake_case     		=	[]
	_snake_case     		=	fairseq_model.state_dict()

	_snake_case     		=	hf_model.feature_extractor
	_snake_case     		=	hf_model.adapter

	for name, value in fairseq_dict.items():
		_snake_case     		=	False
		if "conv_layers" in name:
			load_conv_layer(
			    __A    ,       __A    ,       __A    ,       __A    ,       hf_model.config.feat_extract_norm == 'group'    ,       )
			_snake_case     		=	True
		elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']			):
			load_adapter(__A    ,       __A    ,       __A    ,       __A			)
			_snake_case     		=	True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
					_snake_case     		=	True
					if "*" in mapped_key:
						_snake_case     		=	name.split(__A			)[0].split('.'			)[-2]
						_snake_case     		=	mapped_key.replace('*'    ,       __A			)
					if "weight_g" in name:
						_snake_case     		=	'weight_g'
					elif "weight_v" in name:
						_snake_case     		=	'weight_v'
					elif "bias" in name:
						_snake_case     		=	'bias'
					elif "weight" in name:
						_snake_case     		=	'weight'
					else:
						_snake_case     		=	None
					set_recursively(__A    ,       __A    ,       __A    ,       __A    ,       __A			)
				continue
		if not is_used:
			unused_weights.append(__A			)

	logger.warning(F'Unused weights: {unused_weights}'			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			Dict:
	_snake_case     		=	full_name.split('conv_layers.'			)[-1]
	_snake_case     		=	name.split('.'			)
	_snake_case     		=	int(items[0]			)
	_snake_case     		=	int(items[1]			)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
			)
			_snake_case     		=	value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
			)
			_snake_case     		=	value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
			    " found."
			)
			_snake_case     		=	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
			)
			_snake_case     		=	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
	else:
		unused_weights.append(__A			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A			) ->			Optional[Any]:
	_snake_case     		=	full_name.split('adaptor.'			)[-1]
	_snake_case     		=	name.split('.'			)

	if items[1].isdigit():
		_snake_case     		=	int(items[1]			)
	else:
		_snake_case     		=	None

	if "adaptor" not in full_name:
		if "proj_ln" in full_name:
			# has to be layer norm
			if "bias" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.bias.data.shape
				), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
				_snake_case     		=	value
				logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.'			)
			if "weight" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.weight.data.shape
				), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
				_snake_case     		=	value
		else:
			# has to be projection layer
			if "bias" in name:
				assert (
				    value.shape == adapter.proj.bias.data.shape
				), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
				_snake_case     		=	value
				logger.info(F'Adapter proj layer bias was initialized from {full_name}.'			)
			if "weight" in name:
				assert (
				    value.shape == adapter.proj.weight.data.shape
				), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
				_snake_case     		=	value
				logger.info(F'Adapter proj layer weight was initialized from {full_name}.'			)
	elif isinstance(__A    ,       __A			):
		if "bias" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.bias.data.shape
			), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
			_snake_case     		=	value
			logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'			)
		elif "weight" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.weight.data.shape
			), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
			_snake_case     		=	value
			logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'			)
	else:
		unused_weights.append(__A			)







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	_snake_case		,				_snake_case     		=	emb.weight.shape
	_snake_case     		=	nn.Linear(__A    ,       __A    ,       bias=__A			)
	_snake_case     		=	emb.weight.data
	return lin_layer







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       ) ->			Any:
	_snake_case     		=	WavaVecaConfig.from_pretrained(
	    __A    ,       add_adapter=__A    ,       adapter_stride=__A    ,       adapter_kernel_size=__A    ,       use_auth_token=__A    ,       output_hidden_size=__A    ,       )
	_snake_case     		=	MBartConfig.from_pretrained(__A			)

	# load model
	_snake_case		,				_snake_case		,				_snake_case     		=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]    ,       arg_overrides={
	        'config_yaml': config_yaml_path,
	        'data': '/'.join(dict_path.split('/'			)[:-1]			),
	        'w2v_path': checkpoint_path,
	        'load_pretrained_decoder_from': None,
	    }    ,       )
	_snake_case     		=	model[0].eval()

	# load feature extractor
	_snake_case     		=	WavaVecaFeatureExtractor.from_pretrained(__A    ,       use_auth_token=__A			)

	# set weights for wav2vec2 encoder
	_snake_case     		=	WavaVecaModel(__A			)

	recursively_load_weights_wavaveca(model.encoder    ,       __A			)

	# load decoder weights
	_snake_case     		=	MBartForCausalLM(__A			)
	_snake_case		,				_snake_case     		=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,       strict=__A			)
	logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'			)
	logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'			)

	_snake_case     		=	SpeechEncoderDecoderModel(encoder=__A    ,       decoder=__A			)
	_snake_case     		=	False

	_snake_case     		=	MBartaaTokenizer(__A			)
	tokenizer.save_pretrained(__A			)

	_snake_case     		=	hf_wavavec.config.to_dict()
	_snake_case     		=	tokenizer.pad_token_id
	_snake_case     		=	tokenizer.bos_token_id
	_snake_case     		=	tokenizer.eos_token_id
	_snake_case     		=	'mbart50'
	_snake_case     		=	'wav2vec2'

	_snake_case     		=	tokenizer.eos_token_id
	_snake_case     		=	250_004
	_snake_case     		=	tokenizer.eos_token_id

	_snake_case     		=	SpeechEncoderDecoderConfig.from_dict(__A			)

	hf_wavavec.save_pretrained(__A			)
	feature_extractor.save_pretrained(__A			)


if __name__ == "__main__":
					lowercase					:							Optional[int]   =      argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
					parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
					parser.add_argument(
					    "--encoder_config_path",
					    default="facebook/wav2vec2-xls-r-1b",
					    type=str,
					    help="Path to hf encoder wav2vec2 checkpoint config",
					)
					parser.add_argument(
					    "--decoder_config_path",
					    default="facebook/mbart-large-50-one-to-many-mmt",
					    type=str,
					    help="Path to hf decoder checkpoint config",
					)
					parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
					parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
					parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
					parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
					parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

					lowercase					:							List[str]   =      parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    args.config_yaml_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    add_adapter=args.add_adapter,
					    adapter_kernel_size=args.adapter_kernel_size,
					    adapter_stride=args.adapter_stride,
					    decoder_start_token_id=args.start_token_id,
					    encoder_output_dim=args.encoder_output_dim,
					)



'''simple docstring'''







from __future__ import annotations

import math







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			list:
	if len(__A			) != 2 or len(a[0]			) != 2 or len(__A			) != 2 or len(b[0]			) != 2:
		raise Exception('Matrices are not 2x2'			)
	_snake_case     		=	[
	    [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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Dict:
	return [
	    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]			)			)]
	    for row in range(len(__A			)			)
	]







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Dict:
	return [
	    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]			)			)]
	    for row in range(len(__A			)			)
	]







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			tuple[list, list, list, list]:
	if len(__A			) % 2 != 0 or len(a[0]			) % 2 != 0:
		raise Exception('Odd matrices are not supported!'			)

	_snake_case     		=	len(__A			)
	_snake_case     		=	matrix_length // 2

	_snake_case     		=	[[a[i][j] for j in range(__A    ,       __A			)] for i in range(__A			)]
	_snake_case     		=	[
	    [a[i][j] for j in range(__A    ,       __A			)] for i in range(__A    ,       __A			)
	]

	_snake_case     		=	[[a[i][j] for j in range(__A			)] for i in range(__A			)]
	_snake_case     		=	[[a[i][j] for j in range(__A			)] for i in range(__A    ,       __A			)]

	return top_left, top_right, bot_left, bot_right







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			tuple[int, int]:
	return len(__A			), len(matrix[0]			)







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			None:
	print('\n'.join(str(__A			) for line in matrix			)			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			list:
	if matrix_dimensions(__A			) == (2, 2):
		return default_matrix_multiplication(__A    ,       __A			)

	_snake_case		,				_snake_case		,				_snake_case		,				_snake_case     		=	split_matrix(__A			)
	_snake_case		,				_snake_case		,				_snake_case		,				_snake_case     		=	split_matrix(__A			)

	_snake_case     		=	actual_strassen(__A    ,       matrix_subtraction(__A    ,       __A			)			)
	_snake_case     		=	actual_strassen(matrix_addition(__A    ,       __A			)    ,       __A			)
	_snake_case     		=	actual_strassen(matrix_addition(__A    ,       __A			)    ,       __A			)
	_snake_case     		=	actual_strassen(__A    ,       matrix_subtraction(__A    ,       __A			)			)
	_snake_case     		=	actual_strassen(matrix_addition(__A    ,       __A			)    ,       matrix_addition(__A    ,       __A			)			)
	_snake_case     		=	actual_strassen(matrix_subtraction(__A    ,       __A			)    ,       matrix_addition(__A    ,       __A			)			)
	_snake_case     		=	actual_strassen(matrix_subtraction(__A    ,       __A			)    ,       matrix_addition(__A    ,       __A			)			)

	_snake_case     		=	matrix_addition(matrix_subtraction(matrix_addition(__A    ,       __A			)    ,       __A			)    ,       __A			)
	_snake_case     		=	matrix_addition(__A    ,       __A			)
	_snake_case     		=	matrix_addition(__A    ,       __A			)
	_snake_case     		=	matrix_subtraction(matrix_subtraction(matrix_addition(__A    ,       __A			)    ,       __A			)    ,       __A			)

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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			list:
	if matrix_dimensions(__A			)[1] != matrix_dimensions(__A			)[0]:
		_snake_case     		=	(
		    'Unable to multiply these matrices, please check the dimensions.\n'
		    F'Matrix A: {matrixa}\n'
		    F'Matrix B: {matrixa}'
		)
		raise Exception(__A			)
	_snake_case     		=	matrix_dimensions(__A			)
	_snake_case     		=	matrix_dimensions(__A			)

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

	_snake_case     		=	max(*__A    ,       *__A			)
	_snake_case     		=	int(math.pow(2    ,       math.ceil(math.loga(__A			)			)			)			)
	_snake_case     		=	matrixa
	_snake_case     		=	matrixa

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

	_snake_case     		=	actual_strassen(__A    ,       __A			)

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


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

