



'''simple docstring'''



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCAmelCase	:		Any				=   ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase	:		Dict				=   {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
	raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
lowerCAmelCase	:		Union[str, Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Dict				=   ''' Hello world! cécé herlolip'''

lowerCAmelCase	:		Optional[Any]				=   [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]					):






	'''simple docstring'''




	__lowerCAmelCase												=  [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	]
	for k in ignore_keys:
		state_dict.pop(lowerCamelCase				,		lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  dct.pop(lowerCamelCase					)
	__lowerCAmelCase												=  val



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  torch.load(lowerCamelCase				,		map_location="cpu"					)
	__lowerCAmelCase												=  torch.hub.load("pytorch/fairseq"				,		"bart.large.cnn"					).eval()
	hub_interface.model.load_state_dict(sd["model"]					)
	return hub_interface



def   __lowerCAmelCase					(							lowerCamelCase   :						List[Any]					):






	'''simple docstring'''




	__lowerCAmelCase							,       __lowerCAmelCase												=  emb.weight.shape
	__lowerCAmelCase												=  nn.Linear(lowerCamelCase				,		lowerCamelCase				,		bias=lowerCamelCase					)
	__lowerCAmelCase												=  emb.weight.data
	return lin_layer



@torch.no_grad()
def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Optional[Any]=None					):






	'''simple docstring'''




	if not os.path.exists(lowerCamelCase					):
		__lowerCAmelCase												=  torch.hub.load("pytorch/fairseq"				,		lowerCamelCase					).eval()
	else:
		__lowerCAmelCase												=  load_xsum_checkpoint(lowerCamelCase					)

	bart.model.upgrade_state_dict(bart.model.state_dict()					)
	if hf_checkpoint_name is None:
		__lowerCAmelCase												=  checkpoint_path.replace("."				,		"-"					)
	__lowerCAmelCase												=  BartConfig.from_pretrained(lowerCamelCase					)
	__lowerCAmelCase												=  bart.encode(lowerCamelCase					).unsqueeze(0					)
	__lowerCAmelCase												=  BartTokenizer.from_pretrained(lowerCamelCase					).encode(lowerCamelCase				,		return_tensors="pt"					).unsqueeze(0					)
	if not torch.eq(lowerCamelCase				,		lowerCamelCase					).all():
		raise ValueError(
		    f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''					)

	if checkpoint_path == "bart.large.mnli":
		__lowerCAmelCase												=  bart.state_dict()
		remove_ignore_keys_(lowerCamelCase					)
		__lowerCAmelCase												=  state_dict["model.decoder.embed_tokens.weight"]
		for src, dest in mnli_rename_keys:
			rename_key(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)
		__lowerCAmelCase												=  BartForSequenceClassification(lowerCamelCase					).eval()
		model.load_state_dict(lowerCamelCase					)
		__lowerCAmelCase												=  bart.predict("mnli"				,		lowerCamelCase				,		return_logits=lowerCamelCase					)
		__lowerCAmelCase												=  model(lowerCamelCase					)[0]  # logits
	else:  # no classification heads to worry about
		__lowerCAmelCase												=  bart.model.state_dict()
		remove_ignore_keys_(lowerCamelCase					)
		__lowerCAmelCase												=  state_dict["decoder.embed_tokens.weight"]
		__lowerCAmelCase												=  bart.extract_features(lowerCamelCase					)
		if hf_checkpoint_name == "facebook/bart-large":
			__lowerCAmelCase												=  BartModel(lowerCamelCase					).eval()
			model.load_state_dict(lowerCamelCase					)
			__lowerCAmelCase												=  model(lowerCamelCase					).model[0]
		else:
			__lowerCAmelCase												=  BartForConditionalGeneration(lowerCamelCase					).eval()  # an existing summarization ckpt
			model.model.load_state_dict(lowerCamelCase					)
			if hasattr(lowerCamelCase				,		"lm_head"					):
				__lowerCAmelCase												=  make_linear_from_emb(model.model.shared					)
			__lowerCAmelCase												=  model.model(lowerCamelCase					)[0]

    # Check results
	if fairseq_output.shape != new_model_outputs.shape:
		raise ValueError(
		    f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''					)
	if (fairseq_output != new_model_outputs).any().item():
		raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"					)
	Path(lowerCamelCase					).mkdir(exist_ok=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
	lowerCAmelCase	:		Optional[int]				=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
	)
	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='''Which huggingface architecture to use: bart-large-xsum'''
	)
	lowerCAmelCase	:		List[str]				=   parser.parse_args()
	convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)




'''simple docstring'''



from __future__ import annotations

import math



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''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"					)
	__lowerCAmelCase												=  [
	    [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   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''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   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''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   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




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

	__lowerCAmelCase												=  len(lowerCamelCase					)
	__lowerCAmelCase												=  matrix_length // 2

	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase				,		lowerCamelCase					)] for i in range(lowerCamelCase					)]
	__lowerCAmelCase												=  [
	    [a[i][j] for j in range(lowerCamelCase				,		lowerCamelCase					)] for i in range(lowerCamelCase				,		lowerCamelCase					)
	]

	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase					)] for i in range(lowerCamelCase					)]
	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase					)] for i in range(lowerCamelCase				,		lowerCamelCase					)]

	return top_left, top_right, bot_left, bot_right



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




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

	__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  split_matrix(lowerCamelCase					)
	__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  split_matrix(lowerCamelCase					)

	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)

	__lowerCAmelCase												=  matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_addition(lowerCamelCase				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_addition(lowerCamelCase				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)				,		lowerCamelCase					)

	# construct the new matrix from our 4 quadrants
	__lowerCAmelCase												=  []
	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   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




	if matrix_dimensions(lowerCamelCase					)[1] != matrix_dimensions(lowerCamelCase					)[0]:
		__lowerCAmelCase												=  (
		    "Unable to multiply these matrices, please check the dimensions.\n"
		    f'''Matrix A: {matrixa}\n'''
		    f'''Matrix B: {matrixa}'''
		)
		raise Exception(lowerCamelCase					)
	__lowerCAmelCase												=  matrix_dimensions(lowerCamelCase					)
	__lowerCAmelCase												=  matrix_dimensions(lowerCamelCase					)

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

	__lowerCAmelCase												=  max(*lowerCamelCase				,		*lowerCamelCase					)
	__lowerCAmelCase												=  int(math.pow(2				,		math.ceil(math.loga(lowerCamelCase					)					)					)					)
	__lowerCAmelCase												=  matrixa
	__lowerCAmelCase												=  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					)

	__lowerCAmelCase												=  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__":
	lowerCAmelCase	:		Tuple				=   [
	    [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],
	]
	lowerCAmelCase	:		Any				=   [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
	print(strassen(matrixa, matrixa))