




'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]						)      ->      Union[str, Any]:
			__snake_case			=		[
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(_UpperCAmelCase		,				_UpperCAmelCase						)


def __UpperCAmelCase	(   _UpperCAmelCase		:   Union[str, Any]						)      ->      List[Any]:
			__snake_case		,     __snake_case			=		emb.weight.shape
			__snake_case			=		nn.Linear(_UpperCAmelCase		,				_UpperCAmelCase		,				bias=_UpperCAmelCase						)
			__snake_case			=		emb.weight.data
			return lin_layer


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]						)      ->      List[Any]:
			__snake_case			=		torch.load(_UpperCAmelCase		,				map_location="cpu"						)
			__snake_case			=		mam_aaa["args"] or mam_aaa["cfg"]["model"]
			__snake_case			=		mam_aaa["model"]
			remove_ignore_keys_(_UpperCAmelCase						)
			__snake_case			=		state_dict["encoder.embed_tokens.weight"].shape[0]

			__snake_case			=		MaMaaaConfig(
			    vocab_size=_UpperCAmelCase		,				max_position_embeddings=10_24		,				encoder_layers=args.encoder_layers		,				decoder_layers=args.decoder_layers		,				encoder_attention_heads=args.encoder_attention_heads		,				decoder_attention_heads=args.decoder_attention_heads		,				encoder_ffn_dim=args.encoder_ffn_embed_dim		,				decoder_ffn_dim=args.decoder_ffn_embed_dim		,				d_model=args.encoder_embed_dim		,				encoder_layerdrop=args.encoder_layerdrop		,				decoder_layerdrop=args.decoder_layerdrop		,				dropout=args.dropout		,				attention_dropout=args.attention_dropout		,				activation_dropout=args.activation_dropout		,				activation_function="relu"		,				)

			__snake_case			=		state_dict["decoder.embed_tokens.weight"]
			__snake_case			=		MaMaaaForConditionalGeneration(_UpperCAmelCase						)
			model.model.load_state_dict(_UpperCAmelCase		,				strict=_UpperCAmelCase						)
			__snake_case			=		make_linear_from_emb(model.model.shared						)

			return model


if __name__ == "__main__":
						a       :						List[str]								=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''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.''')
						a       :						Optional[int]								=		parser.parse_args()
						a       :						List[Any]								=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

	# A local function to see if a dot lands in the circle.
	def is_in_circle(UpperCamelCase__					: float     ,       UpperCamelCase__					: float							) -> bool:
		__UpperCAmelCase      					=      sqrt((x**2) + (y**2)							)
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__UpperCAmelCase      					=      mean(
	    int(is_in_circle(uniform(-1.0     ,       1.0							)     ,       uniform(-1.0     ,       1.0							)							)							)
	    for _ in range(UpperCamelCase__							)							)
	# The ratio of the area for circle to square is pi/4.
	__UpperCAmelCase      					=      proportion * 4
	print(f"""The estimated value of pi is {pi_estimate}"""							)
	print(f"""The numpy value of pi is {pi}"""							)
	print(f"""The total error is {abs(pi - pi_estimate							)}"""							)





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: Callable[[float], float]     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0     ,       ):


	"""simple docstring"""
	return mean(
	    function_to_integrate(uniform(UpperCamelCase__     ,       UpperCamelCase__							)							) for _ in range(UpperCamelCase__							)							) * (max_value - min_value)





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0							):


	"""simple docstring"""

	def identity_function(UpperCamelCase__					: float							) -> float:
		return x

	__UpperCAmelCase      					=      area_under_curve_estimator(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	__UpperCAmelCase      					=      (max_value * max_value - min_value * min_value) / 2

	print('''******************'''							)
	print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}"""							)
	print(f"""Estimated value is {estimated_value}"""							)
	print(f"""Expected value is {expected_value}"""							)
	print(f"""Total error is {abs(estimated_value - expected_value							)}"""							)
	print('''******************'''							)





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

	def function_to_integrate(UpperCamelCase__					: float							) -> float:
		return sqrt(4.0 - x * x							)

	__UpperCAmelCase      					=      area_under_curve_estimator(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       0.0     ,       2.0							)

	print('''******************'''							)
	print('''Estimating pi using area_under_curve_estimator'''							)
	print(f"""Estimated value is {estimated_value}"""							)
	print(f"""Expected value is {pi}"""							)
	print(f"""Total error is {abs(estimated_value - pi							)}"""							)
	print('''******************'''							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
