
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def    A (      lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase						=  [
		    'decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowercase					, lowercase				)


def    A (      lowercase				)     ->						Any:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  emb.weight.shape
		UpperCamelCase						=  nn.Linear(lowercase					, lowercase					, bias=lowercase				)
		UpperCamelCase						=  emb.weight.data
		return lin_layer


def    A (      lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase						=  torch.load(lowercase					, map_location='cpu'				)
		UpperCamelCase						=  Namespace(**checkpoint['cfg']['model']				)
		UpperCamelCase						=  checkpoint['model']
		remove_ignore_keys_(lowercase				)
		UpperCamelCase						=  state_dict['decoder.embed_tokens.weight'].shape[0]

		UpperCamelCase						=  {key.replace('decoder'					, 'model'				): val for key, val in state_dict.items()}

		UpperCamelCase						=  XGLMConfig(
		    vocab_size=lowercase					, max_position_embeddings=args.max_target_positions					, num_layers=args.decoder_layers					, attention_heads=args.decoder_attention_heads					, ffn_dim=args.decoder_ffn_embed_dim					, d_model=args.decoder_embed_dim					, layerdrop=args.decoder_layerdrop					, dropout=args.dropout					, attention_dropout=args.attention_dropout					, activation_dropout=args.activation_dropout					, activation_function='gelu'					, scale_embedding=not args.no_scale_embedding					, tie_word_embeddings=args.share_decoder_input_output_embed					, )

		UpperCamelCase						=  XGLMForCausalLM(lowercase				)
		UpperCamelCase						=  model.load_state_dict(lowercase					, strict=lowercase				)
		print(lowercase				)
		UpperCamelCase						=  make_linear_from_emb(model.model.embed_tokens				)

		return model


if __name__ == "__main__":
					_UpperCAmelCase      :				Union[str, Any]          = 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.")
					_UpperCAmelCase      :				List[str]          = parser.parse_args()
					_UpperCAmelCase      :				Union[str, Any]          = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)


import os

_UpperCAmelCase      :				int          = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}


def    A (      lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		UpperCamelCase						=  0
		while index < len(lowercase				) - 1:
				UpperCamelCase						=  SYMBOLS[numerals[index]]
				UpperCamelCase						=  SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''

		UpperCamelCase						=  num // 1_000
		numerals += m_count * "M"
		num %= 1_000

		UpperCamelCase						=  num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		UpperCamelCase						=  num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals


def    A (      lowercase = "/p089_roman.txt"				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		with open(os.path.dirname(lowercase				) + roman_numerals_filename				) as filea:
				UpperCamelCase						=  filea.readlines()

		for line in lines:
				UpperCamelCase						=  line.strip()
				UpperCamelCase						=  parse_roman_numerals(lowercase				)
				UpperCamelCase						=  generate_roman_numerals(lowercase				)
				savings += len(lowercase				) - len(lowercase				)

		return savings


if __name__ == "__main__":
					print(F'''{solution() = }''')
