



"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			[
						    """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(_lowercase,					_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :str						=			list(s_dict.keys()	)
						for key in keys:
												if "transformer_layers" in key:
																		snake_case_ :Optional[Any]						=			s_dict.pop(_lowercase	)
												elif "subsample" in key:
																		snake_case_ :int						=			s_dict.pop(_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_,       snake_case_ :List[str]						=			emb.weight.shape
						snake_case_ :Union[str, Any]						=			nn.Linear(_lowercase,					_lowercase,					bias=_lowercase	)
						snake_case_ :Dict						=			emb.weight.data
						return lin_layer
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			torch.load(_lowercase,					map_location="""cpu"""	)
						snake_case_ :List[Any]						=			mam_aaa["""args"""]
						snake_case_ :int						=			mam_aaa["""model"""]
						snake_case_ :List[Any]						=			state_dict["""decoder.output_projection.weight"""]

						remove_ignore_keys_(_lowercase	)
						rename_keys(_lowercase	)

						snake_case_ :Dict						=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

						snake_case_ :str						=			args.share_decoder_input_output_embed

						snake_case_ :Optional[Any]						=			[int(_lowercase	) for i in args.conv_kernel_sizes.split(""","""	)]
						snake_case_ :Optional[Any]						=			SpeechaTextConfig(
						    vocab_size=_lowercase,					max_source_positions=args.max_source_positions,					max_target_positions=args.max_target_positions,					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,					dropout=args.dropout,					attention_dropout=args.attention_dropout,					activation_dropout=args.activation_dropout,					activation_function="""relu""",					num_conv_layers=len(_lowercase	),					conv_channels=args.conv_channels,					conv_kernel_sizes=_lowercase,					input_feat_per_channel=args.input_feat_per_channel,					input_channels=args.input_channels,					tie_word_embeddings=_lowercase,					num_beams=5,					max_length=200,					use_cache=_lowercase,					decoder_start_token_id=2,					early_stopping=_lowercase,					)

						snake_case_ :Optional[Any]						=			SpeechaTextForConditionalGeneration(_lowercase	)
						snake_case_,       snake_case_ :Union[str, Any]						=			model.model.load_state_dict(_lowercase,					strict=_lowercase	)
						if len(_lowercase	) > 0 and not set(_lowercase	) <= {
						    "encoder.embed_positions.weights",
						    "decoder.embed_positions.weights",
						}:
												raise ValueError(
												    """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
												    f""" but all the following weights are missing {missing}"""	)

						if tie_embeds:
												snake_case_ :Union[str, Any]						=			make_linear_from_emb(model.model.decoder.embed_tokens	)
						else:
												snake_case_ :Optional[int]						=			lm_head_weights

						model.save_pretrained(_lowercase	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						__a      =				parser.parse_args()
						convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





"""simple docstring"""
from math import factorial
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Optional[int]						,     snake_case:							Dict						,     snake_case:							int						)   ->	Tuple:
											snake_case_ :List[Any]						=			real
											if isinstance(snake_case						,     snake_case						):
																	snake_case_ :Tuple						=			[1] * rank
											else:
																	snake_case_ :Optional[Any]						=			rank






					def __repr__(       self:							List[str]						)   ->	Tuple:
											return (
											    f"""{self.real}+"""
											    f"""{'+'.join(str(snake_case						)+'E'+str(n+1						)for n,dual in enumerate(self.duals						)						)}"""
											)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											snake_case_ :Any						=			self.duals.copy()
											while cur[-1] == 0:
																	cur.pop(-1						)
											return Dual(self.real						,     snake_case						)






					def __add__(       self:							Optional[int]						,     snake_case:							Dict						)   ->	List[str]:
											if not isinstance(snake_case						,     snake_case						):
																	return Dual(self.real + other						,     self.duals						)
											snake_case_ :List[Any]						=			self.duals.copy()
											snake_case_ :Tuple						=			other.duals.copy()
											if len(snake_case						) > len(snake_case						):
																	o_dual.extend([1] * (len(snake_case						) - len(snake_case						))						)
											elif len(snake_case						) < len(snake_case						):
																	s_dual.extend([1] * (len(snake_case						) - len(snake_case						))						)
											snake_case_ :Dict						=			[]
											for i in range(len(snake_case						)						):
																	new_duals.append(s_dual[i] + o_dual[i]						)
											return Dual(self.real + other.real						,     snake_case						)

					_A		:			str						=     __add__






					def __sub__(       self:							Tuple						,     snake_case:							Union[str, Any]						)   ->	Tuple:
											return self + other * -1






					def __mul__(       self:							str						,     snake_case:							Tuple						)   ->	Optional[Any]:
											if not isinstance(snake_case						,     snake_case						):
																	snake_case_ :Dict						=			[]
																	for i in self.duals:
																							new_duals.append(i * other						)
																	return Dual(self.real * other						,     snake_case						)
											snake_case_ :int						=			[0] * (len(self.duals						) + len(other.duals						) + 1)
											for i, item in enumerate(self.duals						):
																	for j, jtem in enumerate(other.duals						):
																							new_duals[i + j + 1] += item * jtem
											for k in range(len(self.duals						)						):
																	new_duals[k] += self.duals[k] * other.real
											for index in range(len(other.duals						)						):
																	new_duals[index] += other.duals[index] * self.real
											return Dual(self.real * other.real						,     snake_case						)

					_A		:			int						=     __mul__






					def __truediv__(       self:							List[str]						,     snake_case:							List[str]						)   ->	List[str]:
											if not isinstance(snake_case						,     snake_case						):
																	snake_case_ :Optional[Any]						=			[]
																	for i in self.duals:
																							new_duals.append(i / other						)
																	return Dual(self.real / other						,     snake_case						)
											raise ValueError






					def __floordiv__(       self:							int						,     snake_case:							List[Any]						)   ->	Any:
											if not isinstance(snake_case						,     snake_case						):
																	snake_case_ :Optional[int]						=			[]
																	for i in self.duals:
																							new_duals.append(i // other						)
																	return Dual(self.real // other						,     snake_case						)
											raise ValueError






					def __pow__(       self:							Optional[Any]						,     snake_case:							Optional[int]						)   ->	List[Any]:
											if n < 0 or isinstance(snake_case						,     snake_case						):
																	raise ValueError("""power must be a positive integer"""						)
											if n == 0:
																	return 1
											if n == 1:
																	return self
											snake_case_ :str						=			self
											for _ in range(n - 1						):
																	x *= self
											return x

def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						if not callable(_lowercase	):
												raise ValueError("""differentiate() requires a function as input for func"""	)
						if not isinstance(_lowercase,					(float, int)	):
												raise ValueError("""differentiate() requires a float as input for position"""	)
						if not isinstance(_lowercase,					_lowercase	):
												raise ValueError("""differentiate() requires an int as input for order"""	)
						snake_case_ :Optional[Any]						=			Dual(_lowercase,					1	)
						snake_case_ :List[Any]						=			func(_lowercase	)
						if order == 0:
												return result.real
						return result.duals[order - 1] * factorial(_lowercase	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						def    A_  (			_lowercase	):







												'''simple docstring'''


												return y**2 * y**4

						print(differentiate(f, 9, 2))
