






'''simple docstring'''

import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
						raise Exception("requires fairseq >= 0.9.0")


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

A:   Optional[Any]						=					"Hello world! cécé herlolip"






def 						_UpperCAmelCase     (	a		:						str    ,      a		:						str    ,      a		:						bool				)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : int   	=      FairseqRobertaModel.from_pretrained(a				)
						roberta.eval()  # disable dropout
						lowercase_   : Any   	=      roberta.model.encoder.sentence_encoder
						lowercase_   : Tuple   	=      RobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings    ,      hidden_size=roberta.args.encoder_embed_dim    ,      num_hidden_layers=roberta.args.encoder_layers    ,      num_attention_heads=roberta.args.encoder_attention_heads    ,      intermediate_size=roberta.args.encoder_ffn_embed_dim    ,      max_position_embeddings=5_1_4    ,      type_vocab_size=1    ,      layer_norm_eps=1e-5    ,      )
						if classification_head:
												lowercase_   : Optional[int]   	=      roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'    ,      a				)

						lowercase_   : List[str]   	=      RobertaForSequenceClassification(a				) if classification_head else RobertaForMaskedLM(a				)
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						lowercase_   : Optional[Any]   	=      roberta_sent_encoder.embed_tokens.weight
						lowercase_   : int   	=      roberta_sent_encoder.embed_positions.weight
						lowercase_   : List[Any]   	=      torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.
						lowercase_   : Tuple   	=      roberta_sent_encoder.emb_layer_norm.weight
						lowercase_   : Tuple   	=      roberta_sent_encoder.emb_layer_norm.bias

						for i in range(config.num_hidden_layers				):
												# Encoder: start of layer
												lowercase_   : BertLayer   	=      model.roberta.encoder.layer[i]
												lowercase_   : TransformerSentenceEncoderLayer   	=      roberta_sent_encoder.layers[i]

												# self attention
												lowercase_   : BertSelfAttention   	=      layer.attention.self
												assert (
												    roberta_layer.self_attn.k_proj.weight.data.shape
												    == roberta_layer.self_attn.q_proj.weight.data.shape
												    == roberta_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size)				)
												)

												lowercase_   : Tuple   	=      roberta_layer.self_attn.q_proj.weight
												lowercase_   : str   	=      roberta_layer.self_attn.q_proj.bias
												lowercase_   : List[Any]   	=      roberta_layer.self_attn.k_proj.weight
												lowercase_   : str   	=      roberta_layer.self_attn.k_proj.bias
												lowercase_   : List[str]   	=      roberta_layer.self_attn.v_proj.weight
												lowercase_   : Tuple   	=      roberta_layer.self_attn.v_proj.bias

												# self-attention output
												lowercase_   : BertSelfOutput   	=      layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												lowercase_   : Union[str, Any]   	=      roberta_layer.self_attn.out_proj.weight
												lowercase_   : Any   	=      roberta_layer.self_attn.out_proj.bias
												lowercase_   : int   	=      roberta_layer.self_attn_layer_norm.weight
												lowercase_   : List[Any]   	=      roberta_layer.self_attn_layer_norm.bias

												# intermediate
												lowercase_   : BertIntermediate   	=      layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												lowercase_   : Tuple   	=      roberta_layer.fca.weight
												lowercase_   : str   	=      roberta_layer.fca.bias

												# output
												lowercase_   : BertOutput   	=      layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												lowercase_   : str   	=      roberta_layer.fca.weight
												lowercase_   : Optional[int]   	=      roberta_layer.fca.bias
												lowercase_   : str   	=      roberta_layer.final_layer_norm.weight
												lowercase_   : str   	=      roberta_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												lowercase_   : Any   	=      roberta.model.classification_heads['mnli'].dense.weight
												lowercase_   : Union[str, Any]   	=      roberta.model.classification_heads['mnli'].dense.bias
												lowercase_   : Optional[int]   	=      roberta.model.classification_heads['mnli'].out_proj.weight
												lowercase_   : Any   	=      roberta.model.classification_heads['mnli'].out_proj.bias
						else:
												# LM Head
												lowercase_   : List[str]   	=      roberta.model.encoder.lm_head.dense.weight
												lowercase_   : Dict   	=      roberta.model.encoder.lm_head.dense.bias
												lowercase_   : Any   	=      roberta.model.encoder.lm_head.layer_norm.weight
												lowercase_   : Tuple   	=      roberta.model.encoder.lm_head.layer_norm.bias
												lowercase_   : Optional[Any]   	=      roberta.model.encoder.lm_head.weight
												lowercase_   : List[str]   	=      roberta.model.encoder.lm_head.bias

						# Let's check that we get the same results.
						lowercase_   : torch.Tensor   	=      roberta.encode(a				).unsqueeze(0				)  # batch of size 1

						lowercase_   : List[str]   	=      model(a				)[0]
						if classification_head:
												lowercase_   : List[str]   	=      roberta.model.classification_heads['mnli'](roberta.extract_features(a				)				)
						else:
												lowercase_   : List[Any]   	=      roberta.model(a				)[0]
						print(our_output.shape    ,      their_output.shape				)
						lowercase_   : Union[str, Any]   	=      torch.max(torch.abs(our_output - their_output				)				).item()
						print(f"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
						lowercase_   : Dict   	=      torch.allclose(a    ,      a    ,      atol=1e-3				)
						print('Do both models output the same tensors?'    ,      '🔥' if success else '💩'				)
						if not success:
												raise Exception('Something went wRoNg'				)

						pathlib.Path(a				).mkdir(parents=a    ,      exist_ok=a				)
						print(f"Saving model to {pytorch_dump_folder_path}"				)
						model.save_pretrained(a				)


if __name__ == "__main__":
						A:   Any						=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--classification_head", action="store_true", help="Whether to convert a final classification head."
						)
						A:   int						=					parser.parse_args()
						convert_roberta_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)








'''simple docstring'''

import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm






def 						_UpperCAmelCase     (	a		:						Tuple    ,      a		:						int				)      ->      List[str]:





						"""simple docstring"""
						if isinstance(a    ,      (list, tuple)				):
												return honor_type(a    ,      (move_to_device(a    ,      a				) for t in state)				)
						elif isinstance(a    ,      a				):
												return type(a				)({k: move_to_device(a    ,      a				) for k, v in state.items()}				)
						elif isinstance(a    ,      torch.Tensor				):
												return state.to(a				)
						return state






class 				__magic_name__  (						torch.optim.Optimizer					):







	"""simple docstring"""



	def __init__(				self	,   _lowercase	,   _lowercase=True	,   _lowercase=None							)    ->	int:
							lowercase_   : List[str]   	=      optimizer
							lowercase_   : Optional[int]   	=      scaler
							lowercase_   : Union[str, Any]   	=      AcceleratorState()
							lowercase_   : Optional[Any]   	=      GradientState()
							lowercase_   : Union[str, Any]   	=      device_placement
							lowercase_   : List[str]   	=      False
							lowercase_   : Optional[Any]   	=      None

							# Handle device placement
							if device_placement:
													lowercase_   : str   	=      self.optimizer.state_dict()
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(_lowercase	,   self.accelerator_state.device							)
													else:
																			lowercase_   : List[str]   	=      move_to_device(_lowercase	,   self.accelerator_state.device							)
													self.optimizer.load_state_dict(_lowercase							)
	@property
	def 							lowerCamelCase__		(				self							)    ->	Any:
							return self.optimizer.state
	@state.setter
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Union[str, Any]:
							lowercase_   : Optional[Any]   	=      state
	@property
	def 							lowerCamelCase__		(				self							)    ->	Any:
							return self.optimizer.param_groups
	@param_groups.setter
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Dict   	=      param_groups
	@property
	def 							lowerCamelCase__		(				self							)    ->	int:
							return self.optimizer.defaults
	@defaults.setter
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							lowercase_   : List[Any]   	=      defaults
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Tuple:
							self.optimizer.add_param_group(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
													xm.send_cpu_data_to_device(_lowercase	,   self.accelerator_state.device							)
							self.optimizer.load_state_dict(_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Any:
							return self.optimizer.state_dict()
	def 							lowerCamelCase__		(				self	,   _lowercase=None							)    ->	Optional[Any]:
							if self.gradient_state.sync_gradients:
													lowercase_   : Tuple   	=      'set_to_none' in inspect.signature(self.optimizer.zero_grad							).parameters
													if accept_arg:
																			if set_to_none is None:
																									lowercase_   : Optional[int]   	=      False
																			self.optimizer.zero_grad(set_to_none=_lowercase							)
													else:
																			if set_to_none is not None:
																									raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'							)
																			self.optimizer.zero_grad()
	def 							lowerCamelCase__		(				self	,   _lowercase=None							)    ->	Optional[Any]:
							if self.gradient_state.sync_gradients:
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			lowercase_   : Union[str, Any]   	=      {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer	,   optimizer_args=_lowercase							)
													elif self.scaler is not None:
																			lowercase_   : Optional[Any]   	=      False
																			if self._last_scale is None:
																									# `get_scale` is an async operation requiring full synchronization
																									# on CPU and GPUs before finishing. As a result, we store away
																									# the prior one to reduce the call overhead
																									lowercase_   : Tuple   	=      self.scaler.get_scale()
																									lowercase_   : int   	=      True
																			self.scaler.step(self.optimizer	,   _lowercase							)
																			self.scaler.update()
																			lowercase_   : List[str]   	=      self.scaler.get_scale()
																			if not new_scale:
																									# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																									lowercase_   : Optional[int]   	=      scale_after < self._last_scale
																			lowercase_   : List[str]   	=      scale_after

													else:
																			self.optimizer.step(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							for param_group in self.optimizer.param_groups:
													lowercase_   : Dict   	=      [parameters_map.get(_lowercase	,   _lowercase							) for p in param_group['params']]
	@property
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							warnings.warn(
							    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
							    '`optimizer.step_was_skipped` instead.'	,   _lowercase	,   )
							return self._is_overflow
	@property
	def 							lowerCamelCase__		(				self							)    ->	str:
							return self._is_overflow
	def __getstate__(				self							)    ->	str:
							return self.__dict__.copy()







	def __setstate__(				self	,   _lowercase							)    ->	Union[str, Any]:
							self.__dict__.update(_lowercase							)
