


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      Optional[int] 							=   'Hello world! cécé herlolip'


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = os.path.split(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = DataaVecTextModel.from_pretrained(
				    UpperCAmelCase__   ,checkpoint_file=UpperCAmelCase__							)
				dataavec.eval()  # disable dropout
				_SCREAMING_SNAKE_CASE      = dataavec.models[0]
				_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.sentence_encoder
				_SCREAMING_SNAKE_CASE      = DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,hidden_size=dataavec_model.args.encoder_embed_dim   ,num_hidden_layers=dataavec_model.args.encoder_layers   ,num_attention_heads=dataavec_model.args.encoder_attention_heads   ,intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,max_position_embeddings=514   ,type_vocab_size=1   ,layer_norm_eps=1e-5   ,)
				if classification_head:
								_SCREAMING_SNAKE_CASE      = dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
				print('Our BERT config:'   ,UpperCAmelCase__							)

				_SCREAMING_SNAKE_CASE      = DataaVecTextForSequenceClassification(UpperCAmelCase__							) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase__							)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				_SCREAMING_SNAKE_CASE      = dataavec_sent_encoder.embed_tokens.weight
				_SCREAMING_SNAKE_CASE      = dataavec_sent_encoder.embed_positions.weight
				_SCREAMING_SNAKE_CASE      = torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight							)  # just zero them out b/c data2vec doesn't use them.
				_SCREAMING_SNAKE_CASE      = dataavec_sent_encoder.layernorm_embedding.weight
				_SCREAMING_SNAKE_CASE      = dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers							):
								# Encoder: start of layer
								_SCREAMING_SNAKE_CASE      = model.dataavec_text.encoder.layer[i]
								_SCREAMING_SNAKE_CASE      = dataavec_sent_encoder.layers[i]

								# self attention
								_SCREAMING_SNAKE_CASE      = layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)							), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    f''' {torch.Size((config.hidden_size, config.hidden_size)							)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)							), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    f''' {torch.Size((config.hidden_size, config.hidden_size)							)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)							), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    f''' {torch.Size((config.hidden_size, config.hidden_size)							)}'''
								)

								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.q_proj.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.q_proj.bias
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.k_proj.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.k_proj.bias
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.v_proj.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								_SCREAMING_SNAKE_CASE      = layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), f'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.out_proj.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.out_proj.bias
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn_layer_norm.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								_SCREAMING_SNAKE_CASE      = layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), f'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.bias

								# output
								_SCREAMING_SNAKE_CASE      = layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), f'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.bias
								_SCREAMING_SNAKE_CASE      = dataavec_layer.final_layer_norm.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								_SCREAMING_SNAKE_CASE      = dataavec.model.classification_heads['mnli'].dense.weight
								_SCREAMING_SNAKE_CASE      = dataavec.model.classification_heads['mnli'].dense.bias
								_SCREAMING_SNAKE_CASE      = dataavec.model.classification_heads['mnli'].out_proj.weight
								_SCREAMING_SNAKE_CASE      = dataavec.model.classification_heads['mnli'].out_proj.bias
				else:
								# LM Head
								_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.lm_head.dense.weight
								_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.lm_head.dense.bias
								_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.lm_head.layer_norm.weight
								_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.lm_head.layer_norm.bias
								_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.lm_head.weight
								_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				_SCREAMING_SNAKE_CASE      = dataavec.encode(UpperCAmelCase__							).unsqueeze(0							)  # batch of size 1

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

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


if __name__ == "__main__":
							snake_case			:      Union[str, Any] 							=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--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.'
							)
							snake_case			:      Tuple 							=   parser.parse_args()
							convert_dataavec_checkpoint_to_pytorch(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)




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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






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



class    __lowercase  (       torch.optim.Optimizer							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=True				, A_=None	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = optimizer
											_SCREAMING_SNAKE_CASE      = scaler
											_SCREAMING_SNAKE_CASE      = AcceleratorState()
											_SCREAMING_SNAKE_CASE      = GradientState()
											_SCREAMING_SNAKE_CASE      = device_placement
											_SCREAMING_SNAKE_CASE      = False
											_SCREAMING_SNAKE_CASE      = None

											# Handle device placement
											if device_placement:
															_SCREAMING_SNAKE_CASE      = self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
															else:
																			_SCREAMING_SNAKE_CASE      = move_to_device(A_				, self.accelerator_state.device	)
															self.optimizer.load_state_dict(A_	)





							@property
							def 		__magic_name__			(    self	)->	Tuple:
											return self.optimizer.state





							@state.setter
							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = state





							@property
							def 		__magic_name__			(    self	)->	Optional[Any]:
											return self.optimizer.param_groups





							@param_groups.setter
							def 		__magic_name__			(    self				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = param_groups





							@property
							def 		__magic_name__			(    self	)->	Any:
											return self.optimizer.defaults





							@defaults.setter
							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = defaults





							def 		__magic_name__			(    self				, A_	)->	Any:
											self.optimizer.add_param_group(A_	)





							def 		__magic_name__			(    self				, A_	)->	int:
											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
											self.optimizer.load_state_dict(A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											return self.optimizer.state_dict()





							def 		__magic_name__			(    self				, A_=None	)->	Any:
											if self.gradient_state.sync_gradients:
															_SCREAMING_SNAKE_CASE      = 'set_to_none' in inspect.signature(self.optimizer.zero_grad	).parameters
															if accept_arg:
																			if set_to_none is None:
																							_SCREAMING_SNAKE_CASE      = False
																			self.optimizer.zero_grad(set_to_none=A_	)
															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 		__magic_name__			(    self				, A_=None	)->	Optional[int]:
											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			_SCREAMING_SNAKE_CASE      = {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer				, optimizer_args=A_	)
															elif self.scaler is not None:
																			_SCREAMING_SNAKE_CASE      = 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
																							_SCREAMING_SNAKE_CASE      = self.scaler.get_scale()
																							_SCREAMING_SNAKE_CASE      = True
																			self.scaler.step(self.optimizer				, A_	)
																			self.scaler.update()
																			_SCREAMING_SNAKE_CASE      = 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.
																							_SCREAMING_SNAKE_CASE      = scale_after < self._last_scale
																			_SCREAMING_SNAKE_CASE      = scale_after

															else:
																			self.optimizer.step(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											for param_group in self.optimizer.param_groups:
															_SCREAMING_SNAKE_CASE      = [parameters_map.get(A_				, A_	) for p in param_group['params']]





							@property
							def 		__magic_name__			(    self	)->	str:
											warnings.warn(
											    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
											    '`optimizer.step_was_skipped` instead.'				, A_				, )
											return self._is_overflow





							@property
							def 		__magic_name__			(    self	)->	int:
											return self._is_overflow





							def __getstate__(    self	)->	Dict:
											return self.__dict__.copy()





							def __setstate__(    self				, A_	)->	List[str]:
											self.__dict__.update(A_	)
