


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()
UpperCamelCase :				Optional[Any]						    =       logging.get_logger(__name__)

UpperCamelCase :				Tuple						    =       '''Hello world! cécé herlolip'''
def 							UpperCamelCase_  (				__snake_case			:						str	,			__snake_case			:						str	,			__snake_case			:						bool     )->      Tuple:




						'''simple docstring'''
						__magic_name__ 		=			FairseqRobertaModel.from_pretrained(__snake_case     )
						roberta.eval()  # disable dropout
						__magic_name__ 		=			roberta.model.encoder.sentence_encoder
						__magic_name__ 		=			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_14	,			type_vocab_size=1	,			layer_norm_eps=1E-5	,			)
						if classification_head:
												__magic_name__ 		=			roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'	,			__snake_case     )

						__magic_name__ 		=			RobertaForSequenceClassification(__snake_case     ) if classification_head else RobertaForMaskedLM(__snake_case     )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						__magic_name__ 		=			roberta_sent_encoder.embed_tokens.weight
						__magic_name__ 		=			roberta_sent_encoder.embed_positions.weight
						__magic_name__ 		=			torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.
						__magic_name__ 		=			roberta_sent_encoder.emb_layer_norm.weight
						__magic_name__ 		=			roberta_sent_encoder.emb_layer_norm.bias

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

												# self attention
												__magic_name__ 		=			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)     )
												)

												__magic_name__ 		=			roberta_layer.self_attn.q_proj.weight
												__magic_name__ 		=			roberta_layer.self_attn.q_proj.bias
												__magic_name__ 		=			roberta_layer.self_attn.k_proj.weight
												__magic_name__ 		=			roberta_layer.self_attn.k_proj.bias
												__magic_name__ 		=			roberta_layer.self_attn.v_proj.weight
												__magic_name__ 		=			roberta_layer.self_attn.v_proj.bias

												# self-attention output
												__magic_name__ 		=			layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												__magic_name__ 		=			roberta_layer.self_attn.out_proj.weight
												__magic_name__ 		=			roberta_layer.self_attn.out_proj.bias
												__magic_name__ 		=			roberta_layer.self_attn_layer_norm.weight
												__magic_name__ 		=			roberta_layer.self_attn_layer_norm.bias

												# intermediate
												__magic_name__ 		=			layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												__magic_name__ 		=			roberta_layer.fca.weight
												__magic_name__ 		=			roberta_layer.fca.bias

												# output
												__magic_name__ 		=			layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												__magic_name__ 		=			roberta_layer.fca.weight
												__magic_name__ 		=			roberta_layer.fca.bias
												__magic_name__ 		=			roberta_layer.final_layer_norm.weight
												__magic_name__ 		=			roberta_layer.final_layer_norm.bias
												# end of layer

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

						# Let's check that we get the same results.
						__magic_name__ 		=			roberta.encode(__snake_case     ).unsqueeze(0     )  # batch of size 1

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

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


if __name__ == "__main__":
					UpperCamelCase :				Optional[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.'''
					)
					UpperCamelCase :				Dict						    =       parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,			_lowerCAmelCase  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor_class







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

