






'''simple docstring'''




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :					List[Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Tuple   							=					{
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''xlnet'''
							a__   		=	['''mems''']
							a__   		=	{
							    '''n_token''': '''vocab_size''',  # Backward compatibility
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}

							def __init__(		self			:Any       ,							lowerCAmelCase__			:Optional[int]=32_000       ,							lowerCAmelCase__			:Any=1_024       ,							lowerCAmelCase__			:Tuple=24       ,							lowerCAmelCase__			:str=16       ,							lowerCAmelCase__			:Optional[Any]=4_096       ,							lowerCAmelCase__			:Optional[Any]="gelu"       ,							lowerCAmelCase__			:Optional[int]=True       ,							lowerCAmelCase__			:Optional[int]="bi"       ,							lowerCAmelCase__			:Any=0.0_2       ,							lowerCAmelCase__			:int=1E-1_2       ,							lowerCAmelCase__			:Dict=0.1       ,							lowerCAmelCase__			:Any=512       ,							lowerCAmelCase__			:List[Any]=None       ,							lowerCAmelCase__			:Tuple=True       ,							lowerCAmelCase__			:Tuple=False       ,							lowerCAmelCase__			:Optional[int]=False       ,							lowerCAmelCase__			:Tuple=-1       ,							lowerCAmelCase__			:Any=False       ,							lowerCAmelCase__			:Optional[int]="last"       ,							lowerCAmelCase__			:Dict=True       ,							lowerCAmelCase__			:str="tanh"       ,							lowerCAmelCase__			:Optional[int]=0.1       ,							lowerCAmelCase__			:int=5       ,							lowerCAmelCase__			:Optional[int]=5       ,							lowerCAmelCase__			:Any=5       ,							lowerCAmelCase__			:List[Any]=1       ,							lowerCAmelCase__			:Dict=2       ,							**lowerCAmelCase__			:Tuple       ,							)				->  Any:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       vocab_size
										snake_case_				:					int   =       d_model
										snake_case_				:					List[str]   =       n_layer
										snake_case_				:					Optional[int]   =       n_head
										if d_model % n_head != 0:
													raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''				)
										if "d_head" in kwargs:
													if kwargs["d_head"] != d_model // n_head:
																raise ValueError(
																    F'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''				)
										snake_case_				:					Any   =       d_model // n_head
										snake_case_				:					int   =       ff_activation
										snake_case_				:					str   =       d_inner
										snake_case_				:					Optional[Any]   =       untie_r
										snake_case_				:					Tuple   =       attn_type

										snake_case_				:					Dict   =       initializer_range
										snake_case_				:					int   =       layer_norm_eps

										snake_case_				:					Optional[Any]   =       dropout
										snake_case_				:					Union[str, Any]   =       mem_len
										snake_case_				:					Optional[int]   =       reuse_len
										snake_case_				:					Any   =       bi_data
										snake_case_				:					Any   =       clamp_len
										snake_case_				:					int   =       same_length

										snake_case_				:					Any   =       summary_type
										snake_case_				:					Dict   =       summary_use_proj
										snake_case_				:					str   =       summary_activation
										snake_case_				:					Tuple   =       summary_last_dropout
										snake_case_				:					int   =       start_n_top
										snake_case_				:					str   =       end_n_top

										snake_case_				:					List[Any]   =       bos_token_id
										snake_case_				:					List[str]   =       pad_token_id
										snake_case_				:					Optional[int]   =       eos_token_id

										if "use_cache" in kwargs:
													warnings.warn(
													    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
													    " instead."       ,							lowerCAmelCase__       ,							)
													snake_case_				:					Optional[int]   =       kwargs["use_cache"]

										snake_case_				:					Optional[int]   =       use_mems_eval
										snake_case_				:					Optional[Any]   =       use_mems_train
										super().__init__(pad_token_id=lowerCAmelCase__       ,							bos_token_id=lowerCAmelCase__       ,							eos_token_id=lowerCAmelCase__       ,							**lowerCAmelCase__				)

							@property
							def 							_A	(		self			:List[str]				)				->  Any:


										'''simple docstring'''




										logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''				)
										return -1






							@max_position_embeddings.setter
							def 							_A	(		self			:int       ,							lowerCAmelCase__			:Dict				)				->  Union[str, Any]:


										'''simple docstring'''




										raise NotImplementedError(
										    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''				)












'''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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
		raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Union[str, Any]   							=					'''Hello world! cécé herlolip'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       FairseqRobertaModel.from_pretrained(__magic_name__						)
			roberta.eval()  # disable dropout
			snake_case_				:					Dict   =       roberta.model.encoder.sentence_encoder
			snake_case_				:					List[str]   =       XLMRobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,hidden_size=roberta.cfg.model.encoder_embed_dim     ,num_hidden_layers=roberta.cfg.model.encoder_layers     ,num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,max_position_embeddings=514     ,type_vocab_size=1     ,layer_norm_eps=1E-5     ,)
			if classification_head:
						snake_case_				:					List[str]   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our RoBERTa config:"     ,__magic_name__						)

			snake_case_				:					List[str]   =       XLMRobertaXLForSequenceClassification(__magic_name__						) if classification_head else XLMRobertaXLForMaskedLM(__magic_name__						)
			model.eval()

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

			snake_case_				:					Union[str, Any]   =       roberta_sent_encoder.layer_norm.weight
			snake_case_				:					str   =       roberta_sent_encoder.layer_norm.bias

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

						snake_case_				:					RobertaAttention   =       layer.attention
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.weight
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.bias

						# self attention
						snake_case_				:					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)						)
						)

						snake_case_				:					Dict   =       roberta_layer.self_attn.q_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.q_proj.bias
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.weight
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.bias
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.v_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.v_proj.bias

						# self-attention output
						snake_case_				:					BertSelfOutput   =       layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.self_attn.out_proj.weight
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						snake_case_				:					int   =       roberta_layer.final_layer_norm.weight
						snake_case_				:					Union[str, Any]   =       roberta_layer.final_layer_norm.bias

						# intermediate
						snake_case_				:					BertIntermediate   =       layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.fca.weight
						snake_case_				:					List[Any]   =       roberta_layer.fca.bias

						# output
						snake_case_				:					BertOutput   =       layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					Any   =       roberta_layer.fca.weight
						snake_case_				:					Any   =       roberta_layer.fca.bias
						# end of layer

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

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

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

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


if __name__ == "__main__":
		__lowerCamelCase   :					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.'''
		)
		__lowerCamelCase   :					Tuple   							=					parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




