






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()
__UpperCamelCase   :   str      =    logging.get_logger(__name__)

__UpperCamelCase   :   str      =    """Hello world! cécé herlolip"""





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	FairseqRobertaModel.from_pretrained(lowerCamelCase       )
	roberta.eval()  # disable dropout
	__lowercase   							=	roberta.model.encoder.sentence_encoder
	__lowercase   							=	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:
		__lowercase   							=	roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

	print("""Our RoBERTa config:"""					,	lowerCamelCase       )

	__lowercase   							=	XLMRobertaXLForSequenceClassification(lowerCamelCase       ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase       )
	model.eval()

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

	__lowercase   							=	roberta_sent_encoder.layer_norm.weight
	__lowercase   							=	roberta_sent_encoder.layer_norm.bias

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

		__lowercase   							=	layer.attention
		__lowercase   							=	roberta_layer.self_attn_layer_norm.weight
		__lowercase   							=	roberta_layer.self_attn_layer_norm.bias

		# self attention
		__lowercase   							=	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   							=	roberta_layer.self_attn.q_proj.weight
		__lowercase   							=	roberta_layer.self_attn.q_proj.bias
		__lowercase   							=	roberta_layer.self_attn.k_proj.weight
		__lowercase   							=	roberta_layer.self_attn.k_proj.bias
		__lowercase   							=	roberta_layer.self_attn.v_proj.weight
		__lowercase   							=	roberta_layer.self_attn.v_proj.bias

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

		# this one is final layer norm
		__lowercase   							=	roberta_layer.final_layer_norm.weight
		__lowercase   							=	roberta_layer.final_layer_norm.bias

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

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

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

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

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

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


if __name__ == "__main__":
						__UpperCamelCase   :   List[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   :   List[Any]      =    parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)






"""simple docstring"""







from __future__ import annotations

A		=						'#'

class       _a  :



			def __init__(			self							:				List[Any]		)							->					None:
									snake_case   :			dict						    = {}



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				str		)							->					None:
									snake_case   :			Optional[int]						    = self._trie
									for char in text:
															if char not in trie:
																					snake_case   :			str						    = {}
															snake_case   :			Optional[Any]						    = trie[char]
									snake_case   :			str						    = True



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				str		)							->					tuple | list:
									snake_case   :			Dict						    = self._trie
									for char in prefix:
															if char in trie:
																					snake_case   :			Tuple						    = trie[char]
															else:
																					return []
									return self._elements(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				dict		)							->					tuple:
									snake_case   :			int						    = []
									for c, v in d.items():
															snake_case   :			int						    = [" "] if c == END else [(c + s) for s in self._elements(_lowercase		)]
															result.extend(_lowercase		)
									return tuple(_lowercase		)



A		=						Trie()
A		=						('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
			trie.insert_word(word)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = trie.find_word(lowerCamelCase_ )
						return tuple(string + word for word in suffixes )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print(autocomplete_using_trie("de" ) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



