



'''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()
a     :    Dict      						=					logging.get_logger(__name__)

a     :    Optional[int]      						=					"""Hello world! cécé herlolip"""



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	Union[str, Any]							  =	FairseqRobertaModel.from_pretrained(_lowercase	)
							roberta.eval()  # disable dropout
							UpperCAmelCase  :	int							  =	roberta.model.encoder.sentence_encoder
							UpperCAmelCase  :	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=5_1_4			,       type_vocab_size=1			,       layer_norm_eps=1e-5			,       )
							if classification_head:
														UpperCAmelCase  :	Tuple							  =	roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

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

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

							UpperCAmelCase  :	List[str]							  =	roberta_sent_encoder.layer_norm.weight
							UpperCAmelCase  :	Any							  =	roberta_sent_encoder.layer_norm.bias

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

														UpperCAmelCase  :	Optional[Any]							  =	layer.attention
														UpperCAmelCase  :	Optional[int]							  =	roberta_layer.self_attn_layer_norm.weight
														UpperCAmelCase  :	Optional[int]							  =	roberta_layer.self_attn_layer_norm.bias

														# self attention
														UpperCAmelCase  :	int							  =	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)	)
														)

														UpperCAmelCase  :	List[str]							  =	roberta_layer.self_attn.q_proj.weight
														UpperCAmelCase  :	str							  =	roberta_layer.self_attn.q_proj.bias
														UpperCAmelCase  :	Union[str, Any]							  =	roberta_layer.self_attn.k_proj.weight
														UpperCAmelCase  :	Any							  =	roberta_layer.self_attn.k_proj.bias
														UpperCAmelCase  :	str							  =	roberta_layer.self_attn.v_proj.weight
														UpperCAmelCase  :	Union[str, Any]							  =	roberta_layer.self_attn.v_proj.bias

														# self-attention output
														UpperCAmelCase  :	List[Any]							  =	layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														UpperCAmelCase  :	Any							  =	roberta_layer.self_attn.out_proj.weight
														UpperCAmelCase  :	Optional[Any]							  =	roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														UpperCAmelCase  :	int							  =	roberta_layer.final_layer_norm.weight
														UpperCAmelCase  :	List[Any]							  =	roberta_layer.final_layer_norm.bias

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

														# output
														UpperCAmelCase  :	Dict							  =	layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														UpperCAmelCase  :	Dict							  =	roberta_layer.fca.weight
														UpperCAmelCase  :	List[str]							  =	roberta_layer.fca.bias
														# end of layer

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

							# Let's check that we get the same results.
							UpperCAmelCase  :	List[str]							  =	roberta.encode(_lowercase	).unsqueeze(0	)  # batch of size 1

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

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


if __name__ == "__main__":
							a     :    List[str]      						=					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_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)






'''simple docstring'''




def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Union[str, Any]							  =	set()

							# Replace all the whitespace in our sentence
							UpperCAmelCase  :	List[str]							  =	input_str.replace(""" """			,       """"""	)
							for alpha in input_str:
														if "a" <= alpha.lower() <= "z":
																					frequency.add(alpha.lower()	)
							return len(_lowercase	) == 2_6



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							UpperCAmelCase  :	Tuple							  =	[False] * 2_6
							for char in input_str:
														if char.islower():
																					UpperCAmelCase  :	Any							  =	True
														elif char.isupper():
																					UpperCAmelCase  :	Union[str, Any]							  =	True
							return all(_lowercase	)



def    __lowerCamelCase						(      _lowercase = "The quick brown fox jumps over the lazy dog"			,       )   ->    bool:
							return len({char for char in input_str.lower() if char.isalpha()}	) == 2_6



def    __lowerCamelCase						(      )   ->    None:
							from timeit import timeit

							UpperCAmelCase  :	str							  =	"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
							print(timeit("""is_pangram()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_faster()"""			,       setup=_lowercase	)	)
							print(timeit("""is_pangram_fastest()"""			,       setup=_lowercase	)	)
							# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
							# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							benchmark()

