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

lowercase		:						int			=					'Hello world! cécé herlolip'


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					__a:						Union[str, Any]		    =					FairseqRobertaModel.from_pretrained(UpperCamelCase__ )
					roberta.eval()  # disable dropout
					__a:						Dict		    =					roberta.model.encoder.sentence_encoder
					__a:						int		    =					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:
										__a:						List[Any]		    =					roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("""Our BERT config:"""	,				UpperCamelCase__ )

					__a:						Dict		    =					RobertaForSequenceClassification(UpperCamelCase__ ) if classification_head else RobertaForMaskedLM(UpperCamelCase__ )
					model.eval()

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

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

										# self attention
										__a:						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) )
										)

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

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

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

										# output
										__a:						BertOutput		    =					layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										__a:						int		    =					roberta_layer.fca.weight
										__a:						List[str]		    =					roberta_layer.fca.bias
										__a:						List[str]		    =					roberta_layer.final_layer_norm.weight
										__a:						Any		    =					roberta_layer.final_layer_norm.bias
										# end of layer

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

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

					__a:						int		    =					model(UpperCamelCase__ )[0]
					if classification_head:
										__a:						str		    =					roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__ ) )
					else:
										__a:						int		    =					roberta.model(UpperCamelCase__ )[0]
					print(our_output.shape	,				their_output.shape )
					__a:						List[Any]		    =					torch.max(torch.abs(our_output - their_output ) ).item()
					print(f"""max_absolute_diff = {max_absolute_diff}""" )  # ~ 1e-7
					__a:						List[Any]		    =					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__":
			lowercase		:						Tuple			=					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.'
			)
			lowercase		:						Optional[Any]			=					parser.parse_args()
			convert_roberta_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)


"""simple docstring"""
lowercase		:						str =					{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					__a:						Optional[int]		    =					str(lowerCamelCase_ ).strip()

					# Exceptions
					if not binary_str:
										raise ValueError("""Empty string was passed to the function""" )
					__a:						List[Any]		    =					binary_str[0] == """-"""
					__a:						List[str]		    =					binary_str[1:] if is_negative else binary_str
					if not all(char in """01""" for char in binary_str ):
										raise ValueError("""Non-binary value was passed to the function""" )

					__a:						Any		    =					(
					    """0""" * (4 * (divmod(len(lowerCamelCase_ )	,				4 )[0] + 1) - len(lowerCamelCase_ )) + binary_str
					)

					__a:						int		    =					[]
					for x in range(0	,				len(lowerCamelCase_ )	,				4 ):
										hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]] )
					__a:						Tuple		    =					"""0x""" + """""".join(lowerCamelCase_ )

					return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
			from doctest import testmod

			testmod()
