


"""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__    :							Any            =					logging.get_logger(__name__)

a__    :							List[str]            =					'''Hello world! cécé herlolip'''




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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

					print("Our RoBERTa config:"  ,						lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    XLMRobertaXLForSequenceClassification(lowerCAmelCase_       ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase_       )
					model.eval()

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

					__SCREAMING_SNAKE_CASE						       =    roberta_sent_encoder.layer_norm.weight
					__SCREAMING_SNAKE_CASE						       =    roberta_sent_encoder.layer_norm.bias

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

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

										# self attention
										__SCREAMING_SNAKE_CASE						       =    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)       )
										)

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

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

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

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

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

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

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

					__SCREAMING_SNAKE_CASE						       =    model(lowerCAmelCase_       )[0]
					if classification_head:
										__SCREAMING_SNAKE_CASE						       =    roberta.model.classification_heads["mnli"](roberta.extract_features(lowerCAmelCase_       )       )
					else:
										__SCREAMING_SNAKE_CASE						       =    roberta.model(lowerCAmelCase_       )[0]
					print(our_output.shape  ,						their_output.shape       )
					__SCREAMING_SNAKE_CASE						       =    torch.max(torch.abs(our_output - their_output       )       ).item()
					print(f"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
					__SCREAMING_SNAKE_CASE						       =    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__":
		a__    :							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.'''
		)
		a__    :							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 typing import TYPE_CHECKING

from ...utils import _LazyModule


a__    :							Optional[int]            =					{'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
		from .tokenization_byta import ByTaTokenizer
else:
		import sys

		a__    :							Any            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
