




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()
lowercase__				:     List[str] 		=						logging.get_logger(__name__)

lowercase__				:     List[Any] 		=						"Hello world! cécé herlolip"


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     FairseqRobertaModel.from_pretrained(_lowerCAmelCase							)
	roberta.eval()  # disable dropout
	snake_case_		    =     roberta.model.encoder.sentence_encoder
	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:
		snake_case_		    =     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

	print("Our RoBERTa config:"     ,	_lowerCAmelCase							)

	snake_case_		    =     XLMRobertaXLForSequenceClassification(_lowerCAmelCase							) if classification_head else XLMRobertaXLForMaskedLM(_lowerCAmelCase							)
	model.eval()

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

	snake_case_		    =     roberta_sent_encoder.layer_norm.weight
	snake_case_		    =     roberta_sent_encoder.layer_norm.bias

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

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

		# self attention
		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)							)
		)

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

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

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

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

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

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

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

	snake_case_		    =     model(_lowerCAmelCase							)[0]
	if classification_head:
		snake_case_		    =     roberta.model.classification_heads['mnli'](roberta.extract_features(_lowerCAmelCase							)							)
	else:
		snake_case_		    =     roberta.model(_lowerCAmelCase							)[0]
	print(our_output.shape     ,	their_output.shape							)
	snake_case_		    =     torch.max(torch.abs(our_output - their_output							)							).item()
	print(f"max_absolute_diff = {max_absolute_diff}"							)  # ~ 1e-7
	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__":
		lowercase__				:     int 		=						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__				:     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 OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






