



'''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()
lowerCamelCase							:int     = logging.get_logger(__name__)

lowerCamelCase							:Union[str, Any]     = '''Hello world! cécé herlolip'''



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						int     =					FairseqRobertaModel.from_pretrained(lowerCamelCase__   )
			roberta.eval()  # disable dropout
			A_     :						Dict     =					roberta.model.encoder.sentence_encoder
			A_     :						Any     =					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_14						,				type_vocab_size=1						,				layer_norm_eps=1E-5						,				)
			if classification_head:
						A_     :						Any     =					roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

			A_     :						Union[str, Any]     =					XLMRobertaXLForSequenceClassification(lowerCamelCase__   ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase__   )
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			A_     :						Union[str, Any]     =					roberta_sent_encoder.embed_tokens.weight
			A_     :						Union[str, Any]     =					roberta_sent_encoder.embed_positions.weight
			A_     :						str     =					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.layer_norm.weight
			A_     :						Dict     =					roberta_sent_encoder.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]

						A_     :						RobertaAttention     =					layer.attention
						A_     :						Dict     =					roberta_layer.self_attn_layer_norm.weight
						A_     :						Tuple     =					roberta_layer.self_attn_layer_norm.bias

						# 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_     :						Dict     =					roberta_layer.self_attn.q_proj.weight
						A_     :						Any     =					roberta_layer.self_attn.q_proj.bias
						A_     :						Optional[Any]     =					roberta_layer.self_attn.k_proj.weight
						A_     :						Any     =					roberta_layer.self_attn.k_proj.bias
						A_     :						Any     =					roberta_layer.self_attn.v_proj.weight
						A_     :						str     =					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_     :						Any     =					roberta_layer.self_attn.out_proj.weight
						A_     :						Union[str, Any]     =					roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						A_     :						Tuple     =					roberta_layer.final_layer_norm.weight
						A_     :						int     =					roberta_layer.final_layer_norm.bias

						# intermediate
						A_     :						BertIntermediate     =					layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						A_     :						List[Any]     =					roberta_layer.fca.weight
						A_     :						Dict     =					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_     :						Optional[Any]     =					roberta_layer.fca.bias
						# end of layer

			if classification_head:
						A_     :						List[Any]     =					roberta.model.classification_heads["""mnli"""].dense.weight
						A_     :						Union[str, Any]     =					roberta.model.classification_heads["""mnli"""].dense.bias
						A_     :						str     =					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_     :						List[str]     =					roberta.model.encoder.lm_head.dense.bias
						A_     :						List[Any]     =					roberta.model.encoder.lm_head.layer_norm.weight
						A_     :						Any     =					roberta.model.encoder.lm_head.layer_norm.bias
						A_     :						List[Any]     =					roberta.model.encoder.lm_head.weight
						A_     :						Optional[int]     =					roberta.model.encoder.lm_head.bias

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

			A_     :						Optional[Any]     =					model(lowerCamelCase__   )[0]
			if classification_head:
						A_     :						int     =					roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase__   )   )
			else:
						A_     :						str     =					roberta.model(lowerCamelCase__   )[0]
			print(our_output.shape						,				their_output.shape   )
			A_     :						str     =					torch.max(torch.abs(our_output - their_output   )   ).item()
			print(f'max_absolute_diff = {max_absolute_diff}'   )  # ~ 1e-7
			A_     :						Any     =					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__":
						lowerCamelCase							: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.'''
						)
						lowerCamelCase							:Tuple     = 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_torch_available


lowerCamelCase							:Dict       = {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:str       = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						lowerCamelCase							:Tuple       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)