



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__		:					Any							=logging.get_logger(__name__)

lowerCAmelCase__		:					List[Any]							='''Hello world! cécé herlolip'''






def      __lowercase       (			a__							,       a__							,       a__       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= FairseqRobertaModel.from_pretrained(a__       )
							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=5_14							,       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:'							,       a__       )

							__SCREAMING_SNAKE_CASE									= XLMRobertaXLForSequenceClassification(a__       ) if classification_head else XLMRobertaXLForMaskedLM(a__       )
							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(a__       ).unsqueeze(0       )  # batch of size 1

							__SCREAMING_SNAKE_CASE									= model(a__       )[0]
							if classification_head:
														__SCREAMING_SNAKE_CASE									= roberta.model.classification_heads['mnli'](roberta.extract_features(a__       )       )
							else:
														__SCREAMING_SNAKE_CASE									= roberta.model(a__       )[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(a__							,       a__							,       atol=1E-3       )
							print('Do both models output the same tensors?'							,       '🔥' if success else '💩'       )
							if not success:
														raise Exception('Something went wRoNg'       )

							pathlib.Path(a__       ).mkdir(parents=a__							,       exist_ok=a__       )
							print(f"""Saving model to {pytorch_dump_folder_path}"""       )
							model.save_pretrained(a__       )


if __name__ == "__main__":
			lowerCAmelCase__		:					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.'''
			)
			lowerCAmelCase__		:					Tuple							=parser.parse_args()
			convert_xlm_roberta_xl_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__		:					str							={'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=[
			    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ViTMAEForPreTraining''',
			    '''ViTMAELayer''',
			    '''ViTMAEModel''',
			    '''ViTMAEPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TFViTMAEForPreTraining''',
			    '''TFViTMAEModel''',
			    '''TFViTMAEPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_mae import (
						    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMAEForPreTraining,
						    ViTMAELayer,
						    ViTMAEModel,
						    ViTMAEPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
			import sys

			lowerCAmelCase__		:					Optional[int]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






