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()
lowerCAmelCase__	:  str						=  logging.get_logger(__name__)

lowerCAmelCase__	:  Any						=  '''Hello world! cécé herlolip'''
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__       )  ->   Tuple:
				snake_case__   :      int   				= FairseqRobertaModel.from_pretrained(A__       )
				roberta.eval()  # disable dropout
				snake_case__   :      List[Any]   				= roberta.model.encoder.sentence_encoder
				snake_case__   :      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=514     ,					type_vocab_size=1     ,					layer_norm_eps=1e-5     ,					)
				if classification_head:
								snake_case__   :      Tuple   				= roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
				print('Our BERT config:'     ,					A__       )

				snake_case__   :      Any   				= RobertaForSequenceClassification(A__       ) if classification_head else RobertaForMaskedLM(A__       )
				model.eval()

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

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

								# self attention
								snake_case__   :      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)       )
								)

								snake_case__   :      str   				= roberta_layer.self_attn.q_proj.weight
								snake_case__   :      Tuple   				= roberta_layer.self_attn.q_proj.bias
								snake_case__   :      Optional[Any]   				= roberta_layer.self_attn.k_proj.weight
								snake_case__   :      int   				= roberta_layer.self_attn.k_proj.bias
								snake_case__   :      int   				= roberta_layer.self_attn.v_proj.weight
								snake_case__   :      Optional[Any]   				= roberta_layer.self_attn.v_proj.bias

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

								# intermediate
								snake_case__   :      BertIntermediate   				= layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__   :      Optional[int]   				= roberta_layer.fca.weight
								snake_case__   :      Optional[int]   				= roberta_layer.fca.bias

								# output
								snake_case__   :      BertOutput   				= layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__   :      int   				= roberta_layer.fca.weight
								snake_case__   :      Tuple   				= roberta_layer.fca.bias
								snake_case__   :      Dict   				= roberta_layer.final_layer_norm.weight
								snake_case__   :      Optional[int]   				= roberta_layer.final_layer_norm.bias
								# end of layer

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

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

				snake_case__   :      Optional[int]   				= model(A__       )[0]
				if classification_head:
								snake_case__   :      Union[str, Any]   				= roberta.model.classification_heads['mnli'](roberta.extract_features(A__       )       )
				else:
								snake_case__   :      Dict   				= roberta.model(A__       )[0]
				print(our_output.shape     ,					their_output.shape       )
				snake_case__   :      str   				= torch.max(torch.abs(our_output - their_output       )       ).item()
				print(F"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
				snake_case__   :      Optional[int]   				= 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__	:  Optional[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__	:  Dict						=  parser.parse_args()
				convert_roberta_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_torch_available, is_vision_available


lowerCAmelCase__	:  int						=  {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Union[str, Any]						=  ['''DetaImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  Optional[int]						=  [
				    '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DetaForObjectDetection''',
				    '''DetaModel''',
				    '''DetaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

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


