






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()
_a   			=						logging.get_logger(__name__)

_a   			=						'''Hello world! cécé herlolip'''





def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    FairseqRobertaModel.from_pretrained(__lowerCAmelCase			)
						roberta.eval()  # disable dropout
						_UpperCAmelCase             =    roberta.model.encoder.sentence_encoder
						_UpperCAmelCase             =    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:
												_UpperCAmelCase             =    roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'				,	__lowerCAmelCase			)

						_UpperCAmelCase             =    RobertaForSequenceClassification(__lowerCAmelCase			) if classification_head else RobertaForMaskedLM(__lowerCAmelCase			)
						model.eval()

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

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

												# self attention
												_UpperCAmelCase             =    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)			)
												)

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

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

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

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

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

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

						_UpperCAmelCase             =    model(__lowerCAmelCase			)[0]
						if classification_head:
												_UpperCAmelCase             =    roberta.model.classification_heads['mnli'](roberta.extract_features(__lowerCAmelCase			)			)
						else:
												_UpperCAmelCase             =    roberta.model(__lowerCAmelCase			)[0]
						print(our_output.shape				,	their_output.shape			)
						_UpperCAmelCase             =    torch.max(torch.abs(our_output - their_output			)			).item()
						print(F"""max_absolute_diff = {max_absolute_diff}"""			)  # ~ 1e-7
						_UpperCAmelCase             =    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   			=						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   			=						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


_a   			=						{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

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