



'''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()
_A				:    Dict     						=logging.get_logger(__name__)

_A				:    Optional[int]     						="""Hello world! cécé herlolip"""







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			int:
	_lowercase					:       str       =		FairseqRobertaModel.from_pretrained(_lowercase					)
	roberta.eval()  # disable dropout
	_lowercase					:       str       =		roberta.model.encoder.sentence_encoder
	_lowercase					:       List[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=514,				type_vocab_size=1,				layer_norm_eps=1E-5,				)
	if classification_head:
		_lowercase					:       Union[str, Any]       =		roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

	print('Our RoBERTa config:',				_lowercase					)

	_lowercase					:       Tuple       =		XLMRobertaXLForSequenceClassification(_lowercase					) if classification_head else XLMRobertaXLForMaskedLM(_lowercase					)
	model.eval()

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

	_lowercase					:       str       =		roberta_sent_encoder.layer_norm.weight
	_lowercase					:       Optional[Any]       =		roberta_sent_encoder.layer_norm.bias

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

		_lowercase					:       int       =		layer.attention
		_lowercase					:       Optional[Any]       =		roberta_layer.self_attn_layer_norm.weight
		_lowercase					:       Dict       =		roberta_layer.self_attn_layer_norm.bias

		# self attention
		_lowercase					:       Union[str, Any]       =		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)					)
		)

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

		# self-attention output
		_lowercase					:       Optional[int]       =		layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		_lowercase					:       Optional[int]       =		roberta_layer.self_attn.out_proj.weight
		_lowercase					:       List[Any]       =		roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		_lowercase					:       Any       =		roberta_layer.final_layer_norm.weight
		_lowercase					:       Optional[Any]       =		roberta_layer.final_layer_norm.bias

		# intermediate
		_lowercase					:       int       =		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		_lowercase					:       Tuple       =		roberta_layer.fca.weight
		_lowercase					:       Dict       =		roberta_layer.fca.bias

		# output
		_lowercase					:       Dict       =		layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		_lowercase					:       List[str]       =		roberta_layer.fca.weight
		_lowercase					:       List[Any]       =		roberta_layer.fca.bias
		# end of layer

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

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

	_lowercase					:       Optional[int]       =		model(_lowercase					)[0]
	if classification_head:
		_lowercase					:       Optional[int]       =		roberta.model.classification_heads['mnli'](roberta.extract_features(_lowercase					)					)
	else:
		_lowercase					:       Dict       =		roberta.model(_lowercase					)[0]
	print(our_output.shape,				their_output.shape					)
	_lowercase					:       Optional[int]       =		torch.max(torch.abs(our_output - their_output					)					).item()
	print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-7
	_lowercase					:       Optional[int]       =		torch.allclose(_lowercase,				_lowercase,				atol=1E-3					)
	print('Do both models output the same tensors?',				'🔥' if success else '💩'					)
	if not success:
		raise Exception('Something went wRoNg'					)

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


if __name__ == "__main__":
		_A				:    List[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.'''
		)
		_A				:    int     						=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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A				:    Union[str, Any]     						={'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Dict     						=['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Union[str, Any]     						=['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		_A				:    Union[str, Any]     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
