





'''simple docstring'''

import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


__lowerCamelCase			:			Dict									=      threading.Lock()
__lowerCamelCase			:			int									=      None

__lowerCamelCase			:			List[Any]									=      {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

__lowerCamelCase			:			Optional[Any]									=      logging.WARNING

__lowerCamelCase			:			str									=      True







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				lowercase    =			os.getenv("TRANSFORMERS_VERBOSITY"		,				lowerCamelCase__				)
				if env_level_str:
								if env_level_str in log_levels:
												return log_levels[env_level_str]
								else:
												logging.getLogger().warning(
												    f'Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '
												    f'has to be one of: { ", ".join(log_levels.keys()				) }'				)
				return _default_log_level







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				return __name__.split("."				)[0]







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				return logging.getLogger(_get_library_name()				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				global _default_handler

				with _lock:
								if _default_handler:
												# This library has already configured the library root logger.
												return
								lowercase    =			logging.StreamHandler()  # Set sys.stderr as stream.
								lowercase    =			sys.stderr.flush

								# Apply our default configuration to the library root logger.
								lowercase    =			_get_library_root_logger()
								library_root_logger.addHandler(_default_handler				)
								library_root_logger.setLevel(_get_default_logging_level()				)
								lowercase    =			False







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				global _default_handler

				with _lock:
								if not _default_handler:
												return

								lowercase    =			_get_library_root_logger()
								library_root_logger.removeHandler(_default_handler				)
								library_root_logger.setLevel(logging.NOTSET				)
								lowercase    =			None







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				return log_levels







def 				UpperCAmelCase_     (						lowerCAmelCase_ = None				):

				"""simple docstring"""






				if name is None:
								lowercase    =			_get_library_name()

				_configure_library_root_logger()
				return logging.getLogger(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				_configure_library_root_logger()
				return _get_library_root_logger().getEffectiveLevel()







def 				UpperCAmelCase_     (						lowerCAmelCase_				):

				"""simple docstring"""






				_configure_library_root_logger()
				_get_library_root_logger().setLevel(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				return set_verbosity(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				return set_verbosity(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				return set_verbosity(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				return set_verbosity(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				_configure_library_root_logger()

				assert _default_handler is not None
				_get_library_root_logger().removeHandler(_default_handler				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				_configure_library_root_logger()

				assert _default_handler is not None
				_get_library_root_logger().addHandler(_default_handler				)







def 				UpperCAmelCase_     (						lowerCAmelCase_				):

				"""simple docstring"""






				_configure_library_root_logger()

				assert handler is not None
				_get_library_root_logger().addHandler(lowerCamelCase__				)







def 				UpperCAmelCase_     (						lowerCAmelCase_				):

				"""simple docstring"""






				_configure_library_root_logger()

				assert handler is not None and handler not in _get_library_root_logger().handlers
				_get_library_root_logger().removeHandler(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				_configure_library_root_logger()
				lowercase    =			False







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				_configure_library_root_logger()
				lowercase    =			True







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				lowercase    =			_get_library_root_logger().handlers

				for handler in handlers:
								lowercase    =			logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"				)
								handler.setFormatter(lowerCamelCase__				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				lowercase    =			_get_library_root_logger().handlers

				for handler in handlers:
								handler.setFormatter(lowerCamelCase__				)







def 				UpperCAmelCase_     (						self		,				*lowerCAmelCase_		,				**lowerCAmelCase_				):

				"""simple docstring"""






				lowercase    =			os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"		,				lowerCamelCase__				)
				if no_advisory_warnings:
								return
				self.warning(*lowerCamelCase__		,				**lowerCamelCase__				)


__lowerCamelCase			:			Optional[int]									=      warning_advice







@functools.lru_cache(lowerCamelCase__				)
def 				UpperCAmelCase_     (						self		,				*lowerCAmelCase_		,				**lowerCAmelCase_				):

				"""simple docstring"""






				self.warning(*lowerCamelCase__		,				**lowerCamelCase__				)


__lowerCamelCase			:			Any									=      warning_once

class    UpperCAmelCase			:




		def __init__(self   :						Optional[Any]				,      *A__   :						str				,      **A__   :						str					)       ->			str:  # pylint: disable=unused-argument
						lowercase    =			args[0] if args else None




		def __iter__(self   :						Tuple					)       ->			List[Any]:
						return iter(self._iterator					)




		def __getattr__(self   :						List[str]				,      A__   :						Optional[Any]					)       ->			int:

						def empty_fn(*A__   :						List[str]				,      **A__   :						Union[str, Any]					):  # pylint: disable=unused-argument
										return

						return empty_fn




		def __enter__(self   :						Union[str, Any]					)       ->			Optional[int]:
						return self





		def __exit__(self   :						str				,      A__   :						List[Any]				,      A__   :						Union[str, Any]				,      A__   :						Union[str, Any]					)       ->			Dict:
						return




class    UpperCAmelCase			:




		def __call__(self   :						Optional[int]				,      *A__   :						int				,      **A__   :						Any					)       ->			Optional[Any]:
						if _tqdm_active:
										return tqdm_lib.tqdm(*lowerCamelCase__				,      **lowerCamelCase__					)
						else:
										return EmptyTqdm(*lowerCamelCase__				,      **lowerCamelCase__					)




		def 					UpperCAmelCase__ (self   :						Any				,      *A__   :						str				,      **A__   :						Tuple					)       ->			str:
						lowercase    =			None
						if _tqdm_active:
										return tqdm_lib.tqdm.set_lock(*lowerCamelCase__				,      **lowerCamelCase__					)





		def 					UpperCAmelCase__ (self   :						List[str]					)       ->			Dict:
						if _tqdm_active:
										return tqdm_lib.tqdm.get_lock()




__lowerCamelCase			:			List[str]									=      _tqdm_cls()







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				global _tqdm_active
				return bool(_tqdm_active				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				global _tqdm_active
				lowercase    =			True
				hf_hub_utils.enable_progress_bars()







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				global _tqdm_active
				lowercase    =			False
				hf_hub_utils.disable_progress_bars()








"""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()
__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					'Hello world! cécé herlolip'


def    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							bool  ):
 lowercase__       :       int				=						FairseqRobertaModel.from_pretrained(lowerCamelCase__  )
 roberta.eval()  # disable dropout
 lowercase__       :       Tuple				=						roberta.model.encoder.sentence_encoder
 lowercase__       :       Tuple				=						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:
  lowercase__       :       Any				=						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

 lowercase__       :       List[Any]				=						XLMRobertaXLForSequenceClassification(lowerCamelCase__  ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase__  )
 model.eval()

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

 lowercase__       :       int				=						roberta_sent_encoder.layer_norm.weight
 lowercase__       :       List[Any]				=						roberta_sent_encoder.layer_norm.bias

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

  lowercase__       :       RobertaAttention				=						layer.attention
  lowercase__       :       str				=						roberta_layer.self_attn_layer_norm.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn_layer_norm.bias

  # self attention
  lowercase__       :       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)  )
  )

  lowercase__       :       Optional[Any]				=						roberta_layer.self_attn.q_proj.weight
  lowercase__       :       str				=						roberta_layer.self_attn.q_proj.bias
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.weight
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.bias
  lowercase__       :       int				=						roberta_layer.self_attn.v_proj.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn.v_proj.bias

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

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

  # intermediate
  lowercase__       :       BertIntermediate				=						layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Dict				=						roberta_layer.fca.weight
  lowercase__       :       Any				=						roberta_layer.fca.bias

  # output
  lowercase__       :       BertOutput				=						layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Union[str, Any]				=						roberta_layer.fca.weight
  lowercase__       :       Optional[Any]				=						roberta_layer.fca.bias
  # end of layer

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

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

 lowercase__       :       Any				=						model(lowerCamelCase__  )[0]
 if classification_head:
  lowercase__       :       Optional[Any]				=						roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase__  )  )
 else:
  lowercase__       :       Tuple				=						roberta.model(lowerCamelCase__  )[0]
 print(our_output.shape       ,		their_output.shape  )
 lowercase__       :       Tuple				=						torch.max(torch.abs(our_output - their_output  )  ).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
 lowercase__       :       int				=						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__":
   __snake_case    					=					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.'
   )
   __snake_case    					=					parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )