code: 
  
  
  '''simple docstring'''
  
  
  
  import importlib
  import inspect
  from functools import wraps
  from typing import TYPE_CHECKING, Optional, Union
  
  from .download.streaming_download_manager import (
      xbasename,
      xdirname,
      xet_parse,
      xexists,
      xgetsize,
      xglob,
      xgzip_open,
      xisdir,
      xisfile,
      xjoin,
      xlistdir,
      xnumpy_load,
      xopen,
      xpandas_read_csv,
      xpandas_read_excel,
      xPath,
      xrelpath,
      xsio_loadmat,
      xsplit,
      xsplitext,
      xwalk,
      xxml_dom_minidom_parse,
  )
  from .utils.logging import get_logger
  from .utils.patching import patch_submodule
  from .utils.py_utils import get_imports
  
  
  A__	:      Any				=						get_logger(__name__)
  
  
  if TYPE_CHECKING:
        from .builder import DatasetBuilder
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       Optional[Union[str, bool]] = None							)  ->	Optional[Any]:
       __lowerCamelCase     :			int              =	importlib.import_module(UpperCAmelCase_							)
  
       # TODO(QL): always update the module to add subsequent new authentication
       if hasattr(UpperCAmelCase_   ,					'_patched_for_streaming'							) and module._patched_for_streaming:
            return
  
       def wrap_auth(UpperCAmelCase_					:       List[str]							):
            @wraps(UpperCAmelCase_							)
            def wrapper(*UpperCAmelCase_					:       Union[str, Any]   ,					**UpperCAmelCase_					:       Optional[int]							):
                 return function(*UpperCAmelCase_   ,					use_auth_token=UpperCAmelCase_   ,					**UpperCAmelCase_							)
  
            __lowerCamelCase     :			Any              =	"wrap_auth"
            return wrapper
  
       # open files in a streaming fashion
       patch_submodule(UpperCAmelCase_   ,					'open'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.listdir'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.walk'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'glob.glob'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       # allow to navigate in remote zip files
       patch_submodule(UpperCAmelCase_   ,					'os.path.join'   ,					UpperCAmelCase_							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.dirname'   ,					UpperCAmelCase_							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.basename'   ,					UpperCAmelCase_							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.relpath'   ,					UpperCAmelCase_							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.split'   ,					UpperCAmelCase_							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.splitext'   ,					UpperCAmelCase_							).start()
       # allow checks on paths
       patch_submodule(UpperCAmelCase_   ,					'os.path.exists'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.isdir'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.isfile'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'os.path.getsize'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'pathlib.Path'   ,					UpperCAmelCase_							).start()
       # file readers
       patch_submodule(UpperCAmelCase_   ,					'gzip.open'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'numpy.load'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'pandas.read_csv'   ,					wrap_auth(UpperCAmelCase_							)   ,					attrs=['__version__']							).start()
       patch_submodule(UpperCAmelCase_   ,					'pandas.read_excel'   ,					wrap_auth(UpperCAmelCase_							)   ,					attrs=['__version__']							).start()
       patch_submodule(UpperCAmelCase_   ,					'scipy.io.loadmat'   ,					wrap_auth(UpperCAmelCase_							)   ,					attrs=['__version__']							).start()
       patch_submodule(UpperCAmelCase_   ,					'xml.etree.ElementTree.parse'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       patch_submodule(UpperCAmelCase_   ,					'xml.dom.minidom.parse'   ,					wrap_auth(UpperCAmelCase_							)							).start()
       __lowerCamelCase     :			Any              =	True
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       "DatasetBuilder"							)  ->	List[Any]:
       extend_module_for_streaming(builder.__module__   ,					use_auth_token=builder.use_auth_token							)
       # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
       if not builder.__module__.startswith('datasets.'							):  # check that it's not a packaged builder like csv
            for imports in get_imports(inspect.getfile(builder.__class__							)							):
                 if imports[0] == "internal":
                      __lowerCamelCase     :			int              =	imports[1]
                      __lowerCamelCase     :			Optional[Any]              =	".".join(builder.__module__.split('.'							)[:-1] + [internal_import_name]							)
                      extend_module_for_streaming(UpperCAmelCase_   ,					use_auth_token=builder.use_auth_token							)
  
            # builders can inherit from other builders that might use streaming functionality
            # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
            # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
       from .builder import DatasetBuilder
  
       __lowerCamelCase     :			Optional[Any]              =	[
           cls.__module__
           for cls in type(UpperCAmelCase_							).__mro__[1:]  # make sure it's not the same module we've already patched
           if issubclass(cls   ,					UpperCAmelCase_							) and cls.__module__ != DatasetBuilder.__module__
       ]  # check it's not a standard builder from datasets.builder
       for module in parent_builder_modules:
            extend_module_for_streaming(UpperCAmelCase_   ,					use_auth_token=builder.use_auth_token							)
  
  
  
  
  
  
  
style_context: 
  
  
  '''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 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__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
       __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
       roberta.eval()  # disable dropout
       __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
       __lowerCamelCase     :			Optional[Any]              =	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=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
       if classification_head:
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'   ,					UpperCAmelCase_							)
  
       __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
       model.eval()
  
       # Now let's copy all the weights.
       # Embeddings
       __lowerCamelCase     :			Optional[Any]              =	roberta_sent_encoder.embed_tokens.weight
       __lowerCamelCase     :			List[str]              =	roberta_sent_encoder.embed_positions.weight
       __lowerCamelCase     :			Tuple              =	torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
       __lowerCamelCase     :			Any              =	roberta_sent_encoder.emb_layer_norm.weight
       __lowerCamelCase     :			Tuple              =	roberta_sent_encoder.emb_layer_norm.bias
  
       for i in range(config.num_hidden_layers							):
            # Encoder: start of layer
            __lowerCamelCase     :			BertLayer              =	model.roberta.encoder.layer[i]
            __lowerCamelCase     :			TransformerSentenceEncoderLayer              =	roberta_sent_encoder.layers[i]
  
            # self attention
            __lowerCamelCase     :			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)							)
            )
  
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
            __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias
  
            # self-attention output
            __lowerCamelCase     :			BertSelfOutput              =	layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.out_proj.weight
            __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.out_proj.bias
            __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn_layer_norm.weight
            __lowerCamelCase     :			List[str]              =	roberta_layer.self_attn_layer_norm.bias
  
            # intermediate
            __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias
  
            # output
            __lowerCamelCase     :			BertOutput              =	layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            __lowerCamelCase     :			str              =	roberta_layer.fca.weight
            __lowerCamelCase     :			Tuple              =	roberta_layer.fca.bias
            __lowerCamelCase     :			Optional[int]              =	roberta_layer.final_layer_norm.weight
            __lowerCamelCase     :			Optional[Any]              =	roberta_layer.final_layer_norm.bias
            # end of layer
  
       if classification_head:
            __lowerCamelCase     :			Dict              =	roberta.model.classification_heads['mnli'].dense.weight
            __lowerCamelCase     :			Union[str, Any]              =	roberta.model.classification_heads['mnli'].dense.bias
            __lowerCamelCase     :			Optional[int]              =	roberta.model.classification_heads['mnli'].out_proj.weight
            __lowerCamelCase     :			Any              =	roberta.model.classification_heads['mnli'].out_proj.bias
       else:
            # LM Head
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.dense.weight
            __lowerCamelCase     :			List[Any]              =	roberta.model.encoder.lm_head.dense.bias
            __lowerCamelCase     :			str              =	roberta.model.encoder.lm_head.layer_norm.weight
            __lowerCamelCase     :			int              =	roberta.model.encoder.lm_head.layer_norm.bias
            __lowerCamelCase     :			Optional[Any]              =	roberta.model.encoder.lm_head.weight
            __lowerCamelCase     :			Tuple              =	roberta.model.encoder.lm_head.bias
  
       # Let's check that we get the same results.
       __lowerCamelCase     :			torch.Tensor              =	roberta.encode(UpperCAmelCase_							).unsqueeze(0							)  # batch of size 1
  
       __lowerCamelCase     :			int              =	model(UpperCAmelCase_							)[0]
       if classification_head:
            __lowerCamelCase     :			List[str]              =	roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase_							)							)
       else:
            __lowerCamelCase     :			Dict              =	roberta.model(UpperCAmelCase_							)[0]
       print(our_output.shape   ,					their_output.shape							)
       __lowerCamelCase     :			Union[str, Any]              =	torch.max(torch.abs(our_output - their_output							)							).item()
       print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
       __lowerCamelCase     :			List[str]              =	torch.allclose(UpperCAmelCase_   ,					UpperCAmelCase_   ,					atol=1e-3							)
       print('Do both models output the same tensors?'   ,					'🔥' if success else '💩'							)
       if not success:
            raise Exception('Something went wRoNg'							)
  
       pathlib.Path(UpperCAmelCase_							).mkdir(parents=UpperCAmelCase_   ,					exist_ok=UpperCAmelCase_							)
       print(F'Saving model to {pytorch_dump_folder_path}'							)
       model.save_pretrained(UpperCAmelCase_							)
  
  
  if __name__ == "__main__":
        A__	:      Optional[Any]				=						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__	:      Optional[Any]				=						parser.parse_args()
        convert_roberta_checkpoint_to_pytorch(
            args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
        )
  
  
  
  
  
  
  