


'''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							)










'''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
      )






