"""simple docstring"""



import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


__magic_name__    				=					datasets.utils.logging.get_logger(__name__)

@dataclass
class SCREAMING_SNAKE_CASE     (    datasets.BuilderConfig	):







 """simple docstring"""


 a_       :	Union[str, "sqlalchemy.sql.Selectable"]  			=None
 a_       :	Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]  			=None
 a_       :	Optional[Union[str, List[str]]]  			=None
 a_       :	bool  			=True
 a_       :	Optional[Union[List, Tuple, Dict]]  			=None
 a_       :	Optional[Union[List, Dict]]  			=None
 a_       :	Optional[List[str]]  			=None
 a_       :	Optional[int]  			=1_0_0_0_0
 a_       :	Optional[datasets.Features]  			=None


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




    '''simple docstring'''






    if self.sql is None:
       raise ValueError('sql must be specified'				)
    if self.con is None:
       raise ValueError('con must be specified'				)


 def        _lowerCAmelCase				(   self		:      int					,   _snake_case		:      dict					,   _snake_case		:      Optional[datasets.Features] = None					,   )				->			str:




    '''simple docstring'''






    a__     				=		config_kwargs.copy()
    # We need to stringify the Selectable object to make its hash deterministic

    # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
    a__     				=		config_kwargs['sql']
    if not isinstance(_snake_case					,   _snake_case				):
       if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

          if isinstance(_snake_case					,   sqlalchemy.sql.Selectable				):
             a__     				=		sqlalchemy.create_engine(config_kwargs['con'].split('://'				)[0] + '://'				)
             a__     				=		str(sql.compile(dialect=engine.dialect				)				)
             a__     				=		sql_str
          else:
             raise TypeError(
                 F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(_snake_case				)}: {sql}'''				)
       else:
          raise TypeError(
              F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(_snake_case				)}: {sql}'''				)
    a__     				=		config_kwargs['con']
    if not isinstance(_snake_case					,   _snake_case				):
       a__     				=		id(_snake_case				)
       logger.info(
           F'''SQL connection \'con\' of type {type(_snake_case				)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''				)

    return super().create_config_id(_snake_case					,   custom_features=_snake_case				)



 @property
 def        _lowerCAmelCase				(   self		:      str				)				->			Optional[int]:




    '''simple docstring'''






    a__     				=		{
        'index_col': self.index_col,
        'columns': self.columns,
        'params': self.params,
        'coerce_float': self.coerce_float,
        'parse_dates': self.parse_dates,
    }
    return pd_read_sql_kwargs

class SCREAMING_SNAKE_CASE     (    datasets.ArrowBasedBuilder	):







 """simple docstring"""


 a_       :	Dict  			=SqlConfig


 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Dict:




    '''simple docstring'''






    return datasets.DatasetInfo(features=self.config.features				)


 def        _lowerCAmelCase				(   self		:      Optional[int]					,   _snake_case		:      Union[str, Any]				)				->			int:




    '''simple docstring'''






    return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,   gen_kwargs={}				)]


 def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      pa.Table				)				->			pa.Table:




    '''simple docstring'''






    if self.config.features is not None:
       a__     				=		self.config.features.arrow_schema
       if all(not require_storage_cast(_snake_case				) for feature in self.config.features.values()				):
          # cheaper cast
          a__     				=		pa.Table.from_arrays([pa_table[field.name] for field in schema]					,   schema=_snake_case				)
       else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          a__     				=		table_cast(_snake_case					,   _snake_case				)
    return pa_table



 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Any:




    '''simple docstring'''






    a__     				=		self.config.chunksize
    a__     				=		pd.read_sql(
        self.config.sql					,   self.config.con					,   chunksize=_snake_case					,   **self.config.pd_read_sql_kwargs				)
    a__     				=		[sql_reader] if chunksize is None else sql_reader
    for chunk_idx, df in enumerate(_snake_case				):
       a__     				=		pa.Table.from_pandas(_snake_case				)
       yield chunk_idx, self._cast_table(_snake_case				)



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

__magic_name__    				=					"Hello world! cécé herlolip"





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


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

   a__     				=		RobertaForSequenceClassification(UpperCAmelCase__					) if classification_head else RobertaForMaskedLM(UpperCAmelCase__					)
   model.eval()

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

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

      # self attention
      a__     				=		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)					)
      )

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

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

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

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

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

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

   a__     				=		model(UpperCAmelCase__					)[0]
   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase__					)					)
   else:
      a__     				=		roberta.model(UpperCAmelCase__					)[0]
   print(our_output.shape,their_output.shape					)
   a__     				=		torch.max(torch.abs(our_output - their_output					)					).item()
   print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-7
   a__     				=		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__":
     __magic_name__    				=					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."
     )
     __magic_name__    				=					parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )


