
"""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()
UpperCamelCase     :    List[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    Dict       =			"Hello world! cécé herlolip"






def 							A						(    snake_case				:int    , snake_case				:Optional[Any]    , snake_case				:Tuple					)				->     str:
 __UpperCamelCase      =	FairseqRobertaModel.from_pretrained(_lowerCAmelCase					)
 roberta.eval()  # disable dropout
 __UpperCamelCase      =	roberta.model.encoder.sentence_encoder
 __UpperCamelCase      =	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_1_4    , type_vocab_size=1    , layer_norm_eps=1e-5    , )
 if classification_head:
  __UpperCamelCase      =	roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

 print('Our RoBERTa config:'    , _lowerCAmelCase					)

 __UpperCamelCase      =	XLMRobertaXLForSequenceClassification(_lowerCAmelCase					) if classification_head else XLMRobertaXLForMaskedLM(_lowerCAmelCase					)
 model.eval()

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

 __UpperCamelCase      =	roberta_sent_encoder.layer_norm.weight
 __UpperCamelCase      =	roberta_sent_encoder.layer_norm.bias

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

  __UpperCamelCase      =	layer.attention
  __UpperCamelCase      =	roberta_layer.self_attn_layer_norm.weight
  __UpperCamelCase      =	roberta_layer.self_attn_layer_norm.bias

  # self attention
  __UpperCamelCase      =	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)					)
  )

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

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

  # this one is final layer norm
  __UpperCamelCase      =	roberta_layer.final_layer_norm.weight
  __UpperCamelCase      =	roberta_layer.final_layer_norm.bias

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

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

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

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

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





"""simple docstring"""

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase     ,					split=__UpperCAmelCase     ,					features=__UpperCAmelCase     ,					cache_dir=__UpperCAmelCase     ,					keep_in_memory=__UpperCAmelCase     ,					streaming=__UpperCAmelCase     ,					num_proc=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
      __UpperCamelCase      =	field
      __UpperCamelCase      =	path_or_paths if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else {self.split: path_or_paths}
      __UpperCamelCase      =	Json(
          cache_dir=__UpperCAmelCase     ,					data_files=__UpperCAmelCase     ,					features=__UpperCAmelCase     ,					field=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.streaming:
       __UpperCamelCase      =	self.builder.as_streaming_dataset(split=self.split				)
      # Build regular (map-style) dataset
      else:
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None
       __UpperCamelCase      =	None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase     ,					download_mode=__UpperCAmelCase     ,					verification_mode=__UpperCAmelCase     ,					base_path=__UpperCAmelCase     ,					num_proc=self.num_proc     ,					)
       __UpperCamelCase      =	self.builder.as_dataset(
           split=self.split     ,					verification_mode=__UpperCAmelCase     ,					in_memory=self.keep_in_memory				)
      return dataset


class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      if num_proc is not None and num_proc <= 0:
       raise ValueError(F'num_proc {num_proc} must be an integer > 0.'				)

      __UpperCamelCase      =	dataset
      __UpperCamelCase      =	path_or_buf
      __UpperCamelCase      =	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
      __UpperCamelCase      =	num_proc
      __UpperCamelCase      =	'utf-8'
      __UpperCamelCase      =	to_json_kwargs





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.to_json_kwargs.pop('path_or_buf'     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('orient'     ,					'records'				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('lines'     ,					True if orient == 'records' else False				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('index'     ,					False if orient in ['split', 'table'] else True				)
      __UpperCamelCase      =	self.to_json_kwargs.pop('compression'     ,					__UpperCAmelCase				)

      if compression not in [None, "infer", "gzip", "bz2", "xz"]:
       raise NotImplementedError(F'`datasets` currently does not support {compression} compression'				)

      if isinstance(self.path_or_buf     ,					(str, bytes, os.PathLike)				):
       with fsspec.open(self.path_or_buf     ,					'wb'     ,					compression=__UpperCAmelCase				) as buffer:
        __UpperCamelCase      =	self._write(file_obj=__UpperCAmelCase     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**self.to_json_kwargs				)
      else:
       if compression:
        raise NotImplementedError(
            F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
            ' was passed. Please provide a local path instead.'				)
       __UpperCamelCase      =	self._write(
           file_obj=self.path_or_buf     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**self.to_json_kwargs				)
      return written





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	args

      __UpperCamelCase      =	query_table(
          table=self.dataset.data     ,					key=slice(__UpperCAmelCase     ,					offset + self.batch_size				)     ,					indices=self.dataset._indices     ,					)
      __UpperCamelCase      =	batch.to_pandas().to_json(
          path_or_buf=__UpperCAmelCase     ,					orient=__UpperCAmelCase     ,					lines=__UpperCAmelCase     ,					index=__UpperCAmelCase     ,					**__UpperCAmelCase				)
      if not json_str.endswith('\n'				):
       json_str += "\n"
      return json_str.encode(self.encoding				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	0

      if self.num_proc is None or self.num_proc == 1:
       for offset in logging.tqdm(
           range(0     ,					len(self.dataset				)     ,					self.batch_size				)     ,					unit='ba'     ,					disable=not logging.is_progress_bar_enabled()     ,					desc='Creating json from Arrow format'     ,					):
        __UpperCamelCase      =	self._batch_json((offset, orient, lines, index, to_json_kwargs)				)
        written += file_obj.write(__UpperCAmelCase				)
      else:
       __UpperCamelCase  ,     __UpperCamelCase      =	len(self.dataset				), self.batch_size
       with multiprocessing.Pool(self.num_proc				) as pool:
        for json_str in logging.tqdm(
            pool.imap(
                self._batch_json     ,					[(offset, orient, lines, index, to_json_kwargs) for offset in range(0     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)]     ,					)     ,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,					unit='ba'     ,					disable=not logging.is_progress_bar_enabled()     ,					desc='Creating json from Arrow format'     ,					):
         written += file_obj.write(__UpperCAmelCase				)

      return written



