


'''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 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = False		,      lowerCamelCase_ = False		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    List[Any]:
    super().__init__(
        a_		,      split=a_		,      features=a_		,      cache_dir=a_		,      keep_in_memory=a_		,      streaming=a_		,      num_proc=a_		,      **a_		,      )
    _a					:  List[str]						=  field
    _a					:  Optional[Any]						=  path_or_paths if isinstance(a_		,      a_      ) else {self.split: path_or_paths}
    _a					:  str						=  Json(
        cache_dir=a_		,      data_files=a_		,      features=a_		,      field=a_		,      **a_		,      )




 def   __UpperCamelCase   (    self      )      ->    str:
    # Build iterable dataset
    if self.streaming:
       _a					:  Union[str, Any]						=  self.builder.as_streaming_dataset(split=self.split      )
    # Build regular (map-style) dataset
    else:
       _a					:  List[str]						=  None
       _a					:  Union[str, Any]						=  None
       _a					:  Optional[Any]						=  None
       _a					:  Union[str, Any]						=  None

       self.builder.download_and_prepare(
           download_config=a_		,      download_mode=a_		,      verification_mode=a_		,      base_path=a_		,      num_proc=self.num_proc		,      )
       _a					:  List[str]						=  self.builder.as_dataset(
           split=self.split		,      verification_mode=a_		,      in_memory=self.keep_in_memory      )
    return dataset







class 					a			:
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    Union[str, Any]:
    if num_proc is not None and num_proc <= 0:
       raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''      )

    _a					:  Any						=  dataset
    _a					:  List[str]						=  path_or_buf
    _a					:  Union[str, Any]						=  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    _a					:  Dict						=  num_proc
    _a					:  Dict						=  "utf-8"
    _a					:  Any						=  to_json_kwargs




 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  List[Any]						=  self.to_json_kwargs.pop('path_or_buf'		,      a_      )
    _a					:  List[str]						=  self.to_json_kwargs.pop('orient'		,      'records'      )
    _a					:  Union[str, Any]						=  self.to_json_kwargs.pop('lines'		,      True if orient == 'records' else False      )
    _a					:  Optional[int]						=  self.to_json_kwargs.pop('index'		,      False if orient in ['split', 'table'] else True      )
    _a					:  int						=  self.to_json_kwargs.pop('compression'		,      a_      )

    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=a_      ) as buffer:
          _a					:  Tuple						=  self._write(file_obj=a_		,      orient=a_		,      lines=a_		,      index=a_		,      **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.'      )
       _a					:  Any						=  self._write(
           file_obj=self.path_or_buf		,      orient=a_		,      lines=a_		,      index=a_		,      **self.to_json_kwargs      )
    return written




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    int:
    _a					:  int						=  args

    _a					:  List[str]						=  query_table(
        table=self.dataset.data		,      key=slice(a_		,      offset + self.batch_size      )		,      indices=self.dataset._indices		,      )
    _a					:  Optional[Any]						=  batch.to_pandas().to_json(
        path_or_buf=a_		,      orient=a_		,      lines=a_		,      index=a_		,      **a_      )
    if not json_str.endswith('\n'      ):
       json_str += "\n"
    return json_str.encode(self.encoding      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      **lowerCamelCase_		,      )      ->    int:
    _a					:  int						=  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'		,      ):
          _a					:  Tuple						=  self._batch_json((offset, orient, lines, index, to_json_kwargs)      )
          written += file_obj.write(a_      )
    else:
       _a					:  Optional[int]						=  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		,      a_		,      a_      )]		,      )		,      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(a_      )

    return written






'''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()
lowerCAmelCase   : Optional[int]             =				logging.get_logger(__name__)

lowerCAmelCase   : Tuple             =				"""Hello world! cécé herlolip"""




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

	print("Our RoBERTa config:"							,A							)

	lowercase						:		Union[str, Any]   					=		XLMRobertaXLForSequenceClassification(A							) if classification_head else XLMRobertaXLForMaskedLM(A							)
	model.eval()

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

	lowercase						:		Dict   					=		roberta_sent_encoder.layer_norm.weight
	lowercase						:		str   					=		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						:		List[str]   					=		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						:		str   					=		roberta_layer.self_attn.q_proj.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn.q_proj.bias
		lowercase						:		Union[str, Any]   					=		roberta_layer.self_attn.k_proj.weight
		lowercase						:		List[Any]   					=		roberta_layer.self_attn.k_proj.bias
		lowercase						:		Dict   					=		roberta_layer.self_attn.v_proj.weight
		lowercase						:		Tuple   					=		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						:		Tuple   					=		roberta_layer.self_attn.out_proj.weight
		lowercase						:		str   					=		roberta_layer.self_attn.out_proj.bias

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

		# intermediate
		lowercase						:		BertIntermediate   					=		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		Dict   					=		roberta_layer.fca.bias

		# output
		lowercase						:		BertOutput   					=		layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		str   					=		roberta_layer.fca.bias
		# end of layer

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

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

	lowercase						:		Tuple   					=		model(A							)[0]
	if classification_head:
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"](roberta.extract_features(A							)							)
	else:
		lowercase						:		Optional[Any]   					=		roberta.model(A							)[0]
	print(our_output.shape							,their_output.shape							)
	lowercase						:		Optional[Any]   					=		torch.max(torch.abs(our_output - their_output							)							).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
	lowercase						:		Dict   					=		torch.allclose(A							,A							,atol=1e-3							)
	print("Do both models output the same tensors?"							,"🔥" if success else "💩"							)
	if not success:
		raise Exception("Something went wRoNg"							)

	pathlib.Path(A							).mkdir(parents=A							,exist_ok=A							)
	print(F'''Saving model to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, 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."""
					)
					lowerCAmelCase   : Optional[Any]             =				parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)


