





"""simple docstring"""




import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
lowerCamelCase_	:		Optional[int]         =   logging.get_logger(__name__)

lowerCamelCase_	:		List[Any]         =   'Hello world! cécé herlolip'





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_		,			A_    :			Optional[Any]									=    os.path.split(_UpperCAmelCase   )
  A_    :			Optional[int]									=    DataaVecTextModel.from_pretrained(
      _UpperCAmelCase					,   checkpoint_file=_UpperCAmelCase   )
  dataavec.eval()  # disable dropout
  A_    :			int									=    dataavec.models[0]
  A_    :			int									=    dataavec_model.encoder.sentence_encoder
  A_    :			List[str]									=    DataaVecTextConfig(
      vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings					,   hidden_size=dataavec_model.args.encoder_embed_dim					,   num_hidden_layers=dataavec_model.args.encoder_layers					,   num_attention_heads=dataavec_model.args.encoder_attention_heads					,   intermediate_size=dataavec_model.args.encoder_ffn_embed_dim					,   max_position_embeddings=514					,   type_vocab_size=1					,   layer_norm_eps=1E-5					,   )
  if classification_head:
    A_    :			List[Any]									=    dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
  print('Our BERT config:'					,   _UpperCAmelCase   )

  A_    :			Union[str, Any]									=    DataaVecTextForSequenceClassification(_UpperCAmelCase   ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase   )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  A_    :			int									=    dataavec_sent_encoder.embed_tokens.weight
  A_    :			str									=    dataavec_sent_encoder.embed_positions.weight
  A_    :			Optional[Any]									=    torch.zeros_like(
      model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
  A_    :			Any									=    dataavec_sent_encoder.layernorm_embedding.weight
  A_    :			Any									=    dataavec_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers   ):
    # Encoder: start of layer
    A_    :			BertLayer									=    model.dataavec_text.encoder.layer[i]
    A_    :			TransformerSentenceEncoderLayer									=    dataavec_sent_encoder.layers[i]

    # self attention
    A_    :			BertSelfAttention									=    layer.attention.self
    assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)   ), (
        "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
        f""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
    )
    assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)   ), (
        "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
        f""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
    )
    assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
        (config.hidden_size, config.hidden_size)   ), (
        "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
        f""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
    )

    A_    :			Union[str, Any]									=    dataavec_layer.self_attn.q_proj.weight
    A_    :			int									=    dataavec_layer.self_attn.q_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn.k_proj.weight
    A_    :			Optional[int]									=    dataavec_layer.self_attn.k_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn.v_proj.weight
    A_    :			Optional[Any]									=    dataavec_layer.self_attn.v_proj.bias

    # self-attention output
    A_    :			BertSelfOutput									=    layer.attention.output
    assert (
        self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
    ), f"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
    A_    :			Dict									=    dataavec_layer.self_attn.out_proj.weight
    A_    :			Any									=    dataavec_layer.self_attn.out_proj.bias
    A_    :			Any									=    dataavec_layer.self_attn_layer_norm.weight
    A_    :			Optional[Any]									=    dataavec_layer.self_attn_layer_norm.bias

    # intermediate
    A_    :			BertIntermediate									=    layer.intermediate
    assert (
        intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
    ), f"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
    A_    :			Optional[Any]									=    dataavec_layer.fca.weight
    A_    :			Any									=    dataavec_layer.fca.bias

    # output
    A_    :			BertOutput									=    layer.output
    assert (
        bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
    ), f"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
    A_    :			Optional[Any]									=    dataavec_layer.fca.weight
    A_    :			Union[str, Any]									=    dataavec_layer.fca.bias
    A_    :			Optional[int]									=    dataavec_layer.final_layer_norm.weight
    A_    :			Tuple									=    dataavec_layer.final_layer_norm.bias
    # end of layer

  if classification_head:
    A_    :			Optional[Any]									=    dataavec.model.classification_heads['mnli'].dense.weight
    A_    :			Tuple									=    dataavec.model.classification_heads['mnli'].dense.bias
    A_    :			List[str]									=    dataavec.model.classification_heads['mnli'].out_proj.weight
    A_    :			Any									=    dataavec.model.classification_heads['mnli'].out_proj.bias
  else:
    # LM Head
    A_    :			str									=    dataavec_model.encoder.lm_head.dense.weight
    A_    :			Optional[Any]									=    dataavec_model.encoder.lm_head.dense.bias
    A_    :			Tuple									=    dataavec_model.encoder.lm_head.layer_norm.weight
    A_    :			List[Any]									=    dataavec_model.encoder.lm_head.layer_norm.bias
    A_    :			Optional[int]									=    dataavec_model.encoder.lm_head.weight
    A_    :			Dict									=    dataavec_model.encoder.lm_head.bias

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

  A_    :			Dict									=    model(_UpperCAmelCase   )[0]
  if classification_head:
    A_    :			List[Any]									=    dataavec.model.classification_heads['mnli'](dataavec.extract_features(_UpperCAmelCase   )   )
  else:
    A_    :			Optional[Any]									=    dataavec_model(_UpperCAmelCase   )[0]
  print(our_output.shape					,   their_output.shape   )
  A_    :			str									=    torch.max(torch.abs(our_output - their_output   )   ).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
  A_    :			List[Any]									=    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__":
   lowerCamelCase_	:		str         =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--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_	:		Any         =   parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )

import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

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


SCREAMING_SNAKE_CASE__				=							datasets.utils.logging.get_logger(__name__)
@dataclass
class       A__  (		datasets.BuilderConfig							):
							lowerCAmelCase__						:		Optional[datasets.Features]            =				None
							lowerCAmelCase__						:		str            =				"utf-8"
							lowerCAmelCase__						:		Optional[str]            =				None
							lowerCAmelCase__						:		int            =				10 << 20  # 10MB
							lowerCAmelCase__						:		bool            =				False
							lowerCAmelCase__						:		str            =				"line"






class       A__  (		datasets.ArrowBasedBuilder							):
							lowerCAmelCase__						:		Optional[int]            =				TextConfig





							def      a__					(					self    :  List[str]							)		->						Any:



											"""simple docstring"""


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





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  str							)		->						List[str]:



											"""simple docstring"""


											if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
											__lowercase								=   dl_manager.download_and_extract(self.config.data_files							)
											if isinstance(_UpperCAmelCase	,					(str, list, tuple)							):
															__lowercase								=   data_files
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,					gen_kwargs={'files': files}							)]
											__lowercase								=   []
											for split_name, files in data_files.items():
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															splits.append(datasets.SplitGenerator(name=_UpperCAmelCase	,					gen_kwargs={'files': files}							)							)
											return splits





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pa.Table							)		->						pa.Table:



											"""simple docstring"""


											if self.config.features is not None:
															__lowercase								=   self.config.features.arrow_schema
															if all(not require_storage_cast(_UpperCAmelCase							) for feature in self.config.features.values()							):
																			# cheaper cast
																			__lowercase								=   pa_table.cast(_UpperCAmelCase							)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			__lowercase								=   table_cast(_UpperCAmelCase	,					_UpperCAmelCase							)
															return pa_table
											else:
															return pa_table.cast(pa.schema({'text': pa.string()}							)							)





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]							)		->						List[Any]:



											"""simple docstring"""


											__lowercase								=   list(self.config.features							) if self.config.features is not None else ['text']
											for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase							)							):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(_UpperCAmelCase	,					encoding=self.config.encoding	,					errors=self.config.errors							) as f:
																			if self.config.sample_by == "line":
																							__lowercase								=   0
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not batch:
																															break
																											batch += f.readline()  # finish current line
																											# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																											__lowercase								=   StringIO(_UpperCAmelCase							).readlines()
																											if not self.config.keep_linebreaks:
																															__lowercase								=   [line.rstrip('\n'							) for line in batch]
																											__lowercase								=   pa.Table.from_arrays([pa.array(_UpperCAmelCase							)]	,					names=_UpperCAmelCase							)
																											# Uncomment for debugging (will print the Arrow table size and elements)
																											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																											batch_idx += 1
																			elif self.config.sample_by == "paragraph":
																							__lowercase								=   0
																							__lowercase								=   ''
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not new_batch:
																															break
																											batch += new_batch
																											batch += f.readline()  # finish current line
																											__lowercase								=   batch.split('\n\n'							)
																											__lowercase								=   pa.Table.from_arrays(
																											    [pa.array([example for example in batch[:-1] if example]							)]	,					names=_UpperCAmelCase							)
																											# Uncomment for debugging (will print the Arrow table size and elements)
																											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																											batch_idx += 1
																											__lowercase								=   batch[-1]
																							if batch:
																											__lowercase								=   pa.Table.from_arrays([pa.array([batch]							)]	,					names=_UpperCAmelCase							)
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																			elif self.config.sample_by == "document":
																							__lowercase								=   f.read()
																							__lowercase								=   pa.Table.from_arrays([pa.array([text]							)]	,					names=_UpperCAmelCase							)
																							yield file_idx, self._cast_table(_UpperCAmelCase							)





