
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()
_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Any     ='Hello world! cécé herlolip'



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  bool    ):






  """simple docstring"""




  _lowerCAmelCase	,       _lowerCAmelCase						=		os.path.split(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		DataaVecTextModel.from_pretrained(
      __SCREAMING_SNAKE_CASE		,				checkpoint_file=__SCREAMING_SNAKE_CASE    )
  dataavec.eval()  # disable dropout
  _lowerCAmelCase						=		dataavec.models[0]
  _lowerCAmelCase						=		dataavec_model.encoder.sentence_encoder
  _lowerCAmelCase						=		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:
    _lowerCAmelCase						=		dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
  print('Our BERT config:'		,				__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		DataaVecTextForSequenceClassification(__SCREAMING_SNAKE_CASE    ) if classification_head else DataaVecTextForMaskedLM(__SCREAMING_SNAKE_CASE    )
  model.eval()

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

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

    # self attention
    _lowerCAmelCase						=		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)    )}'''
    )

    _lowerCAmelCase						=		dataavec_layer.self_attn.q_proj.weight
    _lowerCAmelCase						=		dataavec_layer.self_attn.q_proj.bias
    _lowerCAmelCase						=		dataavec_layer.self_attn.k_proj.weight
    _lowerCAmelCase						=		dataavec_layer.self_attn.k_proj.bias
    _lowerCAmelCase						=		dataavec_layer.self_attn.v_proj.weight
    _lowerCAmelCase						=		dataavec_layer.self_attn.v_proj.bias

    # self-attention output
    _lowerCAmelCase						=		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}'''
    _lowerCAmelCase						=		dataavec_layer.self_attn.out_proj.weight
    _lowerCAmelCase						=		dataavec_layer.self_attn.out_proj.bias
    _lowerCAmelCase						=		dataavec_layer.self_attn_layer_norm.weight
    _lowerCAmelCase						=		dataavec_layer.self_attn_layer_norm.bias

    # intermediate
    _lowerCAmelCase						=		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}'''
    _lowerCAmelCase						=		dataavec_layer.fca.weight
    _lowerCAmelCase						=		dataavec_layer.fca.bias

    # output
    _lowerCAmelCase						=		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}'''
    _lowerCAmelCase						=		dataavec_layer.fca.weight
    _lowerCAmelCase						=		dataavec_layer.fca.bias
    _lowerCAmelCase						=		dataavec_layer.final_layer_norm.weight
    _lowerCAmelCase						=		dataavec_layer.final_layer_norm.bias
    # end of layer

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

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

  _lowerCAmelCase						=		model(__SCREAMING_SNAKE_CASE    )[0]
  if classification_head:
    _lowerCAmelCase						=		dataavec.model.classification_heads['mnli'](dataavec.extract_features(__SCREAMING_SNAKE_CASE    )    )
  else:
    _lowerCAmelCase						=		dataavec_model(__SCREAMING_SNAKE_CASE    )[0]
  print(our_output.shape		,				their_output.shape    )
  _lowerCAmelCase						=		torch.max(torch.abs(our_output - their_output    )    ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''    )  # ~ 1e-7
  _lowerCAmelCase						=		torch.allclose(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				atol=1E-3    )
  print('Do both models output the same tensors?'		,				'🔥' if success else '💩'    )
  if not success:
    raise Exception('Something went wRoNg'    )

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


if __name__ == "__main__":
       _UpperCamelCase:					Any     =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.'
       )
       _UpperCamelCase:					Tuple     =parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )


from __future__ import annotations



def 	_a  (					__SCREAMING_SNAKE_CASE					:  list		,				__SCREAMING_SNAKE_CASE					:  list    ):






  """simple docstring"""




  _lowerCAmelCase						=		[None] * (len(__SCREAMING_SNAKE_CASE    ) + len(__SCREAMING_SNAKE_CASE    ))

  _lowerCAmelCase						=		0  # pointer to current index for left Half
  _lowerCAmelCase						=		0  # pointer to current index for the right Half
  _lowerCAmelCase						=		0  # pointer to current index for the sorted array Half

  while pointera < len(__SCREAMING_SNAKE_CASE    ) and pointera < len(__SCREAMING_SNAKE_CASE    ):
    if left_half[pointera] < right_half[pointera]:
      _lowerCAmelCase						=		left_half[pointera]
      pointera += 1
      index += 1
    else:
      _lowerCAmelCase						=		right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(__SCREAMING_SNAKE_CASE    ):
    _lowerCAmelCase						=		left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(__SCREAMING_SNAKE_CASE    ):
    _lowerCAmelCase						=		right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array



def 	_a  (					__SCREAMING_SNAKE_CASE					:  list    ):






  """simple docstring"""




  if len(__SCREAMING_SNAKE_CASE    ) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  _lowerCAmelCase						=		0 + (len(__SCREAMING_SNAKE_CASE    ) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  _lowerCAmelCase						=		array[:middle]
  _lowerCAmelCase						=		array[middle:]

  return merge(merge_sort(__SCREAMING_SNAKE_CASE    )		,				merge_sort(__SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
