

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()
a_         =      logging.get_logger(__name__)

a_         =      'Hello world! cécé herlolip'

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
 SCREAMING_SNAKE_CASE				: Tuple			       =    os.path.split(_a)
 SCREAMING_SNAKE_CASE				: Dict			       =    DataaVecTextModel.from_pretrained(
     _a							,     checkpoint_file=_a)
 dataavec.eval()  # disable dropout
 SCREAMING_SNAKE_CASE				: Optional[int]			       =    dataavec.models[0]
 SCREAMING_SNAKE_CASE				: Dict			       =    dataavec_model.encoder.sentence_encoder
 SCREAMING_SNAKE_CASE				: Dict			       =    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:
  SCREAMING_SNAKE_CASE				: Optional[int]			       =    dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
 print("Our BERT config:"							,     _a)

 SCREAMING_SNAKE_CASE				: Optional[int]			       =    DataaVecTextForSequenceClassification(_a) if classification_head else DataaVecTextForMaskedLM(_a)
 model.eval()

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

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

  # self attention
  SCREAMING_SNAKE_CASE				: 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))}"
  )

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

  # self-attention output
  SCREAMING_SNAKE_CASE				: 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}"
  SCREAMING_SNAKE_CASE				: int			       =    dataavec_layer.self_attn.out_proj.weight
  SCREAMING_SNAKE_CASE				: Optional[Any]			       =    dataavec_layer.self_attn.out_proj.bias
  SCREAMING_SNAKE_CASE				: str			       =    dataavec_layer.self_attn_layer_norm.weight
  SCREAMING_SNAKE_CASE				: int			       =    dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  SCREAMING_SNAKE_CASE				: 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}"
  SCREAMING_SNAKE_CASE				: List[str]			       =    dataavec_layer.fca.weight
  SCREAMING_SNAKE_CASE				: List[str]			       =    dataavec_layer.fca.bias

  # output
  SCREAMING_SNAKE_CASE				: 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}"
  SCREAMING_SNAKE_CASE				: Tuple			       =    dataavec_layer.fca.weight
  SCREAMING_SNAKE_CASE				: Any			       =    dataavec_layer.fca.bias
  SCREAMING_SNAKE_CASE				: int			       =    dataavec_layer.final_layer_norm.weight
  SCREAMING_SNAKE_CASE				: Tuple			       =    dataavec_layer.final_layer_norm.bias
  # end of layer

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

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

 SCREAMING_SNAKE_CASE				: Dict			       =    model(_a)[0]
 if classification_head:
  SCREAMING_SNAKE_CASE				: Dict			       =    dataavec.model.classification_heads["mnli"](dataavec.extract_features(_a))
 else:
  SCREAMING_SNAKE_CASE				: Tuple			       =    dataavec_model(_a)[0]
 print(our_output.shape							,     their_output.shape)
 SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.max(torch.abs(our_output - their_output)).item()
 print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
 SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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__":
   a_         =      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.'
   )
   a_         =      parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )


from __future__ import annotations

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)