
'''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()
__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   'Hello world! cécé herlolip'



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	bool							)       ->	Dict:
 SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[int]								=		os.path.split(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		DataaVecTextModel.from_pretrained(
     snake_case_   ,     checkpoint_file=snake_case_							)
 dataavec.eval()  # disable dropout
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		dataavec.models[0]
 SCREAMING_SNAKE_CASE  :  int								=		dataavec_model.encoder.sentence_encoder
 SCREAMING_SNAKE_CASE  :  Tuple								=		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  :  int								=		dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'   ,     snake_case_							)

 SCREAMING_SNAKE_CASE  :  List[Any]								=		DataaVecTextForSequenceClassification(snake_case_							) if classification_head else DataaVecTextForMaskedLM(snake_case_							)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 SCREAMING_SNAKE_CASE  :  int								=		dataavec_sent_encoder.embed_tokens.weight
 SCREAMING_SNAKE_CASE  :  str								=		dataavec_sent_encoder.embed_positions.weight
 SCREAMING_SNAKE_CASE  :  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  :  Any								=		dataavec_sent_encoder.layernorm_embedding.weight
 SCREAMING_SNAKE_CASE  :  Optional[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  :  Tuple								=		dataavec_layer.self_attn.q_proj.weight
  SCREAMING_SNAKE_CASE  :  Dict								=		dataavec_layer.self_attn.q_proj.bias
  SCREAMING_SNAKE_CASE  :  List[Any]								=		dataavec_layer.self_attn.k_proj.weight
  SCREAMING_SNAKE_CASE  :  Any								=		dataavec_layer.self_attn.k_proj.bias
  SCREAMING_SNAKE_CASE  :  int								=		dataavec_layer.self_attn.v_proj.weight
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		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  :  Optional[Any]								=		dataavec_layer.self_attn.out_proj.weight
  SCREAMING_SNAKE_CASE  :  Dict								=		dataavec_layer.self_attn.out_proj.bias
  SCREAMING_SNAKE_CASE  :  Tuple								=		dataavec_layer.self_attn_layer_norm.weight
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		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  :  int								=		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  :  List[str]								=		dataavec_layer.fca.weight
  SCREAMING_SNAKE_CASE  :  Any								=		dataavec_layer.fca.bias
  SCREAMING_SNAKE_CASE  :  Dict								=		dataavec_layer.final_layer_norm.weight
  SCREAMING_SNAKE_CASE  :  str								=		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  :  Optional[int]								=		dataavec.model.classification_heads['mnli'].dense.bias
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		dataavec.model.classification_heads['mnli'].out_proj.weight
  SCREAMING_SNAKE_CASE  :  int								=		dataavec.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  SCREAMING_SNAKE_CASE  :  Any								=		dataavec_model.encoder.lm_head.dense.weight
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		dataavec_model.encoder.lm_head.dense.bias
  SCREAMING_SNAKE_CASE  :  Dict								=		dataavec_model.encoder.lm_head.layer_norm.weight
  SCREAMING_SNAKE_CASE  :  str								=		dataavec_model.encoder.lm_head.layer_norm.bias
  SCREAMING_SNAKE_CASE  :  List[Any]								=		dataavec_model.encoder.lm_head.weight
  SCREAMING_SNAKE_CASE  :  Tuple								=		dataavec_model.encoder.lm_head.bias

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

 SCREAMING_SNAKE_CASE  :  Any								=		model(snake_case_							)[0]
 if classification_head:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		dataavec.model.classification_heads['mnli'](dataavec.extract_features(snake_case_							)							)
 else:
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		dataavec_model(snake_case_							)[0]
 print(our_output.shape   ,     their_output.shape							)
 SCREAMING_SNAKE_CASE  :  str								=		torch.max(torch.abs(our_output - their_output							)							).item()
 print(f"""max_absolute_diff = {max_absolute_diff}"""							)  # ~ 1e-7
 SCREAMING_SNAKE_CASE  :  int								=		torch.allclose(snake_case_   ,     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(snake_case_							).mkdir(parents=snake_case_   ,     exist_ok=snake_case_							)
 print(f"""Saving model to {pytorch_dump_folder_path}"""							)
 model.save_pretrained(snake_case_							)


if __name__ == "__main__":
   __UpperCAmelCase												=   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												=   parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str = "mytext.txt"   ,     snake_case_				:	bool = False							)       ->	None:
 import socket

 SCREAMING_SNAKE_CASE  :  Optional[int]								=		1_2312  # Reserve a port for your service.
 SCREAMING_SNAKE_CASE  :  Any								=		socket.socket()  # Create a socket object
 SCREAMING_SNAKE_CASE  :  int								=		socket.gethostname()  # Get local machine name
 sock.bind((host, port)							)  # Bind to the port
 sock.listen(5							)  # Now wait for client connection.

 print('Server listening....'							)

 while True:
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[Any]								=		sock.accept()  # Establish connection with client.
  print(f"""Got connection from {addr}"""							)
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		conn.recv(1024							)
  print(f"""Server received: {data = }"""							)

  with open(snake_case_   ,     'rb'							) as in_file:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		in_file.read(1024							)
   while data:
    conn.send(snake_case_							)
    print(f"""Sent {data!r}"""							)
    SCREAMING_SNAKE_CASE  :  List[Any]								=		in_file.read(1024							)

  print('Done sending'							)
  conn.close()
  if testing:  # Allow the test to complete
   break

 sock.shutdown(1							)
 sock.close()


if __name__ == "__main__":
   send_file()


