




"""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()
SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=			"Hello world! cécé herlolip"







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						List[str]:
 A__, A__			=							os.path.split(lowercase_		)
 A__			=							DataaVecTextModel.from_pretrained(
     lowercase_     ,					checkpoint_file=lowercase_		)
 dataavec.eval()  # disable dropout
 A__			=							dataavec.models[0]
 A__			=							dataavec_model.encoder.sentence_encoder
 A__			=							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=5_14     ,					type_vocab_size=1     ,					layer_norm_eps=1E-5     ,					)
 if classification_head:
  A__			=							dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
 print("Our BERT config:"     ,					lowercase_		)

 A__			=							DataaVecTextForSequenceClassification(lowercase_		) if classification_head else DataaVecTextForMaskedLM(lowercase_		)
 model.eval()

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

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

  # self attention
  A__			=							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__			=							dataavec_layer.self_attn.q_proj.weight
  A__			=							dataavec_layer.self_attn.q_proj.bias
  A__			=							dataavec_layer.self_attn.k_proj.weight
  A__			=							dataavec_layer.self_attn.k_proj.bias
  A__			=							dataavec_layer.self_attn.v_proj.weight
  A__			=							dataavec_layer.self_attn.v_proj.bias

  # self-attention output
  A__			=							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__			=							dataavec_layer.self_attn.out_proj.weight
  A__			=							dataavec_layer.self_attn.out_proj.bias
  A__			=							dataavec_layer.self_attn_layer_norm.weight
  A__			=							dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  A__			=							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__			=							dataavec_layer.fca.weight
  A__			=							dataavec_layer.fca.bias

  # output
  A__			=							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__			=							dataavec_layer.fca.weight
  A__			=							dataavec_layer.fca.bias
  A__			=							dataavec_layer.final_layer_norm.weight
  A__			=							dataavec_layer.final_layer_norm.bias
  # end of layer

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

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

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

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


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











"""simple docstring"""



import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


SCREAMING_SNAKE_CASE							=			2 << 20  # 2mb
SCREAMING_SNAKE_CASE							=			get_logger(__name__)




class       UpperCAmelCase_    (		A_				):




    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							self._options.get_all_options().get("runner" )
     return runner in [None, "DirectRunner", "PortableRunner"]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						Dict:
 A__			=							FileSystems
 if fs.exists(lowercase_		):
  if force_upload:
   logger.info(f"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""		)
  else:
   logger.info(f"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""		)
   return
 with fs.create(lowercase_		) as remote_file:
  with open(lowercase_     ,					"rb"		) as local_file:
   A__			=							local_file.read(lowercase_		)
   while chunk:
    remote_file.write(lowercase_		)
    A__			=							local_file.read(lowercase_		)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						int:
 A__			=							FileSystems
 if os.path.exists(lowercase_		):
  if force_download:
   logger.info(f"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""		)
  else:
   logger.info(f"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""		)
   return
 with fs.open(lowercase_		) as remote_file:
  with open(lowercase_     ,					"wb"		) as local_file:
   A__			=							remote_file.read(lowercase_		)
   while chunk:
    local_file.write(lowercase_		)
    A__			=							remote_file.read(lowercase_		)





