




'''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_		:    List[Any]									=		logging.get_logger(__name__)

UpperCamelCase_		:    Optional[int]									=		'''Hello world! cécé herlolip'''





def 			__a			(     _UpperCamelCase:					Optional[Any]      ,		_UpperCamelCase:					int      ,		_UpperCamelCase:					List[Any]					)		->				Optional[Any]:

     """simple docstring"""




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

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

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

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

          # self attention
          _snake_case							=   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)					)}"""
          )

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

          # self-attention output
          _snake_case							=   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}"""
          _snake_case							=   dataavec_layer.self_attn.out_proj.weight
          _snake_case							=   dataavec_layer.self_attn.out_proj.bias
          _snake_case							=   dataavec_layer.self_attn_layer_norm.weight
          _snake_case							=   dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          _snake_case							=   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}"""
          _snake_case							=   dataavec_layer.fca.weight
          _snake_case							=   dataavec_layer.fca.bias

          # output
          _snake_case							=   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}"""
          _snake_case							=   dataavec_layer.fca.weight
          _snake_case							=   dataavec_layer.fca.bias
          _snake_case							=   dataavec_layer.final_layer_norm.weight
          _snake_case							=   dataavec_layer.final_layer_norm.bias
          # end of layer

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

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

     _snake_case							=   model(lowercase_					)[0]
     if classification_head:
          _snake_case							=   dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(lowercase_					)					)
     else:
          _snake_case							=   dataavec_model(lowercase_					)[0]
     print(our_output.shape      ,		their_output.shape					)
     _snake_case							=   torch.max(torch.abs(our_output - their_output					)					).item()
     print(F"""max_absolute_diff = {max_absolute_diff}"""					)  # ~ 1e-7
     _snake_case							=   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__":
   UpperCamelCase_		:    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.'''
   )
   UpperCamelCase_		:    int									=		parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )








'''simple docstring'''





import io
import json
import subprocess


UpperCamelCase_		:    Any									=		[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase_		:    Union[str, Any]									=		8





def 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					Optional[Any]					)		->				Optional[int]:

     """simple docstring"""




     _snake_case							=   {}
     for src, tgt in pairs:
          _snake_case							=   F"""{src}-{tgt}"""
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
          _snake_case							=   subprocess.run(_UpperCamelCase      ,		stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
          _snake_case							=   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
     return text


UpperCamelCase_		:    Tuple									=		get_all_data(pairs, n_objs)
UpperCamelCase_		:    Optional[Any]									=		'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   UpperCamelCase_		:    List[str]									=		json.dump(text, f, indent=2, ensure_ascii=False)


