


'''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()
A__:   int    =	logging.get_logger(__name__)

A__:   Any    =	'''Hello world! cécé herlolip'''


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

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

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

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

              # self attention
              _a   :			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)  )}"
              )

              _a   :			List[Any]					 =dataavec_layer.self_attn.q_proj.weight
              _a   :			List[Any]					 =dataavec_layer.self_attn.q_proj.bias
              _a   :			str					 =dataavec_layer.self_attn.k_proj.weight
              _a   :			Any					 =dataavec_layer.self_attn.k_proj.bias
              _a   :			Any					 =dataavec_layer.self_attn.v_proj.weight
              _a   :			Dict					 =dataavec_layer.self_attn.v_proj.bias

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

              # intermediate
              _a   :			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}"
              _a   :			int					 =dataavec_layer.fca.weight
              _a   :			List[Any]					 =dataavec_layer.fca.bias

              # output
              _a   :			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}"
              _a   :			int					 =dataavec_layer.fca.weight
              _a   :			Optional[int]					 =dataavec_layer.fca.bias
              _a   :			Dict					 =dataavec_layer.final_layer_norm.weight
              _a   :			Any					 =dataavec_layer.final_layer_norm.bias
              # end of layer

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

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

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

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


if __name__ == "__main__":
   A__:   Optional[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.'''
   )
   A__:   Dict    =	parser.parse_args()
   convert_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )






'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class A__	(						unittest.TestCase				):






      def __UpperCAmelCase	(			self      :List[Any]     )	-> List[str]:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =inspect.getfile(accelerate.test_utils     )
             _a   :			Any					 =os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["""scripts""", """test_sync.py"""]     )






      @require_cpu
      def __UpperCAmelCase	(			self      :List[str]     )	-> Optional[Any]:




             '''simple docstring'''
             debug_launcher(test_sync.main					,						num_processes=1     )






      @require_cpu
      def __UpperCAmelCase	(			self      :int     )	-> Union[str, Any]:




             '''simple docstring'''
             debug_launcher(test_sync.main     )






      @require_single_gpu
      def __UpperCAmelCase	(			self      :Any     )	-> Optional[int]:




             '''simple docstring'''
             test_sync.main()






      @require_multi_gpu
      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> int:




             '''simple docstring'''
             print(f"Found {torch.cuda.device_count()} devices."     )
             _a   :			int					 =["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
             with patch_environment(omp_num_threads=1     ):
                    execute_subprocess_async(SCREAMING_SNAKE_CASE					,						env=os.environ.copy()     )


