





"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__UpperCamelCase			:		Optional[Any]		    =   logging.getLogger(__name__)







@dataclass
class 	a				:
 snake_case__    	=    field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
 snake_case__    	=    field(
     default=a__					,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
 snake_case__    	=    field(
     default=a__					,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
 snake_case__    	=    field(default=a__					,      metadata={'''help''': '''Set this flag to use fast tokenization.'''}   )
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 snake_case__    	=    field(
     default=a__					,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,      )


def 						_SCREAMING_SNAKE_CASE       ():
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCAmelCase  							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

 if len(sys.argv       ) == 2 and sys.argv[1].endswith('.json'       ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase  							=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
 else:
  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase  							=      parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir       )
     and os.listdir(training_args.output_dir       )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
      ' --overwrite_output_dir to overcome.'       )

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,			datefmt='%m/%d/%Y %H:%M:%S'   ,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,			)
 logger.warning(
     'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'   ,			training_args.local_rank   ,			training_args.device   ,			training_args.n_gpu   ,			bool(training_args.local_rank != -1       )   ,			training_args.fpaa   ,			)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank       ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('Training/evaluation parameters %s'   ,			_UpperCAmelCase       )

 # Prepare Question-Answering task
 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 lowerCAmelCase  							=      AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,			cache_dir=model_args.cache_dir   ,			)
 lowerCAmelCase  							=      AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,			cache_dir=model_args.cache_dir   ,			use_fast=_UpperCAmelCase   ,			)
 lowerCAmelCase  							=      AutoModelForQuestionAnswering.from_pretrained(
     model_args.model_name_or_path   ,			from_tf=bool('.ckpt' in model_args.model_name_or_path       )   ,			config=_UpperCAmelCase   ,			cache_dir=model_args.cache_dir   ,			)

 # Get datasets
 lowerCAmelCase  							=      hasattr(model.config   ,			'lang2id'       )
 lowerCAmelCase  							=      (
     SquadDataset(
         _UpperCAmelCase   ,			tokenizer=_UpperCAmelCase   ,			is_language_sensitive=_UpperCAmelCase   ,			cache_dir=model_args.cache_dir       )
     if training_args.do_train
     else None
 )
 lowerCAmelCase  							=      (
     SquadDataset(
         _UpperCAmelCase   ,			tokenizer=_UpperCAmelCase   ,			mode='dev'   ,			is_language_sensitive=_UpperCAmelCase   ,			cache_dir=model_args.cache_dir   ,			)
     if training_args.do_eval
     else None
 )

 # Data collator
 lowerCAmelCase  							=      DataCollatorWithPadding(_UpperCAmelCase   ,			pad_to_multiple_of=8       ) if training_args.fpaa else None

 # Initialize our Trainer
 lowerCAmelCase  							=      Trainer(
     model=_UpperCAmelCase   ,			args=_UpperCAmelCase   ,			train_dataset=_UpperCAmelCase   ,			eval_dataset=_UpperCAmelCase   ,			data_collator=_UpperCAmelCase   ,			)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir       )

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Any       ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
       main()







"""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       						=       logging.get_logger(__name__)

__A       						=       'Hello world! cécé herlolip'



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase						,							_lowerCAmelCase								=os.path.split(__UpperCamelCase       )
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase    , checkpoint_file=__UpperCamelCase       )
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=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:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""    , __UpperCamelCase       )

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase       )
	model.eval()

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

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

		# self attention
		_lowerCAmelCase								=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)       )}'''
		)

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

		# self-attention output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=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}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

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

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

	_lowerCAmelCase								=model(__UpperCamelCase       )[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase       )       )
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase       )[0]
	print(our_output.shape    , their_output.shape       )
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output       )       ).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
	_lowerCAmelCase								=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       						=       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
			)



