
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
      raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
snake_case 				=       logging.get_logger(__name__)

snake_case 				=       """Hello world! cécé herlolip"""

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :str			,							snake_case__  :bool     )      ->						List[Any]:
  _lowercase							= FairseqRobertaModel.from_pretrained(snake_case__     )
  roberta.eval()  # disable dropout
  _lowercase							= roberta.model.encoder.sentence_encoder
  _lowercase							= XLMRobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings			,							hidden_size=roberta.cfg.model.encoder_embed_dim			,							num_hidden_layers=roberta.cfg.model.encoder_layers			,							num_attention_heads=roberta.cfg.model.encoder_attention_heads			,							intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim			,							max_position_embeddings=514			,							type_vocab_size=1			,							layer_norm_eps=1E-5			,							)
  if classification_head:
    _lowercase							= roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

  print('Our RoBERTa config:'			,							snake_case__     )

  _lowercase							= XLMRobertaXLForSequenceClassification(snake_case__     ) if classification_head else XLMRobertaXLForMaskedLM(snake_case__     )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  _lowercase							= roberta_sent_encoder.embed_tokens.weight
  _lowercase							= roberta_sent_encoder.embed_positions.weight
  _lowercase							= torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.

  _lowercase							= roberta_sent_encoder.layer_norm.weight
  _lowercase							= roberta_sent_encoder.layer_norm.bias

  for i in range(config.num_hidden_layers     ):
    # Encoder: start of layer
    _lowercase							= model.roberta.encoder.layer[i]
    _lowercase							= roberta_sent_encoder.layers[i]

    _lowercase							= layer.attention
    _lowercase							= roberta_layer.self_attn_layer_norm.weight
    _lowercase							= roberta_layer.self_attn_layer_norm.bias

    # self attention
    _lowercase							= layer.attention.self
    assert (
        roberta_layer.self_attn.k_proj.weight.data.shape
        == roberta_layer.self_attn.q_proj.weight.data.shape
        == roberta_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)     )
    )

    _lowercase							= roberta_layer.self_attn.q_proj.weight
    _lowercase							= roberta_layer.self_attn.q_proj.bias
    _lowercase							= roberta_layer.self_attn.k_proj.weight
    _lowercase							= roberta_layer.self_attn.k_proj.bias
    _lowercase							= roberta_layer.self_attn.v_proj.weight
    _lowercase							= roberta_layer.self_attn.v_proj.bias

    # self-attention output
    _lowercase							= layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    _lowercase							= roberta_layer.self_attn.out_proj.weight
    _lowercase							= roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    _lowercase							= roberta_layer.final_layer_norm.weight
    _lowercase							= roberta_layer.final_layer_norm.bias

    # intermediate
    _lowercase							= layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowercase							= roberta_layer.fca.weight
    _lowercase							= roberta_layer.fca.bias

    # output
    _lowercase							= layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowercase							= roberta_layer.fca.weight
    _lowercase							= roberta_layer.fca.bias
    # end of layer

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

  # Let's check that we get the same results.
  _lowercase							= roberta.encode(snake_case__     ).unsqueeze(0     )  # batch of size 1

  _lowercase							= model(snake_case__     )[0]
  if classification_head:
    _lowercase							= roberta.model.classification_heads['mnli'](roberta.extract_features(snake_case__     )     )
  else:
    _lowercase							= roberta.model(snake_case__     )[0]
  print(our_output.shape			,							their_output.shape     )
  _lowercase							= torch.max(torch.abs(our_output - their_output     )     ).item()
  print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
  _lowercase							= 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__":
      snake_case 				=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--roberta_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."""
      )
      snake_case 				=       parser.parse_args()
      convert_xlm_roberta_xl_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )






from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


snake_case 				=       logging.get_logger(__name__)


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset

      from transformers import Trainer





      class 				A_				(      UpperCAmelCase	):







             """simple docstring"""





             def __init__(    self	:	str						,__A	:	int = 101  )						->					Optional[int]:
               _lowercase							= length







             def __len__(    self	:	List[Any]  )						->					Any:
               return self.length







             def __getitem__(    self	:	List[Any]						,__A	:	Optional[int]  )						->					int:
               return i





      class 				A_				:







             """simple docstring"""





             def __call__(    self	:	str						,__A	:	Union[str, Any]  )						->					Any:
               return {"input_ids": torch.tensor(__A  ), "labels": torch.tensor(__A  )}





      class 				A_				(      nn.Module	):







             """simple docstring"""





             def __init__(    self	:	Dict  )						->					Any:
               super().__init__()
               # Add some (unused) params otherwise DDP will complain.
               _lowercase							= nn.Linear(120						,80  )







             def    __UpperCAmelCase (    self	:	Dict						,__A	:	Dict						,__A	:	Any=None  )						->					Tuple:
               if labels is not None:
                 return torch.tensor(0.0						,device=input_ids.device  ), input_ids
               else:
                 return input_ids





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_neuroncore
       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,env=self.get_env()  )
         # successful return here == success - any errors would have caused an error in the sub-call





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_multi_gpu
       def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
         _lowercase							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['torchrun'] + distributed_args + args
         execute_subprocess_async(__A						,env=self.get_env()  )
         # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
      # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
      #
      # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

      snake_case 				=       HfArgumentParser((TrainingArguments,))
      snake_case 				=       parser.parse_args_into_dataclasses()[0]

      logger.warning(
          F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
          F"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
      )

      # Essentially, what we want to verify in the distributed case is that we get all samples back,
      # in the right order. (this is crucial for prediction for instance)
      for dataset_length in [1_0_1, 4_0, 7]:
            snake_case 				=       DummyDataset(dataset_length)

            def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :EvalPrediction     )      ->						Dict:
              _lowercase							= list(range(len(snake_case__     )     )     )
              _lowercase							= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
              if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""     )
              return {"success": success}

            snake_case 				=       Trainer(
                model=DummyModel(),
                args=training_args,
                data_collator=DummyDataCollator(),
                eval_dataset=dataset,
                compute_metrics=compute_metrics,
            )
            snake_case 				=       trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            snake_case 				=       trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)

            snake_case 				=       2

            snake_case 				=       trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

            snake_case 				=       trainer.predict(dataset)
            logger.info(p.metrics)
            if p.metrics["test_success"] is not True:
                  logger.error(p.metrics)
                  exit(1)







            snake_case 				=       None