
"""simple docstring"""



import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__          = logging.getLogger()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any]		)     ->					Any:
  _snake_case					     =       {}
  _snake_case					     =       os.path.join(__lowerCamelCase ,					'''all_results.json'''		)
  if os.path.exists(__lowerCamelCase		):
    with open(__lowerCamelCase ,					'''r'''		) as f:
      _snake_case					     =       json.load(__lowerCamelCase		)
  else:
    raise ValueError(f'''can\'t find {path}'''		)
  return results


UpperCAmelCase__          = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

@require_torch_tpu
class       lowerCAmelCase__				(						A_	):







     def      lowercase	(							self			:			str			):
       import xla_spawn

       _snake_case					     =       self.get_auto_remove_tmp_dir()
       _snake_case					     =       f'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

       with patch.object(_lowerCamelCase    ,      '''argv'''    ,      _lowerCamelCase			):
         _snake_case					     =       time()
         xla_spawn.main()
         _snake_case					     =       time()

         _snake_case					     =       get_results(_lowerCamelCase			)
         self.assertGreaterEqual(result['''eval_accuracy''']    ,      0.7_5			)

         # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
         self.assertLess(end - start    ,      500			)







     def      lowercase	(							self			:			Optional[int]			):
       import xla_spawn

       _snake_case					     =       '''
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            '''.split()
       with patch.object(_lowerCamelCase    ,      '''argv'''    ,      _lowerCamelCase			):
         xla_spawn.main()





"""simple docstring"""



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCAmelCase__          = ['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase__          = {'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


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

UpperCAmelCase__          = ' Hello world! cécé herlolip'

UpperCAmelCase__          = [
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  _snake_case					     =       [
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCamelCase ,					__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)
  _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					'''bart.large.cnn'''		).eval()
  hub_interface.model.load_state_dict(sd['''model''']		)
  return hub_interface
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Union[str, Any]:
  _snake_case     ,					_snake_case					     =       emb.weight.shape
  _snake_case					     =       nn.Linear(__lowerCamelCase ,					__lowerCamelCase ,					bias=__lowerCamelCase		)
  _snake_case					     =       emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any]=None		)     ->					List[Any]:
  if not os.path.exists(__lowerCamelCase		):
    _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					__lowerCamelCase		).eval()
  else:
    _snake_case					     =       load_xsum_checkpoint(__lowerCamelCase		)

  bart.model.upgrade_state_dict(bart.model.state_dict()		)
  if hf_checkpoint_name is None:
    _snake_case					     =       checkpoint_path.replace('''.''' ,					'''-'''		)
  _snake_case					     =       BartConfig.from_pretrained(__lowerCamelCase		)
  _snake_case					     =       bart.encode(__lowerCamelCase		).unsqueeze(0		)
  _snake_case					     =       BartTokenizer.from_pretrained(__lowerCamelCase		).encode(__lowerCamelCase ,					return_tensors='''pt'''		).unsqueeze(0		)
  if not torch.eq(__lowerCamelCase ,					__lowerCamelCase		).all():
    raise ValueError(
        f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''		)

  if checkpoint_path == "bart.large.mnli":
    _snake_case					     =       bart.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''model.decoder.embed_tokens.weight''']
    for src, dest in mnli_rename_keys:
      rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       BartForSequenceClassification(__lowerCamelCase		).eval()
    model.load_state_dict(__lowerCamelCase		)
    _snake_case					     =       bart.predict('''mnli''' ,					__lowerCamelCase ,					return_logits=__lowerCamelCase		)
    _snake_case					     =       model(__lowerCamelCase		)[0]  # logits
  else:  # no classification heads to worry about
    _snake_case					     =       bart.model.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''decoder.embed_tokens.weight''']
    _snake_case					     =       bart.extract_features(__lowerCamelCase		)
    if hf_checkpoint_name == "facebook/bart-large":
      _snake_case					     =       BartModel(__lowerCamelCase		).eval()
      model.load_state_dict(__lowerCamelCase		)
      _snake_case					     =       model(__lowerCamelCase		).model[0]
    else:
      _snake_case					     =       BartForConditionalGeneration(__lowerCamelCase		).eval()  # an existing summarization ckpt
      model.model.load_state_dict(__lowerCamelCase		)
      if hasattr(__lowerCamelCase ,					'''lm_head'''		):
        _snake_case					     =       make_linear_from_emb(model.model.shared		)
      _snake_case					     =       model.model(__lowerCamelCase		)[0]

    # Check results
  if fairseq_output.shape != new_model_outputs.shape:
    raise ValueError(
        f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''		)
  if (fairseq_output != new_model_outputs).any().item():
    raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  model.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
 )
 UpperCAmelCase__          = parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



