


'''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


a_    :			Dict      				=			["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
a_    :			Any      				=			{"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()
a_    :			List[str]      				=			logging.get_logger(__name__)

a_    :			Any      				=			" Hello world! cécé herlolip"

a_    :			Optional[Any]      				=			[
    ("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 		_A (lowerCAmelCase__     :int						)      ->     Tuple:




     '''simple docstring'''



     _a						=							[
         'encoder.version',
         'decoder.version',
         'model.encoder.version',
         'model.decoder.version',
         '_float_tensor',
     ]
     for k in ignore_keys:
          state_dict.pop(lowerCamelCase__ ,  lowerCamelCase__						)






def 		_A (lowerCAmelCase__     :List[str] ,  lowerCAmelCase__     :Optional[Any] ,  lowerCAmelCase__     :int						)      ->     Tuple:




     '''simple docstring'''



     _a						=							dct.pop(lowerCamelCase__						)
     _a						=							val






def 		_A (lowerCAmelCase__     :Optional[Any]						)      ->     Any:




     '''simple docstring'''



     _a						=							torch.load(lowerCamelCase__ ,  map_location='cpu'						)
     _a						=							torch.hub.load('pytorch/fairseq' ,  'bart.large.cnn'						).eval()
     hub_interface.model.load_state_dict(sd['model']						)
     return hub_interface






def 		_A (lowerCAmelCase__     :Any						)      ->     Optional[int]:




     '''simple docstring'''



     _a					,      _a						=							emb.weight.shape
     _a						=							nn.Linear(lowerCamelCase__ ,  lowerCamelCase__ ,  bias=lowerCamelCase__						)
     _a						=							emb.weight.data
     return lin_layer






@torch.no_grad()
def 		_A (lowerCAmelCase__     :Union[str, Any] ,  lowerCAmelCase__     :Dict ,  lowerCAmelCase__     :List[str]=None						)      ->     List[str]:




     '''simple docstring'''



     if not os.path.exists(lowerCamelCase__						):
          _a						=							torch.hub.load('pytorch/fairseq' ,  lowerCamelCase__						).eval()
     else:
          _a						=							load_xsum_checkpoint(lowerCamelCase__						)

     bart.model.upgrade_state_dict(bart.model.state_dict()						)
     if hf_checkpoint_name is None:
          _a						=							checkpoint_path.replace('.' ,  '-'						)
     _a						=							BartConfig.from_pretrained(lowerCamelCase__						)
     _a						=							bart.encode(lowerCamelCase__						).unsqueeze(0						)
     _a						=							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":
          _a						=							bart.state_dict()
          remove_ignore_keys_(lowerCamelCase__						)
          _a						=							state_dict['model.decoder.embed_tokens.weight']
          for src, dest in mnli_rename_keys:
               rename_key(lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__						)
          _a						=							BartForSequenceClassification(lowerCamelCase__						).eval()
          model.load_state_dict(lowerCamelCase__						)
          _a						=							bart.predict('mnli' ,  lowerCamelCase__ ,  return_logits=lowerCamelCase__						)
          _a						=							model(lowerCamelCase__						)[0]  # logits
     else:  # no classification heads to worry about
          _a						=							bart.model.state_dict()
          remove_ignore_keys_(lowerCamelCase__						)
          _a						=							state_dict['decoder.embed_tokens.weight']
          _a						=							bart.extract_features(lowerCamelCase__						)
          if hf_checkpoint_name == "facebook/bart-large":
               _a						=							BartModel(lowerCamelCase__						).eval()
               model.load_state_dict(lowerCamelCase__						)
               _a						=							model(lowerCamelCase__						).model[0]
          else:
               _a						=							BartForConditionalGeneration(lowerCamelCase__						).eval()  # an existing summarization ckpt
               model.model.load_state_dict(lowerCamelCase__						)
               if hasattr(lowerCamelCase__ ,  'lm_head'						):
                    _a						=							make_linear_from_emb(model.model.shared						)
               _a						=							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__":
 a_    :			Any      				=			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"
 )
 a_    :			Union[str, Any]      				=			parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


