


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


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

SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				" Hello world! cécé herlolip"

SCREAMING_SNAKE_CASE       :		Tuple         =				[
    ("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    (      _a		)					->							Dict:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       dct.pop(_a		)
     lowercase_       :int     =       val




def     UpperCamelCase    (      _a		)					->							List[Any]:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_    ,      lowercase_       :Tuple     =       emb.weight.shape
     lowercase_       :Optional[Any]     =       nn.Linear(_a       ,		_a       ,		bias=_a		)
     lowercase_       :int     =       emb.weight.data
     return lin_layer




@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							int:
     '''simple docstring'''





     if not os.path.exists(_a		):
          lowercase_       :Optional[int]     =       torch.hub.load('''pytorch/fairseq'''       ,		_a		).eval()
     else:
          lowercase_       :Optional[Any]     =       load_xsum_checkpoint(_a		)

     bart.model.upgrade_state_dict(bart.model.state_dict()		)
     if hf_checkpoint_name is None:
          lowercase_       :Dict     =       checkpoint_path.replace('''.'''       ,		'''-'''		)
     lowercase_       :List[str]     =       BartConfig.from_pretrained(_a		)
     lowercase_       :str     =       bart.encode(_a		).unsqueeze(0		)
     lowercase_       :Optional[Any]     =       BartTokenizer.from_pretrained(_a		).encode(_a       ,		return_tensors='''pt'''		).unsqueeze(0		)
     if not torch.eq(_a       ,		_a		).all():
          raise ValueError(
              f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"		)

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		int         =				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"
   )
   SCREAMING_SNAKE_CASE       :		int         =				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_tf_available, is_torch_available


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

