



"""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      =				["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
__a      =				{"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      =				logging.get_logger(__name__)

__a      =				" Hello world! cécé herlolip"

__a      =				[
    ("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_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(_lowercase,					_lowercase	)
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			dct.pop(_lowercase	)
						snake_case_ :Optional[Any]						=			val
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Tuple						=			torch.load(_lowercase,					map_location="""cpu"""	)
						snake_case_ :Union[str, Any]						=			torch.hub.load("""pytorch/fairseq""",					"""bart.large.cnn"""	).eval()
						hub_interface.model.load_state_dict(sd["""model"""]	)
						return hub_interface
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_,       snake_case_ :Any						=			emb.weight.shape
						snake_case_ :Dict						=			nn.Linear(_lowercase,					_lowercase,					bias=_lowercase	)
						snake_case_ :int						=			emb.weight.data
						return lin_layer
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase=None	):







						'''simple docstring'''


						if not os.path.exists(_lowercase	):
												snake_case_ :str						=			torch.hub.load("""pytorch/fairseq""",					_lowercase	).eval()
						else:
												snake_case_ :List[Any]						=			load_xsum_checkpoint(_lowercase	)

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

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


if __name__ == "__main__":
						__a      =				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      =				parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)




"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_fnet import FNetTokenizer
else:
 snake_case__						:   str			 = None

snake_case__						:   Optional[Any]			 = logging.get_logger(__name__)
snake_case__						:   Optional[int]			 = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__						:   Dict			 = {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

snake_case__						:   Any			 = {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

snake_case__						:   Dict			 = '''▁'''



class 						snake_case_(				a__	):
     __UpperCamelCase     =    VOCAB_FILES_NAMES
     __UpperCamelCase     =    PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase     =    ['''input_ids''', '''token_type_ids''']
     __UpperCamelCase     =    FNetTokenizer






     def __init__(		self     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    Any=False    , UpperCamelCase_     :    Any=True    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    Tuple="<unk>"    , UpperCamelCase_     :    List[str]="[SEP]"    , UpperCamelCase_     :    List[Any]="<pad>"    , UpperCamelCase_     :    Union[str, Any]="[CLS]"    , UpperCamelCase_     :    int="[MASK]"    , **UpperCamelCase_     :    Optional[Any]    , ):
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.
           lowerCAmelCase    :		int				=      (
               AddedToken(UpperCamelCase_    , lstrip=UpperCamelCase_    , rstrip=UpperCamelCase_    , normalized=UpperCamelCase_ )
               if isinstance(UpperCamelCase_    , UpperCamelCase_ )
               else mask_token
           )

           super().__init__(
               UpperCamelCase_    , tokenizer_file=UpperCamelCase_    , do_lower_case=UpperCamelCase_    , remove_space=UpperCamelCase_    , keep_accents=UpperCamelCase_    , unk_token=UpperCamelCase_    , sep_token=UpperCamelCase_    , pad_token=UpperCamelCase_    , cls_token=UpperCamelCase_    , mask_token=UpperCamelCase_    , **UpperCamelCase_    , )

           lowerCAmelCase    :		Optional[int]				=      do_lower_case
           lowerCAmelCase    :		str				=      remove_space
           lowerCAmelCase    :		Any				=      keep_accents
           lowerCAmelCase    :		int				=      vocab_file
           lowerCAmelCase    :		List[str]				=      False if not self.vocab_file else True






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		Optional[int]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		List[str]				=      [self.sep_token_id]
           lowerCAmelCase    :		Optional[Any]				=      [self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           if not os.path.isdir(UpperCamelCase_ ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                 return
           lowerCAmelCase    :		str				=      os.path.join(
               UpperCamelCase_    , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
                 copyfile(self.vocab_file    , UpperCamelCase_ )

           return (out_vocab_file,)


