


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_				:			str   =	['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase_				:			Union[str, 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()
UpperCAmelCase_				:			int   =	logging.get_logger(__name__)

UpperCAmelCase_				:			List[str]   =	' Hello world! cécé herlolip'

UpperCAmelCase_				:			List[str]   =	[
    ('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 	SCREAMING_SNAKE_CASE_						(      __A			:      Dict    )     ->		Any:

       """simple docstring"""

       a_ :		List[Any]							=  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(__A  ,   __A    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      Dict    )     ->		int:

       """simple docstring"""

       a_ :		List[str]							=  dct.pop(__A    )
       a_ :		List[str]							=  val







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]    )     ->		List[Any]:

       """simple docstring"""

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Union[str, Any]:

       """simple docstring"""

       a_							,		a_ :		str							=  emb.weight.shape
       a_ :		Optional[int]							=  nn.Linear(__A  ,   __A  ,   bias=__A    )
       a_ :		Optional[int]							=  emb.weight.data
       return lin_layer







@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      Optional[Any]  ,   __A			:      Any=None    )     ->		Tuple:

       """simple docstring"""

       if not os.path.exists(__A    ):
              a_ :		Any							=  torch.hub.load('pytorch/fairseq'  ,   __A    ).eval()
       else:
              a_ :		Tuple							=  load_xsum_checkpoint(__A    )

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






def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		list:

       """simple docstring"""

       a_ :		int							=  len(__A    )
       for _ in range(__A    ):
              for i in range(_ % 2  ,   arr_size - 1  ,   2    ):
                     if arr[i + 1] < arr[i]:
                            a_							,		a_ :		int							=  arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	list(range(10, 0, -1))
   print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')


