'''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__     :	Dict				=			['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCamelCase__     :	Tuple				=			{'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__     :	Optional[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	Dict				=			' 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 __UpperCamelCase(   _A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  [
       '''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					:				Union[str, Any]  ,			_A					:				List[str]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  dct.pop(_A    )
   UpperCAmelCase__							:				Union[str, Any]   				=  val







def __UpperCamelCase(   _A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  torch.load(_A  ,			map_location='''cpu'''    )
   UpperCAmelCase__							:				Optional[int]   				=  torch.hub.load('''pytorch/fairseq'''  ,			'''bart.large.cnn'''    ).eval()
   hub_interface.model.load_state_dict(sd['''model''']    )
   return hub_interface







def __UpperCamelCase(   _A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  emb.weight.shape
   UpperCAmelCase__							:				List[str]   				=  nn.Linear(_A  ,			_A  ,			bias=_A    )
   UpperCAmelCase__							:				Optional[int]   				=  emb.weight.data
   return lin_layer







@torch.no_grad()
def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Any  ,			_A					:				int=None    ):
   '''simple docstring'''
   if not os.path.exists(_A    ):
      UpperCAmelCase__							:				Any   				=  torch.hub.load('''pytorch/fairseq'''  ,			_A    ).eval()
   else:
      UpperCAmelCase__							:				Optional[int]   				=  load_xsum_checkpoint(_A    )

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





'''simple docstring'''

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase__     :	Tuple				=			TypeVar('T')


class 				_lowercase	(      Generic[T]   ):
      '''simple docstring'''


      UpperCAmelCase_      :      deque[T]  # Cache store of keys
      UpperCAmelCase_      :      set[T]  # References of the keys in cache
      UpperCAmelCase_      :      int      							=   10  # Maximum capacity of cache




      def __init__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  deque()
         UpperCAmelCase__							:				Optional[int]   				=  set()
         if not n:
            UpperCAmelCase__							:				List[Any]   				=  sys.maxsize
         elif n < 0:
            raise ValueError('''n should be an integer greater than 0.'''							)
         else:
            UpperCAmelCase__							:				Any   				=  n




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         if x not in self.key_reference:
            if len(self.dq_store							) == LRUCache._MAX_CAPACITY:
               UpperCAmelCase__							:				int   				=  self.dq_store.pop()
               self.key_reference.remove(lowerCamelCase_							)
         else:
            self.dq_store.remove(lowerCamelCase_							)

         self.dq_store.appendleft(lowerCamelCase_							)
         self.key_reference.add(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         for k in self.dq_store:
            print(lowerCamelCase_							)




      def __repr__(				self							)			->			str:



         '''simple docstring'''



         return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store							)}'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCamelCase__     :	LRUCache[str | int]				=			LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




