



from __future__ import annotations

import time

import numpy as np

__UpperCAmelCase          =       [8, 5, 9, 7]
__UpperCAmelCase          =       [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__UpperCAmelCase          =       [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		None:
          UpperCamelCase    :  Optional[int]							=	claim_vector
          UpperCamelCase    :  Any							=	allocated_resources_table
          UpperCamelCase    :  Union[str, Any]							=	maximum_claim_table
       def 					snake_case_       (			self       )     ->		list[int]:
          return [
              sum(p_item[i] for p_item in self.__allocated_resources_table       )
              for i in range(len(self.__allocated_resources_table[0]       )       )
          ]
       def 					snake_case_       (			self       )     ->		list[int]:
          return np.array(self.__claim_vector       ) - np.array(
              self.__processes_resource_summation()       )
       def 					snake_case_       (			self       )     ->		list[list[int]]:
          return [
              list(np.array(self.__maximum_claim_table[i]       ) - np.array(SCREAMING_SNAKE_CASE_       )       )
              for i, allocated_resource in enumerate(self.__allocated_resources_table       )
          ]
       def 					snake_case_       (			self       )     ->		dict[int, list[int]]:
          return {self.__need().index(SCREAMING_SNAKE_CASE_       ): i for i in self.__need()}
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  int							=	self.__need()
          UpperCamelCase    :  int							=	self.__allocated_resources_table
          UpperCamelCase    :  List[Any]							=	self.__available_resources()
          UpperCamelCase    :  str							=	self.__need_index_manager()
          for kw, val in kwargs.items():
             if kw and val is True:
                self.__pretty_data()
          print('_' * 50 + '\n'       )
          while need_list:
             UpperCamelCase    :  Optional[Any]							=	False
             for each_need in need_list:
                UpperCamelCase    :  List[str]							=	True
                for index, need in enumerate(SCREAMING_SNAKE_CASE_       ):
                   if need > available_resources[index]:
                      UpperCamelCase    :  Optional[int]							=	False
                      break
                if execution:
                   UpperCamelCase    :  Any							=	True
                   # get the original index of the process from ind_ctrl db
                   for original_need_index, need_clone in need_index_manager.items():
                      if each_need == need_clone:
                         UpperCamelCase    :  List[str]							=	original_need_index
                   print(F"""Process {process_number + 1} is executing."""       )
                   # remove the process run from stack
                   need_list.remove(SCREAMING_SNAKE_CASE_       )
                   # update available/freed resources stack
                   UpperCamelCase    :  Optional[Any]							=	np.array(SCREAMING_SNAKE_CASE_       ) + np.array(
                       alloc_resources_table[process_number]       )
                   print(
                       'Updated available resource stack for processes: '
                       + ' '.join([str(SCREAMING_SNAKE_CASE_       ) for x in available_resources]       )       )
                   break
             if safe:
                print('The process is in a safe state.\n'       )
             else:
                print('System in unsafe state. Aborting...\n'       )
                break






       def 					snake_case_       (			self       )     ->		Optional[int]:
          print(' ' * 9 + 'Allocated Resource Table'       )
          for item in self.__allocated_resources_table:
             print(
                 F"""P{self.__allocated_resources_table.index(SCREAMING_SNAKE_CASE_       ) + 1}"""
                 + ' '.join(F"""{it:>8}""" for it in item       )
                 + '\n'       )
          print(' ' * 9 + 'System Resource Table'       )
          for item in self.__maximum_claim_table:
             print(
                 F"""P{self.__maximum_claim_table.index(SCREAMING_SNAKE_CASE_       ) + 1}"""
                 + ' '.join(F"""{it:>8}""" for it in item       )
                 + '\n'       )
          print(
              'Current Usage by Active Processes: '
              + ' '.join(str(SCREAMING_SNAKE_CASE_       ) for x in self.__claim_vector       )       )
          print(
              'Initial Available Resources:       '
              + ' '.join(str(SCREAMING_SNAKE_CASE_       ) for x in self.__available_resources()       )       )
          time.sleep(1       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()








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

__UpperCAmelCase          =       ''' Hello world! cécé herlolip'''

__UpperCAmelCase          =       [
    ('''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	(   snake_case__       :    Union[str, Any]	)	->     List[str]:
   UpperCamelCase    :  int							=	[
       'encoder.version',
       'decoder.version',
       'model.encoder.version',
       'model.decoder.version',
       '_float_tensor',
   ]
   for k in ignore_keys:
      state_dict.pop(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Any:
   UpperCamelCase    :  Dict							=	dct.pop(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	val
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     Tuple:
   UpperCamelCase    :  int							=	torch.load(snake_case__       ,     map_location='cpu'	)
   UpperCamelCase    :  Dict							=	torch.hub.load('pytorch/fairseq'       ,     'bart.large.cnn'	).eval()
   hub_interface.model.load_state_dict(sd['model']	)
   return hub_interface
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     Dict:
   UpperCamelCase	,     UpperCamelCase    :  str							=	emb.weight.shape
   UpperCamelCase    :  Optional[int]							=	nn.Linear(snake_case__       ,     snake_case__       ,     bias=snake_case__	)
   UpperCamelCase    :  List[str]							=	emb.weight.data
   return lin_layer
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    str=None	)	->     Optional[Any]:
   if not os.path.exists(snake_case__	):
      UpperCamelCase    :  List[str]							=	torch.hub.load('pytorch/fairseq'       ,     snake_case__	).eval()
   else:
      UpperCamelCase    :  int							=	load_xsum_checkpoint(snake_case__	)

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

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


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



