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

A     :	Optional[Any]    = ' Hello world! cécé herlolip'

A     :	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     __lowerCAmelCase			(			a__  )       ->  Optional[Any]:
				__a										=    [
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''_float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(a__						,    a__  )


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  Optional[Any]:
				__a										=    dct.pop(a__  )
				__a										=    val


def     __lowerCAmelCase			(			a__  )       ->  Optional[int]:
				__a										=    torch.load(a__						,    map_location='''cpu'''  )
				__a										=    torch.hub.load('''pytorch/fairseq'''						,    '''bart.large.cnn'''  ).eval()
				hub_interface.model.load_state_dict(sd['''model''']  )
				return hub_interface


def     __lowerCAmelCase			(			a__  )       ->  Optional[int]:
				__a							,     __a										=    emb.weight.shape
				__a										=    nn.Linear(a__						,    a__						,    bias=a__  )
				__a										=    emb.weight.data
				return lin_layer


@torch.no_grad()
def     __lowerCAmelCase			(			a__						,    a__						,    a__=None  )       ->  List[str]:
				if not os.path.exists(a__  ):
								__a										=    torch.hub.load('''pytorch/fairseq'''						,    a__  ).eval()
				else:
								__a										=    load_xsum_checkpoint(a__  )

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






from __future__ import annotations

import math


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5 ,     int(math.sqrt(UpperCamelCase__  ) + 1  ) ,     6  ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->list[int]:
       A__											=						str(UpperCamelCase__  )
       A__											=						[n]
       for i in range(1 ,     len(UpperCamelCase__  )  ):
              list_nums.append(int(str_num[i:]  )  )
              list_nums.append(int(str_num[:-i]  )  )
       return list_nums


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:
       if len(str(UpperCamelCase__  )  ) > 3:
              if not is_prime(int(str(UpperCamelCase__  )[-3:]  )  ) or not is_prime(int(str(UpperCamelCase__  )[:3]  )  ):
                     return False
       return True


def 	UpperCamelCase__(       UpperCamelCase__			:       int = 11  )->list[int]:
       A__											=						[]
       A__											=						13
       while len(UpperCamelCase__  ) != count:
              if validate(UpperCamelCase__  ):
                     A__											=						list_truncated_nums(UpperCamelCase__  )
                     if all(is_prime(UpperCamelCase__  ) for i in list_nums  ):
                            list_truncated_primes.append(UpperCamelCase__  )
              num += 2
       return list_truncated_primes


def 	UpperCamelCase__(       )->int:
       return sum(compute_truncated_primes(11  )  )


if __name__ == "__main__":
       print(F"{sum(compute_truncated_primes(11)) = }")





