

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


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

lowerCAmelCase_			      =      ''' Hello world! cécé herlolip'''

lowerCAmelCase_			      =      [
    ('''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   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      [
						    '''encoder.version''',
						    '''decoder.version''',
						    '''model.encoder.version''',
						    '''model.decoder.version''',
						    '''_float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      dct.pop(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      val


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      torch.load(SCREAMING_SNAKE_CASE__    ,						map_location='''cpu'''			)
						snake_case_  =      torch.hub.load('''pytorch/fairseq'''    ,						'''bart.large.cnn'''			).eval()
						hub_interface.model.load_state_dict(sd['''model''']			)
						return hub_interface


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_,       snake_case_  =      emb.weight.shape
						snake_case_  =      nn.Linear(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						bias=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      emb.weight.data
						return lin_layer


@torch.no_grad()
def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None			):
						if not os.path.exists(SCREAMING_SNAKE_CASE__			):
												snake_case_  =      torch.hub.load('''pytorch/fairseq'''    ,						SCREAMING_SNAKE_CASE__			).eval()
						else:
												snake_case_  =      load_xsum_checkpoint(SCREAMING_SNAKE_CASE__			)

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

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


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







from collections import defaultdict



def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> bool:
				_A	:				Union[str, Any]   =      first_str.lower().strip()
				_A	:				int   =      second_str.lower().strip()

				# Remove whitespace
				_A	:				int   =      first_str.replace(''' '''					,  ''''''   )
				_A	:				Optional[Any]   =      second_str.replace(''' '''					,  ''''''   )

				# Strings of different lengths are not anagrams
				if len(__lowercase   ) != len(__lowercase   ):
								return False

				# Default values for count should be 0
				_A	:				defaultdict[str, int]   =      defaultdict(__lowercase   )

				# For each character in input strings,
				# increment count in the corresponding
				for i in range(len(__lowercase   )   ):
								count[first_str[i]] += 1
								count[second_str[i]] -= 1

				return all(_count == 0 for _count in count.values()   )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	_UpperCamelCase			:	Dict								=input('Enter the first string ').strip()
	_UpperCamelCase			:	Union[str, Any]								=input('Enter the second string ').strip()

	_UpperCamelCase			:	Dict								=check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')



