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


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

snake_case__				= ' Hello world! cécé herlolip'

snake_case__				= [
    ('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       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				[
          '''encoder.version''',
          '''decoder.version''',
          '''model.encoder.version''',
          '''model.decoder.version''',
          '''_float_tensor''',
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				dct.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				torch.load(__UpperCAmelCase ,						map_location='''cpu'''		)
      _lowerCamelCase			    =				torch.hub.load('''pytorch/fairseq''' ,						'''bart.large.cnn'''		).eval()
      hub_interface.model.load_state_dict(sd['''model''']		)
      return hub_interface
def       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase    ,					_lowerCamelCase			    =				emb.weight.shape
      _lowerCamelCase			    =				nn.Linear(__UpperCAmelCase ,						__UpperCAmelCase ,						bias=__UpperCAmelCase		)
      _lowerCamelCase			    =				emb.weight.data
      return lin_layer
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None		)						->    Union[str, Any]:



      '''simple docstring'''

      if not os.path.exists(__UpperCAmelCase		):
            _lowerCamelCase			    =				torch.hub.load('''pytorch/fairseq''' ,						__UpperCAmelCase		).eval()
      else:
            _lowerCamelCase			    =				load_xsum_checkpoint(__UpperCAmelCase		)

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

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


if __name__ == "__main__":
     snake_case__				= 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'
     )
     snake_case__				= parser.parse_args()
     convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case__				= fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
     # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

     # max-min composition
     # max-product composition

     # Plot each set A, set B and each operation result using plot() and subplot().
     from matplotlib import pyplot as plt

     plt.figure()

     plt.subplot(4, 3, 1)
     plt.plot(X, young)
     plt.title('Young')
     plt.grid(True)

     plt.subplot(4, 3, 2)
     plt.plot(X, middle_aged)
     plt.title('Middle aged')
     plt.grid(True)

     plt.subplot(4, 3, 3)
     plt.plot(X, union)
     plt.title('union')
     plt.grid(True)

     plt.subplot(4, 3, 4)
     plt.plot(X, intersection)
     plt.title('intersection')
     plt.grid(True)

     plt.subplot(4, 3, 5)
     plt.plot(X, complement_a)
     plt.title('complement_a')
     plt.grid(True)

     plt.subplot(4, 3, 6)
     plt.plot(X, difference)
     plt.title('difference a/b')
     plt.grid(True)

     plt.subplot(4, 3, 7)
     plt.plot(X, alg_sum)
     plt.title('alg_sum')
     plt.grid(True)

     plt.subplot(4, 3, 8)
     plt.plot(X, alg_product)
     plt.title('alg_product')
     plt.grid(True)

     plt.subplot(4, 3, 9)
     plt.plot(X, bdd_sum)
     plt.title('bdd_sum')
     plt.grid(True)

     plt.subplot(4, 3, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

     plt.subplots_adjust(hspace=0.5)
     plt.show()