





"""simple docstring"""






import copy
import random

from transformers import CLIPTokenizer



class 	lowerCAmelCase					(      snake_case							):






       def __init__(   self    ,  *a__    ,  **a__     ):
              super().__init__(*a__    ,  **a__     )
              _UpperCAmelCase     =     {}






       def 							__A (   self    ,  a__    ,  *a__    ,  **a__     ):
              _UpperCAmelCase     =     super().add_tokens(a__    ,  *a__    ,  **a__     )
              if num_added_tokens == 0:
                     raise ValueError(
                         f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                         ' `placeholder_token` that is not already in the tokenizer.'     )






       def 							__A (   self    ,  a__    ,  *a__    ,  a__=1    ,  **a__     ):
              _UpperCAmelCase     =     []
              if num_vec_per_token == 1:
                     self.try_adding_tokens(a__    ,  *a__    ,  **a__     )
                     output.append(a__     )
              else:
                     _UpperCAmelCase     =     []
                     for i in range(a__     ):
                            _UpperCAmelCase     =     placeholder_token + f"""_{i}"""
                            self.try_adding_tokens(a__    ,  *a__    ,  **a__     )
                            output.append(a__     )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
              for token in self.token_map:
                     if token in placeholder_token:
                            raise ValueError(
                                f"""The tokenizer already has placeholder token {token} that can get confused with"""
                                f""" {placeholder_token}keep placeholder tokens independent"""     )
              _UpperCAmelCase     =     output






       def 							__A (   self    ,  a__    ,  a__=False    ,  a__=1.0     ):
              if isinstance(a__    ,  a__     ):
                     _UpperCAmelCase     =     []
                     for i in range(len(a__     )     ):
                            output.append(self.replace_placeholder_tokens_in_text(text[i]    ,  vector_shuffle=a__     )     )
                     return output
              for placeholder_token in self.token_map:
                     if placeholder_token in text:
                            _UpperCAmelCase     =     self.token_map[placeholder_token]
                            _UpperCAmelCase     =     tokens[: 1 + int(len(a__     ) * prop_tokens_to_load     )]
                            if vector_shuffle:
                                   _UpperCAmelCase     =     copy.copy(a__     )
                                   random.shuffle(a__     )
                            _UpperCAmelCase     =     text.replace(a__    ,  ' '.join(a__     )     )
              return text






       def __call__(   self    ,  a__    ,  *a__    ,  a__=False    ,  a__=1.0    ,  **a__     ):
              return super().__call__(
                  self.replace_placeholder_tokens_in_text(
                      a__    ,  vector_shuffle=a__    ,  prop_tokens_to_load=a__     )    ,  *a__    ,  **a__    ,  )






       def 							__A (   self    ,  a__    ,  *a__    ,  a__=False    ,  a__=1.0    ,  **a__     ):
              return super().encode(
                  self.replace_placeholder_tokens_in_text(
                      a__    ,  vector_shuffle=a__    ,  prop_tokens_to_load=a__     )    ,  *a__    ,  **a__    ,  )













"""simple docstring"""






from datetime import datetime

import matplotlib.pyplot as plt
import torch

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




       for param in module.parameters():
              _UpperCAmelCase     =     False

def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              _UpperCAmelCase     =     'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'							)
       return device

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     plt.imshow(SCREAMING_SNAKE_CASE							)
       fig.axes.get_xaxis().set_visible(SCREAMING_SNAKE_CASE							)
       fig.axes.get_yaxis().set_visible(SCREAMING_SNAKE_CASE							)
       plt.show()


def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     datetime.now()
       _UpperCAmelCase     =     current_time.strftime('%H:%M:%S'							)
       return timestamp






