




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_					    = logging.get_logger(__name__)

lowerCAmelCase_					    = {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		'canine'

      def __init__(   self      ,				__UpperCAmelCase=768      ,				__UpperCAmelCase=12      ,				__UpperCAmelCase=12      ,				__UpperCAmelCase=3072      ,				__UpperCAmelCase="gelu"      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=1_6384      ,				__UpperCAmelCase=16      ,				__UpperCAmelCase=0.02      ,				__UpperCAmelCase=1e-12      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=0XE0_00      ,				__UpperCAmelCase=0XE0_01      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase=8      ,				__UpperCAmelCase=1_6384      ,				__UpperCAmelCase=128      ,				**__UpperCAmelCase      ,				):
        super().__init__(pad_token_id=__UpperCAmelCase      ,				bos_token_id=__UpperCAmelCase      ,				eos_token_id=__UpperCAmelCase      ,				**__UpperCAmelCase		)

        lowerCAmelCase__				:  int		       =     max_position_embeddings
        lowerCAmelCase__				:  Optional[Any]		       =     hidden_size
        lowerCAmelCase__				:  Any		       =     num_hidden_layers
        lowerCAmelCase__				:  Any		       =     num_attention_heads
        lowerCAmelCase__				:  List[Any]		       =     intermediate_size
        lowerCAmelCase__				:  Any		       =     hidden_act
        lowerCAmelCase__				:  int		       =     hidden_dropout_prob
        lowerCAmelCase__				:  List[str]		       =     attention_probs_dropout_prob
        lowerCAmelCase__				:  Dict		       =     initializer_range
        lowerCAmelCase__				:  Optional[int]		       =     type_vocab_size
        lowerCAmelCase__				:  Union[str, Any]		       =     layer_norm_eps

        # Character config:
        lowerCAmelCase__				:  Any		       =     downsampling_rate
        lowerCAmelCase__				:  Tuple		       =     upsampling_kernel_size
        lowerCAmelCase__				:  Any		       =     num_hash_functions
        lowerCAmelCase__				:  Optional[int]		       =     num_hash_buckets
        lowerCAmelCase__				:  Optional[int]		       =     local_transformer_stride







from datetime import datetime

import matplotlib.pyplot as plt
import torch



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  for param in module.parameters():
    lowerCAmelCase__				:  int		       =     False



def      __lowerCAmelCase			(					)     ->	Optional[Any]:
  lowerCAmelCase__				:  List[Any]		       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    lowerCAmelCase__				:  Optional[Any]		       =     '''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			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     plt.imshow(UpperCamelCase      )
  fig.axes.get_xaxis().set_visible(UpperCamelCase      )
  fig.axes.get_yaxis().set_visible(UpperCamelCase      )
  plt.show()



def      __lowerCAmelCase			(					)     ->	str:
  lowerCAmelCase__				:  Dict		       =     datetime.now()
  lowerCAmelCase__				:  Optional[int]		       =     current_time.strftime('''%H:%M:%S'''      )
  return timestamp

