




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     for param in module.parameters():
          UpperCAmelCase_      :							List[Any]       =      False







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Any       =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
     if torch.backends.mps.is_available() and torch.backends.mps.is_built():
          UpperCAmelCase_      :							List[str]       =      '''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__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      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__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      datetime.now()
     UpperCAmelCase_      :							List[str]       =      current_time.strftime('''%H:%M:%S'''				)
     return timestamp





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =			{
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			['BloomTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BloomForCausalLM',
         'BloomModel',
         'BloomPreTrainedModel',
         'BloomForSequenceClassification',
         'BloomForTokenClassification',
         'BloomForQuestionAnswering',
     ]

if TYPE_CHECKING:
     from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bloom_fast import BloomTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bloom import (
              BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
              BloomForCausalLM,
              BloomForQuestionAnswering,
              BloomForSequenceClassification,
              BloomForTokenClassification,
              BloomModel,
              BloomPreTrainedModel,
          )

else:
     import sys

     __a          =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)