from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__								={
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__								=['''RoFormerTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__								=[
         '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RoFormerForCausalLM''',
         '''RoFormerForMaskedLM''',
         '''RoFormerForMultipleChoice''',
         '''RoFormerForQuestionAnswering''',
         '''RoFormerForSequenceClassification''',
         '''RoFormerForTokenClassification''',
         '''RoFormerLayer''',
         '''RoFormerModel''',
         '''RoFormerPreTrainedModel''',
         '''load_tf_weights_in_roformer''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__								=[
         '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFRoFormerForCausalLM''',
         '''TFRoFormerForMaskedLM''',
         '''TFRoFormerForMultipleChoice''',
         '''TFRoFormerForQuestionAnswering''',
         '''TFRoFormerForSequenceClassification''',
         '''TFRoFormerForTokenClassification''',
         '''TFRoFormerLayer''',
         '''TFRoFormerModel''',
         '''TFRoFormerPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__								=[
         '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''FlaxRoFormerForMaskedLM''',
         '''FlaxRoFormerForMultipleChoice''',
         '''FlaxRoFormerForQuestionAnswering''',
         '''FlaxRoFormerForSequenceClassification''',
         '''FlaxRoFormerForTokenClassification''',
         '''FlaxRoFormerModel''',
         '''FlaxRoFormerPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
     from .tokenization_roformer import RoFormerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_roformer_fast import RoFormerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roformer import (
              ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoFormerForCausalLM,
              RoFormerForMaskedLM,
              RoFormerForMultipleChoice,
              RoFormerForQuestionAnswering,
              RoFormerForSequenceClassification,
              RoFormerForTokenClassification,
              RoFormerLayer,
              RoFormerModel,
              RoFormerPreTrainedModel,
              load_tf_weights_in_roformer,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_roformer import (
              TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForMultipleChoice,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerLayer,
              TFRoFormerModel,
              TFRoFormerPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_roformer import (
              FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerModel,
              FlaxRoFormerPreTrainedModel,
          )


else:
     import sys

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




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def  __UpperCamelCase    (						A							):
     for param in module.parameters():
          UpperCamelCase__										=      False







def  __UpperCamelCase    (						):
     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  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      plt.imshow(A							)
     fig.axes.get_xaxis().set_visible(A							)
     fig.axes.get_yaxis().set_visible(A							)
     plt.show()







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



