


from datetime import datetime

import matplotlib.pyplot as plt
import torch





def     _lowerCamelCase		(				SCREAMING_SNAKE_CASE_					:			str   ):






							"""simple docstring"""



							for param in module.parameters():
														a_							: Union[str, Any]								=						False





def     _lowerCamelCase		(				):






							"""simple docstring"""



							a_							: Optional[int]								=						"""cuda""" if torch.cuda.is_available() else """cpu"""
							if torch.backends.mps.is_available() and torch.backends.mps.is_built():
														a_							: 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		(				SCREAMING_SNAKE_CASE_					:			Optional[Any]   ):






							"""simple docstring"""



							a_							: Any								=						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"""



							a_							: Optional[Any]								=						datetime.now()
							a_							: Optional[Any]								=						current_time.strftime("""%H:%M:%S"""   )
							return timestamp



from typing import TYPE_CHECKING

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


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

				_lowercase:	Optional[int]       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






