



"""simple docstring"""


from typing import TYPE_CHECKING

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


snake_case_								=			{"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			["""PegasusTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			["""PegasusTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """PegasusForCausalLM""",
				    """PegasusForConditionalGeneration""",
				    """PegasusModel""",
				    """PegasusPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """TFPegasusForConditionalGeneration""",
				    """TFPegasusModel""",
				    """TFPegasusPreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_								=			[
				    """FlaxPegasusForConditionalGeneration""",
				    """FlaxPegasusModel""",
				    """FlaxPegasusPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus import PegasusTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus_fast import PegasusTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_pegasus import (
								    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    PegasusForCausalLM,
								    PegasusForConditionalGeneration,
								    PegasusModel,
								    PegasusPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_pegasus import (
								    FlaxPegasusForConditionalGeneration,
								    FlaxPegasusModel,
								    FlaxPegasusPreTrainedModel,
								)

else:
				import sys

				snake_case_								=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



