





import torch
from torch import autograd

class 					__A   (  autograd.Function			):


				@staticmethod
				def _snake_case				(						UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =inputs.numel()
								lowerCamelCase		       =int(0.0_0_5 * nb_elems							) + 1
								if sigmoid:
												lowerCamelCase		       =(torch.sigmoid(UpperCAmelCase_							) > threshold).type(inputs.type()							)
								else:
												lowerCamelCase		       =(inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
												# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
												lowerCamelCase		       =inputs.flatten().kthvalue(max(nb_elems - nb_min      ,       1							)							).values
												lowerCamelCase		       =(inputs > k_threshold).type(inputs.type()							)
								return mask


				@staticmethod
				def _snake_case				(						UpperCAmelCase_      ,       UpperCAmelCase_							):
								return gradOutput, None, None




class 					__A   (  autograd.Function			):


				@staticmethod
				def _snake_case				(						UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =inputs.clone()
								lowerCamelCase       ,     lowerCamelCase		       =inputs.flatten().sort(descending=UpperCAmelCase_							)
								lowerCamelCase		       =int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								lowerCamelCase		       =mask.flatten()
								lowerCamelCase		       =0
								lowerCamelCase		       =1
								return mask


				@staticmethod
				def _snake_case				(						UpperCAmelCase_      ,       UpperCAmelCase_							):
								return gradOutput, None




class 					__A   (  a			):


				@staticmethod
				def _snake_case				(						UpperCAmelCase_      ,       UpperCAmelCase_							):
								lowerCamelCase		       =inputs.clone()
								lowerCamelCase       ,     lowerCamelCase		       =inputs.abs().flatten().sort(descending=UpperCAmelCase_							)
								lowerCamelCase		       =int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								lowerCamelCase		       =mask.flatten()
								lowerCamelCase		       =0
								lowerCamelCase		       =1
								return mask












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,
)


UpperCAmelCase__  :	Optional[Any]     ={'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =['''PegasusTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	int     =['''PegasusTokenizerFast''']

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

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =[
							    '''TFPegasusForConditionalGeneration''',
							    '''TFPegasusModel''',
							    '''TFPegasusPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Dict     =[
							    '''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

							UpperCAmelCase__  :	Union[str, Any]     =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





