





'''simple docstring'''



from typing import TYPE_CHECKING

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


snake_case_   :  Optional[int]								=      {
    '''configuration_utils''': ['''GenerationConfig'''],
    '''streamers''': ['''TextIteratorStreamer''', '''TextStreamer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case_   :  str								=      [
			    '''Constraint''',
			    '''ConstraintListState''',
			    '''DisjunctiveConstraint''',
			    '''PhrasalConstraint''',
			]
			snake_case_   :  Optional[int]								=      [
			    '''BeamHypotheses''',
			    '''BeamScorer''',
			    '''BeamSearchScorer''',
			    '''ConstrainedBeamSearchScorer''',
			]
			snake_case_   :  List[Any]								=      [
			    '''EpsilonLogitsWarper''',
			    '''EtaLogitsWarper''',
			    '''ForcedBOSTokenLogitsProcessor''',
			    '''ForcedEOSTokenLogitsProcessor''',
			    '''HammingDiversityLogitsProcessor''',
			    '''InfNanRemoveLogitsProcessor''',
			    '''LogitsProcessor''',
			    '''LogitsProcessorList''',
			    '''LogitsWarper''',
			    '''MinLengthLogitsProcessor''',
			    '''MinNewTokensLengthLogitsProcessor''',
			    '''NoBadWordsLogitsProcessor''',
			    '''NoRepeatNGramLogitsProcessor''',
			    '''PrefixConstrainedLogitsProcessor''',
			    '''RepetitionPenaltyLogitsProcessor''',
			    '''SequenceBiasLogitsProcessor''',
			    '''EncoderRepetitionPenaltyLogitsProcessor''',
			    '''TemperatureLogitsWarper''',
			    '''TopKLogitsWarper''',
			    '''TopPLogitsWarper''',
			    '''TypicalLogitsWarper''',
			    '''EncoderNoRepeatNGramLogitsProcessor''',
			    '''ExponentialDecayLengthPenalty''',
			    '''LogitNormalization''',
			]
			snake_case_   :  Dict								=      [
			    '''MaxNewTokensCriteria''',
			    '''MaxLengthCriteria''',
			    '''MaxTimeCriteria''',
			    '''StoppingCriteria''',
			    '''StoppingCriteriaList''',
			    '''validate_stopping_criteria''',
			]
			snake_case_   :  Union[str, Any]								=      [
			    '''GenerationMixin''',
			    '''top_k_top_p_filtering''',
			    '''GreedySearchEncoderDecoderOutput''',
			    '''GreedySearchDecoderOnlyOutput''',
			    '''SampleEncoderDecoderOutput''',
			    '''SampleDecoderOnlyOutput''',
			    '''BeamSearchEncoderDecoderOutput''',
			    '''BeamSearchDecoderOnlyOutput''',
			    '''BeamSampleEncoderDecoderOutput''',
			    '''BeamSampleDecoderOnlyOutput''',
			    '''ContrastiveSearchEncoderDecoderOutput''',
			    '''ContrastiveSearchDecoderOnlyOutput''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case_   :  Any								=      [
			    '''TFForcedBOSTokenLogitsProcessor''',
			    '''TFForcedEOSTokenLogitsProcessor''',
			    '''TFLogitsProcessor''',
			    '''TFLogitsProcessorList''',
			    '''TFLogitsWarper''',
			    '''TFMinLengthLogitsProcessor''',
			    '''TFNoBadWordsLogitsProcessor''',
			    '''TFNoRepeatNGramLogitsProcessor''',
			    '''TFRepetitionPenaltyLogitsProcessor''',
			    '''TFTemperatureLogitsWarper''',
			    '''TFTopKLogitsWarper''',
			    '''TFTopPLogitsWarper''',
			    '''TFForceTokensLogitsProcessor''',
			    '''TFSuppressTokensAtBeginLogitsProcessor''',
			    '''TFSuppressTokensLogitsProcessor''',
			]
			snake_case_   :  Union[str, Any]								=      [
			    '''TFGenerationMixin''',
			    '''tf_top_k_top_p_filtering''',
			    '''TFGreedySearchDecoderOnlyOutput''',
			    '''TFGreedySearchEncoderDecoderOutput''',
			    '''TFSampleEncoderDecoderOutput''',
			    '''TFSampleDecoderOnlyOutput''',
			    '''TFBeamSearchEncoderDecoderOutput''',
			    '''TFBeamSearchDecoderOnlyOutput''',
			    '''TFBeamSampleEncoderDecoderOutput''',
			    '''TFBeamSampleDecoderOnlyOutput''',
			    '''TFContrastiveSearchEncoderDecoderOutput''',
			    '''TFContrastiveSearchDecoderOnlyOutput''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case_   :  List[str]								=      [
			    '''FlaxForcedBOSTokenLogitsProcessor''',
			    '''FlaxForcedEOSTokenLogitsProcessor''',
			    '''FlaxLogitsProcessor''',
			    '''FlaxLogitsProcessorList''',
			    '''FlaxLogitsWarper''',
			    '''FlaxMinLengthLogitsProcessor''',
			    '''FlaxTemperatureLogitsWarper''',
			    '''FlaxTopKLogitsWarper''',
			    '''FlaxTopPLogitsWarper''',
			]
			snake_case_   :  Dict								=      [
			    '''FlaxGenerationMixin''',
			    '''FlaxGreedySearchOutput''',
			    '''FlaxSampleOutput''',
			    '''FlaxBeamSearchOutput''',
			]

if TYPE_CHECKING:
			from .configuration_utils import GenerationConfig
			from .streamers import TextIteratorStreamer, TextStreamer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
						from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
						from .logits_process import (
						    EncoderNoRepeatNGramLogitsProcessor,
						    EncoderRepetitionPenaltyLogitsProcessor,
						    EpsilonLogitsWarper,
						    EtaLogitsWarper,
						    ExponentialDecayLengthPenalty,
						    ForcedBOSTokenLogitsProcessor,
						    ForcedEOSTokenLogitsProcessor,
						    HammingDiversityLogitsProcessor,
						    InfNanRemoveLogitsProcessor,
						    LogitNormalization,
						    LogitsProcessor,
						    LogitsProcessorList,
						    LogitsWarper,
						    MinLengthLogitsProcessor,
						    MinNewTokensLengthLogitsProcessor,
						    NoBadWordsLogitsProcessor,
						    NoRepeatNGramLogitsProcessor,
						    PrefixConstrainedLogitsProcessor,
						    RepetitionPenaltyLogitsProcessor,
						    SequenceBiasLogitsProcessor,
						    TemperatureLogitsWarper,
						    TopKLogitsWarper,
						    TopPLogitsWarper,
						    TypicalLogitsWarper,
						)
						from .stopping_criteria import (
						    MaxLengthCriteria,
						    MaxNewTokensCriteria,
						    MaxTimeCriteria,
						    StoppingCriteria,
						    StoppingCriteriaList,
						    validate_stopping_criteria,
						)
						from .utils import (
						    BeamSampleDecoderOnlyOutput,
						    BeamSampleEncoderDecoderOutput,
						    BeamSearchDecoderOnlyOutput,
						    BeamSearchEncoderDecoderOutput,
						    ContrastiveSearchDecoderOnlyOutput,
						    ContrastiveSearchEncoderDecoderOutput,
						    GenerationMixin,
						    GreedySearchDecoderOnlyOutput,
						    GreedySearchEncoderDecoderOutput,
						    SampleDecoderOnlyOutput,
						    SampleEncoderDecoderOutput,
						    top_k_top_p_filtering,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tf_logits_process import (
						    TFForcedBOSTokenLogitsProcessor,
						    TFForcedEOSTokenLogitsProcessor,
						    TFForceTokensLogitsProcessor,
						    TFLogitsProcessor,
						    TFLogitsProcessorList,
						    TFLogitsWarper,
						    TFMinLengthLogitsProcessor,
						    TFNoBadWordsLogitsProcessor,
						    TFNoRepeatNGramLogitsProcessor,
						    TFRepetitionPenaltyLogitsProcessor,
						    TFSuppressTokensAtBeginLogitsProcessor,
						    TFSuppressTokensLogitsProcessor,
						    TFTemperatureLogitsWarper,
						    TFTopKLogitsWarper,
						    TFTopPLogitsWarper,
						)
						from .tf_utils import (
						    TFBeamSampleDecoderOnlyOutput,
						    TFBeamSampleEncoderDecoderOutput,
						    TFBeamSearchDecoderOnlyOutput,
						    TFBeamSearchEncoderDecoderOutput,
						    TFContrastiveSearchDecoderOnlyOutput,
						    TFContrastiveSearchEncoderDecoderOutput,
						    TFGenerationMixin,
						    TFGreedySearchDecoderOnlyOutput,
						    TFGreedySearchEncoderDecoderOutput,
						    TFSampleDecoderOnlyOutput,
						    TFSampleEncoderDecoderOutput,
						    tf_top_k_top_p_filtering,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .flax_logits_process import (
						    FlaxForcedBOSTokenLogitsProcessor,
						    FlaxForcedEOSTokenLogitsProcessor,
						    FlaxLogitsProcessor,
						    FlaxLogitsProcessorList,
						    FlaxLogitsWarper,
						    FlaxMinLengthLogitsProcessor,
						    FlaxTemperatureLogitsWarper,
						    FlaxTopKLogitsWarper,
						    FlaxTopPLogitsWarper,
						)
						from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
			import sys

			snake_case_   :  Dict								=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
													_UpperCamelCase											=   mask_scale
													_UpperCamelCase											=   mask_init
													_UpperCamelCase											=   nn.Parameter(torch.empty(self.weight.size()   )   )
													self.init_mask()




			def       a	(					self   ):
								if self.mask_init == "constant":
													init.constant_(self.mask_scores			,     val=self.mask_scale   )
								elif self.mask_init == "uniform":
													init.uniform_(self.mask_scores			,     a=-self.mask_scale			,     b=self.mask_scale   )
								elif self.mask_init == "kaiming":
													init.kaiming_uniform_(self.mask_scores			,     a=math.sqrt(5   )   )




			def       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								elif self.pruning_method == "l0":
													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   -0.1, 1.1, 2 / 3
													if self.training:
																		_UpperCamelCase											=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0001			,     0.9999   )
																		_UpperCamelCase											=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
													else:
																		_UpperCamelCase											=   torch.sigmoid(self.mask_scores   )
													_UpperCamelCase											=   s * (r - l) + l
													_UpperCamelCase											=   s_bar.clamp(min=0.0			,     max=1.0   )
								# Mask weights with computed mask
								_UpperCamelCase											=   mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(A_			,     A_			,     self.bias   )



