
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class __snake_case	(   nn.Linear  ):
				def __init__(						self	,snake_case	,snake_case	,snake_case = True	,snake_case = "constant"	,snake_case = 0.0	,snake_case = "topK"	,):



											'''simple docstring'''







											super(snake_case	,self   ).__init__(in_features=snake_case	,out_features=snake_case	,bias=snake_case   )
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											lowercase							:  List[Any] 			=   pruning_method

											if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		lowercase							:  List[str] 			=   mask_scale
																		lowercase							:  Optional[int] 			=   mask_init
																		lowercase							:  int 			=   nn.Parameter(torch.empty(self.weight.size()   )   )
																		self.init_mask()
				def    _SCREAMING_SNAKE_CASE (						self   ):



											'''simple docstring'''







											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    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case   ):



											'''simple docstring'''







											if self.pruning_method == "topK":
																		lowercase							:  List[Any] 			=   TopKBinarizer.apply(self.mask_scores	,snake_case   )
											elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		lowercase							:  int 			=   """sigmoied""" in self.pruning_method
																		lowercase							:  List[str] 			=   ThresholdBinarizer.apply(self.mask_scores	,snake_case	,snake_case   )
											elif self.pruning_method == "magnitude":
																		lowercase							:  int 			=   MagnitudeBinarizer.apply(self.weight	,snake_case   )
											elif self.pruning_method == "l0":
																		lowercase							:  Optional[int] 			=   -0.1, 1.1, 2 / 3
																		if self.training:
																									lowercase							:  List[str] 			=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0_001	,0.9_999   )
																									lowercase							:  Any 			=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
																		else:
																									lowercase							:  List[Any] 			=   torch.sigmoid(self.mask_scores   )
																		lowercase							:  Union[str, Any] 			=   s * (r - l) + l
																		lowercase							:  List[str] 			=   s_bar.clamp(min=0.0	,max=1.0   )
											# Mask weights with computed mask
											lowercase							:  Optional[int] 			=   mask * self.weight
											# Compute output (linear layer) with masked weights
											return nn.functional.linear(snake_case	,snake_case	,self.bias   )


from typing import TYPE_CHECKING

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


lowercase						:      Any							=							{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[Any]							=							["""GPT2TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPT2DoubleHeadsModel""",
      """GPT2ForQuestionAnswering""",
      """GPT2ForSequenceClassification""",
      """GPT2ForTokenClassification""",
      """GPT2LMHeadModel""",
      """GPT2Model""",
      """GPT2PreTrainedModel""",
      """load_tf_weights_in_gpt2""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      Tuple							=							[
      """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFGPT2DoubleHeadsModel""",
      """TFGPT2ForSequenceClassification""",
      """TFGPT2LMHeadModel""",
      """TFGPT2MainLayer""",
      """TFGPT2Model""",
      """TFGPT2PreTrainedModel""",
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[str]							=							["""TFGPT2Tokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

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