






"""simple docstring"""




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  (VQDiffusionScheduler,)






    def  A    (self						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							{
          """num_vec_classes""": 4_0_9_7,
          """num_train_timesteps""": 1_0_0,
      }

      config.update(**lowerCamelCase__   )
      return config






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							4
      A__											=							8
      A__											=							8

      A__											=							torch.randint(0						,      lowerCamelCase__						,      (batch_size, height * width)   )

      return sample






    @property
    def  A    (self   ):


      """simple docstring"""
      assert False






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      def model(lowerCamelCase__						,      lowerCamelCase__						,      *lowerCamelCase__   ):
        A__						,A__											=							sample.shape
        A__											=							torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)   )
        A__											=							F.log_softmax(logits.double()						,      dim=1   ).float()
        return return_value

      return model






    def  A    (self   ):


      """simple docstring"""
      for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
        self.check_over_configs(num_vec_classes=lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      for t in [0, 5_0, 9_9]:
        self.check_over_forward(time_step=lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      pass












"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__			=   logging.get_logger(__name__)

lowerCamelCase__			=   {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  "electra"






    def __init__(self						,      lowerCamelCase__=3_0_5_2_2						,      lowerCamelCase__=1_2_8						,      lowerCamelCase__=2_5_6						,      lowerCamelCase__=1_2						,      lowerCamelCase__=4						,      lowerCamelCase__=1_0_2_4						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0.1						,      lowerCamelCase__=5_1_2						,      lowerCamelCase__=2						,      lowerCamelCase__=0.0_2						,      lowerCamelCase__=1E-12						,      lowerCamelCase__="first"						,      lowerCamelCase__=True						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0						,      lowerCamelCase__="absolute"						,      lowerCamelCase__=True						,      lowerCamelCase__=None						,      **lowerCamelCase__						,      ):


      """simple docstring"""
      super().__init__(pad_token_id=lowerCamelCase__						,      **lowerCamelCase__   )

      A__											=							vocab_size
      A__											=							embedding_size
      A__											=							hidden_size
      A__											=							num_hidden_layers
      A__											=							num_attention_heads
      A__											=							intermediate_size
      A__											=							hidden_act
      A__											=							hidden_dropout_prob
      A__											=							attention_probs_dropout_prob
      A__											=							max_position_embeddings
      A__											=							type_vocab_size
      A__											=							initializer_range
      A__											=							layer_norm_eps

      A__											=							summary_type
      A__											=							summary_use_proj
      A__											=							summary_activation
      A__											=							summary_last_dropout
      A__											=							position_embedding_type
      A__											=							use_cache
      A__											=							classifier_dropout







class 					_UpperCamelCase		( __snake_case):






    @property
    def  A    (self   ):


      """simple docstring"""
      if self.task == "multiple-choice":
        A__											=							{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        A__											=							{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
              ("""token_type_ids""", dynamic_axis),
          ]   )




