



'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 							a__(      lowerCamelCase__			):
    lowercase__				=					(DEISMultistepScheduler,)
    lowercase__				=					(("""num_inference_steps""", 25),)







    def     lowercase_ (   self				:						Tuple		,    **__snake_case				:						Any			):
      a		:      Optional[int]       =  {
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**__snake_case			)
      return config







    def     lowercase_ (   self				:						int		,    __snake_case				:						Union[str, Any]=0		,    **__snake_case				:						int			):
      a		:      Optional[int]       =  dict(self.forward_default_kwargs			)
      a		:      Union[str, Any]       =  kwargs.pop('num_inference_steps'		,    __snake_case			)
      a		:      str       =  self.dummy_sample
      a		:      Optional[Any]       =  0.1 * sample
      a		:      Optional[int]       =  [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        a		:      Union[str, Any]       =  self.get_scheduler_config(**__snake_case			)
        a		:      Any       =  scheduler_class(**__snake_case			)
        scheduler.set_timesteps(__snake_case			)
        # copy over dummy past residuals
        a		:      Dict       =  dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__snake_case			)
          a		:      Optional[Any]       =  scheduler_class.from_pretrained(__snake_case			)
          new_scheduler.set_timesteps(__snake_case			)
          # copy over dummy past residuals
          a		:      Optional[Any]       =  dummy_past_residuals[: new_scheduler.config.solver_order]

        a     ,			a		:      str       =  sample, sample
        for t in range(__snake_case		,    time_step + scheduler.config.solver_order + 1			):
          a		:      Optional[Any]       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    **__snake_case			).prev_sample
          a		:      int       =  new_scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    **__snake_case			).prev_sample

          assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"







    def     lowercase_ (   self				:						List[Any]			):
      pass







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Optional[int]=0		,    **__snake_case				:						Tuple			):
      a		:      Tuple       =  dict(self.forward_default_kwargs			)
      a		:      str       =  kwargs.pop('num_inference_steps'		,    __snake_case			)
      a		:      Optional[int]       =  self.dummy_sample
      a		:      Tuple       =  0.1 * sample
      a		:      int       =  [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        a		:      Union[str, Any]       =  self.get_scheduler_config()
        a		:      Tuple       =  scheduler_class(**__snake_case			)
        scheduler.set_timesteps(__snake_case			)

        # copy over dummy past residuals (must be after setting timesteps)
        a		:      str       =  dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__snake_case			)
          a		:      Tuple       =  scheduler_class.from_pretrained(__snake_case			)
          # copy over dummy past residuals
          new_scheduler.set_timesteps(__snake_case			)

          # copy over dummy past residual (must be after setting timesteps)
          a		:      str       =  dummy_past_residuals[: new_scheduler.config.solver_order]

        a		:      List[Any]       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    **__snake_case			).prev_sample
        a		:      Optional[Any]       =  new_scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    **__snake_case			).prev_sample

        assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"







    def     lowercase_ (   self				:						int		,    __snake_case				:						str=None		,    **__snake_case				:						List[Any]			):
      if scheduler is None:
        a		:      Dict       =  self.scheduler_classes[0]
        a		:      List[str]       =  self.get_scheduler_config(**__snake_case			)
        a		:      Dict       =  scheduler_class(**__snake_case			)

      a		:      List[str]       =  self.scheduler_classes[0]
      a		:      Tuple       =  self.get_scheduler_config(**__snake_case			)
      a		:      Union[str, Any]       =  scheduler_class(**__snake_case			)

      a		:      Tuple       =  10
      a		:      List[Any]       =  self.dummy_model()
      a		:      List[str]       =  self.dummy_sample_deter
      scheduler.set_timesteps(__snake_case			)

      for i, t in enumerate(scheduler.timesteps			):
        a		:      int       =  model(__snake_case		,    __snake_case			)
        a		:      Optional[int]       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case			).prev_sample

      return sample







    def     lowercase_ (   self				:						str			):
      a		:      int       =  dict(self.forward_default_kwargs			)

      a		:      str       =  kwargs.pop('num_inference_steps'		,    __snake_case			)

      for scheduler_class in self.scheduler_classes:
        a		:      str       =  self.get_scheduler_config()
        a		:      Optional[int]       =  scheduler_class(**__snake_case			)

        a		:      int       =  self.dummy_sample
        a		:      List[str]       =  0.1 * sample

        if num_inference_steps is not None and hasattr(__snake_case		,    'set_timesteps'			):
          scheduler.set_timesteps(__snake_case			)
        elif num_inference_steps is not None and not hasattr(__snake_case		,    'set_timesteps'			):
          a		:      Union[str, Any]       =  num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        a		:      Optional[Any]       =  [residual + 0.2, residual + 0.15, residual + 0.10]
        a		:      Tuple       =  dummy_past_residuals[: scheduler.config.solver_order]

        a		:      List[Any]       =  scheduler.timesteps[5]
        a		:      Tuple       =  scheduler.timesteps[6]

        a		:      Optional[Any]       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    **__snake_case			).prev_sample
        a		:      Tuple       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    **__snake_case			).prev_sample

        self.assertEqual(output_a.shape		,    sample.shape			)
        self.assertEqual(output_a.shape		,    output_a.shape			)







    def     lowercase_ (   self				:						Dict			):
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      a		:      List[Any]       =  DEISMultistepScheduler(**self.get_scheduler_config()			)
      a		:      Optional[Any]       =  self.full_loop(scheduler=__snake_case			)
      a		:      List[str]       =  torch.mean(torch.abs(__snake_case			)			)

      assert abs(result_mean.item() - 0.23916			) < 1e-3

      a		:      Union[str, Any]       =  DPMSolverSinglestepScheduler.from_config(scheduler.config			)
      a		:      List[Any]       =  DPMSolverMultistepScheduler.from_config(scheduler.config			)
      a		:      int       =  UniPCMultistepScheduler.from_config(scheduler.config			)
      a		:      Optional[int]       =  DEISMultistepScheduler.from_config(scheduler.config			)

      a		:      Any       =  self.full_loop(scheduler=__snake_case			)
      a		:      Optional[Any]       =  torch.mean(torch.abs(__snake_case			)			)

      assert abs(result_mean.item() - 0.23916			) < 1e-3







    def     lowercase_ (   self				:						int			):
      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
        self.check_over_configs(num_train_timesteps=__snake_case			)







    def     lowercase_ (   self				:						List[Any]			):
      self.check_over_configs(thresholding=__snake_case			)
      for order in [1, 2, 3]:
        for solver_type in ["logrho"]:
          for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  thresholding=__snake_case		,    prediction_type=__snake_case		,    sample_max_value=__snake_case		,    algorithm_type='deis'		,    solver_order=__snake_case		,    solver_type=__snake_case		,    )







    def     lowercase_ (   self				:						Optional[int]			):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      for algorithm_type in ["deis"]:
        for solver_type in ["logrho"]:
          for order in [1, 2, 3]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  solver_order=__snake_case		,    solver_type=__snake_case		,    prediction_type=__snake_case		,    algorithm_type=__snake_case		,    )
              a		:      Dict       =  self.full_loop(
                  solver_order=__snake_case		,    solver_type=__snake_case		,    prediction_type=__snake_case		,    algorithm_type=__snake_case		,    )
              assert not torch.isnan(__snake_case			).any(), "Samples have nan numbers"







    def     lowercase_ (   self				:						List[Any]			):
      self.check_over_configs(lower_order_final=__snake_case			)
      self.check_over_configs(lower_order_final=__snake_case			)







    def     lowercase_ (   self				:						Optional[Any]			):
      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
        self.check_over_forward(num_inference_steps=__snake_case		,    time_step=0			)







    def     lowercase_ (   self				:						Tuple			):
      a		:      Union[str, Any]       =  self.full_loop()
      a		:      Optional[int]       =  torch.mean(torch.abs(__snake_case			)			)

      assert abs(result_mean.item() - 0.23916			) < 1e-3







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Union[str, Any]       =  self.full_loop(prediction_type='v_prediction'			)
      a		:      List[Any]       =  torch.mean(torch.abs(__snake_case			)			)

      assert abs(result_mean.item() - 0.091			) < 1e-3







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Any       =  self.scheduler_classes[0]
      a		:      Any       =  self.get_scheduler_config(thresholding=__snake_case		,    dynamic_thresholding_ratio=0			)
      a		:      Any       =  scheduler_class(**__snake_case			)

      a		:      Optional[int]       =  10
      a		:      int       =  self.dummy_model()
      a		:      Tuple       =  self.dummy_sample_deter.half()
      scheduler.set_timesteps(__snake_case			)

      for i, t in enumerate(scheduler.timesteps			):
        a		:      List[str]       =  model(__snake_case		,    __snake_case			)
        a		:      int       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case			).prev_sample

      assert sample.dtype == torch.floataa




'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

lowerCAmelCase:      List[Any]		 =		logging.get_logger(__name__)

lowerCAmelCase:      List[str]		 =		{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class 							a__(      lowerCamelCase__			):
    lowercase__				=					"""imagegpt"""
    lowercase__				=					["""past_key_values"""]
    lowercase__				=					{
        """hidden_size""": """n_embd""",
        """max_position_embeddings""": """n_positions""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(   self				:						List[str]		,    __snake_case				:						int=5_12 + 1		,    __snake_case				:						Optional[Any]=32 * 32		,    __snake_case				:						Optional[Any]=5_12		,    __snake_case				:						List[str]=24		,    __snake_case				:						Tuple=8		,    __snake_case				:						Optional[int]=None		,    __snake_case				:						Dict="quick_gelu"		,    __snake_case				:						Dict=0.1		,    __snake_case				:						List[str]=0.1		,    __snake_case				:						Optional[Any]=0.1		,    __snake_case				:						Tuple=1e-5		,    __snake_case				:						Union[str, Any]=0.02		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						Dict=True		,    __snake_case				:						str=False		,    __snake_case				:						Any=False		,    __snake_case				:						List[Any]=False		,    **__snake_case				:						List[str]		,    ):
      a		:      Optional[Any]       =  vocab_size
      a		:      Union[str, Any]       =  n_positions
      a		:      int       =  n_embd
      a		:      int       =  n_layer
      a		:      str       =  n_head
      a		:      int       =  n_inner
      a		:      int       =  activation_function
      a		:      Any       =  resid_pdrop
      a		:      Tuple       =  embd_pdrop
      a		:      List[Any]       =  attn_pdrop
      a		:      Tuple       =  layer_norm_epsilon
      a		:      Any       =  initializer_range
      a		:      str       =  scale_attn_weights
      a		:      List[Any]       =  use_cache
      a		:      Any       =  scale_attn_by_inverse_layer_idx
      a		:      Optional[Any]       =  reorder_and_upcast_attn
      a		:      str       =  tie_word_embeddings

      super().__init__(tie_word_embeddings=__snake_case		,    **__snake_case			)

class 							a__(      lowerCamelCase__			):







    @property
    def     lowercase_ (   self				:						Union[str, Any]			):
      return OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'sequence'}),
          ]			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						"FeatureExtractionMixin"		,    __snake_case				:						int = 1		,    __snake_case				:						int = -1		,    __snake_case				:						bool = False		,    __snake_case				:						Optional["TensorType"] = None		,    __snake_case				:						int = 3		,    __snake_case				:						int = 32		,    __snake_case				:						int = 32		,    ):
      a		:      Dict       =  self._generate_dummy_images(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)
      a		:      Union[str, Any]       =  dict(preprocessor(images=__snake_case		,    return_tensors=__snake_case			)			)

      return inputs