






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       							=  {
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE       							=  ['OpenAIGPTTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE       							=  [
      'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OpenAIGPTDoubleHeadsModel',
      'OpenAIGPTForSequenceClassification',
      'OpenAIGPTLMHeadModel',
      'OpenAIGPTModel',
      'OpenAIGPTPreTrainedModel',
      'load_tf_weights_in_openai_gpt',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE       							=  [
      'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFOpenAIGPTDoubleHeadsModel',
      'TFOpenAIGPTForSequenceClassification',
      'TFOpenAIGPTLMHeadModel',
      'TFOpenAIGPTMainLayer',
      'TFOpenAIGPTModel',
      'TFOpenAIGPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE       							=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     (LMSDiscreteScheduler,)
    __magic_name__         =     10





    def 				lowerCamelCase__      (							self	:					Any				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			Optional[Any]				=   {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**A			)
      return config





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,		[0.00_02, 0.0_02, 0.02]			):
        self.check_over_configs(beta_start=A				,		beta_end=A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=A			)





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      for t in [0, 5_0_0, 8_0_0]:
        self.check_over_forward(time_step=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Any				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			List[Any]				=   self.dummy_model()
      a     :			str				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			int				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Union[str, Any]				=   output.prev_sample

      a     :			Any				=   torch.sum(torch.abs(A			)			)
      a     :			List[str]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[str]				=   self.scheduler_classes[0]
      a     :			Any				=   self.get_scheduler_config(prediction_type='v_prediction'			)
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			Tuple				=   self.dummy_model()
      a     :			Tuple				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Any				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Tuple				=   output.prev_sample

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Optional[int]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 0.00_17			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Optional[Any]				=   self.scheduler_classes[0]
      a     :			Union[str, Any]				=   self.get_scheduler_config()
      a     :			Union[str, Any]				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Optional[Any]				=   self.dummy_model()
      a     :			Dict				=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a     :			Union[str, Any]				=   sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Optional[int]				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			str				=   output.prev_sample

      a     :			Dict				=   torch.sum(torch.abs(A			)			)
      a     :			Tuple				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			Tuple				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			Optional[Any]				=   scheduler_class(**A				,		use_karras_sigmas=A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Any				=   self.dummy_model()
      a     :			Union[str, Any]				=   self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma
      a     :			Any				=   sample.to(A			)

      for t in scheduler.timesteps:
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			Tuple				=   model(A				,		A			)

        a     :			List[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			int				=   output.prev_sample

      a     :			Tuple				=   torch.sum(torch.abs(A			)			)
      a     :			Any				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 38_12.99_27			) < 2E-2
      assert abs(result_mean.item() - 4.96_48			) < 1E-3




