



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					(DPMSolverSinglestepScheduler,)
     UpperCamelCase__		     =					(('''num_inference_steps''', 25),)




     def 		snake_case_		(					self			,    **a__):
         A__					=       {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
             '''sample_max_value''': 1.0,
             '''algorithm_type''': '''dpmsolver++''',
             '''solver_type''': '''midpoint''',
             '''lambda_min_clipped''': -float('''inf'''),
             '''variance_type''': None,
         }

         config.update(**a__)
         return config




     def 		snake_case_		(					self			,    a__=0			,    **a__):
         A__					=       dict(self.forward_default_kwargs)
         A__					=       kwargs.pop('''num_inference_steps'''			,    a__)
         A__					=       self.dummy_sample
         A__					=       0.1 * sample
         A__					=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
             A__					=       self.get_scheduler_config(**a__)
             A__					=       scheduler_class(**a__)
             scheduler.set_timesteps(a__)
             # copy over dummy past residuals
             A__					=       dummy_past_residuals[: scheduler.config.solver_order]

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

             A__			,						A__					=       sample, sample
             for t in range(a__			,    time_step + scheduler.config.solver_order + 1):
                 A__					=       scheduler.step(a__			,    a__			,    a__			,    **a__).prev_sample
                 A__					=       new_scheduler.step(a__			,    a__			,    a__			,    **a__).prev_sample

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




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self			,    a__=0			,    **a__):
         A__					=       dict(self.forward_default_kwargs)
         A__					=       kwargs.pop('''num_inference_steps'''			,    a__)
         A__					=       self.dummy_sample
         A__					=       0.1 * sample
         A__					=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
             A__					=       self.get_scheduler_config()
             A__					=       scheduler_class(**a__)
             scheduler.set_timesteps(a__)

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

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

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

             A__					=       scheduler.step(a__			,    a__			,    a__			,    **a__).prev_sample
             A__					=       new_scheduler.step(a__			,    a__			,    a__			,    **a__).prev_sample

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




     def 		snake_case_		(					self			,    a__=None			,    **a__):
         if scheduler is None:
             A__					=       self.scheduler_classes[0]
             A__					=       self.get_scheduler_config(**a__)
             A__					=       scheduler_class(**a__)

         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config(**a__)
         A__					=       scheduler_class(**a__)

         A__					=       1_0
         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter
         scheduler.set_timesteps(a__)

         for i, t in enumerate(scheduler.timesteps):
             A__					=       model(a__			,    a__)
             A__					=       scheduler.step(a__			,    a__			,    a__).prev_sample

         return sample




     def 		snake_case_		(					self):
         A__					=       DPMSolverSinglestepScheduler(**self.get_scheduler_config())
         A__					=       5_0
         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter
         scheduler.set_timesteps(a__)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]):
             A__					=       model(a__			,    a__)
             A__					=       scheduler.step(a__			,    a__			,    a__).prev_sample

         A__					=       torch.mean(torch.abs(a__))

         assert abs(result_mean.item() - 0.2_5_7_4) < 1e-3




     def 		snake_case_		(					self):
         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(					self):
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         A__					=       DPMSolverSinglestepScheduler(**self.get_scheduler_config())
         A__					=       self.full_loop(scheduler=a__)
         A__					=       torch.mean(torch.abs(a__))

         assert abs(result_mean.item() - 0.2_7_9_1) < 1e-3

         A__					=       DEISMultistepScheduler.from_config(scheduler.config)
         A__					=       DPMSolverMultistepScheduler.from_config(scheduler.config)
         A__					=       UniPCMultistepScheduler.from_config(scheduler.config)
         A__					=       DPMSolverSinglestepScheduler.from_config(scheduler.config)

         A__					=       self.full_loop(scheduler=a__)
         A__					=       torch.mean(torch.abs(a__))

         assert abs(result_mean.item() - 0.2_7_9_1) < 1e-3




     def 		snake_case_		(					self):
         self.check_over_configs(thresholding=a__)
         for order in [1, 2, 3]:
             for solver_type in ["midpoint", "heun"]:
                 for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                         self.check_over_configs(
                             thresholding=a__			,    prediction_type=a__			,    sample_max_value=a__			,    algorithm_type='''dpmsolver++'''			,    solver_order=a__			,    solver_type=a__			,    )




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




     def 		snake_case_		(					self):
         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
             for solver_type in ["midpoint", "heun"]:
                 for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                         self.check_over_configs(
                             solver_order=a__			,    solver_type=a__			,    prediction_type=a__			,    algorithm_type=a__			,    )
                         A__					=       self.full_loop(
                             solver_order=a__			,    solver_type=a__			,    prediction_type=a__			,    algorithm_type=a__			,    )
                         assert not torch.isnan(a__).any(), "Samples have nan numbers"




     def 		snake_case_		(					self):
         self.check_over_configs(lower_order_final=a__)
         self.check_over_configs(lower_order_final=a__)




     def 		snake_case_		(					self):
         self.check_over_configs(lambda_min_clipped=-float('''inf'''))
         self.check_over_configs(lambda_min_clipped=-5.1)




     def 		snake_case_		(					self):
         self.check_over_configs(variance_type=a__)
         self.check_over_configs(variance_type='''learned_range''')




     def 		snake_case_		(					self):
         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_forward(num_inference_steps=a__			,    time_step=0)




     def 		snake_case_		(					self):
         A__					=       self.full_loop()
         A__					=       torch.mean(torch.abs(a__))

         assert abs(result_mean.item() - 0.2_7_9_1) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.full_loop(use_karras_sigmas=a__)
         A__					=       torch.mean(torch.abs(a__))

         assert abs(result_mean.item() - 0.2_2_4_8) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.full_loop(prediction_type='''v_prediction''')
         A__					=       torch.mean(torch.abs(a__))

         assert abs(result_mean.item() - 0.1_4_5_3) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.full_loop(prediction_type='''v_prediction'''			,    use_karras_sigmas=a__)
         A__					=       torch.mean(torch.abs(a__))

         assert abs(result_mean.item() - 0.0_6_4_9) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config(thresholding=a__			,    dynamic_thresholding_ratio=0)
         A__					=       scheduler_class(**a__)

         A__					=       1_0
         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__)

         for i, t in enumerate(scheduler.timesteps):
             A__					=       model(a__			,    a__)
             A__					=       scheduler.step(a__			,    a__			,    a__).prev_sample

         assert sample.dtype == torch.floataa





from typing import TYPE_CHECKING

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


_lowercase    	=		{
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2TokenizerFast"]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2FeatureExtractor"]
   _lowercase    	=		["LayoutLMv2ImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMv2ForQuestionAnswering",
       "LayoutLMv2ForSequenceClassification",
       "LayoutLMv2ForTokenClassification",
       "LayoutLMv2Layer",
       "LayoutLMv2Model",
       "LayoutLMv2PreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
