





'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       ['image_processor', 'tokenizer']
       _snake_case      :	int									=       'BlipImageProcessor'
       _snake_case      :	Tuple									=       ('BertTokenizer', 'BertTokenizerFast')





       def __init__(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						List[str]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			False
            super().__init__(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			self.image_processor





       def __call__(				self  :						int			,					lowerCAmelCase__  :						ImageInput = None			,					lowerCAmelCase__  :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Union[bool, str, PaddingStrategy] = False			,					lowerCAmelCase__  :						Union[bool, str, TruncationStrategy] = None			,					lowerCAmelCase__  :						Optional[int] = None			,					lowerCAmelCase__  :						int = 0			,					lowerCAmelCase__  :						Optional[int] = None			,					lowerCAmelCase__  :						Optional[bool] = None			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Optional[Union[str, TensorType]] = None			,					**lowerCAmelCase__  :						Optional[Any]			,					)       ->	BatchEncoding:

            '''simple docstring'''




            if images is None and text is None:
                 raise ValueError('''You have to specify either images or text.'''	)

            # Get only text
            if images is None:
                 _UpperCamelCase							       =			self.tokenizer
                 _UpperCamelCase							       =			self.tokenizer(
                     text=lowerCAmelCase__			,					add_special_tokens=lowerCAmelCase__			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					stride=lowerCAmelCase__			,					pad_to_multiple_of=lowerCAmelCase__			,					return_attention_mask=lowerCAmelCase__			,					return_overflowing_tokens=lowerCAmelCase__			,					return_special_tokens_mask=lowerCAmelCase__			,					return_offsets_mapping=lowerCAmelCase__			,					return_token_type_ids=lowerCAmelCase__			,					return_length=lowerCAmelCase__			,					verbose=lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					**lowerCAmelCase__			,					)
                 return text_encoding

            # add pixel_values
            _UpperCamelCase							       =			self.image_processor(lowerCAmelCase__			,					return_tensors=lowerCAmelCase__	)

            if text is not None:
                 _UpperCamelCase							       =			self.tokenizer(
                     text=lowerCAmelCase__			,					add_special_tokens=lowerCAmelCase__			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					stride=lowerCAmelCase__			,					pad_to_multiple_of=lowerCAmelCase__			,					return_attention_mask=lowerCAmelCase__			,					return_overflowing_tokens=lowerCAmelCase__			,					return_special_tokens_mask=lowerCAmelCase__			,					return_offsets_mapping=lowerCAmelCase__			,					return_token_type_ids=lowerCAmelCase__			,					return_length=lowerCAmelCase__			,					verbose=lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					**lowerCAmelCase__			,					)
            else:
                 _UpperCamelCase							       =			None

            if text_encoding is not None:
                 encoding_image_processor.update(lowerCAmelCase__	)

            return encoding_image_processor





       def snake_case__		(				self  :						Any			,					*lowerCAmelCase__  :						Union[str, Any]			,					**lowerCAmelCase__  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            return self.tokenizer.batch_decode(*lowerCAmelCase__			,					**lowerCAmelCase__	)





       def snake_case__		(				self  :						Tuple			,					*lowerCAmelCase__  :						int			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




            return self.tokenizer.decode(*lowerCAmelCase__			,					**lowerCAmelCase__	)







       @property
       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.model_input_names
            _UpperCamelCase							       =			self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=(DEISMultistepScheduler,)
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(("num_inference_steps", 25),)

 def      __lowerCAmelCase  (						self			:			Tuple					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**SCREAMING_SNAKE_CASE__   )
      return config

 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			Dict=0					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0					, **SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

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

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

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

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

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

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      if scheduler is None:
           UpperCamelCase						    =					self.scheduler_classes[0]
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

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

      return sample

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )

      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self.dummy_sample
           UpperCamelCase						    =					0.1 * sample

           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                UpperCamelCase						    =					num_inference_steps

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

           UpperCamelCase						    =					scheduler.timesteps[5]
           UpperCamelCase						    =					scheduler.timesteps[6]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

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

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					DEISMultistepScheduler(**self.get_scheduler_config()   )
      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

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

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

      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

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

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      self.check_over_configs(thresholding=SCREAMING_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=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, sample_max_value=SCREAMING_SNAKE_CASE__					, algorithm_type='deis'					, solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      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=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          UpperCamelCase						    =					self.full_loop(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          assert not torch.isnan(SCREAMING_SNAKE_CASE__   ).any(), "Samples have nan numbers"

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )
      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop()
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

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

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop(prediction_type='v_prediction'   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

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






 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__					, dynamic_thresholding_ratio=0   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter.half()
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

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

      assert sample.dtype == torch.floataa


