




"""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				(     UpperCamelCase__						):
   _lowerCamelCase				:Tuple	      =	["image_processor", "tokenizer"]
   _lowerCamelCase				:Union[str, Any]	      =	"BlipImageProcessor"
   _lowerCamelCase				:str	      =	("BertTokenizer", "BertTokenizerFast")






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

         """simple docstring"""
         lowerCAmelCase__			: Union[str, Any]  					=       False
         super().__init__(_a    ,	_a    )
         lowerCAmelCase__			: int  					=       self.image_processor






   def __call__(		self   :		Union[str, Any]    ,	UpperCamelCase   :		str = None    ,	UpperCamelCase   :		str = None    ,	UpperCamelCase   :		Any = True    ,	UpperCamelCase   :		Any = False    ,	UpperCamelCase   :		str = None    ,	UpperCamelCase   :		str = None    ,	UpperCamelCase   :		Any = 0    ,	UpperCamelCase   :		List[Any] = None    ,	UpperCamelCase   :		List[Any] = None    ,	UpperCamelCase   :		str = False    ,	UpperCamelCase   :		List[Any] = False    ,	UpperCamelCase   :		Union[str, Any] = False    ,	UpperCamelCase   :		Dict = False    ,	UpperCamelCase   :		Dict = False    ,	UpperCamelCase   :		Dict = True    ,	UpperCamelCase   :		Any = None    ,	**UpperCamelCase   :		Tuple    ,	) ->						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:
               lowerCAmelCase__			: Dict  					=       self.tokenizer
               lowerCAmelCase__			: Optional[int]  					=       self.tokenizer(
                   text=_a    ,	add_special_tokens=_a    ,	padding=_a    ,	truncation=_a    ,	max_length=_a    ,	stride=_a    ,	pad_to_multiple_of=_a    ,	return_attention_mask=_a    ,	return_overflowing_tokens=_a    ,	return_special_tokens_mask=_a    ,	return_offsets_mapping=_a    ,	return_token_type_ids=_a    ,	return_length=_a    ,	verbose=_a    ,	return_tensors=_a    ,	**_a    ,	)
               return text_encoding

         # add pixel_values
         lowerCAmelCase__			: Any  					=       self.image_processor(_a    ,	return_tensors=_a    )

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

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

         return encoding_image_processor






   def        _lowerCAmelCase    (		self   :		List[Any]    ,	*UpperCamelCase   :		List[Any]    ,	**UpperCamelCase   :		List[str]    ) ->						int:

         """simple docstring"""
         return self.tokenizer.batch_decode(*_a    ,	**_a    )






   def        _lowerCAmelCase    (		self   :		Any    ,	*UpperCamelCase   :		List[Any]    ,	**UpperCamelCase   :		Optional[int]    ) ->						Optional[int]:

         """simple docstring"""
         return self.tokenizer.decode(*_a    ,	**_a    )






   @property
   def        _lowerCAmelCase    (		self   :		int    ) ->						int:

         """simple docstring"""
         lowerCAmelCase__			: Dict  					=       self.tokenizer.model_input_names
         lowerCAmelCase__			: Tuple  					=       self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )











import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						(DPMSolverSDEScheduler,)
     _a							 =						1_0

     def 					a__	(  self		,     **_a				)						->							Optional[Any]:
           _A							:     str			=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """noise_sampler_seed""": 0,
           }

           config.update(**_a				)
           return config

     def 					a__	(  self				)						->							Tuple:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,     [0.0002, 0.002, 0.02]				):
                 self.check_over_configs(beta_start=_a		,     beta_end=_a				)

     def 					a__	(  self				)						->							Any:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a				)

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Any			=						self.scheduler_classes[0]
           _A							:     List[str]			=						self.get_scheduler_config()
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Dict			=						self.dummy_model()
           _A							:     Any			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Dict			=						sample.to(_a				)

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

                 _A							:     str			=						model(_a		,     _a				)

                 _A							:     List[Any]			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Dict			=						torch.sum(torch.abs(_a				)				)
           _A							:     Dict			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.47821044921875				) < 1e-2
                 assert abs(result_mean.item() - 0.2178705964565277				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59352111816406				) < 1e-2
                 assert abs(result_mean.item() - 0.22342906892299652				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Dict			=						self.scheduler_classes[0]
           _A							:     Optional[int]			=						self.get_scheduler_config(prediction_type="""v_prediction"""				)
           _A							:     Optional[Any]			=						scheduler_class(**_a				)

           scheduler.set_timesteps(self.num_inference_steps				)

           _A							:     Tuple			=						self.dummy_model()
           _A							:     int			=						self.dummy_sample_deter * scheduler.init_noise_sigma
           _A							:     Tuple			=						sample.to(_a				)

           for i, t in enumerate(scheduler.timesteps				):
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     Tuple			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Optional[int]			=						output.prev_sample

           _A							:     Optional[Any]			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[Any]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 124.77149200439453				) < 1e-2
                 assert abs(result_mean.item() - 0.16226289014816284				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 128.1663360595703				) < 1e-2
                 assert abs(result_mean.item() - 0.16688326001167297				) < 1e-3
           else:
                 assert abs(result_sum.item() - 119.8487548828125				) < 1e-2
                 assert abs(result_mean.item() - 0.1560530662536621				) < 1e-3

     def 					a__	(  self				)						->							List[str]:
           _A							:     Union[str, Any]			=						self.scheduler_classes[0]
           _A							:     List[Any]			=						self.get_scheduler_config()
           _A							:     List[str]			=						scheduler_class(**_a				)

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

           _A							:     Union[str, Any]			=						self.dummy_model()
           _A							:     Optional[Any]			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 _A							:     int			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     Dict			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     str			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.46957397460938				) < 1e-2
                 assert abs(result_mean.item() - 0.21805934607982635				) < 1e-3
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59353637695312				) < 1e-2
                 assert abs(result_mean.item() - 0.22342908382415771				) < 1e-3
           else:
                 assert abs(result_sum.item() - 162.52383422851562				) < 1e-2
                 assert abs(result_mean.item() - 0.211619570851326				) < 1e-3



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.scheduler_classes[0]
           _A							:     Optional[Any]			=						self.get_scheduler_config()
           _A							:     int			=						scheduler_class(**_a		,     use_karras_sigmas=_a				)

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

           _A							:     Optional[Any]			=						self.dummy_model()
           _A							:     Dict			=						self.dummy_sample_deter.to(_a				) * scheduler.init_noise_sigma
           _A							:     str			=						sample.to(_a				)

           for t in scheduler.timesteps:
                 _A							:     Optional[int]			=						scheduler.scale_model_input(_a		,     _a				)

                 _A							:     List[Any]			=						model(_a		,     _a				)

                 _A							:     Dict			=						scheduler.step(_a		,     _a		,     _a				)
                 _A							:     List[str]			=						output.prev_sample

           _A							:     str			=						torch.sum(torch.abs(_a				)				)
           _A							:     List[str]			=						torch.mean(torch.abs(_a				)				)

           if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 176.66974135742188				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 177.63653564453125				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2
           else:
                 assert abs(result_sum.item() - 170.3135223388672				) < 1e-2
                 assert abs(result_mean.item() - 0.23003872730981811				) < 1e-2

