






"""simple docstring"""



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   (UnCLIPScheduler,)




					def   A  (				self     :						Tuple    ,						**A     :						Any   )  ->  Any:
							lowercase_ : Dict							       =   {
							    "num_train_timesteps": 10_00,
							    "variance_type": "fixed_small_log",
							    "clip_sample": True,
							    "clip_sample_range": 1.0,
							    "prediction_type": "epsilon",
							}

							config.update(**__lowerCamelCase   )
							return config




					def   A  (				self     :						List[str]   )  ->  Dict:
							for timesteps in [1, 5, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=__lowerCamelCase   )




					def   A  (				self     :						Union[str, Any]   )  ->  str:
							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=__lowerCamelCase   )




					def   A  (				self     :						int   )  ->  Any:
							for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=__lowerCamelCase   )




					def   A  (				self     :						List[str]   )  ->  int:
							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=__lowerCamelCase   )




					def   A  (				self     :						Optional[Any]   )  ->  List[Any]:
							for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=__lowerCamelCase   )




					def   A  (				self     :						Optional[Any]   )  ->  Dict:
							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=__lowerCamelCase    ,						prev_timestep=__lowerCamelCase   )




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : List[Any]							       =   self.scheduler_classes[0]
							lowercase_ : Dict							       =   self.get_scheduler_config(variance_type='''fixed_small_log'''   )
							lowercase_ : Optional[Any]							       =   scheduler_class(**__lowerCamelCase   )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000e-10   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0549625   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.9994987   )   ) < 1e-5




					def   A  (				self     :						Tuple   )  ->  str:
							lowercase_ : List[Any]							       =   self.scheduler_classes[0]
							lowercase_ : Union[str, Any]							       =   self.get_scheduler_config(variance_type='''learned_range'''   )
							lowercase_ : List[str]							       =   scheduler_class(**__lowerCamelCase   )

							lowercase_ : Any							       =   0.5

							assert scheduler._get_variance(1    ,						predicted_variance=__lowerCamelCase   ) - -10.1_71_27_90 < 1e-5
							assert scheduler._get_variance(4_87    ,						predicted_variance=__lowerCamelCase   ) - -5.7998052 < 1e-5
							assert scheduler._get_variance(9_99    ,						predicted_variance=__lowerCamelCase   ) - -0.0010011 < 1e-5




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : List[str]							       =   self.scheduler_classes[0]
							lowercase_ : Dict							       =   self.get_scheduler_config()
							lowercase_ : List[str]							       =   scheduler_class(**__lowerCamelCase   )

							lowercase_ : Optional[int]							       =   scheduler.timesteps

							lowercase_ : Union[str, Any]							       =   self.dummy_model()
							lowercase_ : List[Any]							       =   self.dummy_sample_deter
							lowercase_ : Any							       =   torch.manual_seed(0   )

							for i, t in enumerate(__lowerCamelCase   ):
									# 1. predict noise residual
									lowercase_ : Optional[Any]							       =   model(__lowerCamelCase    ,						__lowerCamelCase   )

									# 2. predict previous mean of sample x_t-1
									lowercase_ : Optional[int]							       =   scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						generator=__lowerCamelCase   ).prev_sample

									lowercase_ : List[Any]							       =   pred_prev_sample

							lowercase_ : str							       =   torch.sum(torch.abs(__lowerCamelCase   )   )
							lowercase_ : Optional[Any]							       =   torch.mean(torch.abs(__lowerCamelCase   )   )

							assert abs(result_sum.item() - 2_52.2_68_24_95   ) < 1e-2
							assert abs(result_mean.item() - 0.3284743   ) < 1e-3




					def   A  (				self     :						int   )  ->  Any:
							lowercase_ : Optional[Any]							       =   self.scheduler_classes[0]
							lowercase_ : Union[str, Any]							       =   self.get_scheduler_config()
							lowercase_ : Dict							       =   scheduler_class(**__lowerCamelCase   )

							scheduler.set_timesteps(25   )

							lowercase_ : Dict							       =   scheduler.timesteps

							lowercase_ : List[Any]							       =   self.dummy_model()
							lowercase_ : Optional[int]							       =   self.dummy_sample_deter
							lowercase_ : str							       =   torch.manual_seed(0   )

							for i, t in enumerate(__lowerCamelCase   ):
									# 1. predict noise residual
									lowercase_ : Optional[int]							       =   model(__lowerCamelCase    ,						__lowerCamelCase   )

									if i + 1 == timesteps.shape[0]:
											lowercase_ : List[str]							       =   None
									else:
											lowercase_ : int							       =   timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									lowercase_ : Any							       =   scheduler.step(
									    __lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						prev_timestep=__lowerCamelCase    ,						generator=__lowerCamelCase   ).prev_sample

									lowercase_ : Dict							       =   pred_prev_sample

							lowercase_ : Dict							       =   torch.sum(torch.abs(__lowerCamelCase   )   )
							lowercase_ : Any							       =   torch.mean(torch.abs(__lowerCamelCase   )   )

							assert abs(result_sum.item() - 2_58.2_04_49_83   ) < 1e-2
							assert abs(result_mean.item() - 0.3362038   ) < 1e-3




					def   A  (				self     :						List[str]   )  ->  str:
							pass






					def   A  (				self     :						Optional[int]   )  ->  int:
							pass







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "OwlViTImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       int				    = None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead." ,						__lowerCamelCase ,						)
                   _A      :       List[Any]				    = kwargs.pop("feature_extractor")

            _A      :       Dict				    = image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`.")
            if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`.")

            super().__init__(__lowerCamelCase ,						__lowerCamelCase)


     def __call__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="max_length" ,						__lowerCamelCase="np" ,						**__lowerCamelCase)			->				Any:

            if text is None and query_images is None and images is None:
                   raise ValueError(
                       "You have to specify at least one text or query image or image. All three cannot be none.")

            if text is not None:
                   if isinstance(__lowerCamelCase ,						__lowerCamelCase) or (isinstance(__lowerCamelCase ,						__lowerCamelCase) and not isinstance(text[0] ,						__lowerCamelCase)):
                          _A      :       Union[str, Any]				    = [self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)]

                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase) and isinstance(text[0] ,						__lowerCamelCase):
                          _A      :       Optional[Any]				    = []

                          # Maximum number of queries across batch
                          _A      :       str				    = max([len(__lowerCamelCase) for t in text])

                          # Pad all batch samples to max number of text queries
                          for t in text:
                                 if len(__lowerCamelCase) != max_num_queries:
                                        _A      :       Optional[int]				    = t + [" "] * (max_num_queries - len(__lowerCamelCase))

                                 _A      :       List[Any]				    = self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
                                 encodings.append(__lowerCamelCase)
                   else:
                          raise TypeError("Input text should be a string, a list of strings or a nested list of strings")

                   if return_tensors == "np":
                          _A      :       Tuple				    = np.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[Any]				    = np.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "jax" and is_flax_available():
                          import jax.numpy as jnp

                          _A      :       Optional[int]				    = jnp.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[int]				    = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "pt" and is_torch_available():
                          import torch

                          _A      :       Optional[Any]				    = torch.cat([encoding["input_ids"] for encoding in encodings] ,						dim=0)
                          _A      :       Union[str, Any]				    = torch.cat([encoding["attention_mask"] for encoding in encodings] ,						dim=0)

                   elif return_tensors == "tf" and is_tf_available():
                          import tensorflow as tf

                          _A      :       Any				    = tf.stack([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Tuple				    = tf.stack([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   else:
                          raise ValueError("Target return tensor type could not be returned")

                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       Tuple				    = input_ids
                   _A      :       Dict				    = attention_mask

            if query_images is not None:
                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       List[str]				    = self.image_processor(
                       __lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase).pixel_values
                   _A      :       Union[str, Any]				    = query_pixel_values

            if images is not None:
                   _A      :       int				    = self.image_processor(__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)

            if text is not None and images is not None:
                   _A      :       Tuple				    = image_features.pixel_values
                   return encoding
            elif query_images is not None and images is not None:
                   _A      :       int				    = image_features.pixel_values
                   return encoding
            elif text is not None or query_images is not None:
                   return encoding
            else:
                   return BatchEncoding(data=dict(**__lowerCamelCase) ,						tensor_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.image_processor.post_process(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.image_processor.post_process_object_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				int:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				int:
            warnings.warn(
                "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,						__lowerCamelCase ,						)
            return self.image_processor_class


     @property
     def  _lowerCamelCase (						self)			->				List[str]:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__lowerCamelCase ,						)
            return self.image_processor
