






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 		_snake_case  (  A__					):
			_lowercase		:    str			     =							(UnCLIPScheduler,)






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= {
						    'num_train_timesteps': 1000,
						    'variance_type': 'fixed_small_log',
						    'clip_sample': True,
						    'clip_sample_range': 1.0,
						    'prediction_type': 'epsilon',
						}

						config.update(**a)
						return config






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						for timesteps in [1, 5, 100, 1000]:
									self.check_over_configs(num_train_timesteps=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						for time_step in [0, 500, 999]:
									for prev_timestep in [None, 5, 100, 250, 500, 750]:
												if prev_timestep is not None and prev_timestep >= time_step:
															continue

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE						= self.get_scheduler_config(variance_type='fixed_small_log')
						SCREAMING_SNAKE_CASE						= scheduler_class(**a)

						assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.00_00E-10)) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_54_96_25)) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9_99_49_87)) < 1E-5






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE						= self.get_scheduler_config(variance_type='learned_range')
						SCREAMING_SNAKE_CASE						= scheduler_class(**a)

						SCREAMING_SNAKE_CASE						= 0.5

						assert scheduler._get_variance(1		,					predicted_variance=a) - -10.1_71_27_90 < 1E-5
						assert scheduler._get_variance(487		,					predicted_variance=a) - -5.7_99_80_52 < 1E-5
						assert scheduler._get_variance(999		,					predicted_variance=a) - -0.0_01_00_11 < 1E-5






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE						= self.get_scheduler_config()
						SCREAMING_SNAKE_CASE						= scheduler_class(**a)

						SCREAMING_SNAKE_CASE						= scheduler.timesteps

						SCREAMING_SNAKE_CASE						= self.dummy_model()
						SCREAMING_SNAKE_CASE						= self.dummy_sample_deter
						SCREAMING_SNAKE_CASE						= torch.manual_seed(0)

						for i, t in enumerate(a):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE						= model(a		,					a)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE						= scheduler.step(a		,					a		,					a		,					generator=a).prev_sample

									SCREAMING_SNAKE_CASE						= pred_prev_sample

						SCREAMING_SNAKE_CASE						= torch.sum(torch.abs(a))
						SCREAMING_SNAKE_CASE						= torch.mean(torch.abs(a))

						assert abs(result_sum.item() - 2_52.2_68_24_95) < 1E-2
						assert abs(result_mean.item() - 0.3_28_47_43) < 1E-3






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE						= self.get_scheduler_config()
						SCREAMING_SNAKE_CASE						= scheduler_class(**a)

						scheduler.set_timesteps(25)

						SCREAMING_SNAKE_CASE						= scheduler.timesteps

						SCREAMING_SNAKE_CASE						= self.dummy_model()
						SCREAMING_SNAKE_CASE						= self.dummy_sample_deter
						SCREAMING_SNAKE_CASE						= torch.manual_seed(0)

						for i, t in enumerate(a):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE						= model(a		,					a)

									if i + 1 == timesteps.shape[0]:
												SCREAMING_SNAKE_CASE						= None
									else:
												SCREAMING_SNAKE_CASE						= timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE						= scheduler.step(
									    a		,					a		,					a		,					prev_timestep=a		,					generator=a).prev_sample

									SCREAMING_SNAKE_CASE						= pred_prev_sample

						SCREAMING_SNAKE_CASE						= torch.sum(torch.abs(a))
						SCREAMING_SNAKE_CASE						= torch.mean(torch.abs(a))

						assert abs(result_sum.item() - 2_58.2_04_49_83) < 1E-2
						assert abs(result_mean.item() - 0.3_36_20_38) < 1E-3






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						pass







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












from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						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 and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= 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

						if not self.image_processor.is_vqa:
									# add pixel_values
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					**a)
						else:
									# add pixel_values and bbox
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					header_text=a		,					**a)

						if text is not None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= 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		,					)

									if "attention_mask" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('input_ids')
						else:
									SCREAMING_SNAKE_CASE						= None

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

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






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







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




