





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'char'
     __magic_name__   =			'bpe'
     __magic_name__   =			'wp'




__A							:			int         =			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['image_processor', 'char_tokenizer']
     __magic_name__   =			'ViTImageProcessor'
     __magic_name__   =			'MgpstrTokenizer'




     def __init__(    self				,     snake_case_=None				,     snake_case_=None				,     **snake_case_							):
         _A     =							None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'				,     snake_case_				,     )
             _A     =							kwargs.pop('feature_extractor'							)

         _A     =							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`.'							)

         _A     =							tokenizer
         _A     =							AutoTokenizer.from_pretrained('gpt2'							)
         _A     =							AutoTokenizer.from_pretrained('bert-base-uncased'							)

         super().__init__(snake_case_				,     snake_case_							)




     def __call__(    self				,     snake_case_=None				,     snake_case_=None				,     snake_case_=None				,     **snake_case_							):
         if images is None and text is None:
             raise ValueError('You need to specify either an `images` or `text` input to process.'							)

         if images is not None:
             _A     =							self.image_processor(snake_case_				,     return_tensors=snake_case_				,     **snake_case_							)
         if text is not None:
             _A     =							self.char_tokenizer(snake_case_				,     return_tensors=snake_case_				,     **snake_case_							)

         if text is None:
             return inputs
         elif images is None:
             return encodings
         else:
             _A     =							encodings['input_ids']
             return inputs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A,			_A,			_A     =							sequences
         _A     =							char_preds.size(0							)

         _A,			_A     =							self._decode_helper(snake_case_				,     'char'							)
         _A,			_A     =							self._decode_helper(snake_case_				,     'bpe'							)
         _A,			_A     =							self._decode_helper(snake_case_				,     'wp'							)

         _A     =							[]
         _A     =							[]
         for i in range(snake_case_							):
             _A     =							[char_scores[i], bpe_scores[i], wp_scores[i]]
             _A     =							[char_strs[i], bpe_strs[i], wp_strs[i]]
             _A     =							scores.index(max(snake_case_							)							)
             final_strs.append(strs[max_score_index]							)
             final_scores.append(scores[max_score_index]							)

         _A     =							{}
         _A     =							final_strs
         _A     =							final_scores
         _A     =							char_strs
         _A     =							bpe_strs
         _A     =							wp_strs
         return out




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         if format == DecodeType.CHARACTER:
             _A     =							self.char_decode
             _A     =							1
             _A     =							'[s]'
         elif format == DecodeType.BPE:
             _A     =							self.bpe_decode
             _A     =							2
             _A     =							'#'
         elif format == DecodeType.WORDPIECE:
             _A     =							self.wp_decode
             _A     =							102
             _A     =							'[SEP]'
         else:
             raise ValueError(F"Format {format} is not supported."							)

         _A,			_A     =							[], []
         _A     =							pred_logits.size(0							)
         _A     =							pred_logits.size(1							)
         _A,			_A     =							pred_logits.topk(1				,     dim=-1				,     largest=snake_case_				,     sorted=snake_case_							)
         _A     =							preds_index.view(-1				,     snake_case_							)[:, 1:]
         _A     =							decoder(snake_case_							)
         _A,			_A     =							torch.nn.functional.softmax(snake_case_				,     dim=2							).max(dim=2							)
         _A     =							preds_max_prob[:, 1:]

         for index in range(snake_case_							):
             _A     =							preds_str[index].find(snake_case_							)
             _A     =							preds_str[index][:pred_eos]
             _A     =							preds_index[index].cpu().tolist()
             _A     =							pred_index.index(snake_case_							) if eos_token in pred_index else -1
             _A     =							preds_max_prob[index][: pred_eos_index + 1]
             _A     =							pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
             dec_strs.append(snake_case_							)
             conf_scores.append(snake_case_							)

         return dec_strs, conf_scores




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							[seq.replace(' '				,     ''							) for seq in self.char_tokenizer.batch_decode(snake_case_							)]
         return decode_strs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return self.bpe_tokenizer.batch_decode(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							[seq.replace(' '				,     ''							) for seq in self.wp_tokenizer.batch_decode(snake_case_							)]
         return decode_strs












'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(DPMSolverSinglestepScheduler,)
					SCREAMING_SNAKE_CASE__				=	(('''num_inference_steps''', 25),)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			{
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """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(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			List[str]=0      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[str]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				str		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Any		=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.get_scheduler_config(**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE :				Any		=			dummy_past_residuals[: scheduler.config.solver_order]

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

									SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			sample, sample
									for t in range(lowerCamelCase_      ,						time_step + scheduler.config.solver_order + 1						):
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
											SCREAMING_SNAKE_CASE :				Optional[int]		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

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


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							pass


					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=0      ,						**lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)

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

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class.from_pretrained(lowerCamelCase_						)
											# copy over dummy past residuals
											new_scheduler.set_timesteps(lowerCamelCase_						)

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

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
									SCREAMING_SNAKE_CASE :				Dict		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

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


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			int=None      ,						**lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							if scheduler is None:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config(**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_scheduler_config(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			10
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				Dict		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							return sample


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			50
							SCREAMING_SNAKE_CASE :				Dict		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]						):
									SCREAMING_SNAKE_CASE :				str		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_574						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				int		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3

							SCREAMING_SNAKE_CASE :				List[str]		=			DEISMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Any		=			UniPCMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverSinglestepScheduler.from_config(scheduler.config						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.check_over_configs(thresholding=lowerCamelCase_						)
							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=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						sample_max_value=lowerCamelCase_      ,						algorithm_type="""dpmsolver++"""      ,						solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



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


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

							'''simple docstring'''



							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=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															SCREAMING_SNAKE_CASE :				int		=			self.full_loop(
															    solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															assert not torch.isnan(lowerCamelCase_						).any(), "Samples have nan numbers"


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							self.check_over_configs(lower_order_final=lowerCamelCase_						)
							self.check_over_configs(lower_order_final=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							self.check_over_configs(lambda_min_clipped=-float("""inf"""						)						)
							self.check_over_configs(lambda_min_clipped=-5.1						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							self.check_over_configs(variance_type=lowerCamelCase_						)
							self.check_over_configs(variance_type="""learned_range"""						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''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=lowerCamelCase_      ,						time_step=0						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.full_loop()
							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.full_loop(use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_248						) < 1e-3


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.full_loop(prediction_type="""v_prediction"""						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.1_453						) < 1e-3


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.full_loop(prediction_type="""v_prediction"""      ,						use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.0_649						) < 1e-3



					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_scheduler_config(thresholding=lowerCamelCase_      ,						dynamic_thresholding_ratio=0						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			10
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				int		=			self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				List[Any]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Any		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							assert sample.dtype == torch.floataa



