


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class      snake_case__	(   __SCREAMING_SNAKE_CASE	):
				__lowerCAmelCase		=   (DDIMScheduler,)
				__lowerCAmelCase		=   (("eta", 0.0), ("num_inference_steps", 50))
				def 			UpperCamelCase_    (	self  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   {
										    """num_train_timesteps""": 1000,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """clip_sample""": True,
										}

										config.update(**__UpperCAmelCase   )
										return config
				def 			UpperCamelCase_    (	self  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   self.scheduler_classes[0]
										__magic_name__					:				List[Any]				=   self.get_scheduler_config(**__UpperCAmelCase   )
										__magic_name__					:				Any				=   scheduler_class(**__UpperCAmelCase   )

										__magic_name__					:				int				=   10, 0.0

										__magic_name__					:				int				=   self.dummy_model()
										__magic_name__					:				List[Any]				=   self.dummy_sample_deter

										scheduler.set_timesteps(__UpperCAmelCase   )

										for t in scheduler.timesteps:
																__magic_name__					:				Dict				=   model(__UpperCAmelCase  ,   __UpperCAmelCase   )
																__magic_name__					:				List[str]				=   scheduler.step(__UpperCAmelCase  ,   __UpperCAmelCase  ,   __UpperCAmelCase  ,   __UpperCAmelCase   ).prev_sample

										return sample
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for timesteps in [100, 500, 1000]:
																self.check_over_configs(num_train_timesteps=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=__UpperCAmelCase   )

										__magic_name__					:				Any				=   self.scheduler_classes[0]
										__magic_name__					:				Union[str, Any]				=   self.get_scheduler_config(steps_offset=1   )
										__magic_name__					:				Optional[int]				=   scheduler_class(**__UpperCAmelCase   )
										scheduler.set_timesteps(5   )
										assert torch.equal(scheduler.timesteps  ,   torch.LongTensor([801, 601, 401, 201, 1]   )   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,   [0.002, 0.02, 0.2, 2]   ):
																self.check_over_configs(beta_start=__UpperCAmelCase  ,   beta_end=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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




										"""simple docstring"""




										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for timestep_spacing in ["trailing", "leading"]:
																self.check_over_configs(timestep_spacing=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										self.check_over_configs(thresholding=__UpperCAmelCase   )
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=__UpperCAmelCase  ,   prediction_type=__UpperCAmelCase  ,   sample_max_value=__UpperCAmelCase  ,   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for t in [1, 10, 49]:
																self.check_over_forward(time_step=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for t, num_inference_steps in zip([1, 10, 50]  ,   [10, 50, 500]   ):
																self.check_over_forward(time_step=__UpperCAmelCase  ,   num_inference_steps=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for t, eta in zip([1, 10, 49]  ,   [0.0, 0.5, 1.0]   ):
																self.check_over_forward(time_step=__UpperCAmelCase  ,   eta=__UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   self.scheduler_classes[0]
										__magic_name__					:				Optional[Any]				=   self.get_scheduler_config()
										__magic_name__					:				Dict				=   scheduler_class(**__UpperCAmelCase   )

										assert torch.sum(torch.abs(scheduler._get_variance(0  ,   0   ) - 0.0   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(420  ,   400   ) - 0.14771   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980  ,   960   ) - 0.32460   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0  ,   0   ) - 0.0   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487  ,   486   ) - 0.00979   )   ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999  ,   998   ) - 0.02   )   ) < 1E-5
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   self.full_loop()

										__magic_name__					:				Any				=   torch.sum(torch.abs(__UpperCAmelCase   )   )
										__magic_name__					:				List[Any]				=   torch.mean(torch.abs(__UpperCAmelCase   )   )

										assert abs(result_sum.item() - 172.0067   ) < 1E-2
										assert abs(result_mean.item() - 0.223967   ) < 1E-3
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   self.full_loop(prediction_type='v_prediction'   )

										__magic_name__					:				Dict				=   torch.sum(torch.abs(__UpperCAmelCase   )   )
										__magic_name__					:				List[str]				=   torch.mean(torch.abs(__UpperCAmelCase   )   )

										assert abs(result_sum.item() - 52.5302   ) < 1E-2
										assert abs(result_mean.item() - 0.0684   ) < 1E-3
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.full_loop(set_alpha_to_one=__UpperCAmelCase  ,   beta_start=0.01   )
										__magic_name__					:				Any				=   torch.sum(torch.abs(__UpperCAmelCase   )   )
										__magic_name__					:				Optional[Any]				=   torch.mean(torch.abs(__UpperCAmelCase   )   )

										assert abs(result_sum.item() - 149.8295   ) < 1E-2
										assert abs(result_mean.item() - 0.1951   ) < 1E-3





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   self.full_loop(set_alpha_to_one=__UpperCAmelCase  ,   beta_start=0.01   )
										__magic_name__					:				Optional[Any]				=   torch.sum(torch.abs(__UpperCAmelCase   )   )
										__magic_name__					:				Optional[int]				=   torch.mean(torch.abs(__UpperCAmelCase   )   )

										assert abs(result_sum.item() - 149.0784   ) < 1E-2
										assert abs(result_mean.item() - 0.1941   ) < 1E-3







'''simple docstring'''
from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest



@require_tokenizers
class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     List[str]       =      DPRContextEncoderTokenizer
		_SCREAMING_SNAKE_CASE       :     Optional[int]       =      DPRContextEncoderTokenizerFast
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      True



@require_tokenizers
class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     str       =      DPRQuestionEncoderTokenizer
		_SCREAMING_SNAKE_CASE       :     List[str]       =      DPRQuestionEncoderTokenizerFast
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      True



@require_tokenizers
class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     List[str]       =      DPRReaderTokenizer
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      DPRReaderTokenizerFast
		_SCREAMING_SNAKE_CASE       :     Dict       =      True





		@slow
		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  self.tokenizer_class.from_pretrained("""bert-base-uncased"""					)

					__UpperCAmelCase :   int    					=  tokenizer.encode("""question sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Optional[Any]    					=  tokenizer.encode("""title sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Any    					=  tokenizer.encode("""text sequence """ * 4			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Tuple    					=  [[101] + text_a + [102] + text_a + [102] + text_a]
					__UpperCAmelCase :   Optional[int]    					=  BatchEncoding({"""input_ids""": input_ids}					)

					__UpperCAmelCase :   str    					=  [[0] * len(input_ids[0]					)]
					__UpperCAmelCase :   str    					=  [[0] * len(input_ids[0]					)]
					__UpperCAmelCase :   Optional[int]    					=  [0]
					__UpperCAmelCase :   List[str]    					=  DPRReaderOutput(__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase					)

					__UpperCAmelCase					,	__UpperCAmelCase :   Any    					=  8, 9
					__UpperCAmelCase :   Optional[int]    					=  10
					__UpperCAmelCase :   int    					=  10
					__UpperCAmelCase :   str    					=  tokenizer.decode_best_spans(__UpperCAmelCase			,							__UpperCAmelCase					)
					self.assertEqual(predicted_spans[0].start_index			,							__UpperCAmelCase					)
					self.assertEqual(predicted_spans[0].end_index			,							__UpperCAmelCase					)
					self.assertEqual(predicted_spans[0].doc_id			,							0					)





		@slow
		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase :   Any    					=  self.tokenizer_class.from_pretrained("""bert-base-uncased"""					)

					__UpperCAmelCase :   List[Any]    					=  tokenizer.encode("""question sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   List[str]    					=  tokenizer.encode("""title sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Union[str, Any]    					=  tokenizer.encode("""text sequence"""			,							add_special_tokens=__UpperCAmelCase					)
					__UpperCAmelCase :   Optional[int]    					=  [101] + text_a + [102] + text_a + [102] + text_a
					__UpperCAmelCase :   int    					=  tokenizer(questions=["""question sequence"""]			,							titles=["""title sequence"""]			,							texts=["""text sequence"""]					)
					self.assertIn("""input_ids"""			,							__UpperCAmelCase					)
					self.assertIn("""attention_mask"""			,							__UpperCAmelCase					)
					self.assertListEqual(encoded_input["""input_ids"""][0]			,							__UpperCAmelCase					)




