
"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast



@require_vision
class  lowercase				(   unittest.TestCase		):






			def 				UpperCAmelCase       (self     :  str )					->  str:





				"""simple docstring"""


				lowerCAmelCase				       =     tempfile.mkdtemp()

				lowerCAmelCase				       =     CLIPImageProcessor()
				lowerCAmelCase				       =     BertTokenizer.from_pretrained(
				    '''hf-internal-testing/tiny-random-BertModel'''			,model_input_names=['''input_ids''', '''attention_mask'''] )

				lowerCAmelCase				       =     GitProcessor(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

				processor.save_pretrained(self.tmpdirname )






			def 				UpperCAmelCase       (self     :  Any			,**SCREAMING_SNAKE_CASE_     :  str )					->  Optional[int]:





				"""simple docstring"""


				return AutoProcessor.from_pretrained(self.tmpdirname			,**SCREAMING_SNAKE_CASE_ ).tokenizer






			def 				UpperCAmelCase       (self     :  Union[str, Any]			,**SCREAMING_SNAKE_CASE_     :  int )					->  str:





				"""simple docstring"""


				return AutoProcessor.from_pretrained(self.tmpdirname			,**SCREAMING_SNAKE_CASE_ ).image_processor






			def 				UpperCAmelCase       (self     :  Optional[int] )					->  int:





				"""simple docstring"""


				shutil.rmtree(self.tmpdirname )






			def 				UpperCAmelCase       (self     :  Tuple )					->  List[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     [np.random.randint(255			,size=(3, 30, 400)			,dtype=np.uinta )]

				lowerCAmelCase				       =     [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_			,0			,-1 ) ) for x in image_inputs]

				return image_inputs






			def 				UpperCAmelCase       (self     :  List[Any] )					->  Tuple:





				"""simple docstring"""


				lowerCAmelCase				       =     GitProcessor(tokenizer=self.get_tokenizer()			,image_processor=self.get_image_processor() )
				processor.save_pretrained(self.tmpdirname )

				lowerCAmelCase				       =     self.get_tokenizer(bos_token='''(BOS)'''			,eos_token='''(EOS)''' )
				lowerCAmelCase				       =     self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_			,padding_value=1.0 )

				lowerCAmelCase				       =     GitProcessor.from_pretrained(
				    self.tmpdirname			,bos_token='''(BOS)'''			,eos_token='''(EOS)'''			,do_normalize=SCREAMING_SNAKE_CASE_			,padding_value=1.0 )

				self.assertEqual(processor.tokenizer.get_vocab()			,tokenizer_add_kwargs.get_vocab() )
				self.assertIsInstance(processor.tokenizer			,SCREAMING_SNAKE_CASE_ )

				self.assertEqual(processor.image_processor.to_json_string()			,image_processor_add_kwargs.to_json_string() )
				self.assertIsInstance(processor.image_processor			,SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.get_image_processor()
				lowerCAmelCase				       =     self.get_tokenizer()

				lowerCAmelCase				       =     GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,image_processor=SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     self.prepare_image_inputs()

				lowerCAmelCase				       =     image_processor(SCREAMING_SNAKE_CASE_			,return_tensors='''np''' )
				lowerCAmelCase				       =     processor(images=SCREAMING_SNAKE_CASE_			,return_tensors='''np''' )

				for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()			,input_processor[key].sum()			,delta=1e-2 )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Any:





				"""simple docstring"""


				lowerCAmelCase				       =     self.get_image_processor()
				lowerCAmelCase				       =     self.get_tokenizer()

				lowerCAmelCase				       =     GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,image_processor=SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     '''lower newer'''

				lowerCAmelCase				       =     processor(text=SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     tokenizer(SCREAMING_SNAKE_CASE_			,return_token_type_ids=SCREAMING_SNAKE_CASE_ )

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]			,encoded_processor[key] )






			def 				UpperCAmelCase       (self     :  int )					->  Tuple:





				"""simple docstring"""


				lowerCAmelCase				       =     self.get_image_processor()
				lowerCAmelCase				       =     self.get_tokenizer()

				lowerCAmelCase				       =     GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,image_processor=SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     '''lower newer'''
				lowerCAmelCase				       =     self.prepare_image_inputs()

				lowerCAmelCase				       =     processor(text=SCREAMING_SNAKE_CASE_			,images=SCREAMING_SNAKE_CASE_ )

				self.assertListEqual(list(inputs.keys() )			,['''input_ids''', '''attention_mask''', '''pixel_values'''] )

				# test if it raises when no input is passed
				with pytest.raises(SCREAMING_SNAKE_CASE_ ):
					processor()






			def 				UpperCAmelCase       (self     :  Dict )					->  Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.get_image_processor()
				lowerCAmelCase				       =     self.get_tokenizer()

				lowerCAmelCase				       =     GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,image_processor=SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				lowerCAmelCase				       =     processor.batch_decode(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )

				self.assertListEqual(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  List[Any] )					->  Optional[int]:





				"""simple docstring"""


				lowerCAmelCase				       =     self.get_image_processor()
				lowerCAmelCase				       =     self.get_tokenizer()

				lowerCAmelCase				       =     GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,image_processor=SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     '''lower newer'''
				lowerCAmelCase				       =     self.prepare_image_inputs()

				lowerCAmelCase				       =     processor(text=SCREAMING_SNAKE_CASE_			,images=SCREAMING_SNAKE_CASE_ )

				# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
				self.assertListEqual(list(inputs.keys() )			,['''input_ids''', '''attention_mask''', '''pixel_values'''] )




"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  lowercase				(   lowercase__		):
			lowercase			     =    (KDPMaAncestralDiscreteScheduler,)
			lowercase			     =    10






			def 				UpperCAmelCase       (self     :  Optional[int]			,**SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     {
				    '''num_train_timesteps''': 1_100,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**SCREAMING_SNAKE_CASE_ )
				return config






			def 				UpperCAmelCase       (self     :  Any )					->  str:





				"""simple docstring"""


				for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  int:





				"""simple docstring"""


				for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,[0.00_02, 0.0_02, 0.02] ):
					self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_			,beta_end=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Optional[int]:





				"""simple docstring"""


				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Dict:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 1_38_49.38_77 ) < 1e-2
				assert abs(result_mean.item() - 18.03_31 ) < 5e-3






			def 				UpperCAmelCase       (self     :  str )					->  List[Any]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  List[Any] )					->  List[str]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config(prediction_type='''v_prediction''' )
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 3_28.99_70 ) < 1e-2
				assert abs(result_mean.item() - 0.42_84 ) < 1e-3






			def 				UpperCAmelCase       (self     :  Dict )					->  Union[str, Any]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps			,device=SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_ ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 1_38_49.38_18 ) < 1e-1
				assert abs(result_mean.item() - 18.03_31 ) < 1e-3


