





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, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        A__											=						tempfile.mkdtemp()

        A__											=						BlipImageProcessor()
        A__											=						BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''' )

        A__											=						BlipProcessor(__lowerCamelCase,__lowerCamelCase )

        processor.save_pretrained(self.tmpdirname )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase ).tokenizer



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase ).image_processor



 def 				UpperCamelCase			(							self ):
        shutil.rmtree(self.tmpdirname )



 def 				UpperCamelCase			(							self ):
        A__											=						[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta )]

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

        return image_inputs



 def 				UpperCamelCase			(							self ):
        A__											=						BlipProcessor(tokenizer=self.get_tokenizer(),image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

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

        A__											=						BlipProcessor.from_pretrained(
            self.tmpdirname,bos_token='''(BOS)''',eos_token='''(EOS)''',do_normalize=__lowerCamelCase,padding_value=1.0 )

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

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_tokenizer()

        A__											=						BlipProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase )

        A__											=						self.prepare_image_inputs()

        A__											=						image_processor(__lowerCamelCase,return_tensors='''np''' )
        A__											=						processor(images=__lowerCamelCase,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 ):
        A__											=						self.get_image_processor()
        A__											=						self.get_tokenizer()

        A__											=						BlipProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase )

        A__											=						'''lower newer'''

        A__											=						processor(text=__lowerCamelCase )

        A__											=						tokenizer(__lowerCamelCase,return_token_type_ids=__lowerCamelCase )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_tokenizer()

        A__											=						BlipProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase )

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

        A__											=						processor(text=__lowerCamelCase,images=__lowerCamelCase )

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

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_tokenizer()

        A__											=						BlipProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase )

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

        A__											=						processor.batch_decode(__lowerCamelCase )
        A__											=						tokenizer.batch_decode(__lowerCamelCase )

        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )




 def 				UpperCamelCase			(							self ):
        A__											=						self.get_image_processor()
        A__											=						self.get_tokenizer()

        A__											=						BlipProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase )

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

        A__											=						processor(text=__lowerCamelCase,images=__lowerCamelCase )

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












import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



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



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        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=__lowerCamelCase,beta_end=__lowerCamelCase )



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



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



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



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



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



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



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



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



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



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        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 ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        A__											=						self.dummy_sample_deter + 0.1
        A__											=						self.dummy_sample_deter - 0.1

        A__											=						samplea.shape[0]
        A__											=						torch.stack([samplea, samplea, samplea],dim=0 )
        A__											=						torch.arange(__lowerCamelCase )[0:3, None].repeat(1,__lowerCamelCase )

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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





