
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     snake_case       :					Any        =  False

if is_vision_available():
     from PIL import Image

     from transformers import (
         AutoProcessor,
         PixaStructImageProcessor,
         PixaStructProcessor,
         PreTrainedTokenizerFast,
         TaTokenizer,
     )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,					reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'		,					)
@require_vision
@require_torch
class 	_snake_case						(   unittest.TestCase							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= tempfile.mkdtemp()

       a		:str											= PixaStructImageProcessor()
       a		:Dict											= TaTokenizer.from_pretrained('''t5-small'''					)

       a		:Optional[Any]											= PixaStructProcessor(_lowerCamelCase      ,						_lowerCamelCase					)

       processor.save_pretrained(self.tmpdirname					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,						**_lowerCamelCase					).tokenizer




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,						**_lowerCamelCase					).image_processor




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Union[str, Any]											= [np.random.randint(255      ,						size=(3, 30, 400)      ,						dtype=np.uinta					)]

       a		:str											= [Image.fromarray(np.moveaxis(_lowerCamelCase      ,						0      ,						-1					)					) for x in image_inputs]

       return image_inputs




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= PixaStructProcessor(tokenizer=self.get_tokenizer()      ,						image_processor=self.get_image_processor()					)
       processor.save_pretrained(self.tmpdirname					)

       a		:Optional[int]											= self.get_tokenizer(bos_token='''(BOS)'''      ,						eos_token='''(EOS)'''					)
       a		:Dict											= self.get_image_processor(do_normalize=_lowerCamelCase      ,						padding_value=1.0					)

       a		:int											= PixaStructProcessor.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  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= self.get_image_processor()
       a		:int											= self.get_tokenizer()

       a		:str											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Optional[Any]											= self.prepare_image_inputs()

       a		:Any											= image_processor(_lowerCamelCase      ,						return_tensors='''np'''					)
       a		:List[Any]											= 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  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= self.get_image_processor()
       a		:Optional[Any]											= self.get_tokenizer()

       a		:Dict											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Union[str, Any]											= '''lower newer'''

       a		:Any											= processor(text=_lowerCamelCase					)

       a		:Optional[Any]											= tokenizer(_lowerCamelCase      ,						return_token_type_ids=_lowerCamelCase      ,						add_special_tokens=_lowerCamelCase					)

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= self.get_image_processor()
       a		:Optional[Any]											= self.get_tokenizer()

       a		:Any											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Any											= '''lower newer'''
       a		:int											= self.prepare_image_inputs()

       a		:Dict											= processor(text=_lowerCamelCase      ,						images=_lowerCamelCase					)

       self.assertListEqual(
           list(inputs.keys()					)      ,						['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= self.get_image_processor()
       a		:Tuple											= self.get_tokenizer()

       a		:Optional[int]											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Union[str, Any]											= '''lower newer'''
       a		:str											= self.prepare_image_inputs()

       a		:Any											= processor(text=_lowerCamelCase      ,						images=_lowerCamelCase					)

       a		:Any											= [512, 1024, 2048, 4096]
       a		:List[str]											= [770, 770, 770, 770]
       # with text
       for i, max_patch in enumerate(_lowerCamelCase					):
             a		:Tuple											= processor(text=_lowerCamelCase      ,						images=_lowerCamelCase      ,						max_patches=_lowerCamelCase					)
             self.assertEqual(inputs['''flattened_patches'''][0].shape[0]      ,						_lowerCamelCase					)
             self.assertEqual(inputs['''flattened_patches'''][0].shape[1]      ,						expected_hidden_size[i]					)

       # without text input
       for i, max_patch in enumerate(_lowerCamelCase					):
             a		:str											= processor(images=_lowerCamelCase      ,						max_patches=_lowerCamelCase					)
             self.assertEqual(inputs['''flattened_patches'''][0].shape[0]      ,						_lowerCamelCase					)
             self.assertEqual(inputs['''flattened_patches'''][0].shape[1]      ,						expected_hidden_size[i]					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= self.get_image_processor()
       a		:Tuple											= self.get_tokenizer()

       a		:List[str]											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:str											= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       a		:List[str]											= processor.batch_decode(_lowerCamelCase					)
       a		:str											= tokenizer.batch_decode(_lowerCamelCase					)

       self.assertListEqual(_lowerCamelCase      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= self.get_image_processor()
       a		:List[str]											= self.get_tokenizer()

       a		:Any											= PixaStructProcessor(tokenizer=_lowerCamelCase      ,						image_processor=_lowerCamelCase					)

       a		:Any											= '''lower newer'''
       a		:Optional[Any]											= self.prepare_image_inputs()

       a		:Dict											= processor(text=_lowerCamelCase      ,						images=_lowerCamelCase					)

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

       a		:Dict											= processor(text=_lowerCamelCase					)

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









import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             self.assertEqual(output_a.shape      ,						sample.shape					)
             self.assertEqual(output_a.shape      ,						output_a.shape					)





