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)
 __lowerCamelCase    :   Any     =      (ScoreSdeVeScheduler,)
 __lowerCamelCase    :   List[Any]     =      ()
 @property
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =4
       UpperCAmelCase    : Optional[int]      =3
       UpperCAmelCase    : Dict      =8
       UpperCAmelCase    : List[str]      =8

       UpperCAmelCase    : str      =torch.rand((batch_size, num_channels, height, width)						)

       return sample
 @property
 def 	UpperCAmelCase__       (  self						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =4
       UpperCAmelCase    : str      =3
       UpperCAmelCase    : Union[str, Any]      =8
       UpperCAmelCase    : Tuple      =8

       UpperCAmelCase    : Dict      =batch_size * num_channels * height * width
       UpperCAmelCase    : Tuple      =torch.arange(snake_case__						)
       UpperCAmelCase    : Tuple      =sample.reshape(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__						)
       UpperCAmelCase    : int      =sample / num_elems
       UpperCAmelCase    : List[str]      =sample.permute(3		,   0		,   1		,   2						)

       return sample
 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       def model(snake_case__		,   snake_case__		,   *snake_case__						):
             return sample * t / (t + 1)

       return model
 def 	UpperCAmelCase__       (  self		,   **snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      ={
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**snake_case__						)
       return config
 def 	UpperCAmelCase__       (  self		,   snake_case__=0		,   **snake_case__						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Any      =dict(self.forward_default_kwargs						)

       for scheduler_class in self.scheduler_classes:
             UpperCAmelCase    : Dict      =self.dummy_sample
             UpperCAmelCase    : Optional[Any]      =0.1 * sample

             UpperCAmelCase    : Optional[Any]      =self.get_scheduler_config(**snake_case__						)
             UpperCAmelCase    : Optional[Any]      =scheduler_class(**snake_case__						)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(snake_case__						)
                   UpperCAmelCase    : int      =scheduler_class.from_pretrained(snake_case__						)

             UpperCAmelCase    : int      =scheduler.step_pred(
                 snake_case__		,   snake_case__		,   snake_case__		,   generator=torch.manual_seed(0						)		,   **snake_case__						).prev_sample
             UpperCAmelCase    : Optional[int]      =new_scheduler.step_pred(
                 snake_case__		,   snake_case__		,   snake_case__		,   generator=torch.manual_seed(0						)		,   **snake_case__						).prev_sample

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

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

             assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler correction are not identical"
 def 	UpperCAmelCase__       (  self		,   snake_case__=0		,   **snake_case__						)	->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =dict(self.forward_default_kwargs						)
       kwargs.update(snake_case__						)

       for scheduler_class in self.scheduler_classes:
             UpperCAmelCase    : Tuple      =self.dummy_sample
             UpperCAmelCase    : List[str]      =0.1 * sample

             UpperCAmelCase    : Dict      =self.get_scheduler_config()
             UpperCAmelCase    : Union[str, Any]      =scheduler_class(**snake_case__						)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(snake_case__						)
                   UpperCAmelCase    : int      =scheduler_class.from_pretrained(snake_case__						)

             UpperCAmelCase    : Tuple      =scheduler.step_pred(
                 snake_case__		,   snake_case__		,   snake_case__		,   generator=torch.manual_seed(0						)		,   **snake_case__						).prev_sample
             UpperCAmelCase    : Any      =new_scheduler.step_pred(
                 snake_case__		,   snake_case__		,   snake_case__		,   generator=torch.manual_seed(0						)		,   **snake_case__						).prev_sample

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

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

             assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler correction are not identical"
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]		,   [1, 100, 1000]						):
             self.check_over_configs(sigma_min=snake_case__		,   sigma_max=snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =dict(self.forward_default_kwargs						)

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

       UpperCAmelCase    : Dict      =3

       UpperCAmelCase    : Dict      =self.dummy_model()
       UpperCAmelCase    : int      =self.dummy_sample_deter

       scheduler.set_sigmas(snake_case__						)
       scheduler.set_timesteps(snake_case__						)
       UpperCAmelCase    : Any      =torch.manual_seed(0						)

       for i, t in enumerate(scheduler.timesteps						):
             UpperCAmelCase    : List[str]      =scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps						):
                   with torch.no_grad():
                         UpperCAmelCase    : Tuple      =model(snake_case__		,   snake_case__						)
                   UpperCAmelCase    : Optional[Any]      =scheduler.step_correct(snake_case__		,   snake_case__		,   generator=snake_case__		,   **snake_case__						).prev_sample

             with torch.no_grad():
                   UpperCAmelCase    : Optional[int]      =model(snake_case__		,   snake_case__						)

             UpperCAmelCase    : Any      =scheduler.step_pred(snake_case__		,   snake_case__		,   snake_case__		,   generator=snake_case__		,   **snake_case__						)
             UpperCAmelCase  ,						UpperCAmelCase    : int      =output.prev_sample, output.prev_sample_mean

       UpperCAmelCase    : int      =torch.sum(torch.abs(snake_case__						)						)
       UpperCAmelCase    : str      =torch.mean(torch.abs(snake_case__						)						)

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






 def 	UpperCAmelCase__       (  self						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =dict(self.forward_default_kwargs						)

       UpperCAmelCase    : int      =kwargs.pop('''num_inference_steps'''		,   snake_case__						)

       for scheduler_class in self.scheduler_classes:
             UpperCAmelCase    : Tuple      =self.get_scheduler_config()
             UpperCAmelCase    : Any      =scheduler_class(**snake_case__						)

             UpperCAmelCase    : Any      =self.dummy_sample
             UpperCAmelCase    : Tuple      =0.1 * sample

             if num_inference_steps is not None and hasattr(snake_case__		,   '''set_timesteps'''						):
                   scheduler.set_timesteps(snake_case__						)
             elif num_inference_steps is not None and not hasattr(snake_case__		,   '''set_timesteps'''						):
                   UpperCAmelCase    : Optional[Any]      =num_inference_steps

             UpperCAmelCase    : List[str]      =scheduler.step_pred(snake_case__		,   0		,   snake_case__		,   generator=torch.manual_seed(0						)		,   **snake_case__						).prev_sample
             UpperCAmelCase    : Optional[int]      =scheduler.step_pred(snake_case__		,   1		,   snake_case__		,   generator=torch.manual_seed(0						)		,   **snake_case__						).prev_sample

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

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard







class 			__snake_case   (     unittest.TestCase   ):
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      ={
           '''model_details''': {
               '''Organization''': '''testing''',
               '''Model date''': '''today''',
               '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
               '''Architecture''': '''Convolutional Neural Network.''',
           },
           '''metrics''': '''BLEU and ROUGE-1''',
           '''evaluation_data''': {
               '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
               '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
           },
           '''training_data''': {
               '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
               '''Preprocessing''': (
                   '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
                   ''' https://arxiv.org/pdf/1810.03993.pdf'''
               ),
           },
           '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
       }
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =ModelCard.from_dict(self.inputs_dict						)
       self.assertTrue(hasattr(snake_case__		,   '''model_details'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''intended_use'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''factors'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''metrics'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''evaluation_data'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''training_data'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''quantitative_analyses'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''ethical_considerations'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''caveats_and_recommendations'''						)						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =ModelCard.from_dict(self.inputs_dict						)
       UpperCAmelCase    : int      =json.loads(modelcard.to_json_string()						)
       for key, value in self.inputs_dict.items():
             self.assertEqual(obj[key]		,   snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCAmelCase    : Optional[Any]      =os.path.join(snake_case__		,   '''modelcard.json'''						)
             model_card_first.to_json_file(snake_case__						)
             UpperCAmelCase    : Tuple      =ModelCard.from_json_file(snake_case__						)

       self.assertEqual(model_card_second.to_dict()		,   model_card_first.to_dict()						)






 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             model_card_first.save_pretrained(snake_case__						)
             UpperCAmelCase    : Dict      =ModelCard.from_pretrained(snake_case__						)

       self.assertEqual(model_card_second.to_dict()		,   model_card_first.to_dict()						)
