



import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard



class 						_snake_case (     unittest.TestCase			):





 """simple docstring"""



 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      {
       '''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''': 5_5.1, '''ROUGE-1''': 7_6},
   }
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   _A          =      ModelCard.from_dict(self.inputs_dict   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''model_details'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''intended_use'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''factors'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''metrics'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''evaluation_data'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''training_data'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''quantitative_analyses'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''ethical_considerations'''   )   )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE		,      '''caveats_and_recommendations'''   )   )
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A          =      ModelCard.from_dict(self.inputs_dict   )
   _A          =      json.loads(modelcard.to_json_string()   )
   for key, value in self.inputs_dict.items():
     self.assertEqual(obj[key]		,      __SCREAMING_SNAKE_CASE   )
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   _A          =      ModelCard.from_dict(self.inputs_dict   )

   with tempfile.TemporaryDirectory() as tmpdirname:
     _A          =      os.path.join(__SCREAMING_SNAKE_CASE		,      '''modelcard.json'''   )
     model_card_first.to_json_file(__SCREAMING_SNAKE_CASE   )
     _A          =      ModelCard.from_json_file(__SCREAMING_SNAKE_CASE   )

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



 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   _A          =      ModelCard.from_dict(self.inputs_dict   )

   with tempfile.TemporaryDirectory() as tmpdirname:
     model_card_first.save_pretrained(__SCREAMING_SNAKE_CASE   )
     _A          =      ModelCard.from_pretrained(__SCREAMING_SNAKE_CASE   )

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




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     (EulerAncestralDiscreteScheduler,)
 lowerCamelCase_       =     1_0
 def lowercase_						(  self		,      **a   )		->							Tuple:




   """simple docstring"""







   _A          =      {
       '''num_train_timesteps''': 1_1_0_0,
       '''beta_start''': 0.0_001,
       '''beta_end''': 0.02,
       '''beta_schedule''': '''linear''',
   }

   config.update(**a   )
   return config
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=a   )
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,      [0.0_002, 0.002, 0.02]   ):
     self.check_over_configs(beta_start=a		,      beta_end=a   )
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=a   )
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=a   )
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config(prediction_type='''v_prediction'''   )
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma
   _A          =      sample.to(a   )

   for i, t in enumerate(scheduler.timesteps   ):
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 108.4_439   ) < 1e-2
   assert abs(result_mean.item() - 0.1_412   ) < 1e-3



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




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps		,      device=a   )
   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

   for t in scheduler.timesteps:
     _A          =      scheduler.scale_model_input(a		,      a   )

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3