from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch







@require_torch
@require_tf
class __snake_case				(						__UpperCAmelCase		):




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SMALL_MODEL_IDENTIFIER
     SCREAMING_SNAKE_CASE__										=      """pt"""
     SCREAMING_SNAKE_CASE__										=      """tf"""




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      AutoModel.from_pretrained(self.test_model      )
     model_pt.save_pretrained(UpperCamelCase__      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      TFAutoModel.from_pretrained(self.test_model       ,	from_pt=UpperCamelCase__      )
     model_tf.save_pretrained(UpperCamelCase__      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """mock_framework"""

     # Framework provided - return whatever the user provides
     SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(self.test_model       ,	UpperCamelCase__      )
     self.assertEqual(UpperCamelCase__       ,	UpperCamelCase__      )

     # Local checkpoint and framework provided - return provided framework
     # PyTorch checkpoint
     with TemporaryDirectory() as local_pt_ckpt:
       self._setup_pt_ckpt(UpperCamelCase__      )
       SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(UpperCamelCase__       ,	UpperCamelCase__      )
       self.assertEqual(UpperCamelCase__       ,	UpperCamelCase__      )

     # TensorFlow checkpoint
     with TemporaryDirectory() as local_tf_ckpt:
       self._setup_tf_ckpt(UpperCamelCase__      )
       SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(UpperCamelCase__       ,	UpperCamelCase__      )
       self.assertEqual(UpperCamelCase__       ,	UpperCamelCase__      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     with TemporaryDirectory() as local_pt_ckpt:
       self._setup_pt_ckpt(UpperCamelCase__      )
       SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(UpperCamelCase__      )
       self.assertEqual(UpperCamelCase__       ,	self.framework_pt      )

     # TensorFlow checkpoint
     with TemporaryDirectory() as local_tf_ckpt:
       self._setup_tf_ckpt(UpperCamelCase__      )
       SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(UpperCamelCase__      )
       self.assertEqual(UpperCamelCase__       ,	self.framework_tf      )

     # Invalid local checkpoint
     with TemporaryDirectory() as local_invalid_ckpt:
       with self.assertRaises(UpperCamelCase__      ):
         SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(UpperCamelCase__      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      MagicMock(return_value=UpperCamelCase__      )
     with patch("""transformers.onnx.features.is_tf_available"""       ,	UpperCamelCase__      ):
       SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(self.test_model      )
       self.assertEqual(UpperCamelCase__       ,	self.framework_pt      )

     # PyTorch not in environment -> use TensorFlow
     SCREAMING_SNAKE_CASE__										=      MagicMock(return_value=UpperCamelCase__      )
     with patch("""transformers.onnx.features.is_torch_available"""       ,	UpperCamelCase__      ):
       SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(self.test_model      )
       self.assertEqual(UpperCamelCase__       ,	self.framework_tf      )

     # Both in environment -> use PyTorch
     SCREAMING_SNAKE_CASE__										=      MagicMock(return_value=UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      MagicMock(return_value=UpperCamelCase__      )
     with patch("""transformers.onnx.features.is_tf_available"""       ,	UpperCamelCase__      ), patch(
         """transformers.onnx.features.is_torch_available"""       ,	UpperCamelCase__      ):
       SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(self.test_model      )
       self.assertEqual(UpperCamelCase__       ,	self.framework_pt      )

     # Both not in environment -> raise error
     SCREAMING_SNAKE_CASE__										=      MagicMock(return_value=UpperCamelCase__      )
     SCREAMING_SNAKE_CASE__										=      MagicMock(return_value=UpperCamelCase__      )
     with patch("""transformers.onnx.features.is_tf_available"""       ,	UpperCamelCase__      ), patch(
         """transformers.onnx.features.is_torch_available"""       ,	UpperCamelCase__      ):
       with self.assertRaises(UpperCamelCase__      ):
         SCREAMING_SNAKE_CASE__										=      FeaturesManager.determine_framework(self.test_model      )






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for time_step in [0, 500, 999]:
														for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																										continue

																				self.check_over_forward(time_step=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

								lowerCAmelCase_      			=  self.dummy_model()
								lowerCAmelCase_      			=  self.dummy_sample_deter
								lowerCAmelCase_      			=  torch.manual_seed(0   )

								for i, t in enumerate(UpperCamelCase__   ):
														# 1. predict noise residual
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   )

														if i + 1 == timesteps.shape[0]:
																				lowerCAmelCase_      			=  None
														else:
																				lowerCAmelCase_      			=  timesteps[i + 1]

														# 2. predict previous mean of sample x_t-1
														lowerCAmelCase_      			=  scheduler.step(
														    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	prev_timestep=UpperCamelCase__,	generator=UpperCamelCase__   ).prev_sample

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




