


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class     UpperCamelCase(  __A   ):
     snake_case_		:				Union[str, Any]										=	(KDPMaDiscreteScheduler,)
     snake_case_		:				Tuple										=	10


     def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	Tuple:




           '''simple docstring'''

           __snake_case				=		{
               'num_train_timesteps': 1_1_0_0,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
           }

           config.update(**SCREAMING_SNAKE_CASE						)
           return config


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[str]:




           '''simple docstring'''

           for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE						)


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	int:




           '''simple docstring'''

           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,    [0.0002, 0.002, 0.02]						):
                 self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE     ,    beta_end=SCREAMING_SNAKE_CASE						)


     def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Union[str, Any]:




           '''simple docstring'''

           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE						)


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Tuple:




           '''simple docstring'''

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


     def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	int:




           '''simple docstring'''

           __snake_case				=		self.scheduler_classes[0]
           __snake_case				=		self.get_scheduler_config(prediction_type="v_prediction"						)
           __snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

           scheduler.set_timesteps(self.num_inference_steps						)

           __snake_case				=		self.dummy_model()
           __snake_case				=		self.dummy_sample_deter * scheduler.init_noise_sigma
           __snake_case				=		sample.to(SCREAMING_SNAKE_CASE						)

           for i, t in enumerate(scheduler.timesteps						):
                 __snake_case				=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

                 __snake_case				=		model(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

                 __snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
                 __snake_case				=		output.prev_sample

           __snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
           __snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_9_3_4e-0_7						) < 1e-2
                 assert abs(result_mean.item() - 6.1_1_1_2e-1_0						) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7						) < 1e-2
                 assert abs(result_mean.item() - 0.0002						) < 1e-3


     def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Dict:




           '''simple docstring'''

           if torch_device == "mps":
                 return
           __snake_case				=		self.scheduler_classes[0]
           __snake_case				=		self.get_scheduler_config()
           __snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

           scheduler.set_timesteps(self.num_inference_steps						)

           __snake_case				=		self.dummy_model()
           __snake_case				=		self.dummy_sample_deter * scheduler.init_noise_sigma
           __snake_case				=		sample.to(SCREAMING_SNAKE_CASE						)

           for i, t in enumerate(scheduler.timesteps						):
                 __snake_case				=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

                 __snake_case				=		model(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

                 __snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
                 __snake_case				=		output.prev_sample

           __snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
           __snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 20.4125						) < 1e-2
                 assert abs(result_mean.item() - 0.0266						) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.4125						) < 1e-2
                 assert abs(result_mean.item() - 0.0266						) < 1e-3






     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Union[str, Any]:




           '''simple docstring'''

           if torch_device == "mps":
                 return
           __snake_case				=		self.scheduler_classes[0]
           __snake_case				=		self.get_scheduler_config()
           __snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

           scheduler.set_timesteps(self.num_inference_steps     ,    device=SCREAMING_SNAKE_CASE						)

           __snake_case				=		self.dummy_model()
           __snake_case				=		self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE						) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __snake_case				=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

                 __snake_case				=		model(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

                 __snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
                 __snake_case				=		output.prev_sample

           __snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
           __snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

           if str(SCREAMING_SNAKE_CASE						).startswith("cpu"						):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 20.4125						) < 1e-2
                 assert abs(result_mean.item() - 0.0266						) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.4125						) < 1e-2
                 assert abs(result_mean.item() - 0.0266						) < 1e-3









import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image





def 	_lowerCAmelCase       (    _lowerCAmelCase="" )						->	str:


						'''simple docstring'''






						__snake_case				=		tempfile.mkdtemp()
						return os.path.join(_lowerCAmelCase      ,	str(uuid.uuida() ) + suffix )






@require_soundfile
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)

											del agent_type

											# Ensure the path remains even after the object deletion
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)

											# Ensure that the file contains the same value as the original tensor
											__snake_case		,					__snake_case				=		sf.read(SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    torch.tensor(SCREAMING_SNAKE_CASE						)     ,    atol=1e-4						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		get_new_path(suffix=".wav"						)
											sf.write(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1_6_0_0_0						)

											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)

											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)
											self.assertEqual(agent_type.to_string()     ,    SCREAMING_SNAKE_CASE						)






@require_vision
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.randint(0     ,    2_5_6     ,    (6_4, 6_4, 3)						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type._tensor     ,    atol=1e-4						)						)

											self.assertIsInstance(agent_type.to_raw()     ,    Image.Image						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Any:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertTrue(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertFalse(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"Hey!"
											__snake_case				=		AgentText(SCREAMING_SNAKE_CASE						)

											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_string()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)





