






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     a     (		lowerCamelCase_=""							):






				'''simple docstring'''
				lowercase__  					=     tempfile.mkdtemp()
				return os.path.join(lowerCamelCase_						,  str(uuid.uuida()							) + suffix							)
@require_soundfile
@require_torch
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     torch.rand(12,    dtype=torch.floataa    ) - 0.5
								lowercase__  					=     AgentAudio(lowerCamelCase    )
								lowercase__  					=     str(agent_type.to_string()    )

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

								del agent_type

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

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




				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     torch.rand(12,    dtype=torch.floataa    ) - 0.5
								lowercase__  					=     get_new_path(suffix='''.wav'''    )
								sf.write(lowerCamelCase,    lowerCamelCase,    16_000    )

								lowercase__  					=     AgentAudio(lowerCamelCase    )

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



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

				"""simple docstring"""




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     torch.randint(0,    256,    (64, 64, 3)    )
								lowercase__  					=     AgentImage(lowerCamelCase    )
								lowercase__  					=     str(agent_type.to_string()    )

								# Ensure that the tensor and the agent_type's tensor are the same
								self.assertTrue(torch.allclose(lowerCamelCase,    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(lowerCamelCase    )    )




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     Path(get_tests_dir('''fixtures/tests_samples/COCO'''    )    ) / '''000000039769.png'''
								lowercase__  					=     Image.open(lowerCamelCase    )
								lowercase__  					=     AgentImage(lowerCamelCase    )

								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(lowerCamelCase    )    )




				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     Path(get_tests_dir('''fixtures/tests_samples/COCO'''    )    ) / '''000000039769.png'''
								lowercase__  					=     Image.open(lowerCamelCase    )
								lowercase__  					=     AgentImage(lowerCamelCase    )

								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(lowerCamelCase    )    )



class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     '''Hey!'''
								lowercase__  					=     AgentText(lowerCamelCase    )

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












import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(IPNDMScheduler,)
				lowercase__											=			(("""num_inference_steps""", 50),)




				def 							lowercase__  (	self		:       Union[str, Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_train_timesteps''': 1_000}
								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Any=0,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )
												# copy over dummy past residuals
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																new_scheduler.set_timesteps(lowerCamelCase    )
																# copy over dummy past residuals
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[int]=0,    **lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(lowerCamelCase    )

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       int,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     10
								lowercase__  					=     self.dummy_model()
								lowercase__  					=     self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase    )

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								return sample




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )

								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )

												lowercase__  					=     self.dummy_sample
												lowercase__  					=     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'''    ):
																lowercase__  					=     num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.timesteps[5]
												lowercase__  					=     scheduler.timesteps[6]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								for timesteps in [100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]    ):
												self.check_over_forward(num_inference_steps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.full_loop()
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_mean.item() - 2_540_529    ) < 10




