
"""simple docstring"""


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__      (				UpperCAmelCase_=""					)->				str:







       """simple docstring"""
       UpperCamelCase       			=		tempfile.mkdtemp()
       return os.path.join(__A		,   str(uuid.uuida()					) + suffix					)



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






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







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

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

              del agent_type

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

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






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







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

              UpperCamelCase       			=		AgentAudio(lowercase__      )

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





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






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[Any]:







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

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






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Optional[Any]:







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

              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(lowercase__      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







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

              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(lowercase__      )      )





class 	__a	(   unittest.TestCase						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		"Hey!"
              UpperCamelCase       			=		AgentText(lowercase__      )

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








import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





@slow
@require_torch_gpu
class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-1


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_dpmpp_2m'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe(
                 [prompt] ,   generator=lowercase__ ,   guidance_scale=7.5 ,   num_inference_steps=1_5 ,   output_type='''np''' ,   use_karras_sigmas=lowercase__ ,   )

             __A    =output.images

             __A    =image[0, -3:, -3:, -1]

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array(
                 [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2


