
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase__		:Union[str, Any]   =    {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:str   =    [
							    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "UniSpeechForCTC",
							    "UniSpeechForPreTraining",
							    "UniSpeechForSequenceClassification",
							    "UniSpeechModel",
							    "UniSpeechPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							lowercase__		:Dict   =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""
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 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
			__snake_case	:						List[str]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

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

			__snake_case	:						Tuple						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						str						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						List[str]						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Dict						=				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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

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

			__snake_case	:						str						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						List[str]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						Any						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Tuple						=				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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

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

			__snake_case	:						int						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						Optional[Any]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe(
			    [prompt]				,       generator=a_				,       guidance_scale=7.5				,       num_inference_steps=15				,       output_type='''np'''				,       use_karras_sigmas=a_				,       )

			__snake_case	:						Optional[int]						=				output.images

			__snake_case	:						Any						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Any						=				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





