




"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a_        =       False

class   UpperCAmelCase_    (			unittest.TestCase		):
		pass


@nightly
@require_torch_gpu
class   UpperCAmelCase_    (			unittest.TestCase		):
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
		def 	_lowerCamelCase							(						self       )						->       int:
					__lowercase			:  Optional[int]								=       VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(UpperCamelCase_       )
					pipe.set_progress_bar_config(disable=UpperCamelCase_       )

					__lowercase			:  Optional[int]								=       '''A painting of a squirrel eating a burger '''
					__lowercase			:  Optional[int]								=       torch.manual_seed(0       )
					__lowercase			:  Any								=       pipe(
					    prompt=UpperCamelCase_				,						generator=UpperCamelCase_				,						guidance_scale=7.5				,						num_inference_steps=2				,						output_type='''numpy'''       ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
								pipe.save_pretrained(UpperCamelCase_       )
								__lowercase			:  Optional[Any]								=       VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCamelCase_       )
					pipe.to(UpperCamelCase_       )
					pipe.set_progress_bar_config(disable=UpperCamelCase_       )

					__lowercase			:  Union[str, Any]								=       generator.manual_seed(0       )
					__lowercase			:  List[Any]								=       pipe(
					    prompt=UpperCamelCase_				,						generator=UpperCamelCase_				,						guidance_scale=7.5				,						num_inference_steps=2				,						output_type='''numpy'''       ).images

					assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"
		def 	_lowerCamelCase							(						self       )						->       Optional[int]:
					__lowercase			:  str								=       VersatileDiffusionTextToImagePipeline.from_pretrained(
					    '''shi-labs/versatile-diffusion'''				,						torch_dtype=torch.floataa       )
					pipe.to(UpperCamelCase_       )
					pipe.set_progress_bar_config(disable=UpperCamelCase_       )

					__lowercase			:  str								=       '''A painting of a squirrel eating a burger '''
					__lowercase			:  Any								=       torch.manual_seed(0       )
					__lowercase			:  str								=       pipe(
					    prompt=UpperCamelCase_				,						generator=UpperCamelCase_				,						guidance_scale=7.5				,						num_inference_steps=50				,						output_type='''numpy'''       ).images

					__lowercase			:  List[str]								=       image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__lowercase			:  Union[str, Any]								=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

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








"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
A		=						'\nHuman: <<task>>\n\nAssistant: '


A		=						'huggingface-tools/default-prompts'
A		=						{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple="run" ):
						"""simple docstring"""






						if prompt_or_repo_id is None:
												snake_case   :			Any						    = DEFAULT_PROMPTS_REPO

						# prompt is considered a repo ID when it does not contain any kind of space
						if re.search("\\s"     ,		lowerCamelCase_ ) is not None:
												return prompt_or_repo_id

						snake_case   :			Optional[int]						    = cached_file(
						    lowerCamelCase_     ,		PROMPT_FILES[mode]     ,		repo_type="dataset"     ,		user_agent={"agent": agent_name} )
						with open(lowerCamelCase_     ,		"r"     ,		encoding="utf-8" ) as f:
												return f.read()



