



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      :  List[str]			     =			False







class 				_a				(   unittest.TestCase					):
			pass







@nightly
@require_torch_gpu
class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   str:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Tuple							      =     VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""					)
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__lowerCAmelCase					)
						pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

						UpperCAmelCase_:		List[Any]							      =     """A painting of a squirrel eating a burger """
						UpperCAmelCase_:		List[str]							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[int]							      =     pipe(
						    prompt=__lowerCAmelCase,   generator=__lowerCAmelCase,   guidance_scale=7.5,   num_inference_steps=2,   output_type="""numpy"""					).images

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

						UpperCAmelCase_:		Optional[Any]							      =     generator.manual_seed(0					)
						UpperCAmelCase_:		List[Any]							      =     pipe(
						    prompt=__lowerCAmelCase,   generator=__lowerCAmelCase,   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      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		List[Any]							      =     VersatileDiffusionTextToImagePipeline.from_pretrained(
						    """shi-labs/versatile-diffusion""",   torch_dtype=torch.floataa					)
						pipe.to(__lowerCAmelCase					)
						pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

						UpperCAmelCase_:		Union[str, Any]							      =     """A painting of a squirrel eating a burger """
						UpperCAmelCase_:		Tuple							      =     torch.manual_seed(0					)
						UpperCAmelCase_:		List[str]							      =     pipe(
						    prompt=__lowerCAmelCase,   generator=__lowerCAmelCase,   guidance_scale=7.5,   num_inference_steps=50,   output_type="""numpy"""					).images

						UpperCAmelCase_:		int							      =     image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						UpperCAmelCase_:		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













# 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
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


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





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

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

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




