
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


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





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       1
		lowercase	    =       3
		lowercase	    =       (32, 32)

		lowercase	    =       floats_tensor((batch_size, num_channels) + sizes						,       rng=random.Random(0			)			).to(snake_case			)
		return image





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)						,       layers_per_block=2						,       sample_size=32						,       in_channels=7						,       out_channels=4						,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')						,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')						,       cross_attention_dim=32						,       attention_head_dim=8						,       use_linear_projection=snake_case						,       only_cross_attention=(True, True, False)						,       num_class_embeds=100						,       )
		return model





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       AutoencoderKL(
		    block_out_channels=[32, 32, 64]						,       in_channels=3						,       out_channels=3						,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']						,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']						,       latent_channels=4						,       )
		return model





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       CLIPTextConfig(
		    bos_token_id=0						,       eos_token_id=2						,       hidden_size=32						,       intermediate_size=37						,       layer_norm_eps=1E-05						,       num_attention_heads=4						,       num_hidden_layers=5						,       pad_token_id=1						,       vocab_size=1000						,       hidden_act='gelu'						,       projection_dim=512						,       )
		return CLIPTextModel(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
		lowercase	    =       self.dummy_cond_unet_upscale
		lowercase	    =       DDPMScheduler()
		lowercase	    =       DDIMScheduler(prediction_type='v_prediction'			)
		lowercase	    =       self.dummy_vae
		lowercase	    =       self.dummy_text_encoder
		lowercase	    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

		lowercase	    =       self.dummy_image.cpu().permute(0						,       2						,       3						,       1			)[0]
		lowercase	    =       Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			).resize((64, 64)			)

		# make sure here that pndm scheduler skips prk
		lowercase	    =       StableDiffusionUpscalePipeline(
		    unet=snake_case						,       low_res_scheduler=snake_case						,       scheduler=snake_case						,       vae=snake_case						,       text_encoder=snake_case						,       tokenizer=snake_case						,       max_noise_level=350						,       )
		lowercase	    =       sd_pipe.to(snake_case			)
		sd_pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       'A painting of a squirrel eating a burger'
		lowercase	    =       torch.Generator(device=snake_case			).manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       )

		lowercase	    =       output.images

		lowercase	    =       torch.Generator(device=snake_case			).manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       return_dict=snake_case						,       )[0]

		lowercase	    =       image[0, -3:, -3:, -1]
		lowercase	    =       image_from_tuple[0, -3:, -3:, -1]

		lowercase	    =       low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		lowercase	    =       np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]			)

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
		lowercase	    =       self.dummy_cond_unet_upscale
		lowercase	    =       DDPMScheduler()
		lowercase	    =       DDIMScheduler(prediction_type='v_prediction'			)
		lowercase	    =       self.dummy_vae
		lowercase	    =       self.dummy_text_encoder
		lowercase	    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

		lowercase	    =       self.dummy_image.cpu().permute(0						,       2						,       3						,       1			)[0]
		lowercase	    =       Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			).resize((64, 64)			)

		# make sure here that pndm scheduler skips prk
		lowercase	    =       StableDiffusionUpscalePipeline(
		    unet=snake_case						,       low_res_scheduler=snake_case						,       scheduler=snake_case						,       vae=snake_case						,       text_encoder=snake_case						,       tokenizer=snake_case						,       max_noise_level=350						,       )
		lowercase	    =       sd_pipe.to(snake_case			)
		sd_pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       'A painting of a squirrel eating a burger'
		lowercase	    =       sd_pipe(
		    2 * [prompt]						,       image=2 * [low_res_image]						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       )
		lowercase	    =       output.images
		assert image.shape[0] == 2

		lowercase	    =       torch.Generator(device=snake_case			).manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       num_images_per_prompt=2						,       guidance_scale=6.0						,       noise_level=20						,       num_inference_steps=2						,       output_type='np'						,       )
		lowercase	    =       output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'						,       'This test requires a GPU'			)
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.dummy_cond_unet_upscale
		lowercase	    =       DDPMScheduler()
		lowercase	    =       DDIMScheduler(prediction_type='v_prediction'			)
		lowercase	    =       self.dummy_vae
		lowercase	    =       self.dummy_text_encoder
		lowercase	    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

		lowercase	    =       self.dummy_image.cpu().permute(0						,       2						,       3						,       1			)[0]
		lowercase	    =       Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			).resize((64, 64)			)

		# put models in fp16, except vae as it overflows in fp16
		lowercase	    =       unet.half()
		lowercase	    =       text_encoder.half()

		# make sure here that pndm scheduler skips prk
		lowercase	    =       StableDiffusionUpscalePipeline(
		    unet=snake_case						,       low_res_scheduler=snake_case						,       scheduler=snake_case						,       vae=snake_case						,       text_encoder=snake_case						,       tokenizer=snake_case						,       max_noise_level=350						,       )
		lowercase	    =       sd_pipe.to(snake_case			)
		sd_pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       'A painting of a squirrel eating a burger'
		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       sd_pipe(
		    [prompt]						,       image=snake_case						,       generator=snake_case						,       num_inference_steps=2						,       output_type='np'						,       ).images

		lowercase	    =       low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)







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




	'''simple docstring'''


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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'			)
		lowercase	    =       load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'			)

		lowercase	    =       'stabilityai/stable-diffusion-x4-upscaler'
		lowercase	    =       StableDiffusionUpscalePipeline.from_pretrained(snake_case			)
		pipe.to(snake_case			)
		pipe.set_progress_bar_config(disable=snake_case			)
		pipe.enable_attention_slicing()

		lowercase	    =       'a cat sitting on a park bench'

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       pipe(
		    prompt=snake_case						,       image=snake_case						,       generator=snake_case						,       output_type='np'						,       )
		lowercase	    =       output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image			).max() < 1E-3





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'			)
		lowercase	    =       load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'			)

		lowercase	    =       'stabilityai/stable-diffusion-x4-upscaler'
		lowercase	    =       StableDiffusionUpscalePipeline.from_pretrained(
		    snake_case						,       torch_dtype=torch.floataa						,       )
		pipe.to(snake_case			)
		pipe.set_progress_bar_config(disable=snake_case			)
		pipe.enable_attention_slicing()

		lowercase	    =       'a cat sitting on a park bench'

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       pipe(
		    prompt=snake_case						,       image=snake_case						,       generator=snake_case						,       output_type='np'						,       )
		lowercase	    =       output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image			).max() < 5E-1





	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		lowercase	    =       load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'			)

		lowercase	    =       'stabilityai/stable-diffusion-x4-upscaler'
		lowercase	    =       StableDiffusionUpscalePipeline.from_pretrained(
		    snake_case						,       torch_dtype=torch.floataa						,       )
		pipe.to(snake_case			)
		pipe.set_progress_bar_config(disable=snake_case			)
		pipe.enable_attention_slicing(1			)
		pipe.enable_sequential_cpu_offload()

		lowercase	    =       'a cat sitting on a park bench'

		lowercase	    =       torch.manual_seed(0			)
		lowercase	    =       pipe(
		    prompt=snake_case						,       image=snake_case						,       generator=snake_case						,       num_inference_steps=5						,       output_type='np'						,       )

		lowercase	    =       torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9








'''simple docstring'''






from datetime import datetime as dt
import os

from github import Github


A__							:  str    =[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				Github(os.environ["""GITHUB_TOKEN"""]    )
      _lowerCAmelCase					=				g.get_repo("""huggingface/transformers"""    )
      _lowerCAmelCase					=				repo.get_issues(state="""open"""    )

      for issue in open_issues:
            _lowerCAmelCase					=				sorted([comment for comment in issue.get_comments()]				,      key=lambda lowerCAmelCase    : i.created_at				,      reverse=lowerCAmelCase    )
            _lowerCAmelCase					=				comments[0] if len(lowerCAmelCase    ) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state="""closed"""    )
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      """This issue has been automatically marked as stale because it has not had """
                      """recent activity. If you think this still needs to be addressed """
                      """please comment on this thread.\n\nPlease note that issues that do not follow the """
                      """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""    )


if __name__ == "__main__":
      main()






