



'''simple docstring'''


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					AltDiffusionPipeline
	__A			  =					TEXT_TO_IMAGE_PARAMS
	__A			  =					TEXT_TO_IMAGE_BATCH_PARAMS
	__A			  =					TEXT_TO_IMAGE_IMAGE_PARAMS
	__A			  =					TEXT_TO_IMAGE_IMAGE_PARAMS







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							torch.manual_seed(0						)
							lowercase        =							UNetaDConditionModel(
							    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=4 ,				out_channels=4 ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,				cross_attention_dim=32 ,				)
							lowercase        =							DDIMScheduler(
							    beta_start=0.0_0085 ,				beta_end=0.012 ,				beta_schedule="""scaled_linear""" ,				clip_sample=_lowerCAmelCase ,				set_alpha_to_one=_lowerCAmelCase ,				)
							torch.manual_seed(0						)
							lowercase        =							AutoencoderKL(
							    block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,				latent_channels=4 ,				)

							# TODO: address the non-deterministic text encoder (fails for save-load tests)
							# torch.manual_seed(0)
							# text_encoder_config = RobertaSeriesConfig(
							#     hidden_size=32,
							#     project_dim=32,
							#     intermediate_size=37,
							#     layer_norm_eps=1e-05,
							#     num_attention_heads=4,
							#     num_hidden_layers=5,
							#     vocab_size=5002,
							# )
							# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

							torch.manual_seed(0						)
							lowercase        =							CLIPTextConfig(
							    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				projection_dim=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=5002 ,				)
							lowercase        =							CLIPTextModel(_lowerCAmelCase						)

							lowercase        =							XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""						)
							lowercase        =							77

							lowercase        =							{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=0						)    ->    Dict:
							'''simple docstring'''






							if str(_lowerCAmelCase						).startswith("""mps"""						):
													lowercase        =							torch.manual_seed(_lowerCAmelCase						)
							else:
													lowercase        =							torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
							lowercase        =							{
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							super().test_attention_slicing_forward_pass(expected_max_diff=3E-3						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator

							lowercase        =							self.get_dummy_components()
							torch.manual_seed(0						)
							lowercase        =							RobertaSeriesConfig(
							    hidden_size=32 ,				project_dim=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				vocab_size=5002 ,				)
							# TODO: remove after fixing the non-deterministic text encoder
							lowercase        =							RobertaSeriesModelWithTransformation(_lowerCAmelCase						)
							lowercase        =							text_encoder

							lowercase        =							AltDiffusionPipeline(**_lowerCAmelCase						)
							lowercase        =							alt_pipe.to(_lowerCAmelCase						)
							alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							lowercase        =							self.get_dummy_inputs(_lowerCAmelCase						)
							lowercase        =							"""A photo of an astronaut"""
							lowercase        =							alt_pipe(**_lowerCAmelCase						)
							lowercase        =							output.images
							lowercase        =							image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowercase        =							np.array(
							    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]						)

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







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							"""cpu"""  # ensure determinism for the device-dependent torch.Generator

							lowercase        =							self.get_dummy_components()
							lowercase        =							PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
							torch.manual_seed(0						)
							lowercase        =							RobertaSeriesConfig(
							    hidden_size=32 ,				project_dim=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				vocab_size=5002 ,				)
							# TODO: remove after fixing the non-deterministic text encoder
							lowercase        =							RobertaSeriesModelWithTransformation(_lowerCAmelCase						)
							lowercase        =							text_encoder
							lowercase        =							AltDiffusionPipeline(**_lowerCAmelCase						)
							lowercase        =							alt_pipe.to(_lowerCAmelCase						)
							alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							lowercase        =							self.get_dummy_inputs(_lowerCAmelCase						)
							lowercase        =							alt_pipe(**_lowerCAmelCase						)
							lowercase        =							output.images
							lowercase        =							image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowercase        =							np.array(
							    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]						)

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




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







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" ,				safety_checker=_lowerCAmelCase						)
							lowercase        =							alt_pipe.to(_lowerCAmelCase						)
							alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							lowercase        =							"""A painting of a squirrel eating a burger"""
							lowercase        =							torch.manual_seed(0						)
							lowercase        =							alt_pipe([prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=6.0 ,				num_inference_steps=20 ,				output_type="""np"""						)

							lowercase        =							output.images

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

							assert image.shape == (1, 512, 512, 3)
							lowercase        =							np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]						)

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







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							DDIMScheduler.from_pretrained("""BAAI/AltDiffusion""" ,				subfolder="""scheduler"""						)

							lowercase        =							AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion""" ,				scheduler=_lowerCAmelCase ,				safety_checker=_lowerCAmelCase						)
							lowercase        =							alt_pipe.to(_lowerCAmelCase						)
							alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							lowercase        =							"""A painting of a squirrel eating a burger"""
							lowercase        =							torch.manual_seed(0						)

							lowercase        =							alt_pipe([prompt] ,				generator=_lowerCAmelCase ,				num_inference_steps=2 ,				output_type="""numpy"""						)
							lowercase        =							output.images

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

							assert image.shape == (1, 512, 512, 3)
							lowercase        =							np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]						)

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







'''simple docstring'''


from __future__ import annotations

import os
from typing import Any

import requests

lowercase_      :				List[str]   					=    '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_      :				Any   					=    BASE_URL + '''/user'''

# https://github.com/settings/tokens
lowercase_      :				Union[str, Any]   					=    os.environ.get('''USER_TOKEN''', '''''')







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						lowercase        =							{
						    """Authorization""": F"""token {auth_token}""",
						    """Accept""": """application/vnd.github.v3+json""",
						}
						return requests.get(lowercase_ ,  headers=lowercase_						).json()


if __name__ == "__main__":  # pragma: no cover
						if USER_TOKEN:
												for key, value in fetch_github_info(USER_TOKEN).items():
																		print(f'''{key}: {value}''')
						else:
												raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')


