



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a			      = StableDiffusionSAGPipeline
							_a			      = TEXT_TO_IMAGE_PARAMS
							_a			      = TEXT_TO_IMAGE_BATCH_PARAMS
							_a			      = TEXT_TO_IMAGE_IMAGE_PARAMS
							_a			      = TEXT_TO_IMAGE_IMAGE_PARAMS
							_a			      = False




							def    UpperCAmelCase__			(       self     :       str):
									torch.manual_seed(0)
									lowerCAmelCase_			:     Optional[Any]									= UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)				,				layers_per_block=2				,				sample_size=3_2				,				in_channels=4				,				out_channels=4				,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,				cross_attention_dim=3_2				,				)
									lowerCAmelCase_			:     Optional[int]									= DDIMScheduler(
									    beta_start=0.0_0085				,				beta_end=0.012				,				beta_schedule='''scaled_linear'''				,				clip_sample=A_				,				set_alpha_to_one=A_				,				)
									torch.manual_seed(0)
									lowerCAmelCase_			:     int									= AutoencoderKL(
									    block_out_channels=[3_2, 6_4]				,				in_channels=3				,				out_channels=3				,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,				latent_channels=4				,				)
									torch.manual_seed(0)
									lowerCAmelCase_			:     int									= CLIPTextConfig(
									    bos_token_id=0				,				eos_token_id=2				,				hidden_size=3_2				,				intermediate_size=3_7				,				layer_norm_eps=1e-05				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_0_0_0				,				)
									lowerCAmelCase_			:     List[Any]									= CLIPTextModel(A_)
									lowerCAmelCase_			:     int									= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

									lowerCAmelCase_			:     Optional[Any]									= {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       List[str]				,				A_     :       Dict=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_			:     str									= torch.manual_seed(A_)
									else:
											lowerCAmelCase_			:     str									= torch.Generator(device=A_).manual_seed(A_)
									lowerCAmelCase_			:     Optional[int]									= {
									    '''prompt''': '''.''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 1.0,
									    '''sag_scale''': 1.0,
									    '''output_type''': '''numpy''',
									}
									return inputs







							def    UpperCAmelCase__			(       self     :       int):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3)





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




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




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_			:     Optional[int]									= StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
									lowerCAmelCase_			:     Optional[Any]									= sag_pipe.to(A_)
									sag_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_			:     Optional[Any]									= '''.'''
									lowerCAmelCase_			:     Union[str, Any]									= torch.manual_seed(0)
									lowerCAmelCase_			:     List[str]									= sag_pipe(
									    [prompt]				,				generator=A_				,				guidance_scale=7.5				,				sag_scale=1.0				,				num_inference_steps=2_0				,				output_type='''np''')

									lowerCAmelCase_			:     List[Any]									= output.images

									lowerCAmelCase_			:     str									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									lowerCAmelCase_			:     Any									= np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Optional[int]									= StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
									lowerCAmelCase_			:     Any									= sag_pipe.to(A_)
									sag_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_			:     Optional[Any]									= '''.'''
									lowerCAmelCase_			:     str									= torch.manual_seed(0)
									lowerCAmelCase_			:     Tuple									= sag_pipe(
									    [prompt]				,				generator=A_				,				guidance_scale=7.5				,				sag_scale=1.0				,				num_inference_steps=2_0				,				output_type='''np''')

									lowerCAmelCase_			:     Any									= output.images

									lowerCAmelCase_			:     Optional[int]									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									lowerCAmelCase_			:     int									= np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     Optional[Any]									= StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
									lowerCAmelCase_			:     int									= sag_pipe.to(A_)
									sag_pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_			:     List[str]									= '''.'''
									lowerCAmelCase_			:     str									= torch.manual_seed(0)
									lowerCAmelCase_			:     Dict									= sag_pipe(
									    [prompt]				,				width=7_6_8				,				height=5_1_2				,				generator=A_				,				guidance_scale=7.5				,				sag_scale=1.0				,				num_inference_steps=2_0				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Union[str, Any]									= output.images

									assert image.shape == (1, 5_1_2, 7_6_8, 3)










# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


A__  :  Tuple								=					get_logger(__name__)



class 				__snake_case						:
							_a			      = '''dummy_data'''
							_a			      = '''datasets'''
							_a			      = False




							def __init__(       self     :       Optional[Any]				,				A_     :       str				,				A_     :       str				,				A_     :       Union[Version, str]				,				A_     :       Optional[str] = None				,				A_     :       bool = False				,				A_     :       bool = True				,				A_     :       Optional[List[Callable]] = None				,				):
									lowerCAmelCase_			:     List[Any]									= 0
									lowerCAmelCase_			:     Any									= dataset_name
									lowerCAmelCase_			:     Union[str, Any]									= cache_dir
									lowerCAmelCase_			:     List[Any]									= use_local_dummy_data
									lowerCAmelCase_			:     Optional[Any]									= config
									# download_callbacks take a single url as input
									lowerCAmelCase_			:     List[Callable]									= download_callbacks or []
									# if False, it doesn't load existing files and it returns the paths of the dummy files relative
									# to the dummy_data zip file root
									lowerCAmelCase_			:     Tuple									= load_existing_dummy_data

									# TODO(PVP, QL) might need to make this more general
									lowerCAmelCase_			:     int									= str(A_)
									# to be downloaded
									lowerCAmelCase_			:     Dict									= None
									lowerCAmelCase_			:     Optional[int]									= None




							@property
							def    UpperCAmelCase__			(       self     :       List[str]):
									if self._dummy_file is None:
											lowerCAmelCase_			:     int									= self.download_dummy_data()
									return self._dummy_file




							@property
							def    UpperCAmelCase__			(       self     :       str):
									if self.config is not None:
											# structure is dummy / config_name / version_name
											return os.path.join('''dummy'''				,				self.config.name				,				self.version_name)
									# structure is dummy / version_name
									return os.path.join('''dummy'''				,				self.version_name)




							@property
							def    UpperCAmelCase__			(       self     :       str):
									return os.path.join(self.dummy_data_folder				,				'''dummy_data.zip''')




							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     Any									= (
									    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
									)

									lowerCAmelCase_			:     Union[str, Any]									= cached_path(
									    A_				,				cache_dir=self.cache_dir				,				extract_compressed_file=A_				,				force_extract=A_)

									return os.path.join(A_				,				self.dummy_file_name)




							@property
							def    UpperCAmelCase__			(       self     :       List[str]):
									return os.path.join(self.datasets_scripts_dir				,				self.dataset_name				,				self.dummy_zip_file)




							@property
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									if self._bucket_url is None:
											lowerCAmelCase_			:     str									= hf_github_url(self.dataset_name				,				self.dummy_zip_file.replace(os.sep				,				'''/'''))
									return self._bucket_url




							@property
							def    UpperCAmelCase__			(       self     :       List[Any]):
									# return full path if its a dir
									if os.path.isdir(self.dummy_file):
											return self.dummy_file
									# else cut off path to file -> example `xsum`.
									return "/".join(self.dummy_file.replace(os.sep				,				'''/''').split('''/''')[:-1])




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict				,				*A_     :       List[Any]):
									if self.load_existing_dummy_data:
											# dummy data is downloaded and tested
											lowerCAmelCase_			:     Union[str, Any]									= self.dummy_file
									else:
											# dummy data cannot be downloaded and only the path to dummy file is returned
											lowerCAmelCase_			:     Optional[int]									= self.dummy_file_name

									# special case when data_url is a dict
									if isinstance(A_				,				A_):
											return self.create_dummy_data_dict(A_				,				A_)
									elif isinstance(A_				,				(list, tuple)):
											return self.create_dummy_data_list(A_				,				A_)
									else:
											return self.create_dummy_data_single(A_				,				A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Tuple				,				*A_     :       int):
									return self.download_and_extract(A_)




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       List[str]				,				A_     :       Optional[Any]):
									return self.download_and_extract(A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				*A_     :       str				,				**A_     :       List[Any]):
									return path




							def    UpperCAmelCase__			(       self     :       Tuple):
									return {}




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Union[str, Any]				,				A_     :       List[Any]):
									lowerCAmelCase_			:     Union[str, Any]									= {}
									for key, single_urls in data_url.items():
											for download_callback in self.download_callbacks:
													if isinstance(A_				,				A_):
															for single_url in single_urls:
																	download_callback(A_)
													else:
															lowerCAmelCase_			:     Any									= single_urls
															download_callback(A_)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
											if isinstance(A_				,				A_):
													lowerCAmelCase_			:     Any									= [os.path.join(A_				,				urllib.parse.quote_plus(Path(A_).name)) for x in single_urls]
											else:
													lowerCAmelCase_			:     Optional[int]									= single_urls
													lowerCAmelCase_			:     List[str]									= os.path.join(A_				,				urllib.parse.quote_plus(Path(A_).name))
											lowerCAmelCase_			:     Dict									= value

									# make sure that values are unique
									if all(isinstance(A_				,				A_) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
									    dummy_data_dict.values()):
											# append key to value to make its name unique
											lowerCAmelCase_			:     Tuple									= {key: value + key for key, value in dummy_data_dict.items()}

									return dummy_data_dict




							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       List[str]				,				A_     :       str):
									lowerCAmelCase_			:     Optional[Any]									= []
									# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
									lowerCAmelCase_			:     str									= all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''				,				A_)) for url in data_url)
									lowerCAmelCase_			:     Optional[Any]									= all(
									    url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''') for url in data_url)
									if data_url and (is_tf_records or is_pubmed_records):
											lowerCAmelCase_			:     Any									= [data_url[0]] * len(A_)
									for single_url in data_url:
											for download_callback in self.download_callbacks:
													download_callback(A_)
											# we force the name of each key to be the last file / folder name of the url path
											# if the url has arguments, we need to encode them with urllib.parse.quote_plus
											lowerCAmelCase_			:     int									= os.path.join(A_				,				urllib.parse.quote_plus(single_url.split('''/''')[-1]))
											dummy_data_list.append(A_)
									return dummy_data_list




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[Any]				,				A_     :       Tuple):
									for download_callback in self.download_callbacks:
											download_callback(A_)
									# we force the name of each key to be the last file / folder name of the url path
									# if the url has arguments, we need to encode them with urllib.parse.quote_plus
									lowerCAmelCase_			:     Tuple									= os.path.join(A_				,				urllib.parse.quote_plus(data_url.split('''/''')[-1]))
									if os.path.exists(A_) or not self.load_existing_dummy_data:
											return value
									else:
											# Backward compatibility, maybe deprecate at one point.
											# For many datasets with single url calls to dl_manager.download_and_extract,
											# the dummy_data.zip file is actually the zipped downloaded file
											# while now we expected the dummy_data.zip file to be a directory containing
											# the downloaded file.
											return path_to_dummy_data




							def    UpperCAmelCase__			(       self     :       int):
									pass




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									pass




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       str):
									def _iter_archive_members(A_     :       Any):
											# this preserves the order of the members inside the ZIP archive
											lowerCAmelCase_			:     Optional[int]									= Path(self.dummy_file).parent
											lowerCAmelCase_			:     Optional[int]									= path.relative_to(A_)
											with ZipFile(self.local_path_to_dummy_data) as zip_file:
													lowerCAmelCase_			:     Tuple									= zip_file.namelist()
											for member in members:
													if member.startswith(relative_path.as_posix()):
															yield dummy_parent_path.joinpath(A_)

									lowerCAmelCase_			:     List[Any]									= Path(A_)
									lowerCAmelCase_			:     Optional[int]									= _iter_archive_members(A_) if self.use_local_dummy_data else path.rglob('''*''')
									for file_path in file_paths:
											if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')):
													yield file_path.relative_to(A_).as_posix(), file_path.open('''rb''')







							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       Any):
									if not isinstance(A_				,				A_):
											lowerCAmelCase_			:     Dict									= [paths]
									for path in paths:
											if os.path.isfile(A_):
													if os.path.basename(A_).startswith(('''.''', '''__''')):
															return
													yield path
											else:
													for dirpath, dirnames, filenames in os.walk(A_):
															if os.path.basename(A_).startswith(('''.''', '''__''')):
																	continue
															dirnames.sort()
															for filename in sorted(A_):
																	if filename.startswith(('''.''', '''__''')):
																			continue
																	yield os.path.join(A_				,				A_)





