






import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					:		Optional[Any]	  =						logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Path       ,	UpperCamelCase:      Union[str, None] = None       ,	UpperCamelCase:      Union[List[str], None] = None       ,	UpperCamelCase:      Union[str, List[str], None] = None       ,	UpperCamelCase:      bool = True       ,	)		->		int:
											snake_case__									=     [file for file in os.listdir(UpperCamelCase						) if os.path.isfile(os.path.join(UpperCamelCase       ,	UpperCamelCase						)						)]

											if identifier is not None:
															snake_case__									=     [file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			for n_ in n_identifier:
																							snake_case__									=     [file for file in files if n_ not in file]
															else:
																			snake_case__									=     [file for file in files if n_identifier not in file]

											snake_case__									=     ignore_files or []
											ignore_files.append('__init__.py'						)
											snake_case__									=     [file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('Testing'       ,	UpperCamelCase						)

															if only_modules:
																			snake_case__									=     file.split('.'						)[0]
																			try:
																							snake_case__									=     getattr(UpperCamelCase       ,	UpperCamelCase						)
																							snake_case__									=     doctest.DocTestSuite(UpperCamelCase						)
																							snake_case__									=     unittest.TextTestRunner().run(UpperCamelCase						)
																							self.assertIs(len(result.failures						)       ,	0						)
																			except AttributeError:
																							logger.info(F'''{module_identifier} is not a module.'''						)
															else:
																			snake_case__									=     doctest.testfile(str('..' / directory / file						)       ,	optionflags=doctest.ELLIPSIS						)
																			self.assertIs(result.failed       ,	0						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'modeling'
											snake_case__									=     [
											    'modeling_ctrl.py',
											    'modeling_tf_ctrl.py',
											]
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase       ,	ignore_files=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'tokenization'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'configuration'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     ['configuration', 'modeling', 'tokenization']
											self.analyze_directory(UpperCamelCase       ,	n_identifier=UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     Path('docs/source'						)
											snake_case__									=     ['favicon.ico']
											self.analyze_directory(UpperCamelCase       ,	ignore_files=UpperCamelCase       ,	only_modules=UpperCamelCase						)











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   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableDiffusionSAGPipeline
							_UpperCAmelCase     =       TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											torch.manual_seed(0						)
											snake_case__									=     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       ,	)
											snake_case__									=     DDIMScheduler(
											    beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='scaled_linear'       ,	clip_sample=UpperCamelCase       ,	set_alpha_to_one=UpperCamelCase       ,	)
											torch.manual_seed(0						)
											snake_case__									=     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       ,	)
											torch.manual_seed(0						)
											snake_case__									=     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=10_00       ,	)
											snake_case__									=     CLIPTextModel(UpperCamelCase						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											snake_case__									=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=0						)		->		Union[str, Any]:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    'prompt': '.',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 1.0,
											    'sag_scale': 1.0,
											    'output_type': 'numpy',
											}
											return inputs


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

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

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

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

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]						)

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


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	width=7_68       ,	height=5_12       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'       ,	)

											snake_case__									=     output.images

											assert image.shape == (1, 5_12, 7_68, 3)




