


'''simple docstring'''
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 					__lowerCAmelCase   (   lowercase				,			lowercase				,			unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[Any]   						=StableDiffusionSAGPipeline
						_UpperCAmelCase		:       Dict   						=TEXT_TO_IMAGE_PARAMS
						_UpperCAmelCase		:       str   						=TEXT_TO_IMAGE_BATCH_PARAMS
						_UpperCAmelCase		:       int   						=TEXT_TO_IMAGE_IMAGE_PARAMS
						_UpperCAmelCase		:       Optional[Any]   						=TEXT_TO_IMAGE_IMAGE_PARAMS
						_UpperCAmelCase		:       List[str]   						=False






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													torch.manual_seed(0	)
													A_			    =		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 ,  )
													A_			    =		DDIMScheduler(
													    beta_start=0.0_0_0_8_5 ,  beta_end=0.0_1_2 ,  beta_schedule="scaled_linear" ,  clip_sample=lowerCAmelCase ,  set_alpha_to_one=lowerCAmelCase ,  )
													torch.manual_seed(0	)
													A_			    =		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	)
													A_			    =		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 ,  )
													A_			    =		CLIPTextModel(lowerCAmelCase	)
													A_			    =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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






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






						def 				_UpperCAmelCase       (  self							:      Dict	):
													super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






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

						"""simple docstring"""






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






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	)
													A_			    =		sag_pipe.to(lowerCAmelCase	)
													sag_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		"."
													A_			    =		torch.manual_seed(0	)
													A_			    =		sag_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=20 ,  output_type="np"	)

													A_			    =		output.images

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

													assert image.shape == (1, 5_12, 5_12, 3)
													A_			    =		np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]	)

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






						def 				_UpperCAmelCase       (  self							:      Dict	):
													A_			    =		StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"	)
													A_			    =		sag_pipe.to(lowerCAmelCase	)
													sag_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		"."
													A_			    =		torch.manual_seed(0	)
													A_			    =		sag_pipe(
													    [prompt] ,  generator=lowerCAmelCase ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=20 ,  output_type="np"	)

													A_			    =		output.images

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

													assert image.shape == (1, 5_12, 5_12, 3)
													A_			    =		np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]	)

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






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"	)
													A_			    =		sag_pipe.to(lowerCAmelCase	)
													sag_pipe.set_progress_bar_config(disable=lowerCAmelCase	)

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

													A_			    =		output.images

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









'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





