"""simple docstring"""




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class 						__A							( unittest.TestCase		):






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






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     1
       _lowerCAmelCase						:       Tuple  			=     3
       _lowerCAmelCase						:       Optional[int]  			=     (32, 32)

       _lowerCAmelCase						:       int  			=     floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0  )  ).to(a__  )
       return image






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Dict  			=     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				,     )
       return model






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     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				,     )
       return model






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     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				,     )
       return CLIPTextModel(a__  )






     @property
     def     __A  (		self  ):
       def extract(*a__				,     **a__  ):
         class 						__A							:






              def __init__(		self  ):
                _lowerCAmelCase						:       Dict  			=     torch.ones([0]  )






              def     __A  (		self				,     a__  ):
                self.pixel_values.to(a__  )
                return self







         return Out()

       return extract






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase						:       Optional[Any]  			=     self.dummy_cond_unet
       _lowerCAmelCase						:       Any  			=     DDIMScheduler(
           beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="""scaled_linear"""				,     clip_sample=a__				,     set_alpha_to_one=a__				,     )

       _lowerCAmelCase						:       str  			=     self.dummy_vae
       _lowerCAmelCase						:       Tuple  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       Optional[Any]  			=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

       # make sure here that pndm scheduler skips prk
       _lowerCAmelCase						:       Optional[int]  			=     StableDiffusionPipeline(
           unet=a__				,     scheduler=a__				,     vae=a__				,     text_encoder=a__				,     tokenizer=a__				,     safety_checker=a__				,     feature_extractor=self.dummy_extractor				,     )
       _lowerCAmelCase						:       int  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     """A painting of a squirrel eating a burger"""

       _lowerCAmelCase						:       List[Any]  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     sd_pipe([prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""  )
       _lowerCAmelCase						:       Any  			=     output.images

       _lowerCAmelCase						:       str  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       int  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Tuple  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Dict  			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _lowerCAmelCase						:       Dict  			=     np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]  )

       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     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase						:       int  			=     self.dummy_cond_unet
       _lowerCAmelCase						:       Optional[int]  			=     PNDMScheduler(skip_prk_steps=a__  )
       _lowerCAmelCase						:       List[str]  			=     self.dummy_vae
       _lowerCAmelCase						:       Any  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       Any  			=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

       # make sure here that pndm scheduler skips prk
       _lowerCAmelCase						:       str  			=     StableDiffusionPipeline(
           unet=a__				,     scheduler=a__				,     vae=a__				,     text_encoder=a__				,     tokenizer=a__				,     safety_checker=a__				,     feature_extractor=self.dummy_extractor				,     )
       _lowerCAmelCase						:       Optional[Any]  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       str  			=     """A painting of a squirrel eating a burger"""
       _lowerCAmelCase						:       str  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     sd_pipe([prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""  )

       _lowerCAmelCase						:       Tuple  			=     output.images

       _lowerCAmelCase						:       Dict  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       List[str]  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Any  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[Any]  			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _lowerCAmelCase						:       Union[str, Any]  			=     np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]  )

       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     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     StableDiffusionPipeline.from_pretrained(
           """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""				,     safety_checker=a__  )
       assert isinstance(a__				,     a__  )
       assert isinstance(pipe.scheduler				,     a__  )
       assert pipe.safety_checker is None

       _lowerCAmelCase						:       Any  			=     pipe("""example prompt"""				,     num_inference_steps=2  ).images[0]
       assert image is not None

       # check that there's no error when saving a pipeline with one of the models being None
       with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__  )
         _lowerCAmelCase						:       str  			=     StableDiffusionPipeline.from_pretrained(a__  )

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       _lowerCAmelCase						:       Optional[Any]  			=     pipe("""example prompt"""				,     num_inference_steps=2  ).images[0]
       assert image is not None






     @unittest.skipIf(torch_device != """cuda"""				,     """This test requires a GPU"""  )
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.dummy_cond_unet
       _lowerCAmelCase						:       str  			=     PNDMScheduler(skip_prk_steps=a__  )
       _lowerCAmelCase						:       Any  			=     self.dummy_vae
       _lowerCAmelCase						:       Dict  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       Tuple  			=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

       # put models in fp16
       _lowerCAmelCase						:       str  			=     unet.half()
       _lowerCAmelCase						:       List[str]  			=     vae.half()
       _lowerCAmelCase						:       Tuple  			=     bert.half()

       # make sure here that pndm scheduler skips prk
       _lowerCAmelCase						:       Dict  			=     StableDiffusionPipeline(
           unet=a__				,     scheduler=a__				,     vae=a__				,     text_encoder=a__				,     tokenizer=a__				,     safety_checker=a__				,     feature_extractor=self.dummy_extractor				,     )
       _lowerCAmelCase						:       int  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       int  			=     """A painting of a squirrel eating a burger"""
       _lowerCAmelCase						:       Dict  			=     sd_pipe([prompt]				,     num_inference_steps=2				,     output_type="""np"""  ).images

       assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 						__A							( unittest.TestCase		):






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=a__  )
       _lowerCAmelCase						:       List[Any]  			=     LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
       _lowerCAmelCase						:       Optional[int]  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     (
           """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
           """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
           """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
           """ children from bahnhof zoo, detailed """
       )
       _lowerCAmelCase						:       List[Any]  			=     4003660346
       _lowerCAmelCase						:       List[Any]  			=     7

       # without safety guidance (sld_guidance_scale = 0)
       _lowerCAmelCase						:       int  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       Dict  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

       _lowerCAmelCase						:       int  			=     output.images
       _lowerCAmelCase						:       Optional[int]  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Dict  			=     [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

       assert image.shape == (1, 512, 512, 3)

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

       # without safety guidance (strong configuration)
       _lowerCAmelCase						:       Optional[int]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       List[str]  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2000				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

       _lowerCAmelCase						:       Optional[int]  			=     output.images
       _lowerCAmelCase						:       Tuple  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       str  			=     [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

       assert image.shape == (1, 512, 512, 3)

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=a__  )
       _lowerCAmelCase						:       List[str]  			=     LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
       _lowerCAmelCase						:       Optional[Any]  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       List[str]  			=     """padme amidala taking a bath artwork, safe for work, no nudity"""
       _lowerCAmelCase						:       Tuple  			=     2734971755
       _lowerCAmelCase						:       Union[str, Any]  			=     7

       _lowerCAmelCase						:       Optional[int]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       Any  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

       _lowerCAmelCase						:       int  			=     output.images
       _lowerCAmelCase						:       int  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Dict  			=     [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

       assert image.shape == (1, 512, 512, 3)

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

       _lowerCAmelCase						:       Optional[Any]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       Any  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2000				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

       _lowerCAmelCase						:       List[Any]  			=     output.images
       _lowerCAmelCase						:       List[Any]  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Optional[int]  			=     [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

       assert image.shape == (1, 512, 512, 3)

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""  )
       _lowerCAmelCase						:       Any  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     (
           """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
           """ leyendecker"""
       )
       _lowerCAmelCase						:       int  			=     1044355234
       _lowerCAmelCase						:       Tuple  			=     12

       _lowerCAmelCase						:       int  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       str  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

       _lowerCAmelCase						:       List[str]  			=     output.images
       _lowerCAmelCase						:       List[str]  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       int  			=     np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]  )

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-7

       _lowerCAmelCase						:       Optional[int]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       List[Any]  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2000				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

       _lowerCAmelCase						:       List[str]  			=     output.images
       _lowerCAmelCase						:       Any  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[str]  			=     np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]  )
       assert image.shape == (1, 512, 512, 3)

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






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
