"""simple docstring"""





import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase   :						int      =    get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase_	(    _a ,							unittest.TestCase):
      lowerCamelCase__		:    Optional[int]   =  ReformerTokenizer
      lowerCamelCase__		:    Tuple   =  ReformerTokenizerFast
      lowerCamelCase__		:    Optional[int]   =  True
      lowerCamelCase__		:    List[Any]   =  False
      lowerCamelCase__		:    Union[str, Any]   =  True


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             super().setUp()

             lowercase__			:		Union[str, Any]       =	ReformerTokenizer(a		,			keep_accents=a							)
             tokenizer.save_pretrained(self.tmpdirname							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	'<s>'
             lowercase__			:		Any       =	1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(a							)		,			a							)
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(a							)		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		List[Any]       =	list(self.get_tokenizer().get_vocab().keys()							)

             self.assertEqual(vocab_keys[0]		,			'<unk>'							)
             self.assertEqual(vocab_keys[1]		,			'<s>'							)
             self.assertEqual(vocab_keys[-1]		,			'j'							)
             self.assertEqual(len(a							)		,			1_0_0_0							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             self.assertEqual(self.get_tokenizer().vocab_size		,			1_0_0_0							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             if not self.test_rust_tokenizer:
                    return

             lowercase__			:		List[Any]       =	self.get_tokenizer()
             lowercase__			:		Dict       =	self.get_rust_tokenizer()

             lowercase__			:		List[Any]       =	'I was born in 92000, and this is falsé.'

             lowercase__			:		Dict       =	tokenizer.tokenize(a							)
             lowercase__			:		Tuple       =	rust_tokenizer.tokenize(a							)
             self.assertListEqual(a		,			a							)

             lowercase__			:		str       =	tokenizer.encode(a		,			add_special_tokens=a							)
             lowercase__			:		Tuple       =	rust_tokenizer.encode(a		,			add_special_tokens=a							)
             self.assertListEqual(a		,			a							)

             lowercase__			:		Any       =	self.get_rust_tokenizer()
             lowercase__			:		Optional[int]       =	tokenizer.encode(a							)
             lowercase__			:		Tuple       =	rust_tokenizer.encode(a							)
             self.assertListEqual(a		,			a							)


      def 		_UpperCAmelCase       (   self		,			a=1_5							)       ->					str:
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                           lowercase__			:		Tuple       =	self.rust_tokenizer_class.from_pretrained(a		,			**a							)

                           # Simple input
                           lowercase__			:		Tuple       =	'This is a simple input'
                           lowercase__			:		List[str]       =	['This is a simple input 1', 'This is a simple input 2']
                           lowercase__			:		Optional[int]       =	('This is a simple input', 'This is a pair')
                           lowercase__			:		List[Any]       =	[
                               ('This is a simple input 1', 'This is a simple input 2'),
                               ('This is a simple pair 1', 'This is a simple pair 2'),
                           ]

                           # Simple input tests
                           self.assertRaises(a		,			tokenizer_r.encode		,			a		,			max_length=a		,			padding='max_length'							)

                           # Simple input
                           self.assertRaises(a		,			tokenizer_r.encode_plus		,			a		,			max_length=a		,			padding='max_length'							)

                           # Simple input
                           self.assertRaises(
                               a		,			tokenizer_r.batch_encode_plus		,			a		,			max_length=a		,			padding='max_length'		,			)

                           # Pair input
                           self.assertRaises(a		,			tokenizer_r.encode		,			a		,			max_length=a		,			padding='max_length'							)

                           # Pair input
                           self.assertRaises(a		,			tokenizer_r.encode_plus		,			a		,			max_length=a		,			padding='max_length'							)

                           # Pair input
                           self.assertRaises(
                               a		,			tokenizer_r.batch_encode_plus		,			a		,			max_length=a		,			padding='max_length'		,			)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Union[str, Any]       =	ReformerTokenizer(a		,			keep_accents=a							)

             lowercase__			:		str       =	tokenizer.tokenize('This is a test'							)
             self.assertListEqual(a		,			['▁This', '▁is', '▁a', '▁t', 'est']							)

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(a							)		,			[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]		,			)

             lowercase__			:		int       =	tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
             self.assertListEqual(
                 a		,			[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]		,			)
             lowercase__			:		int       =	tokenizer.convert_tokens_to_ids(a							)
             self.assertListEqual(
                 a		,			[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]		,			)

             lowercase__			:		List[str]       =	tokenizer.convert_ids_to_tokens(a							)
             self.assertListEqual(
                 a		,			[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]		,			)


      @cached_property
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		Union[str, Any]       =	'Hello World!'
             lowercase__			:		Optional[int]       =	[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

             self.assertListEqual(a		,			self.big_tokenizer.encode(a							)							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		List[str]       =	(
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             lowercase__			:		int       =	[
                 1_0_8,
                 2_6_5,
                 2_4,
                 1_1_1,
                 4,
                 2_5_8,
                 1_5_6,
                 3_5,
                 2_8,
                 2_7_5,
                 3,
                 2_5_9,
                 2_9_7,
                 2_6_0,
                 8_4,
                 4,
                 3_5,
                 1_1_0,
                 4_4,
                 8,
                 2_5_9,
                 9_1,
                 2_6_8,
                 2_1,
                 1_1,
                 2_0_9,
                 2_7_4,
                 1_0_9,
                 2_6_6,
                 2_7_7,
                 1_1_7,
                 8_6,
                 9_3,
                 3_1_5,
                 2_5_8,
                 2_7_8,
                 2_5_8,
                 2_7_7,
                 2_5_8,
                 0,
                 2_5_8,
                 2_8_8,
                 2_5_8,
                 3_1_9,
                 2_5_8,
                 0,
                 2_5_8,
                 0,
                 2_5_8,
                 0,
                 2_5_8,
                 0,
                 2_5_8,
                 2_8_7,
                 2_5_8,
                 3_1_5,
                 2_5_8,
                 2_8_9,
                 2_5_8,
                 2_7_8,
                 9_9,
                 2_6_9,
                 2_6_6,
                 2_6_2,
                 8,
                 2_5_9,
                 2_4_1,
                 4,
                 2_1_7,
                 2_3_0,
                 2_6_8,
                 2_6_6,
                 5_5,
                 1_6_8,
                 1_0_6,
                 7_5,
                 1_9_3,
                 2_6_6,
                 2_2_3,
                 2_7,
                 4_9,
                 2_6,
                 2_8_2,
                 2_5,
                 2_6_4,
                 2_9_9,
                 1_9,
                 2_6,
                 0,
                 2_5_8,
                 2_7_7,
                 1_1_7,
                 8_6,
                 9_3,
                 1_7_6,
                 1_8_3,
                 2_7_0,
                 1_1,
                 2_6_2,
                 4_2,
                 6_1,
                 2_6_5,
             ]

             self.assertListEqual(a		,			self.big_tokenizer.encode(a							)							)


      @require_torch
      @slow
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             import torch

             from transformers import ReformerConfig, ReformerModel

             # Build sequence
             lowercase__			:		str       =	list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
             lowercase__			:		List[Any]       =	' '.join(a							)
             lowercase__			:		List[Any]       =	self.big_tokenizer.encode_plus(a		,			return_tensors='pt'							)
             lowercase__			:		Optional[int]       =	self.big_tokenizer.batch_encode_plus([sequence, sequence]		,			return_tensors='pt'							)

             lowercase__			:		Optional[int]       =	ReformerConfig()
             # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
             lowercase__			:		Dict       =	encoded_sequence['input_ids'].shape
             lowercase__			:		Dict       =	ReformerModel(a							)

             # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
             assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

             with torch.no_grad():
                    model(**a							)
                    model(**a							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             # fmt: off
             lowercase__			:		Optional[Any]       =	{'input_ids': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
             # fmt: on

             # This tokenizer does not know some characters like ")".
             # That is the reason why we use very simple texts here.
             # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
             lowercase__			:		List[Any]       =	[
                 'This is a very simple sentence.',
                 'The quick brown fox jumps over the lazy dog.',
             ]

             self.tokenizer_integration_test_util(
                 expected_encoding=a		,			model_name='google/reformer-crime-and-punishment'		,			revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'		,			padding=a		,			sequences=a		,			)





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return image
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							List[Any]      		=		[image]

    if isinstance(image[0]							, PIL.Image.Image ):
        _lowercase       :							Tuple      		=		[np.array(i.resize((w, h)							, resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
        _lowercase       :							str      		=		np.concatenate(lowerCamelCase_							, axis=0 )
        _lowercase       :							Dict      		=		np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_55.0
        _lowercase       :							Optional[int]      		=		image.transpose(0							, 3							, 1							, 2 )
        _lowercase       :							str      		=		2.0 * image - 1.0
        _lowercase       :							Tuple      		=		torch.from_numpy(lowerCamelCase_ )
    elif isinstance(image[0]							, torch.Tensor ):
        _lowercase       :							Any      		=		torch.cat(lowerCamelCase_							, dim=0 )
    return image



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=0.99_95 )    ->						Tuple:
    if not isinstance(lowerCamelCase_							, np.ndarray ):
        _lowercase       :							List[Any]      		=		True
        _lowercase       :							Any      		=		va.device
        _lowercase       :							Union[str, Any]      		=		va.cpu().numpy()
        _lowercase       :							int      		=		va.cpu().numpy()

    _lowercase       :							int      		=		np.sum(va * va / (np.linalg.norm(lowerCamelCase_ ) * np.linalg.norm(lowerCamelCase_ )) )
    if np.abs(lowerCamelCase_ ) > DOT_THRESHOLD:
        _lowercase       :							Any      		=		(1 - t) * va + t * va
    else:
        _lowercase       :							Dict      		=		np.arccos(lowerCamelCase_ )
        _lowercase       :							str      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							int      		=		theta_a * t
        _lowercase       :							Dict      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							Any      		=		np.sin(theta_a - theta_t ) / sin_theta_a
        _lowercase       :							List[Any]      		=		sin_theta_t / sin_theta_a
        _lowercase       :							Dict      		=		sa * va + sa * va

    if inputs_are_torch:
        _lowercase       :							Optional[Any]      		=		torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )

    return va



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    for param in model.parameters():
        _lowercase       :							Any      		=		value






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=None,      )       ->   Tuple:




           """simple docstring"""

           super().__init__()
           self.register_modules(
               vae=lowerCamelCase,      text_encoder=lowerCamelCase,      clip_model=lowerCamelCase,      tokenizer=lowerCamelCase,      unet=lowerCamelCase,      scheduler=lowerCamelCase,      feature_extractor=lowerCamelCase,      coca_model=lowerCamelCase,      coca_tokenizer=lowerCamelCase,      coca_transform=lowerCamelCase,      )
           _lowercase       :							Tuple      		=		(
               feature_extractor.size
               if isinstance(feature_extractor.size,      lowerCamelCase)
               else feature_extractor.size['shortest_edge']
           )
           _lowercase       :							Union[str, Any]      		=		transforms.Normalize(mean=feature_extractor.image_mean,      std=feature_extractor.image_std)
           set_requires_grad(self.text_encoder,      lowerCamelCase)
           set_requires_grad(self.clip_model,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase = "auto")       ->   Any:




           """simple docstring"""

           if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _lowercase       :							Optional[Any]      		=		self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           self.enable_attention_slicing(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		min(int(num_inference_steps * strength),      lowerCamelCase)

           _lowercase       :							List[Any]      		=		max(num_inference_steps - init_timestep,      0)
           _lowercase       :							int      		=		self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None)       ->   Optional[Any]:




           """simple docstring"""

           if not isinstance(lowerCamelCase,      torch.Tensor):
               raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase)}''')

           _lowercase       :							Any      		=		image.to(device=lowerCamelCase,      dtype=lowerCamelCase)

           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Dict      		=		[
                   self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(lowerCamelCase)
               ]
               _lowercase       :							int      		=		torch.cat(lowerCamelCase,      dim=0)
           else:
               _lowercase       :							int      		=		self.vae.encode(lowerCamelCase).latent_dist.sample(lowerCamelCase)

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							str      		=		0.1_8_2_1_5 * init_latents
           _lowercase       :							List[str]      		=		init_latents.repeat_interleave(lowerCamelCase,      dim=0)

           _lowercase       :							List[str]      		=		randn_tensor(init_latents.shape,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)

           # get latents
           _lowercase       :							Any      		=		self.scheduler.add_noise(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							str      		=		init_latents

           return latents
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		self.coca_transform(lowerCamelCase).unsqueeze(0)
           with torch.no_grad(), torch.cuda.amp.autocast():
               _lowercase       :							List[str]      		=		self.coca_model.generate(transformed_image.to(device=self.device,      dtype=self.coca_model.dtype))
           _lowercase       :							int      		=		self.coca_tokenizer.decode(generated[0].cpu().numpy())
           return generated.split('<end_of_text>')[0].replace('<start_of_text>',      '').rstrip(' .,')
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.feature_extractor.preprocess(lowerCamelCase)
           _lowercase       :							List[str]      		=		torch.from_numpy(clip_image_input['pixel_values'][0]).unsqueeze(0).to(self.device).half()
           _lowercase       :							int      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							Dict      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)
           _lowercase       :							int      		=		image_embeddings_clip.repeat_interleave(lowerCamelCase,      dim=0)
           return image_embeddings_clip
       @torch.enable_grad()
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		latents.detach().requires_grad_()

           _lowercase       :							Union[str, Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

           # predict the noise residual
           _lowercase       :							Tuple      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

           if isinstance(self.scheduler,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
               _lowercase       :							Any      		=		self.scheduler.alphas_cumprod[timestep]
               _lowercase       :							Any      		=		1 - alpha_prod_t
               # compute predicted original sample from predicted noise also called
               # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
               _lowercase       :							List[Any]      		=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

               _lowercase       :							List[str]      		=		torch.sqrt(lowerCamelCase)
               _lowercase       :							Dict      		=		pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Dict      		=		self.scheduler.sigmas[index]
               _lowercase       :							List[Any]      		=		latents - sigma * noise_pred
           else:
               raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Dict      		=		1 / 0.1_8_2_1_5 * sample
           _lowercase       :							Optional[Any]      		=		self.vae.decode(lowerCamelCase).sample
           _lowercase       :							int      		=		(image / 2 + 0.5).clamp(0,      1)

           _lowercase       :							Any      		=		transforms.Resize(self.feature_extractor_size)(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.normalize(lowerCamelCase).to(latents.dtype)

           _lowercase       :							List[str]      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							List[Any]      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		spherical_dist_loss(lowerCamelCase,      lowerCamelCase).mean() * clip_guidance_scale

           _lowercase       :							str      		=		-torch.autograd.grad(lowerCamelCase,      lowerCamelCase)[0]

           if isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Union[str, Any]      		=		latents.detach() + grads * (sigma**2)
               _lowercase       :							List[str]      		=		noise_pred_original
           else:
               _lowercase       :							List[Any]      		=		noise_pred_original - torch.sqrt(lowerCamelCase) * grads
           return noise_pred, latents




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = 5_12,      lowerCamelCase = 5_12,      lowerCamelCase = 0.6,      lowerCamelCase = 50,      lowerCamelCase = 7.5,      lowerCamelCase = 1,      lowerCamelCase = 0.0,      lowerCamelCase = 1_00,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      lowerCamelCase = 0.8,      lowerCamelCase = 0.1,      lowerCamelCase = 0.1,      )       ->   int:




           """simple docstring"""

           if isinstance(lowerCamelCase,      lowerCamelCase) and len(lowerCamelCase) != batch_size:
               raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowerCamelCase)} generators.''')

           if height % 8 != 0 or width % 8 != 0:
               raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

           if isinstance(lowerCamelCase,      torch.Generator) and batch_size > 1:
               _lowercase       :							Dict      		=		[generator] + [None] * (batch_size - 1)

           _lowercase       :							Optional[int]      		=		[
               ('model', self.coca_model is None),
               ('tokenizer', self.coca_tokenizer is None),
               ('transform', self.coca_transform is None),
           ]
           _lowercase       :							Optional[int]      		=		[x[0] for x in coca_is_none if x[1]]
           _lowercase       :							str      		=		', '.join(lowerCamelCase)
           # generate prompts with coca model if prompt is None
           if content_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							List[Any]      		=		self.get_image_description(lowerCamelCase)
           if style_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							Dict      		=		self.get_image_description(lowerCamelCase)

           # get prompt text embeddings for content and style
           _lowercase       :							Optional[int]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							Optional[int]      		=		self.text_encoder(content_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Union[str, Any]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							List[Any]      		=		self.text_encoder(style_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Any      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # duplicate text embeddings for each generation per prompt
           _lowercase       :							Dict      		=		text_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

           # set timesteps
           _lowercase       :							Dict      		=		'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_offset:
               _lowercase       :							Any      		=		1

           self.scheduler.set_timesteps(lowerCamelCase,      **lowerCamelCase)
           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           self.scheduler.timesteps.to(self.device)

           _lowercase		,				_lowercase       :							List[Any]      		=		self.get_timesteps(lowerCamelCase,      lowerCamelCase,      self.device)
           _lowercase       :							str      		=		timesteps[:1].repeat(lowerCamelCase)

           # Preprocess image
           _lowercase       :							str      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							int      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							Optional[int]      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           if clip_guidance_scale > 0:
               _lowercase       :							Optional[int]      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Dict      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Optional[int]      		=		slerp(
                   lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           _lowercase       :							Dict      		=		guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
               _lowercase       :							Tuple      		=		content_text_input.input_ids.shape[-1]
               _lowercase       :							Union[str, Any]      		=		self.tokenizer([''],      padding='max_length',      max_length=lowerCamelCase,      return_tensors='pt')
               _lowercase       :							int      		=		self.text_encoder(uncond_input.input_ids.to(self.device))[0]
               # duplicate unconditional embeddings for each generation per prompt
               _lowercase       :							Union[str, Any]      		=		uncond_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               _lowercase       :							Optional[Any]      		=		torch.cat([uncond_embeddings, text_embeddings])

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           _lowercase       :							Tuple      		=		(batch_size, self.unet.config.in_channels, height // 8, width // 8)
           _lowercase       :							Optional[int]      		=		text_embeddings.dtype
           if latents is None:
               if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   _lowercase       :							List[Any]      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device='cpu',      dtype=lowerCamelCase).to(
                       self.device)
               else:
                   _lowercase       :							Any      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device=self.device,      dtype=lowerCamelCase)
           else:
               if latents.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
               _lowercase       :							Tuple      		=		latents.to(self.device)

           # scale the initial noise by the standard deviation required by the scheduler
           _lowercase       :							List[Any]      		=		latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           _lowercase       :							Dict      		=		'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_eta:
               _lowercase       :							List[Any]      		=		eta

           # check if the scheduler accepts generator
           _lowercase       :							Dict      		=		'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
           if accepts_generator:
               _lowercase       :							str      		=		generator

           with self.progress_bar(total=lowerCamelCase):
               for i, t in enumerate(lowerCamelCase):
                   # expand the latents if we are doing classifier free guidance
                   _lowercase       :							List[str]      		=		torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                   _lowercase       :							List[Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

                   # predict the noise residual
                   _lowercase       :							Dict      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

                   # perform classifier free guidance
                   if do_classifier_free_guidance:
                       _lowercase		,				_lowercase       :							Optional[Any]      		=		noise_pred.chunk(2)
                       _lowercase       :							Optional[Any]      		=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   # perform clip guidance
                   if clip_guidance_scale > 0:
                       _lowercase       :							Tuple      		=		(
                           text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                       )
                       _lowercase		,				_lowercase       :							List[Any]      		=		self.cond_fn(
                           lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )

                   # compute the previous noisy sample x_t -> x_t-1
                   _lowercase       :							Optional[Any]      		=		self.scheduler.step(lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Any      		=		1 / 0.1_8_2_1_5 * latents
           _lowercase       :							List[str]      		=		self.vae.decode(lowerCamelCase).sample

           _lowercase       :							Tuple      		=		(image / 2 + 0.5).clamp(0,      1)
           _lowercase       :							List[Any]      		=		image.cpu().permute(0,      2,      3,      1).numpy()

           if output_type == "pil":
               _lowercase       :							List[Any]      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image, None)

           return StableDiffusionPipelineOutput(images=lowerCamelCase,      nsfw_content_detected=lowerCamelCase)


