




import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						__lowercase		(_UpperCamelCase					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						RobertaTokenizer
      _snake_case												=						RobertaTokenizerFast
      _snake_case												=						True
      _snake_case												=						{'cls_token': '<s>'}

      def      UpperCAmelCase				(				self     )		->    str:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         snake_case				:    Union[str, Any]           =	[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         snake_case				:    List[str]           =	dict(zip(_UpperCAmelCase    ,				range(len(_UpperCAmelCase     )     )     )     )
         snake_case				:    List[Any]           =	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         snake_case				:    List[Any]           =	{'unk_token': '<unk>'}

         snake_case				:    str           =	os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""vocab_file"""]     )
         snake_case				:    Optional[Any]           =	os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""merges_file"""]     )
         with open(self.vocab_file    ,				"""w"""    ,				encoding="""utf-8"""     ) as fp:
            fp.write(json.dumps(_UpperCAmelCase     ) + """\n"""     )
         with open(self.merges_file    ,				"""w"""    ,				encoding="""utf-8"""     ) as fp:
            fp.write("""\n""".join(_UpperCAmelCase     )     )

      def      UpperCAmelCase				(				self    ,				**A     )		->    Dict:
         kwargs.update(self.special_tokens_map     )
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,				**_UpperCAmelCase     )

      def      UpperCAmelCase				(				self    ,				**A     )		->    str:
         kwargs.update(self.special_tokens_map     )
         return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,				**_UpperCAmelCase     )

      def      UpperCAmelCase				(				self    ,				A     )		->    Optional[int]:
         snake_case				:    Union[str, Any]           =	'lower newer'
         snake_case				:    str           =	'lower newer'
         return input_text, output_text

      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    str           =	self.tokenizer_class(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map     )
         snake_case				:    str           =	'lower newer'
         snake_case				:    Tuple           =	['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         snake_case				:    Tuple           =	tokenizer.tokenize(_UpperCAmelCase     )  # , add_prefix_space=True)
         self.assertListEqual(_UpperCAmelCase    ,				_UpperCAmelCase     )

         snake_case				:    List[Any]           =	tokens + [tokenizer.unk_token]
         snake_case				:    List[str]           =	[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )    ,				_UpperCAmelCase     )

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         snake_case				:    List[str]           =	self.get_tokenizer()

         self.assertListEqual(tokenizer.encode("""Hello world!"""    ,				add_special_tokens=_UpperCAmelCase     )    ,				[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]     )
         self.assertListEqual(
             tokenizer.encode("""Hello world! cécé herlolip 418"""    ,				add_special_tokens=_UpperCAmelCase     )    ,				[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]    ,				)

      @slow
      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         snake_case				:    Tuple           =	self.tokenizer_class.from_pretrained("""roberta-base"""     )

         snake_case				:    Any           =	tokenizer.encode("""sequence builders"""    ,				add_special_tokens=_UpperCAmelCase     )
         snake_case				:    Dict           =	tokenizer.encode("""multi-sequence build"""    ,				add_special_tokens=_UpperCAmelCase     )

         snake_case				:    Dict           =	tokenizer.encode(
             """sequence builders"""    ,				add_special_tokens=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase     )
         snake_case				:    str           =	tokenizer.encode(
             """sequence builders"""    ,				"""multi-sequence build"""    ,				add_special_tokens=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase     )

         snake_case				:    List[str]           =	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase     )
         snake_case				:    Optional[int]           =	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase    ,				_UpperCAmelCase     )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode

      def      UpperCAmelCase				(				self     )		->    Tuple:
         snake_case				:    str           =	self.get_tokenizer()

         snake_case				:    List[Any]           =	'Encode this sequence.'
         snake_case				:    Any           =	tokenizer.byte_encoder[' '.encode("""utf-8"""     )[0]]

         # Testing encoder arguments
         snake_case				:    List[Any]           =	tokenizer.encode(_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase     )
         snake_case				:    int           =	tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
         self.assertNotEqual(_UpperCAmelCase    ,				_UpperCAmelCase     )

         snake_case				:    Optional[int]           =	tokenizer.encode(_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase     )
         snake_case				:    Optional[Any]           =	tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
         self.assertEqual(_UpperCAmelCase    ,				_UpperCAmelCase     )

         tokenizer.add_special_tokens({"""bos_token""": """<s>"""}     )
         snake_case				:    Any           =	tokenizer.encode(_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
         snake_case				:    str           =	tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
         self.assertNotEqual(_UpperCAmelCase    ,				_UpperCAmelCase     )

         # Testing spaces after special tokens
         snake_case				:    Optional[int]           =	'<mask>'
         tokenizer.add_special_tokens(
             {"""mask_token""": AddedToken(_UpperCAmelCase    ,				lstrip=_UpperCAmelCase    ,				rstrip=_UpperCAmelCase     )}     )  # mask token has a left space
         snake_case				:    Dict           =	tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )

         snake_case				:    List[str]           =	'Encode <mask> sequence'
         snake_case				:    List[Any]           =	'Encode <mask>sequence'

         snake_case				:    Tuple           =	tokenizer.encode(_UpperCAmelCase     )
         snake_case				:    List[Any]           =	encoded.index(_UpperCAmelCase     )
         snake_case				:    Optional[Any]           =	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
         self.assertEqual(_UpperCAmelCase    ,				_UpperCAmelCase     )

         snake_case				:    Optional[Any]           =	tokenizer.encode(_UpperCAmelCase     )
         snake_case				:    str           =	encoded.index(_UpperCAmelCase     )
         snake_case				:    Optional[int]           =	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
         self.assertNotEqual(_UpperCAmelCase    ,				_UpperCAmelCase     )

      def      UpperCAmelCase				(				self     )		->    Tuple:
         pass

      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
               snake_case				:    Union[str, Any]           =	self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase    ,				**_UpperCAmelCase     )
               snake_case				:    Dict           =	self.tokenizer_class.from_pretrained(_UpperCAmelCase    ,				**_UpperCAmelCase     )
               snake_case				:    int           =	'A, <mask> AllenNLP sentence.'
               snake_case				:    Optional[int]           =	tokenizer_r.encode_plus(_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase    ,				return_token_type_ids=_UpperCAmelCase     )
               snake_case				:    Dict           =	tokenizer_p.encode_plus(_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase    ,				return_token_type_ids=_UpperCAmelCase     )

               # token_type_ids should put 0 everywhere
               self.assertEqual(sum(tokens_r["""token_type_ids"""]     )    ,				sum(tokens_p["""token_type_ids"""]     )     )

               # attention_mask should put 1 everywhere, so sum over length should be 1
               self.assertEqual(
                   sum(tokens_r["""attention_mask"""]     ) / len(tokens_r["""attention_mask"""]     )    ,				sum(tokens_p["""attention_mask"""]     ) / len(tokens_p["""attention_mask"""]     )    ,				)

               snake_case				:    Tuple           =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]     )
               snake_case				:    List[Any]           =	tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]     )

               # Rust correctly handles the space before the mask while python doesnt
               self.assertSequenceEqual(tokens_p["""input_ids"""]    ,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )
               self.assertSequenceEqual(tokens_r["""input_ids"""]    ,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )

               self.assertSequenceEqual(
                   _UpperCAmelCase    ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]     )
               self.assertSequenceEqual(
                   _UpperCAmelCase    ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]     )

      def      UpperCAmelCase				(				self     )		->    int:
         for trim_offsets, add_prefix_space in itertools.product([True, False]    ,				repeat=2     ):
            snake_case				:    Optional[int]           =	self.rust_tokenizer_class.from_pretrained(
                self.tmpdirname    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )

            snake_case				:    int           =	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
            snake_case				:    List[str]           =	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

            self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]    ,				_UpperCAmelCase     )

            self.assertEqual(post_processor_state["""add_prefix_space"""]    ,				_UpperCAmelCase     )
            self.assertEqual(post_processor_state["""trim_offsets"""]    ,				_UpperCAmelCase     )

      def      UpperCAmelCase				(				self     )		->    Optional[Any]:
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
               snake_case				:    Union[str, Any]           =	'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
               snake_case				:    Union[str, Any]           =	f"""{text_of_1_token} {text_of_1_token}"""

               snake_case				:    Optional[Any]           =	self.rust_tokenizer_class.from_pretrained(
                   _UpperCAmelCase    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )
               snake_case				:    str           =	tokenizer_r(_UpperCAmelCase    ,				return_offsets_mapping=_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
               self.assertEqual(encoding.offset_mapping[0]    ,				(0, len(_UpperCAmelCase     ))     )
               self.assertEqual(
                   encoding.offset_mapping[1]    ,				(len(_UpperCAmelCase     ) + 1, len(_UpperCAmelCase     ) + 1 + len(_UpperCAmelCase     ))    ,				)

               snake_case				:    Tuple           =	self.rust_tokenizer_class.from_pretrained(
                   _UpperCAmelCase    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )
               snake_case				:    Any           =	tokenizer_r(_UpperCAmelCase    ,				return_offsets_mapping=_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
               self.assertEqual(encoding.offset_mapping[0]    ,				(0, len(_UpperCAmelCase     ))     )
               self.assertEqual(
                   encoding.offset_mapping[1]    ,				(len(_UpperCAmelCase     ) + 1, len(_UpperCAmelCase     ) + 1 + len(_UpperCAmelCase     ))    ,				)

               snake_case				:    str           =	self.rust_tokenizer_class.from_pretrained(
                   _UpperCAmelCase    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )
               snake_case				:    List[Any]           =	tokenizer_r(_UpperCAmelCase    ,				return_offsets_mapping=_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
               self.assertEqual(encoding.offset_mapping[0]    ,				(0, len(_UpperCAmelCase     ))     )
               self.assertEqual(
                   encoding.offset_mapping[1]    ,				(len(_UpperCAmelCase     ), len(_UpperCAmelCase     ) + 1 + len(_UpperCAmelCase     ))    ,				)

               snake_case				:    Dict           =	self.rust_tokenizer_class.from_pretrained(
                   _UpperCAmelCase    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )
               snake_case				:    str           =	tokenizer_r(_UpperCAmelCase    ,				return_offsets_mapping=_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
               self.assertEqual(encoding.offset_mapping[0]    ,				(0, len(_UpperCAmelCase     ))     )
               self.assertEqual(
                   encoding.offset_mapping[1]    ,				(len(_UpperCAmelCase     ), len(_UpperCAmelCase     ) + 1 + len(_UpperCAmelCase     ))    ,				)

               snake_case				:    List[Any]           =	f""" {text}"""

               # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
               #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
               # )
               # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
               # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
               # self.assertEqual(
               #     encoding.offset_mapping[1],
               #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
               # )

               snake_case				:    Optional[int]           =	self.rust_tokenizer_class.from_pretrained(
                   _UpperCAmelCase    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )
               snake_case				:    Optional[Any]           =	tokenizer_r(_UpperCAmelCase    ,				return_offsets_mapping=_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
               self.assertEqual(encoding.offset_mapping[0]    ,				(1, 1 + len(_UpperCAmelCase     ))     )
               self.assertEqual(
                   encoding.offset_mapping[1]    ,				(1 + len(_UpperCAmelCase     ) + 1, 1 + len(_UpperCAmelCase     ) + 1 + len(_UpperCAmelCase     ))    ,				)

               snake_case				:    Optional[int]           =	self.rust_tokenizer_class.from_pretrained(
                   _UpperCAmelCase    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )
               snake_case				:    List[str]           =	tokenizer_r(_UpperCAmelCase    ,				return_offsets_mapping=_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
               self.assertEqual(encoding.offset_mapping[0]    ,				(0, 1 + len(_UpperCAmelCase     ))     )
               self.assertEqual(
                   encoding.offset_mapping[1]    ,				(1 + len(_UpperCAmelCase     ), 1 + len(_UpperCAmelCase     ) + 1 + len(_UpperCAmelCase     ))    ,				)

               snake_case				:    int           =	self.rust_tokenizer_class.from_pretrained(
                   _UpperCAmelCase    ,				use_fast=_UpperCAmelCase    ,				add_prefix_space=_UpperCAmelCase    ,				trim_offsets=_UpperCAmelCase     )
               snake_case				:    Any           =	tokenizer_r(_UpperCAmelCase    ,				return_offsets_mapping=_UpperCAmelCase    ,				add_special_tokens=_UpperCAmelCase     )
               self.assertEqual(encoding.offset_mapping[0]    ,				(0, 1 + len(_UpperCAmelCase     ))     )
               self.assertEqual(
                   encoding.offset_mapping[1]    ,				(1 + len(_UpperCAmelCase     ), 1 + len(_UpperCAmelCase     ) + 1 + len(_UpperCAmelCase     ))    ,				)











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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Union[str, Any]:
   if isinstance(lowercase						,torch.Tensor					):
      return image
   elif isinstance(lowercase						,PIL.Image.Image					):
      snake_case				:    str           =	[image]

   if isinstance(image[0]						,PIL.Image.Image					):
      snake_case				:    List[Any]           =	[np.array(i.resize((w, h)						,resample=PIL_INTERPOLATION["""lanczos"""]					)					)[None, :] for i in image]
      snake_case				:    Optional[int]           =	np.concatenate(lowercase						,axis=0					)
      snake_case				:    str           =	np.array(lowercase					).astype(np.floataa					) / 255.0
      snake_case				:    List[str]           =	image.transpose(0						,3						,1						,2					)
      snake_case				:    Any           =	2.0 * image - 1.0
      snake_case				:    Optional[Any]           =	torch.from_numpy(lowercase					)
   elif isinstance(image[0]						,torch.Tensor					):
      snake_case				:    Optional[int]           =	torch.cat(lowercase						,dim=0					)
   return image




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase=0.9995					)  ->       Optional[int]:
   if not isinstance(lowercase						,np.ndarray					):
      snake_case				:    Any           =	True
      snake_case				:    str           =	va.device
      snake_case				:    Optional[Any]           =	va.cpu().numpy()
      snake_case				:    str           =	va.cpu().numpy()

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

   if inputs_are_torch:
      snake_case				:    List[Any]           =	torch.from_numpy(lowercase					).to(lowercase					)

   return va




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Dict           =	F.normalize(lowercase						,dim=-1					)
   snake_case				:    Optional[Any]           =	F.normalize(lowercase						,dim=-1					)
   return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:
   for param in model.parameters():
      snake_case				:    Tuple           =	value




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A=None    ,				A=None    ,				A=None    ,				)		->    List[Any]:
         super().__init__()
         self.register_modules(
             vae=A    ,				text_encoder=A    ,				clip_model=A    ,				tokenizer=A    ,				unet=A    ,				scheduler=A    ,				feature_extractor=A    ,				coca_model=A    ,				coca_tokenizer=A    ,				coca_transform=A    ,				)
         snake_case				:    Optional[int]           =	(
             feature_extractor.size
             if isinstance(feature_extractor.size    ,				A     )
             else feature_extractor.size["""shortest_edge"""]
         )
         snake_case				:    Union[str, Any]           =	transforms.Normalize(mean=feature_extractor.image_mean    ,				std=feature_extractor.image_std     )
         set_requires_grad(self.text_encoder    ,				A     )
         set_requires_grad(self.clip_model    ,				A     )

      def      UpperCAmelCase				(				self    ,				A = "auto"     )		->    Tuple:
         if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            snake_case				:    List[str]           =	self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(A     )

      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         self.enable_attention_slicing(A     )

      def      UpperCAmelCase				(				self     )		->    Any:
         set_requires_grad(self.vae    ,				A     )

      def      UpperCAmelCase				(				self     )		->    List[Any]:
         set_requires_grad(self.vae    ,				A     )

      def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
         set_requires_grad(self.unet    ,				A     )

      def      UpperCAmelCase				(				self     )		->    Tuple:
         set_requires_grad(self.unet    ,				A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A     )		->    Dict:
         # get the original timestep using init_timestep
         snake_case				:    Tuple           =	min(int(num_inference_steps * strength     )    ,				A     )

         snake_case				:    List[str]           =	max(num_inference_steps - init_timestep    ,				0     )
         snake_case				:    List[str]           =	self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A=None     )		->    List[str]:
         if not isinstance(A    ,				torch.Tensor     ):
            raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(A     )}"""     )

         snake_case				:    str           =	image.to(device=A    ,				dtype=A     )

         if isinstance(A    ,				A     ):
            snake_case				:    int           =	[
                self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(A     )
            ]
            snake_case				:    str           =	torch.cat(A    ,				dim=0     )
         else:
            snake_case				:    List[Any]           =	self.vae.encode(A     ).latent_dist.sample(A     )

         # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
         snake_case				:    Dict           =	0.1_82_15 * init_latents
         snake_case				:    Tuple           =	init_latents.repeat_interleave(A    ,				dim=0     )

         snake_case				:    Optional[int]           =	randn_tensor(init_latents.shape    ,				generator=A    ,				device=A    ,				dtype=A     )

         # get latents
         snake_case				:    Union[str, Any]           =	self.scheduler.add_noise(A    ,				A    ,				A     )
         snake_case				:    List[Any]           =	init_latents

         return latents

      def      UpperCAmelCase				(				self    ,				A     )		->    int:
         snake_case				:    Optional[Any]           =	self.coca_transform(A     ).unsqueeze(0     )
         with torch.no_grad(), torch.cuda.amp.autocast():
            snake_case				:    Optional[Any]           =	self.coca_model.generate(transformed_image.to(device=self.device    ,				dtype=self.coca_model.dtype     )     )
         snake_case				:    int           =	self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
         return generated.split("""<end_of_text>"""     )[0].replace("""<start_of_text>"""    ,				""""""     ).rstrip(""" .,"""     )

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    List[Any]:
         snake_case				:    Tuple           =	self.feature_extractor.preprocess(A     )
         snake_case				:    List[Any]           =	torch.from_numpy(clip_image_input["""pixel_values"""][0]     ).unsqueeze(0     ).to(self.device     ).half()
         snake_case				:    Optional[int]           =	self.clip_model.get_image_features(A     )
         snake_case				:    List[str]           =	image_embeddings_clip / image_embeddings_clip.norm(p=2    ,				dim=-1    ,				keepdim=A     )
         snake_case				:    Tuple           =	image_embeddings_clip.repeat_interleave(A    ,				dim=0     )
         return image_embeddings_clip

      @torch.enable_grad()
      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A    ,				)		->    Any:
         snake_case				:    Dict           =	latents.detach().requires_grad_()

         snake_case				:    str           =	self.scheduler.scale_model_input(A    ,				A     )

         # predict the noise residual
         snake_case				:    str           =	self.unet(A    ,				A    ,				encoder_hidden_states=A     ).sample

         if isinstance(self.scheduler    ,				(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
            snake_case				:    int           =	self.scheduler.alphas_cumprod[timestep]
            snake_case				:    Tuple           =	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
            snake_case				:    Any           =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

            snake_case				:    str           =	torch.sqrt(A     )
            snake_case				:    str           =	pred_original_sample * (fac) + latents * (1 - fac)
         elif isinstance(self.scheduler    ,				A     ):
            snake_case				:    int           =	self.scheduler.sigmas[index]
            snake_case				:    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
         snake_case				:    List[str]           =	1 / 0.1_82_15 * sample
         snake_case				:    str           =	self.vae.decode(A     ).sample
         snake_case				:    Any           =	(image / 2 + 0.5).clamp(0    ,				1     )

         snake_case				:    str           =	transforms.Resize(self.feature_extractor_size     )(A     )
         snake_case				:    Dict           =	self.normalize(A     ).to(latents.dtype     )

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

         snake_case				:    Optional[int]           =	spherical_dist_loss(A    ,				A     ).mean() * clip_guidance_scale

         snake_case				:    int           =	-torch.autograd.grad(A    ,				A     )[0]

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

      @torch.no_grad()
      def __call__(				self    ,				A    ,				A    ,				A = None    ,				A = None    ,				A = 5_1_2    ,				A = 5_1_2    ,				A = 0.6    ,				A = 5_0    ,				A = 7.5    ,				A = 1    ,				A = 0.0    ,				A = 1_0_0    ,				A = None    ,				A = "pil"    ,				A = True    ,				A = 0.8    ,				A = 0.1    ,				A = 0.1    ,				)		->    Union[str, Any]:
         if isinstance(A    ,				A     ) and len(A     ) != batch_size:
            raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(A     )} 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(A    ,				torch.Generator     ) and batch_size > 1:
            snake_case				:    Dict           =	[generator] + [None] * (batch_size - 1)

         snake_case				:    Tuple           =	[
             ("""model""", self.coca_model is None),
             ("""tokenizer""", self.coca_tokenizer is None),
             ("""transform""", self.coca_transform is None),
         ]
         snake_case				:    List[str]           =	[x[0] for x in coca_is_none if x[1]]
         snake_case				:    Optional[int]           =	""", """.join(A     )
         # generate prompts with coca model if prompt is None
         if content_prompt is None:
            if len(A     ):
               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."""     )
            snake_case				:    Tuple           =	self.get_image_description(A     )
         if style_prompt is None:
            if len(A     ):
               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."""     )
            snake_case				:    List[Any]           =	self.get_image_description(A     )

         # get prompt text embeddings for content and style
         snake_case				:    Dict           =	self.tokenizer(
             A    ,				padding="""max_length"""    ,				max_length=self.tokenizer.model_max_length    ,				truncation=A    ,				return_tensors="""pt"""    ,				)
         snake_case				:    str           =	self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

         snake_case				:    Dict           =	self.tokenizer(
             A    ,				padding="""max_length"""    ,				max_length=self.tokenizer.model_max_length    ,				truncation=A    ,				return_tensors="""pt"""    ,				)
         snake_case				:    Tuple           =	self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

         snake_case				:    List[str]           =	slerp(A    ,				A    ,				A     )

         # duplicate text embeddings for each generation per prompt
         snake_case				:    List[Any]           =	text_embeddings.repeat_interleave(A    ,				dim=0     )

         # set timesteps
         snake_case				:    Union[str, Any]           =	"""offset""" in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
         snake_case				:    Optional[Any]           =	{}
         if accepts_offset:
            snake_case				:    Dict           =	1

         self.scheduler.set_timesteps(A    ,				**A     )
         # 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     )

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

         # Preprocess image
         snake_case				:    Dict           =	preprocess(A    ,				A    ,				A     )
         snake_case				:    List[Any]           =	self.prepare_latents(
             A    ,				A    ,				A    ,				text_embeddings.dtype    ,				self.device    ,				A     )

         snake_case				:    Optional[int]           =	preprocess(A    ,				A    ,				A     )
         snake_case				:    Optional[Any]           =	self.prepare_latents(
             A    ,				A    ,				A    ,				text_embeddings.dtype    ,				self.device    ,				A     )

         snake_case				:    str           =	slerp(A    ,				A    ,				A     )

         if clip_guidance_scale > 0:
            snake_case				:    List[Any]           =	self.get_clip_image_embeddings(A    ,				A     )
            snake_case				:    Any           =	self.get_clip_image_embeddings(A    ,				A     )
            snake_case				:    Tuple           =	slerp(
                A    ,				A    ,				A     )

         # 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.
         snake_case				:    Optional[Any]           =	guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
            snake_case				:    List[str]           =	content_text_input.input_ids.shape[-1]
            snake_case				:    Any           =	self.tokenizer([""""""]    ,				padding="""max_length"""    ,				max_length=A    ,				return_tensors="""pt"""     )
            snake_case				:    Tuple           =	self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
            # duplicate unconditional embeddings for each generation per prompt
            snake_case				:    Tuple           =	uncond_embeddings.repeat_interleave(A    ,				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
            snake_case				:    str           =	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`.
         snake_case				:    Tuple           =	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
         snake_case				:    List[Any]           =	text_embeddings.dtype
         if latents is None:
            if self.device.type == "mps":
               # randn does not work reproducibly on mps
               snake_case				:    List[Any]           =	torch.randn(A    ,				generator=A    ,				device="""cpu"""    ,				dtype=A     ).to(
                   self.device     )
            else:
               snake_case				:    Optional[int]           =	torch.randn(A    ,				generator=A    ,				device=self.device    ,				dtype=A     )
         else:
            if latents.shape != latents_shape:
               raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""     )
            snake_case				:    Union[str, Any]           =	latents.to(self.device     )

         # scale the initial noise by the standard deviation required by the scheduler
         snake_case				:    Dict           =	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]
         snake_case				:    Tuple           =	"""eta""" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
         snake_case				:    Dict           =	{}
         if accepts_eta:
            snake_case				:    Union[str, Any]           =	eta

         # check if the scheduler accepts generator
         snake_case				:    List[Any]           =	"""generator""" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
         if accepts_generator:
            snake_case				:    List[str]           =	generator

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

               # predict the noise residual
               snake_case				:    Any           =	self.unet(A    ,				A    ,				encoder_hidden_states=A     ).sample

               # perform classifier free guidance
               if do_classifier_free_guidance:
                  snake_case     , snake_case				:    int           =	noise_pred.chunk(2     )
                  snake_case				:    Tuple           =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

               # compute the previous noisy sample x_t -> x_t-1
               snake_case				:    Tuple           =	self.scheduler.step(A    ,				A    ,				A    ,				**A     ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
         snake_case				:    str           =	1 / 0.1_82_15 * latents
         snake_case				:    Optional[Any]           =	self.vae.decode(A     ).sample

         snake_case				:    Union[str, Any]           =	(image / 2 + 0.5).clamp(0    ,				1     )
         snake_case				:    str           =	image.cpu().permute(0    ,				2    ,				3    ,				1     ).numpy()

         if output_type == "pil":
            snake_case				:    Tuple           =	self.numpy_to_pil(A     )

         if not return_dict:
            return (image, None)

         return StableDiffusionPipelineOutput(images=A    ,				nsfw_content_detected=A     )





