





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      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Any:






				"""simple docstring"""


				if isinstance(__a ,				torch.Tensor ):
								return image
				elif isinstance(__a ,				PIL.Image.Image ):
								snake_case_						:    str				=  [image]

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






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=0.9_995 )		->						str:






				"""simple docstring"""


				if not isinstance(__a ,				np.ndarray ):
								snake_case_						:    int				=  True
								snake_case_						:    Dict				=  va.device
								snake_case_						:    List[Any]				=  va.cpu().numpy()
								snake_case_						:    List[str]				=  va.cpu().numpy()

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

				if inputs_are_torch:
								snake_case_						:    int				=  torch.from_numpy(__a ).to(__a )

				return va






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    str				=  F.normalize(__a ,				dim=-1 )
				snake_case_						:    Union[str, Any]				=  F.normalize(__a ,				dim=-1 )
				return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				for param in model.parameters():
								snake_case_						:    Any				=  value





class 					__lowerCAmelCase       (			__SCREAMING_SNAKE_CASE     ):






			def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=None					,							)     ->   List[str]:


							'''simple docstring'''



							super().__init__()
							self.register_modules(
							    vae=UpperCAmelCase_					,							text_encoder=UpperCAmelCase_					,							clip_model=UpperCAmelCase_					,							tokenizer=UpperCAmelCase_					,							unet=UpperCAmelCase_					,							scheduler=UpperCAmelCase_					,							feature_extractor=UpperCAmelCase_					,							coca_model=UpperCAmelCase_					,							coca_tokenizer=UpperCAmelCase_					,							coca_transform=UpperCAmelCase_					,							)
							snake_case_						:    Tuple				=  (
							    feature_extractor.size
							    if isinstance(feature_extractor.size					,							UpperCAmelCase_    )
							    else feature_extractor.size["shortest_edge"]
							)
							snake_case_						:    int				=  transforms.Normalize(mean=feature_extractor.image_mean					,							std=feature_extractor.image_std    )
							set_requires_grad(self.text_encoder					,							UpperCAmelCase_    )
							set_requires_grad(self.clip_model					,							UpperCAmelCase_    )






			def 			lowerCamelCase  (self					,							__magic_name__ = "auto"    )     ->   Union[str, Any]:


							'''simple docstring'''



							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(UpperCAmelCase_    )






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							self.enable_attention_slicing(UpperCAmelCase_    )






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							set_requires_grad(self.vae					,							UpperCAmelCase_    )






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							set_requires_grad(self.vae					,							UpperCAmelCase_    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							set_requires_grad(self.unet					,							UpperCAmelCase_    )






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							set_requires_grad(self.unet					,							UpperCAmelCase_    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							snake_case_						:    str				=  min(int(num_inference_steps * strength    )					,							UpperCAmelCase_    )

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

							return timesteps, num_inference_steps - t_start






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=None    )     ->   Union[str, Any]:


							'''simple docstring'''



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

							snake_case_						:    Optional[int]				=  image.to(device=UpperCAmelCase_					,							dtype=UpperCAmelCase_    )

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

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							snake_case_						:    List[str]				=  0.18_215 * init_latents
							snake_case_						:    Optional[int]				=  init_latents.repeat_interleave(UpperCAmelCase_					,							dim=0    )

							snake_case_						:    List[Any]				=  randn_tensor(init_latents.shape					,							generator=UpperCAmelCase_					,							device=UpperCAmelCase_					,							dtype=UpperCAmelCase_    )

							# get latents
							snake_case_						:    str				=  self.scheduler.add_noise(UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_    )
							snake_case_						:    Optional[int]				=  init_latents

							return latents






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.feature_extractor.preprocess(UpperCAmelCase_    )
							snake_case_						:    Optional[Any]				=  torch.from_numpy(clip_image_input['''pixel_values'''][0]    ).unsqueeze(0    ).to(self.device    ).half()
							snake_case_						:    Any				=  self.clip_model.get_image_features(UpperCAmelCase_    )
							snake_case_						:    int				=  image_embeddings_clip / image_embeddings_clip.norm(p=2					,							dim=-1					,							keepdim=UpperCAmelCase_    )
							snake_case_						:    Union[str, Any]				=  image_embeddings_clip.repeat_interleave(UpperCAmelCase_					,							dim=0    )
							return image_embeddings_clip






			@torch.enable_grad()
			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							snake_case_						:    str				=  latents.detach().requires_grad_()

							snake_case_						:    int				=  self.scheduler.scale_model_input(UpperCAmelCase_					,							UpperCAmelCase_    )

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

							if isinstance(self.scheduler					,							(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)    ):
											snake_case_						:    List[Any]				=  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_						:    int				=  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

											snake_case_						:    Any				=  torch.sqrt(UpperCAmelCase_    )
											snake_case_						:    Optional[int]				=  pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler					,							UpperCAmelCase_    ):
											snake_case_						:    Any				=  self.scheduler.sigmas[index]
											snake_case_						:    List[str]				=  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_						:    Any				=  1 / 0.18_215 * sample
							snake_case_						:    Any				=  self.vae.decode(UpperCAmelCase_    ).sample
							snake_case_						:    List[Any]				=  (image / 2 + 0.5).clamp(0					,							1    )

							snake_case_						:    List[Any]				=  transforms.Resize(self.feature_extractor_size    )(UpperCAmelCase_    )
							snake_case_						:    Dict				=  self.normalize(UpperCAmelCase_    ).to(latents.dtype    )

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

							snake_case_						:    List[Any]				=  spherical_dist_loss(UpperCAmelCase_					,							UpperCAmelCase_    ).mean() * clip_guidance_scale

							snake_case_						:    Dict				=  -torch.autograd.grad(UpperCAmelCase_					,							UpperCAmelCase_    )[0]

							if isinstance(self.scheduler					,							UpperCAmelCase_    ):
											snake_case_						:    Dict				=  latents.detach() + grads * (sigma**2)
											snake_case_						:    List[str]				=  noise_pred_original
							else:
											snake_case_						:    int				=  noise_pred_original - torch.sqrt(UpperCAmelCase_    ) * grads
							return noise_pred, latents






			@torch.no_grad()
			def __call__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = 512					,							__magic_name__ = 512					,							__magic_name__ = 0.6					,							__magic_name__ = 50					,							__magic_name__ = 7.5					,							__magic_name__ = 1					,							__magic_name__ = 0.0					,							__magic_name__ = 100					,							__magic_name__ = None					,							__magic_name__ = "pil"					,							__magic_name__ = True					,							__magic_name__ = 0.8					,							__magic_name__ = 0.1					,							__magic_name__ = 0.1					,							)     ->   Any:


							'''simple docstring'''



							if isinstance(UpperCAmelCase_					,							UpperCAmelCase_    ) and len(UpperCAmelCase_    ) != batch_size:
											raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(UpperCAmelCase_    )} 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(UpperCAmelCase_					,							torch.Generator    ) and batch_size > 1:
											snake_case_						:    Optional[Any]				=  [generator] + [None] * (batch_size - 1)

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

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

							snake_case_						:    Tuple				=  self.tokenizer(
							    UpperCAmelCase_					,							padding='''max_length'''					,							max_length=self.tokenizer.model_max_length					,							truncation=UpperCAmelCase_					,							return_tensors='''pt'''					,							)
							snake_case_						:    Any				=  self.text_encoder(style_text_input.input_ids.to(self.device    )    )[0]

							snake_case_						:    Tuple				=  slerp(UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_    )

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

							# set timesteps
							snake_case_						:    Dict				=  "offset" in set(inspect.signature(self.scheduler.set_timesteps    ).parameters.keys()    )
							snake_case_						:    str				=  {}
							if accepts_offset:
											snake_case_						:    Tuple				=  1

							self.scheduler.set_timesteps(UpperCAmelCase_					,							**UpperCAmelCase_    )
							# 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_						:    Optional[Any]				=  self.get_timesteps(UpperCAmelCase_					,							UpperCAmelCase_					,							self.device    )
							snake_case_						:    Optional[Any]				=  timesteps[:1].repeat(UpperCAmelCase_    )

							# Preprocess image
							snake_case_						:    List[Any]				=  preprocess(UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_    )
							snake_case_						:    List[str]				=  self.prepare_latents(
							    UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							text_embeddings.dtype					,							self.device					,							UpperCAmelCase_    )

							snake_case_						:    Tuple				=  preprocess(UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_    )
							snake_case_						:    int				=  self.prepare_latents(
							    UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							text_embeddings.dtype					,							self.device					,							UpperCAmelCase_    )

							snake_case_						:    Tuple				=  slerp(UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_    )

							if clip_guidance_scale > 0:
											snake_case_						:    Optional[int]				=  self.get_clip_image_embeddings(UpperCAmelCase_					,							UpperCAmelCase_    )
											snake_case_						:    List[Any]				=  self.get_clip_image_embeddings(UpperCAmelCase_					,							UpperCAmelCase_    )
											snake_case_						:    Optional[Any]				=  slerp(
											    UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_    )

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

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

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

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

															# predict the noise residual
															snake_case_						:    int				=  self.unet(UpperCAmelCase_					,							UpperCAmelCase_					,							encoder_hidden_states=UpperCAmelCase_    ).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																			snake_case_						:    Any				=  noise_pred.chunk(2    )
																			snake_case_						:    Dict				=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																			snake_case_						:    str				=  (
																			    text_embeddings.chunk(2    )[1] if do_classifier_free_guidance else text_embeddings
																			)
																			snake_case_						:    Tuple				=  self.cond_fn(
																			    UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							UpperCAmelCase_					,							)

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

															 # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							snake_case_						:    int				=  1 / 0.18_215 * latents
							snake_case_						:    str				=  self.vae.decode(UpperCAmelCase_    ).sample

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

							if output_type == "pil":
											snake_case_						:    Union[str, Any]				=  self.numpy_to_pil(UpperCAmelCase_    )

							if not return_dict:
											return (image, None)

							return StableDiffusionPipelineOutput(images=UpperCAmelCase_					,							nsfw_content_detected=UpperCAmelCase_    )







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_       , tokenizer_file=UpperCAmelCase_       , do_lower_case=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , tokenize_chinese_chars=UpperCAmelCase_       , strip_accents=UpperCAmelCase_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =[self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      Optional[int]			  =[self.cls_token_id]
   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

