



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Optional[int]		      =		'''char'''
       UpperCamelCase__						:					List[Any]		      =		'''bpe'''
       UpperCamelCase__						:					Dict		      =		'''wp'''


__snake_case		:Any   	=  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Any		      =		['''image_processor''', '''char_tokenizer''']
       UpperCamelCase__						:					int		      =		'''ViTImageProcessor'''
       UpperCamelCase__						:					Union[str, Any]		      =		'''MgpstrTokenizer'''



       def __init__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=None  ,	**__SCREAMING_SNAKE_CASE							:  str):


           '''simple docstring'''

           __a		 =       None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''  ,	__SCREAMING_SNAKE_CASE  ,	)
               __a		 =       kwargs.pop('''feature_extractor''')

           __a		 =       image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.''')
           if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.''')

           __a		 =       tokenizer
           __a		 =       AutoTokenizer.from_pretrained('''gpt2''')
           __a		 =       AutoTokenizer.from_pretrained('''bert-base-uncased''')

           super().__init__(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)



       def __call__(	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Dict=None  ,	__SCREAMING_SNAKE_CASE							:  List[str]=None  ,	__SCREAMING_SNAKE_CASE							:  int=None  ,	**__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           if images is None and text is None:
               raise ValueError('''You need to specify either an `images` or `text` input to process.''')

           if images is not None:
               __a		 =       self.image_processor(__SCREAMING_SNAKE_CASE  ,	return_tensors=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)
           if text is not None:
               __a		 =       self.char_tokenizer(__SCREAMING_SNAKE_CASE  ,	return_tensors=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)

           if text is None:
               return inputs
           elif images is None:
               return encodings
           else:
               __a		 =       encodings['''input_ids''']
               return inputs



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           __a ,     __a ,     __a		 =       sequences
           __a		 =       char_preds.size(0)

           __a ,     __a		 =       self._decode_helper(__SCREAMING_SNAKE_CASE  ,	'''char''')
           __a ,     __a		 =       self._decode_helper(__SCREAMING_SNAKE_CASE  ,	'''bpe''')
           __a ,     __a		 =       self._decode_helper(__SCREAMING_SNAKE_CASE  ,	'''wp''')

           __a		 =       []
           __a		 =       []
           for i in range(__SCREAMING_SNAKE_CASE):
               __a		 =       [char_scores[i], bpe_scores[i], wp_scores[i]]
               __a		 =       [char_strs[i], bpe_strs[i], wp_strs[i]]
               __a		 =       scores.index(max(__SCREAMING_SNAKE_CASE))
               final_strs.append(strs[max_score_index])
               final_scores.append(scores[max_score_index])

           __a		 =       {}
           __a		 =       final_strs
           __a		 =       final_scores
           __a		 =       char_strs
           __a		 =       bpe_strs
           __a		 =       wp_strs
           return out



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           if format == DecodeType.CHARACTER:
               __a		 =       self.char_decode
               __a		 =       1
               __a		 =       '''[s]'''
           elif format == DecodeType.BPE:
               __a		 =       self.bpe_decode
               __a		 =       2
               __a		 =       '''#'''
           elif format == DecodeType.WORDPIECE:
               __a		 =       self.wp_decode
               __a		 =       102
               __a		 =       '''[SEP]'''
           else:
               raise ValueError(F'Format {format} is not supported.')

           __a ,     __a		 =       [], []
           __a		 =       pred_logits.size(0)
           __a		 =       pred_logits.size(1)
           __a ,     __a		 =       pred_logits.topk(1  ,	dim=-1  ,	largest=__SCREAMING_SNAKE_CASE  ,	sorted=__SCREAMING_SNAKE_CASE)
           __a		 =       preds_index.view(-1  ,	__SCREAMING_SNAKE_CASE)[:, 1:]
           __a		 =       decoder(__SCREAMING_SNAKE_CASE)
           __a ,     __a		 =       torch.nn.functional.softmax(__SCREAMING_SNAKE_CASE  ,	dim=2).max(dim=2)
           __a		 =       preds_max_prob[:, 1:]

           for index in range(__SCREAMING_SNAKE_CASE):
               __a		 =       preds_str[index].find(__SCREAMING_SNAKE_CASE)
               __a		 =       preds_str[index][:pred_eos]
               __a		 =       preds_index[index].cpu().tolist()
               __a		 =       pred_index.index(__SCREAMING_SNAKE_CASE) if eos_token in pred_index else -1
               __a		 =       preds_max_prob[index][: pred_eos_index + 1]
               __a		 =       pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
               dec_strs.append(__SCREAMING_SNAKE_CASE)
               conf_scores.append(__SCREAMING_SNAKE_CASE)

           return dec_strs, conf_scores



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

           __a		 =       [seq.replace(''' '''  ,	'''''') for seq in self.char_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)]
           return decode_strs



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           return self.bpe_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)





       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       [seq.replace(''' '''  ,	'''''') for seq in self.wp_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)]
           return decode_strs










import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def   __snake_case			(    _UpperCAmelCase       ):
    __a ,     __a		 =       image.size
    __a ,     __a		 =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    __a		 =       image.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']       )
    __a		 =       np.array(_UpperCAmelCase       ).astype(np.floataa       ) / 2_55.0
    __a		 =       image[None].transpose(0     ,				3     ,				1     ,				2       )
    __a		 =       torch.from_numpy(_UpperCAmelCase       )
    return 2.0 * image - 1.0

class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  VQModel  ,	__SCREAMING_SNAKE_CASE							:  UNetaDModel  ,	__SCREAMING_SNAKE_CASE							:  Union[
               DDIMScheduler,
               PNDMScheduler,
               LMSDiscreteScheduler,
               EulerDiscreteScheduler,
               EulerAncestralDiscreteScheduler,
               DPMSolverMultistepScheduler,
           ]  ,	):


           '''simple docstring'''

           super().__init__()
           self.register_modules(vqvae=__SCREAMING_SNAKE_CASE  ,	unet=__SCREAMING_SNAKE_CASE  ,	scheduler=__SCREAMING_SNAKE_CASE)





       @torch.no_grad()
       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[torch.Tensor, PIL.Image.Image] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 100  ,	__SCREAMING_SNAKE_CASE							:  Optional[float] = 0.0  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = "pil"  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       1
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor):
               __a		 =       image.shape[0]
           else:
               raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE)}')

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       preprocess(__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       image.shape[-2:]

           # in_channels should be 6: 3 for latents, 3 for low resolution image
           __a		 =       (batch_size, self.unet.config.in_channels // 2, height, width)
           __a		 =       next(self.unet.parameters()).dtype

           __a		 =       randn_tensor(__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           __a		 =       image.to(device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,	device=self.device)
           __a		 =       self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           __a		 =       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]
           __a		 =       '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
           __a		 =       {}
           if accepts_eta:
               __a		 =       eta

           for t in self.progress_bar(__SCREAMING_SNAKE_CASE):
               # concat latents and low resolution image in the channel dimension.
               __a		 =       torch.cat([latents, image]  ,	dim=1)
               __a		 =       self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               # predict the noise residual
               __a		 =       self.unet(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE).sample
               # compute the previous noisy sample x_t -> x_t-1
               __a		 =       self.scheduler.step(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE).prev_sample

           # decode the image latents with the VQVAE
           __a		 =       self.vqvae.decode(__SCREAMING_SNAKE_CASE).sample
           __a		 =       torch.clamp(__SCREAMING_SNAKE_CASE  ,	-1.0  ,	1.0)
           __a		 =       image / 2 + 0.5
           __a		 =       image.cpu().permute(0  ,	2  ,	3  ,	1).numpy()

           if output_type == "pil":
               __a		 =       self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)





