from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   42
      UpperCAmelCase_      :      List[Any]      							=   42




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         super().__init__()
         self.register_modules(unet=_lowerCAmelCase					,scheduler=_lowerCAmelCase							)




      @torch.no_grad()
      def __call__(				self					,lowerCamelCase_ = 1					,lowerCamelCase_ = 50					,lowerCamelCase_ = None					,lowerCamelCase_ = "pil"					,lowerCamelCase_ = True					,**lowerCamelCase_					,)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.unet.config.sample_size
         UpperCAmelCase__							:				Optional[Any]   				=  (batch_size, 3, img_size, img_size)

         UpperCAmelCase__							:				Any   				=  self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         UpperCAmelCase__							:				Optional[Any]   				=  randn_tensor(_lowerCAmelCase					,generator=_lowerCAmelCase					,device=self.device							) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(_lowerCAmelCase							)

         for t in self.progress_bar(self.scheduler.timesteps							):
            # here sigma_t == t_i from the paper
            UpperCAmelCase__							:				int   				=  self.scheduler.schedule[t]
            UpperCAmelCase__							:				str   				=  self.scheduler.schedule[t - 1] if t > 0 else 0

            # 1. Select temporarily increased noise level sigma_hat
            # 2. Add new noise to move from sample_i to sample_hat
            UpperCAmelCase__				,      UpperCAmelCase__							:				List[str]   				=  self.scheduler.add_noise_to_input(_lowerCAmelCase					,_lowerCAmelCase					,generator=_lowerCAmelCase							)

            # 3. Predict the noise residual given the noise magnitude `sigma_hat`
            # The model inputs and output are adjusted by following eq. (213) in [1].
            UpperCAmelCase__							:				Any   				=  (sigma_hat / 2) * model((sample_hat + 1) / 2					,sigma_hat / 2							).sample

            # 4. Evaluate dx/dt at sigma_hat
            # 5. Take Euler step from sigma to sigma_prev
            UpperCAmelCase__							:				Tuple   				=  self.scheduler.step(_lowerCAmelCase					,_lowerCAmelCase					,_lowerCAmelCase					,_lowerCAmelCase							)

            if sigma_prev != 0:
               # 6. Apply 2nd order correction
               # The model inputs and output are adjusted by following eq. (213) in [1].
               UpperCAmelCase__							:				List[Any]   				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2					,sigma_prev / 2							).sample
               UpperCAmelCase__							:				str   				=  self.scheduler.step_correct(
                   _lowerCAmelCase					,_lowerCAmelCase					,_lowerCAmelCase					,_lowerCAmelCase					,step_output.prev_sample					,step_output['''derivative''']					,)
            UpperCAmelCase__							:				int   				=  step_output.prev_sample

         UpperCAmelCase__							:				Dict   				=  (sample / 2 + 0.5).clamp(0					,1							)
         UpperCAmelCase__							:				List[str]   				=  sample.cpu().permute(0					,2					,3					,1							).numpy()
         if output_type == "pil":
            UpperCAmelCase__							:				Optional[Any]   				=  self.numpy_to_pil(_lowerCAmelCase							)

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase							)





'''simple docstring'''

import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__     :	int				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			{'vocab_file': 'vocab.json'}

UpperCamelCase__     :	Tuple				=			{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

UpperCamelCase__     :	List[Any]				=			{'mgp-str': 27}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      int      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[GO]"					,lowerCamelCase_="[s]"					,lowerCamelCase_="[GO]"					,**lowerCamelCase_							)			->			str:



         '''simple docstring'''



         super().__init__(
             unk_token=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,**lowerCamelCase_					,)

         with open(lowerCamelCase_					,encoding='''utf-8'''							) as vocab_handle:
            UpperCAmelCase__							:				Optional[Any]   				=  json.load(lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  {v: k for k, v in self.vocab.items()}




      @property
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         return len(self.vocab							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         return dict(self.vocab					,**self.added_tokens_encoder							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  []
         for s in text:
            char_tokens.extend(lowerCamelCase_							)
         return char_tokens




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.vocab.get(lowerCamelCase_					,self.vocab.get(self.unk_token							)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         return self.decoder.get(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         with open(lowerCamelCase_					,'''w'''					,encoding='''utf-8'''							) as f:
            f.write(json.dumps(self.vocab					,indent=2					,sort_keys=lowerCamelCase_					,ensure_ascii=lowerCamelCase_							) + '''\n'''							)

         return (vocab_file,)




