






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     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	42
				a__       							=	42







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__()
								self.register_modules(unet=__lowerCAmelCase							,       scheduler=__lowerCAmelCase  )







				@torch.no_grad()
				def __call__(    self							,       __lowerCAmelCase = 1							,       __lowerCAmelCase = 5_0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = "pil"							,       __lowerCAmelCase = True							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.unet.config.sample_size
								__magic_name__		:Tuple   =   (batch_size, 3, img_size, img_size)

								__magic_name__		:str   =   self.unet

								# sample x_0 ~ N(0, sigma_0^2 * I)
								__magic_name__		:Optional[int]   =   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
												__magic_name__		:Tuple   =   self.scheduler.schedule[t]
												__magic_name__		:int   =   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
												__magic_name__  ,					__magic_name__		:Dict   =   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].
												__magic_name__		:int   =   (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
												__magic_name__		:List[str]   =   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].
																__magic_name__		:List[Any]   =   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,       sigma_prev / 2  ).sample
																__magic_name__		:str   =   self.scheduler.step_correct(
																    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       step_output.prev_sample							,       step_output['''derivative''']							,       )
												__magic_name__		:Dict   =   step_output.prev_sample

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

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=__lowerCAmelCase  )














import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							List[str]						= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a   :							Dict						= {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__a   :							List[str]						= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE     =					RobertaTokenizer




     def __init__(					self							:      List[Any]			,						UpperCamelCase_							:      Any=None			,						UpperCamelCase_							:      Tuple=None			,						UpperCamelCase_							:      Optional[int]=None			,						UpperCamelCase_							:      Union[str, Any]="replace"			,						UpperCamelCase_							:      Any="<s>"			,						UpperCamelCase_							:      Tuple="</s>"			,						UpperCamelCase_							:      Optional[Any]="</s>"			,						UpperCamelCase_							:      Dict="<s>"			,						UpperCamelCase_							:      int="<unk>"			,						UpperCamelCase_							:      Optional[int]="<pad>"			,						UpperCamelCase_							:      str="<mask>"			,						UpperCamelCase_							:      Tuple=False			,						UpperCamelCase_							:      Optional[Any]=True			,						**UpperCamelCase_							:      Union[str, Any]			,						):


         """simple docstring"""



         super().__init__(
             UpperCamelCase_			,						UpperCamelCase_			,						tokenizer_file=UpperCamelCase_			,						errors=UpperCamelCase_			,						bos_token=UpperCamelCase_			,						eos_token=UpperCamelCase_			,						sep_token=UpperCamelCase_			,						cls_token=UpperCamelCase_			,						unk_token=UpperCamelCase_			,						pad_token=UpperCamelCase_			,						mask_token=UpperCamelCase_			,						add_prefix_space=UpperCamelCase_			,						trim_offsets=UpperCamelCase_			,						**UpperCamelCase_			,						)

         __A       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
         if pre_tok_state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
             __A       =						getattr(UpperCamelCase_			,						pre_tok_state.pop("""type"""					)					)
             __A       =						add_prefix_space
             __A       =						pre_tok_class(**UpperCamelCase_					)

         __A       =						add_prefix_space

         __A       =						"""post_processor"""
         __A       =						getattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)
         if tokenizer_component_instance:
             __A       =						json.loads(tokenizer_component_instance.__getstate__()					)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __A       =						tuple(state["""sep"""]					)
             if "cls" in state:
                 __A       =						tuple(state["""cls"""]					)

             __A       =						False

             if state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
                 __A       =						add_prefix_space
                 __A       =						True

             if state.get("""trim_offsets"""			,						UpperCamelCase_					) != trim_offsets:
                 __A       =						trim_offsets
                 __A       =						True

             if changes_to_apply:
                 __A       =						getattr(UpperCamelCase_			,						state.pop("""type"""					)					)
                 __A       =						component_class(**UpperCamelCase_					)
                 setattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)




     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         if self._mask_token is None:
             if self.verbose:
                 logger.error("""Using mask_token, but it is not set yet."""					)
             return None
         return str(self._mask_token					)




     @mask_token.setter
     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else value
         __A       =						value




     def 		lowerCAmelCase_  (					self							:      Tuple			,						*UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Optional[int]			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         __A       =						self._tokenizer.model.save(UpperCamelCase_			,						name=UpperCamelCase_					)
         return tuple(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      int=None					):


         """simple docstring"""



         __A       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[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 + sep + token_ids_a + sep					) * [0]




