


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        _a							(			UpperCamelCase__			):

							def __init__(							self:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int							)			->   Optional[int]:





									"""simple docstring"""







									super().__init__()

									# make sure scheduler can always be converted to DDIM
									lowercase__            =      DDIMScheduler.from_config(scheduler.config							)

									self.register_modules(unet=UpperCamelCase_    ,						scheduler=UpperCamelCase_							)


							@torch.no_grad()
							def __call__(							self:	int    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						UpperCamelCase_:	float = 0.0    ,						UpperCamelCase_:	int = 50    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	Optional[str] = "pil"    ,						UpperCamelCase_:	bool = True    ,						)			->   Union[ImagePipelineOutput, Tuple]:





									"""simple docstring"""







									if isinstance(self.unet.config.sample_size    ,						UpperCamelCase_							):
											lowercase__            =      (
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
									else:
											lowercase__            =      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(UpperCamelCase_    ,						UpperCamelCase_							) and len(UpperCamelCase_							) != batch_size:
											raise ValueError(
											    f'You have passed a list of generators of length {len(UpperCamelCase_							)}, but requested an effective batch'
											    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'							)

									lowercase__            =      randn_tensor(UpperCamelCase_    ,						generator=UpperCamelCase_    ,						device=self.device    ,						dtype=self.unet.dtype							)

									# set step values
									self.scheduler.set_timesteps(UpperCamelCase_							)

									for t in self.progress_bar(self.scheduler.timesteps							):
											# 1. predict noise model_output
											lowercase__            =      self.unet(UpperCamelCase_    ,						UpperCamelCase_							).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											lowercase__            =      self.scheduler.step(
											    UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						eta=UpperCamelCase_    ,						use_clipped_model_output=UpperCamelCase_    ,						generator=UpperCamelCase_							).prev_sample

									lowercase__            =      (image / 2 + 0.5).clamp(0    ,						1							)
									lowercase__            =      image.cpu().permute(0    ,						2    ,						3    ,						1							).numpy()
									if output_type == "pil":
											lowercase__            =      self.numpy_to_pil(UpperCamelCase_							)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=UpperCamelCase_							)






'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[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 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[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]





