

"""simple docstring"""


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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__A						   =			logging.get_logger(__name__)  # pylint: disable=invalid-name






def     a__    (    __SCREAMING_SNAKE_CASE )	->	str:
		warnings.warn(
		    "The preprocess method is deprecated and will be removed in a future version. Please"
		    " use VaeImageProcessor.preprocess instead"					, __SCREAMING_SNAKE_CASE					, )
		if isinstance(__SCREAMING_SNAKE_CASE					, torch.Tensor ):
				return image
		elif isinstance(__SCREAMING_SNAKE_CASE					, PIL.Image.Image ):
				__lowerCAmelCase:       Optional[Any]          =			[image]

		if isinstance(image[0]					, PIL.Image.Image ):
				__lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			image[0].size
				__lowerCAmelCase      , __lowerCAmelCase:       Tuple          =			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				__lowerCAmelCase:       Union[str, Any]          =			[np.array(i.resize((w, h)					, resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
				__lowerCAmelCase:       Optional[Any]          =			np.concatenate(__SCREAMING_SNAKE_CASE					, axis=0 )
				__lowerCAmelCase:       int          =			np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa ) / 255.0
				__lowerCAmelCase:       Any          =			image.transpose(0					, 3					, 1					, 2 )
				__lowerCAmelCase:       Optional[int]          =			2.0 * image - 1.0
				__lowerCAmelCase:       int          =			torch.from_numpy(__SCREAMING_SNAKE_CASE )
		elif isinstance(image[0]					, torch.Tensor ):
				__lowerCAmelCase:       List[Any]          =			torch.cat(__SCREAMING_SNAKE_CASE					, dim=0 )
		return image






def     a__    (    __SCREAMING_SNAKE_CASE )	->	List[Any]:
		if isinstance(__SCREAMING_SNAKE_CASE					, torch.Tensor ):
				return mask
		elif isinstance(__SCREAMING_SNAKE_CASE					, PIL.Image.Image ):
				__lowerCAmelCase:       str          =			[mask]

		if isinstance(mask[0]					, PIL.Image.Image ):
				__lowerCAmelCase      , __lowerCAmelCase:       str          =			mask[0].size
				__lowerCAmelCase      , __lowerCAmelCase:       Union[str, Any]          =			(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
				__lowerCAmelCase:       List[Any]          =			[np.array(m.convert("L" ).resize((w, h)					, resample=PIL_INTERPOLATION["nearest"] ) )[None, :] for m in mask]
				__lowerCAmelCase:       str          =			np.concatenate(__SCREAMING_SNAKE_CASE					, axis=0 )
				__lowerCAmelCase:       Any          =			mask.astype(np.floataa ) / 255.0
				__lowerCAmelCase:       Optional[int]          =			0
				__lowerCAmelCase:       List[str]          =			1
				__lowerCAmelCase:       int          =			torch.from_numpy(__SCREAMING_SNAKE_CASE )
		elif isinstance(mask[0]					, torch.Tensor ):
				__lowerCAmelCase:       Union[str, Any]          =			torch.cat(__SCREAMING_SNAKE_CASE					, dim=0 )
		return mask





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      UNetaDModel
				SCREAMING_SNAKE_CASE_   :      RePaintScheduler

				def __init__(	self							:				Optional[Any]     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				str)->   Tuple:



						'''simple docstring'''


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




				@torch.no_grad()
				def __call__(	self							:				List[Any]     ,  UpperCamelCase__							:				Union[torch.Tensor, PIL.Image.Image]     ,  UpperCamelCase__							:				Union[torch.Tensor, PIL.Image.Image]     ,  UpperCamelCase__							:				int = 2_5_0     ,  UpperCamelCase__							:				float = 0.0     ,  UpperCamelCase__							:				int = 1_0     ,  UpperCamelCase__							:				int = 1_0     ,  UpperCamelCase__							:				Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  UpperCamelCase__							:				Optional[str] = "pil"     ,  UpperCamelCase__							:				bool = True     ,  )->   Union[ImagePipelineOutput, Tuple]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			image

						__lowerCAmelCase:       int          =			_preprocess_image(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			original_image.to(device=self.device     ,  dtype=self.unet.dtype)
						__lowerCAmelCase:       List[str]          =			_preprocess_mask(UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			mask_image.to(device=self.device     ,  dtype=self.unet.dtype)

						__lowerCAmelCase:       List[str]          =			original_image.shape[0]

						# sample gaussian noise to begin the loop
						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.")

						__lowerCAmelCase:       int          =			original_image.shape
						__lowerCAmelCase:       List[str]          =			randn_tensor(UpperCamelCase__     ,  generator=UpperCamelCase__     ,  device=self.device     ,  dtype=self.unet.dtype)

						# set step values
						self.scheduler.set_timesteps(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  self.device)
						__lowerCAmelCase:       Union[str, Any]          =			eta

						__lowerCAmelCase:       Dict          =			self.scheduler.timesteps[0] + 1
						__lowerCAmelCase:       Tuple          =			generator[0] if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
								if t < t_last:
										# predict the noise residual
										__lowerCAmelCase:       Dict          =			self.unet(UpperCamelCase__     ,  UpperCamelCase__).sample
										# compute previous image: x_t -> x_t-1
										__lowerCAmelCase:       Optional[Any]          =			self.scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__).prev_sample

								else:
										# compute the reverse: x_t-1 -> x_t
										__lowerCAmelCase:       Union[str, Any]          =			self.scheduler.undo_step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__)
								__lowerCAmelCase:       str          =			t

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

						if not return_dict:
								return (image,)

						return ImagePipelineOutput(images=UpperCamelCase__)





"""simple docstring"""


import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

__A						   =			logging.get_logger(__name__)


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

__A						   =			{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__A						   =			{"facebook/blenderbot-3B": 128}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      str         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  BlenderbotTokenizer

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



						'''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__     ,  )

						__lowerCAmelCase:       Any          =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  pre_tok_state.pop("type"))
								__lowerCAmelCase:       int          =			add_prefix_space
								__lowerCAmelCase:       str          =			pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			add_prefix_space

						__lowerCAmelCase:       Union[str, Any]          =			"post_processor"
						__lowerCAmelCase:       Tuple          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       List[Any]          =			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:
										__lowerCAmelCase:       str          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       Any          =			tuple(state["cls"])

								__lowerCAmelCase:       int          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Dict          =			add_prefix_space
										__lowerCAmelCase:       Optional[Any]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Dict          =			trim_offsets
										__lowerCAmelCase:       Tuple          =			True

								if changes_to_apply:
										__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       str          =			component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)

				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 							lowercase_     (	self							:				Optional[Any])->   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 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       Any          =			value

				def 							lowercase_     (	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				List[str]     ,  **UpperCamelCase__							:				Optional[int])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			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 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				Optional[int]     ,  **UpperCamelCase__							:				int)->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			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 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[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]

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[Any]:



						'''simple docstring'''


						return token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				"Conversation")->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[]
						for is_user, text in conversation.iter_texts():
								if is_user:
										# We need to space prefix as it's being done within blenderbot
										inputs.append(" " + text)
								else:
										# Generated responses should contain them already.
										inputs.append(UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			"  ".join(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			self.encode(UpperCamelCase__)
						if len(UpperCamelCase__) > self.model_max_length:
								__lowerCAmelCase:       int          =			input_ids[-self.model_max_length :]
								logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.")
						return input_ids


