






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 						_a    (      a :List[str] )							->			str:
 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(a ).astype(np.floataa ) / 255.0
 a		  =      image[None].transpose(0	,							3	,							1	,							2 )
 a		  =      torch.from_numpy(a )
 return 2.0 * image - 1.0






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						VQModel ,					__UpperCAmelCase      :						UNetaDModel ,					__UpperCAmelCase      :						Union[
              DDIMScheduler,
              PNDMScheduler,
              LMSDiscreteScheduler,
              EulerDiscreteScheduler,
              EulerAncestralDiscreteScheduler,
              DPMSolverMultistepScheduler,
          ] ,					)    ->Dict:




       """simple docstring"""


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







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




       """simple docstring"""


       if isinstance(__UpperCAmelCase ,					PIL.Image.Image  ):
        a		  =      1
       elif isinstance(__UpperCAmelCase ,					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(__UpperCAmelCase  )}"""  )

       if isinstance(__UpperCAmelCase ,					PIL.Image.Image  ):
        a		  =      preprocess(__UpperCAmelCase  )

       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(__UpperCAmelCase ,					generator=__UpperCAmelCase ,					device=self.device ,					dtype=__UpperCAmelCase  )

       a		  =      image.to(device=self.device ,					dtype=__UpperCAmelCase  )

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(__UpperCAmelCase ,					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(__UpperCAmelCase  ):
        # concat latents and low resolution image in the channel dimension.
        a		  =      torch.cat([latents, image] ,					dim=1  )
        a		  =      self.scheduler.scale_model_input(__UpperCAmelCase ,					__UpperCAmelCase  )
        # predict the noise residual
        a		  =      self.unet(__UpperCAmelCase ,					__UpperCAmelCase  ).sample
        # compute the previous noisy sample x_t -> x_t-1
        a		  =      self.scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase  ).prev_sample

       # decode the image latents with the VQVAE
       a		  =      self.vqvae.decode(__UpperCAmelCase  ).sample
       a		  =      torch.clamp(__UpperCAmelCase ,					-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(__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


