





import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch


_snake_case  :   Dict						   =  logging.get_logger(__name__)



class     _UpperCAmelCase							:

  """simple docstring"""


  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		uuid.UUID = None ,  lowerCAmelCase_			:		str=None ,  lowerCAmelCase_			:		Optional[Any]=None					)							->						Dict:


     if not conversation_id:
        __lowerCAmelCase							     =     uuid.uuida()
     if past_user_inputs is None:
        __lowerCAmelCase							     =     []
     if generated_responses is None:
        __lowerCAmelCase							     =     []

     __lowerCAmelCase							     =     conversation_id
     __lowerCAmelCase							     =     past_user_inputs
     __lowerCAmelCase							     =     generated_responses
     __lowerCAmelCase							     =     text






  def __eq__(    self			:		List[str] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						str:


     if not isinstance(_A ,  _A					):
        return False
     if self.uuid == other.uuid:
        return True
     return (
         self.new_user_input == other.new_user_input
         and self.past_user_inputs == other.past_user_inputs
         and self.generated_responses == other.generated_responses
     )






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		bool = False					)							->						List[str]:


     if self.new_user_input:
        if overwrite:
           logger.warning(
               f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
               f"""with: \"{text}\"."""					)
           __lowerCAmelCase							     =     text
        else:
           logger.warning(
               f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
               f"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""					)
     else:
        __lowerCAmelCase							     =     text






  def     lowercase		(    self			:		Optional[int]					)							->						Union[str, Any]:


     if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input					)
     __lowerCAmelCase							     =     None






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     self.generated_responses.append(_A					)






  def     lowercase		(    self			:		Tuple					)							->						Union[str, Any]:


     for user_input, generated_response in zip(self.past_user_inputs ,  self.generated_responses					):
        yield True, user_input
        yield False, generated_response
     if self.new_user_input:
        yield True, self.new_user_input






  def __repr__(    self			:		Tuple					)							->						str:
     __lowerCAmelCase							     =     f"""Conversation id: {self.uuid} \n"""
     for is_user, text in self.iter_texts():
        __lowerCAmelCase							     =     'user' if is_user else 'bot'
        output += f"""{name} >> {text} \n"""
     return output





@add_end_docstrings(
    snake_case_		,   R"""\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    """		,   )
class     _UpperCAmelCase							(   snake_case_   ):

  """simple docstring"""


  def __init__(    self			:		Union[str, Any] ,  *lowerCAmelCase_			:		Dict ,  **lowerCAmelCase_			:		Any					)							->						Optional[Any]:
     super().__init__(*_A ,  **_A					)
     if self.tokenizer.pad_token_id is None:
        __lowerCAmelCase							     =     self.tokenizer.eos_token






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		List[Any]=None ,  **lowerCAmelCase_			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     {}
     __lowerCAmelCase							     =     {}
     __lowerCAmelCase							     =     {}

     if min_length_for_response is not None:
        __lowerCAmelCase							     =     min_length_for_response
     if minimum_tokens is not None:
        __lowerCAmelCase							     =     minimum_tokens

     if "max_length" in generate_kwargs:
        __lowerCAmelCase							     =     generate_kwargs['max_length']
        # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
     if clean_up_tokenization_spaces is not None:
        __lowerCAmelCase							     =     clean_up_tokenization_spaces

     if generate_kwargs:
        forward_params.update(_A					)
     return preprocess_params, forward_params, postprocess_params






  def __call__(    self			:		List[Any] ,  lowerCAmelCase_			:		Union[Conversation, List[Conversation]] ,  lowerCAmelCase_			:		List[Any]=0 ,  **lowerCAmelCase_			:		Optional[int]					)							->						str:
     __lowerCAmelCase							     =     super().__call__(_A ,  num_workers=_A ,  **_A					)
     if isinstance(_A ,  _A					) and len(_A					) == 1:
        return outputs[0]
     return outputs






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		Conversation ,  lowerCAmelCase_			:		str=3_2					)							->						Dict[str, Any]:


     if not isinstance(_A ,  _A					):
        raise ValueError('ConversationalPipeline, expects Conversation as inputs'					)
     if conversation.new_user_input is None:
        raise ValueError(
            f"""Conversation with UUID {type(conversation.uuid					)} does not contain new user input to process. """
            'Add user inputs with the conversation\'s `add_user_input` method'					)
     if hasattr(self.tokenizer ,  '_build_conversation_input_ids'					):
        __lowerCAmelCase							     =     self.tokenizer._build_conversation_input_ids(_A					)
     else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        __lowerCAmelCase							     =     self._legacy_parse_and_tokenize(_A					)

     if self.framework == "pt":
        __lowerCAmelCase							     =     torch.LongTensor([input_ids]					)
     elif self.framework == "tf":
        __lowerCAmelCase							     =     tf.constant([input_ids]					)
     return {"input_ids": input_ids, "conversation": conversation}






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Union[str, Any]=1_0 ,  **lowerCAmelCase_			:		Tuple					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     generate_kwargs.get('max_length' ,  self.model.config.max_length					)

     __lowerCAmelCase							     =     model_inputs['input_ids'].shape[1]
     if max_length - minimum_tokens < n:
        logger.warning(f"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""					)
        __lowerCAmelCase							     =     max_length - minimum_tokens
        __lowerCAmelCase							     =     model_inputs['input_ids'][:, -trim:]
        if "attention_mask" in model_inputs:
           __lowerCAmelCase							     =     model_inputs['attention_mask'][:, -trim:]
     __lowerCAmelCase							     =     model_inputs.pop('conversation'					)
     __lowerCAmelCase							     =     max_length
     __lowerCAmelCase							     =     self.model.generate(**_A ,  **_A					)
     if self.model.config.is_encoder_decoder:
        __lowerCAmelCase							     =     1
     else:
        __lowerCAmelCase							     =     n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		str=True					)							->						Any:
     __lowerCAmelCase							     =     model_outputs['output_ids']
     __lowerCAmelCase							     =     self.tokenizer.decode(
         output_ids[0] ,  skip_special_tokens=_A ,  clean_up_tokenization_spaces=_A ,  )
     __lowerCAmelCase							     =     model_outputs['conversation']
     conversation.mark_processed()
     conversation.append_response(_A					)
     return conversation






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Conversation					)							->						Dict:
     __lowerCAmelCase							     =     self.tokenizer.eos_token_id
     __lowerCAmelCase							     =     []
     for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
           input_ids.extend(self.tokenizer.encode(_A ,  add_special_tokens=_A					) + [eos_token_id]					)
        else:
           input_ids.extend(self.tokenizer.encode(_A ,  add_special_tokens=_A					)					)

     if len(_A					) > self.tokenizer.model_max_length:
        __lowerCAmelCase							     =     input_ids[-self.tokenizer.model_max_length :]
     return input_ids








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


_snake_case  :   Union[str, Any]						   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   warnings.warn(
       'The preprocess method is deprecated and will be removed in a future version. Please'
       ' use VaeImageProcessor.preprocess instead',     lowerCAmelCase_,     )
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return image
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [image]

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

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

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [mask]

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




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)






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

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __lowerCAmelCase							     =     mask_image.to(device=self.device ,  dtype=self.unet.dtype					)

     __lowerCAmelCase							     =     original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) and len(lowerCAmelCase_					) != batch_size:
        raise ValueError(
            f"""You have passed a list of generators of length {len(lowerCAmelCase_					)}, but requested an effective batch"""
            f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

     __lowerCAmelCase							     =     original_image.shape
     __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

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

        else:
           # compute the reverse: x_t-1 -> x_t
           __lowerCAmelCase							     =     self.scheduler.undo_step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     t

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)

