




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    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	str = None					, UpperCAmelCase_       :	uuid.UUID = None					, UpperCAmelCase_       :	Optional[int]=None					, UpperCAmelCase_       :	Union[str, Any]=None      ):

       if not conversation_id:
         SCREAMING_SNAKE_CASE       :      List[str]							=    uuid.uuida()
       if past_user_inputs is None:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
       if generated_responses is None:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    []

       SCREAMING_SNAKE_CASE       :      List[str]							=    conversation_id
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    past_user_inputs
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    generated_responses
       SCREAMING_SNAKE_CASE       :      Tuple							=    text


     def __eq__(				self       :	str					, UpperCAmelCase_       :	Tuple      ):

       if not isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         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 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	bool = False      ):

       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}\".'''      )
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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:
         SCREAMING_SNAKE_CASE       :      List[str]							=    text


     def 				_A       (				self       :	int      ):

       if self.new_user_input:
         self.past_user_inputs.append(self.new_user_input      )
       SCREAMING_SNAKE_CASE       :      Dict							=    None


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str      ):
       self.generated_responses.append(UpperCAmelCase_      )


     def 				_A       (				self       :	int      ):

       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       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Any							=    f'''Conversation id: {self.uuid} \n'''
       for is_user, text in self.iter_texts():
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    "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  SCREAMING_SNAKE_CASE			(			_snake_case					):
     '''simple docstring'''


     def __init__(				self       :	str					, *UpperCAmelCase_       :	Tuple					, **UpperCAmelCase_       :	str      ):
       super().__init__(*UpperCAmelCase_					, **UpperCAmelCase_      )
       if self.tokenizer.pad_token_id is None:
         SCREAMING_SNAKE_CASE       :      str							=    self.tokenizer.eos_token


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	Tuple=None					, UpperCAmelCase_       :	Union[str, Any]=None					, **UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    {}
       SCREAMING_SNAKE_CASE       :      Tuple							=    {}
       SCREAMING_SNAKE_CASE       :      str							=    {}

       if min_length_for_response is not None:
         SCREAMING_SNAKE_CASE       :      List[str]							=    min_length_for_response
       if minimum_tokens is not None:
         SCREAMING_SNAKE_CASE       :      int							=    minimum_tokens

       if "max_length" in generate_kwargs:
         SCREAMING_SNAKE_CASE       :      str							=    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:
         SCREAMING_SNAKE_CASE       :      str							=    clean_up_tokenization_spaces

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


     def __call__(				self       :	List[Any]					, UpperCAmelCase_       :	Union[Conversation, List[Conversation]]					, UpperCAmelCase_       :	Tuple=0					, **UpperCAmelCase_       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    super().__call__(UpperCAmelCase_					, num_workers=UpperCAmelCase_					, **UpperCAmelCase_      )
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) and len(UpperCAmelCase_      ) == 1:
         return outputs[0]
       return outputs


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Conversation					, UpperCAmelCase_       :	Optional[int]=32      ):

       if not isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         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"      ):
         SCREAMING_SNAKE_CASE       :      str							=    self.tokenizer._build_conversation_input_ids(UpperCAmelCase_      )
       else:
         # If the tokenizer cannot handle conversations, we default to only the old version
         SCREAMING_SNAKE_CASE       :      List[Any]							=    self._legacy_parse_and_tokenize(UpperCAmelCase_      )

       if self.framework == "pt":
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.LongTensor([input_ids]      )
       elif self.framework == "tf":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tf.constant([input_ids]      )
       return {"input_ids": input_ids, "conversation": conversation}


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Tuple=10					, **UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      str							=    generate_kwargs.get("max_length"					, self.model.config.max_length      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    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})'''      )
         SCREAMING_SNAKE_CASE       :      str							=    max_length - minimum_tokens
         SCREAMING_SNAKE_CASE       :      List[str]							=    model_inputs["input_ids"][:, -trim:]
         if "attention_mask" in model_inputs:
           SCREAMING_SNAKE_CASE       :      Tuple							=    model_inputs["attention_mask"][:, -trim:]
       SCREAMING_SNAKE_CASE       :      Any							=    model_inputs.pop("conversation"      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    max_length
       SCREAMING_SNAKE_CASE       :      Any							=    self.model.generate(**UpperCAmelCase_					, **UpperCAmelCase_      )
       if self.model.config.is_encoder_decoder:
         SCREAMING_SNAKE_CASE       :      List[str]							=    1
       else:
         SCREAMING_SNAKE_CASE       :      str							=    n
       return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Union[str, Any]=True      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model_outputs["output_ids"]
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.tokenizer.decode(
           output_ids[0]					, skip_special_tokens=UpperCAmelCase_					, clean_up_tokenization_spaces=UpperCAmelCase_					, )
       SCREAMING_SNAKE_CASE       :      Dict							=    model_outputs["conversation"]
       conversation.mark_processed()
       conversation.append_response(UpperCAmelCase_      )
       return conversation






     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Conversation      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.tokenizer.eos_token_id
       SCREAMING_SNAKE_CASE       :      List[str]							=    []
       for is_user, text in conversation.iter_texts():
         if eos_token_id is not None:
           input_ids.extend(self.tokenizer.encode(UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_      ) + [eos_token_id]      )
         else:
           input_ids.extend(self.tokenizer.encode(UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_      )      )

       if len(UpperCAmelCase_      ) > self.tokenizer.model_max_length:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    input_ids[-self.tokenizer.model_max_length :]
       return input_ids














# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		torch.FloatTensor
     UpperCamelCase_							:		Optional[torch.FloatTensor]			=	None





def   lowerCamelCase__			(     lowercase						,     lowercase=0.999						,     lowercase="cosine"						,     ):

  """simple docstring"""






  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase				):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowercase				):
      return math.exp(t * -12.0				)

  else:
    raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
  for i in range(lowercase				):
    SCREAMING_SNAKE_CASE       :      str							=    i / num_diffusion_timesteps
    SCREAMING_SNAKE_CASE       :      List[str]							=    (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase				) / alpha_bar_fn(lowercase				)						,     lowercase				)				)
  return torch.tensor(lowercase						,     dtype=torch.floataa				)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	1


     @register_to_config
     def __init__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	int = 1000					, UpperCAmelCase_       :	float = 0.0_001					, UpperCAmelCase_       :	float = 0.02					, UpperCAmelCase_       :	str = "linear"					, UpperCAmelCase_       :	Optional[Union[np.ndarray, List[float]]] = None					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	int = 0					, UpperCAmelCase_       :	str = "epsilon"					, UpperCAmelCase_       :	float = 1.0					, **UpperCAmelCase_       :	Dict					, ):
       if kwargs.get("set_alpha_to_one"					, UpperCAmelCase_      ) is not None:
         SCREAMING_SNAKE_CASE       :      int							=    (
             "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
         )
         deprecate("set_alpha_to_one"					, "1.0.0"					, UpperCAmelCase_					, standard_warn=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      int							=    kwargs["set_alpha_to_one"]
       if trained_betas is not None:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.tensor(UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "linear":
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.linspace(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
             torch.linspace(beta_start**0.5					, beta_end**0.5					, UpperCAmelCase_					, dtype=torch.floataa      ) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         SCREAMING_SNAKE_CASE       :      int							=    betas_for_alpha_bar(UpperCAmelCase_      )
       else:
         raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1.0 - self.betas
       SCREAMING_SNAKE_CASE       :      Any							=    torch.cumprod(self.alphas					, dim=0      )

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE       :      List[str]							=    1.0

       # setable values
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    None
       SCREAMING_SNAKE_CASE       :      List[str]							=    torch.from_numpy(np.arange(0					, UpperCAmelCase_      ).copy().astype(np.intaa      )      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	Optional[int] = None      ):
       return sample


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Union[str, torch.device] = None      ):

       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
             f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
             f''' maximal {self.config.num_train_timesteps} timesteps.'''      )

       SCREAMING_SNAKE_CASE       :      Dict							=    num_inference_steps
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       SCREAMING_SNAKE_CASE       :      str							=    (np.arange(0					, UpperCAmelCase_      ) * step_ratio).round().copy().astype(np.intaa      )
       SCREAMING_SNAKE_CASE       :      str							=    torch.from_numpy(UpperCAmelCase_      ).to(UpperCAmelCase_      )
       self.timesteps += self.config.steps_offset


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	torch.FloatTensor					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	bool = True					, ):
       # 1. get previous step value (=t+1)
       SCREAMING_SNAKE_CASE       :      Tuple							=    timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.alphas_cumprod[timestep]
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       SCREAMING_SNAKE_CASE       :      Tuple							=    1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
         SCREAMING_SNAKE_CASE       :      Any							=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model_output
       elif self.config.prediction_type == "sample":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model_output
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         SCREAMING_SNAKE_CASE       :      Tuple							=    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
             " `v_prediction`"      )

       # 4. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
         SCREAMING_SNAKE_CASE       :      List[str]							=    pred_original_sample.clamp(
             -self.config.clip_sample_range					, self.config.clip_sample_range      )

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      Dict							=    (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       SCREAMING_SNAKE_CASE       :      str							=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
         return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_					, pred_original_sample=UpperCAmelCase_      )






     def __len__(				self       :	Dict      ):
       return self.config.num_train_timesteps






