



"""simple docstring"""
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


_lowerCAmelCase    :Union[str, Any]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A = None       ,	A = None       ,	A=None       ,	A=None		)							->					Any:
         if not conversation_id:
             _UpperCAmelCase							:  Dict											=    uuid.uuida()
         if past_user_inputs is None:
             _UpperCAmelCase							:  Any											=    []
         if generated_responses is None:
             _UpperCAmelCase							:  List[str]											=    []

         _UpperCAmelCase							:  uuid.UUID											=    conversation_id
         _UpperCAmelCase							:  List[str]											=    past_user_inputs
         _UpperCAmelCase							:  List[str]											=    generated_responses
         _UpperCAmelCase							:  Optional[str]											=    text





     def __eq__(					self       ,	A		)							->					List[Any]:
         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  __lowerCAmelCase	(					self       ,	A       ,	A = False		)							->					List[Any]:
         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}".'		)
                 _UpperCAmelCase							:  int											=    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:
             _UpperCAmelCase							:  Optional[int]											=    text





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         if self.new_user_input:
             self.past_user_inputs.append(self.new_user_input		)
         _UpperCAmelCase							:  List[Any]											=    None





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[Any]:
         self.generated_responses.append(A		)





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



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





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					str:
         super().__init__(*A       ,	**A		)
         if self.tokenizer.pad_token_id is None:
             _UpperCAmelCase							:  Dict											=    self.tokenizer.eos_token





     def  __lowerCAmelCase	(					self       ,	A=None       ,	A=None       ,	A=None       ,	**A		)							->					Tuple:
         _UpperCAmelCase							:  List[str]											=    {}
         _UpperCAmelCase							:  Union[str, Any]											=    {}
         _UpperCAmelCase							:  Dict											=    {}

         if min_length_for_response is not None:
             _UpperCAmelCase							:  Tuple											=    min_length_for_response
         if minimum_tokens is not None:
             _UpperCAmelCase							:  int											=    minimum_tokens

         if "max_length" in generate_kwargs:
             _UpperCAmelCase							:  Dict											=    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:
             _UpperCAmelCase							:  Optional[int]											=    clean_up_tokenization_spaces

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





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





     def  __lowerCAmelCase	(					self       ,	A       ,	A=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'''		):
             _UpperCAmelCase							:  int											=    self.tokenizer._build_conversation_input_ids(A		)
         else:
             # If the tokenizer cannot handle conversations, we default to only the old version
             _UpperCAmelCase							:  Any											=    self._legacy_parse_and_tokenize(A		)

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A=1_0       ,	**A		)							->					int:
         _UpperCAmelCase							:  Optional[Any]											=    generate_kwargs.get('''max_length'''       ,	self.model.config.max_length		)

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





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





     def  __lowerCAmelCase	(					self       ,	A		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    self.tokenizer.eos_token_id
         _UpperCAmelCase							:  Tuple											=    []
         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:
             _UpperCAmelCase							:  str											=    input_ids[-self.tokenizer.model_max_length :]
         return input_ids







"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


