






"""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


A__       :				Dict 					=					logging.get_logger(__name__)







class    lowercase__			:


			def __init__(      self     :  Tuple   ,       snake_case__     :  str = None   ,       snake_case__     :  uuid.UUID = None   ,       snake_case__     :  Optional[Any]=None   ,       snake_case__     :  List[Any]=None					):






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

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


			def __eq__(      self     :  str   ,       snake_case__     :  Union[str, Any]					):






										if not isinstance(snake_case__   ,       snake_case__					):
																	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 	UpperCAmelCase__						(      self     :  Union[str, Any]   ,       snake_case__     :  str   ,       snake_case__     :  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}\"."""					)
																								lowerCamelCase_       :							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:
																	lowerCamelCase_       :							Any									=text


			def 	UpperCAmelCase__						(      self     :  int					):






										if self.new_user_input:
																	self.past_user_inputs.append(self.new_user_input					)
										lowerCamelCase_       :							Tuple									=None


			def 	UpperCAmelCase__						(      self     :  Tuple   ,       snake_case__     :  str					):
										self.generated_responses.append(snake_case__					)


			def 	UpperCAmelCase__						(      self     :  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     :  List[Any]					):
										lowerCamelCase_       :							List[str]									=F"""Conversation id: {self.uuid} \n"""
										for is_user, text in self.iter_texts():
																	lowerCamelCase_       :							List[str]									="""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    lowercase__			(							snake_case__   ):


			def __init__(      self     :  Optional[int]   ,       *snake_case__     :  Tuple   ,       **snake_case__     :  Dict					):
										super().__init__(*snake_case__   ,       **snake_case__					)
										if self.tokenizer.pad_token_id is None:
																	lowerCamelCase_       :							Tuple									=self.tokenizer.eos_token


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]   ,       snake_case__     :  int=None   ,       snake_case__     :  Optional[int]=None   ,       snake_case__     :  List[Any]=None   ,       **snake_case__     :  List[Any]					):
										lowerCamelCase_       :							List[Any]									={}
										lowerCamelCase_       :							str									={}
										lowerCamelCase_       :							Union[str, Any]									={}

										if min_length_for_response is not None:
																	lowerCamelCase_       :							Any									=min_length_for_response
										if minimum_tokens is not None:
																	lowerCamelCase_       :							Dict									=minimum_tokens

										if "max_length" in generate_kwargs:
																	lowerCamelCase_       :							Any									=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_       :							Any									=clean_up_tokenization_spaces

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


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


			def 	UpperCAmelCase__						(      self     :  Optional[Any]   ,       snake_case__     :  Conversation   ,       snake_case__     :  List[Any]=32					):






										if not isinstance(snake_case__   ,       snake_case__					):
																	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_       :							Any									=self.tokenizer._build_conversation_input_ids(snake_case__					)
										else:
																	# If the tokenizer cannot handle conversations, we default to only the old version
																	lowerCamelCase_       :							str									=self._legacy_parse_and_tokenize(snake_case__					)

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


			def 	UpperCAmelCase__						(      self     :  int   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Optional[int]=10   ,       **snake_case__     :  Dict					):
										lowerCamelCase_       :							List[Any]									=generate_kwargs.get("max_length"   ,       self.model.config.max_length					)

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


			def 	UpperCAmelCase__						(      self     :  int   ,       snake_case__     :  Tuple   ,       snake_case__     :  List[str]=True					):
										lowerCamelCase_       :							Tuple									=model_outputs["""output_ids"""]
										lowerCamelCase_       :							Optional[int]									=self.tokenizer.decode(
										    output_ids[0]   ,       skip_special_tokens=snake_case__   ,       clean_up_tokenization_spaces=snake_case__   ,       )
										lowerCamelCase_       :							Dict									=model_outputs["""conversation"""]
										conversation.mark_processed()
										conversation.append_response(snake_case__					)
										return conversation


			def 	UpperCAmelCase__						(      self     :  Optional[Any]   ,       snake_case__     :  Conversation					):
										lowerCamelCase_       :							Optional[Any]									=self.tokenizer.eos_token_id
										lowerCamelCase_       :							Union[str, Any]									=[]
										for is_user, text in conversation.iter_texts():
																	if eos_token_id is not None:
																								input_ids.extend(self.tokenizer.encode(snake_case__   ,       add_special_tokens=snake_case__					) + [eos_token_id]					)
																	else:
																								input_ids.extend(self.tokenizer.encode(snake_case__   ,       add_special_tokens=snake_case__					)					)

										if len(snake_case__					) > self.tokenizer.model_max_length:
																	lowerCamelCase_       :							Union[str, Any]									=input_ids[-self.tokenizer.model_max_length :]
										return input_ids







"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
