'''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 						A   :





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

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

								__UpperCAmelCase							=			conversation_id
								__UpperCAmelCase							=			past_user_inputs
								__UpperCAmelCase							=			generated_responses
								__UpperCAmelCase							=			text





							def __eq__(      self				:							str     ,		__a				:							Tuple	)							->	List[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       snake_case__						(      self				:							Tuple     ,		__a				:							str     ,		__a				:							bool = False	)							->	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}\"."""	)
										__UpperCAmelCase							=			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							=			text





							def       snake_case__						(      self				:							int	)							->	Optional[Any]:

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





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							str	)							->	Tuple:
								self.generated_responses.append(__a	)





							def       snake_case__						(      self				:							int	)							->	Dict:

								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	)							->	List[Any]:
								__UpperCAmelCase							=			f"""Conversation id: {self.uuid} \n"""
								for is_user, text in self.iter_texts():
									__UpperCAmelCase							=			'''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 						A   (   _snake_case   ):





							def __init__(      self				:							str     ,		*__a				:							Tuple     ,		**__a				:							str	)							->	Any:
								super().__init__(*__a     ,		**__a	)
								if self.tokenizer.pad_token_id is None:
									__UpperCAmelCase							=			self.tokenizer.eos_token





							def       snake_case__						(      self				:							List[str]     ,		__a				:							Union[str, Any]=None     ,		__a				:							Tuple=None     ,		__a				:							Union[str, Any]=None     ,		**__a				:							Optional[Any]	)							->	Optional[int]:
								__UpperCAmelCase							=			{}
								__UpperCAmelCase							=			{}
								__UpperCAmelCase							=			{}

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

								if "max_length" in generate_kwargs:
									__UpperCAmelCase							=			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							=			clean_up_tokenization_spaces

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





							def __call__(      self				:							List[Any]     ,		__a				:							Union[Conversation, List[Conversation]]     ,		__a				:							Tuple=0     ,		**__a				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			super().__call__(__a     ,		num_workers=__a     ,		**__a	)
								if isinstance(__a     ,		__a	) and len(__a	) == 1:
									return outputs[0]
								return outputs





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							Conversation     ,		__a				:							Optional[int]=3_2	)							->	Tuple:

								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							=			self.tokenizer._build_conversation_input_ids(__a	)
								else:
									# If the tokenizer cannot handle conversations, we default to only the old version
									__UpperCAmelCase							=			self._legacy_parse_and_tokenize(__a	)

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





							def       snake_case__						(      self				:							int     ,		__a				:							List[str]     ,		__a				:							Tuple=1_0     ,		**__a				:							List[str]	)							->	Optional[Any]:
								__UpperCAmelCase							=			generate_kwargs.get('''max_length'''     ,		self.model.config.max_length	)

								__UpperCAmelCase							=			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							=			max_length - minimum_tokens
									__UpperCAmelCase							=			model_inputs['''input_ids'''][:, -trim:]
									if "attention_mask" in model_inputs:
										__UpperCAmelCase							=			model_inputs['''attention_mask'''][:, -trim:]
								__UpperCAmelCase							=			model_inputs.pop('''conversation'''	)
								__UpperCAmelCase							=			max_length
								__UpperCAmelCase							=			self.model.generate(**__a     ,		**__a	)
								if self.model.config.is_encoder_decoder:
									__UpperCAmelCase							=			1
								else:
									__UpperCAmelCase							=			n
								return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							str     ,		__a				:							Union[str, Any]=True	)							->	Union[str, Any]:
								__UpperCAmelCase							=			model_outputs['''output_ids''']
								__UpperCAmelCase							=			self.tokenizer.decode(
								    output_ids[0]     ,		skip_special_tokens=__a     ,		clean_up_tokenization_spaces=__a     ,		)
								__UpperCAmelCase							=			model_outputs['''conversation''']
								conversation.mark_processed()
								conversation.append_response(__a	)
								return conversation






							def       snake_case__						(      self				:							Dict     ,		__a				:							Conversation	)							->	Tuple:
								__UpperCAmelCase							=			self.tokenizer.eos_token_id
								__UpperCAmelCase							=			[]
								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							=			input_ids[-self.tokenizer.model_max_length :]
								return input_ids

'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)
