





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


__lowercase       =  logging.get_logger(__name__)



class    _a     :






							"""simple docstring"""

							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:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					uuid.uuida()
													if past_user_inputs is None:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[]
													if generated_responses is None:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					[]

													SCREAMING_SNAKE_CASE__     :				int   =					conversation_id
													SCREAMING_SNAKE_CASE__     :				Dict   =					past_user_inputs
													SCREAMING_SNAKE_CASE__     :				str   =					generated_responses
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					text

							def __eq__(      self				:     str							,	a				:     Tuple    )	->Optional[int]:

													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      A_  (      self				:     Tuple							,	a				:     str							,	a				:     bool = False    )	->Dict:

													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[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:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					text

							def      A_  (      self				:     int    )	->str:

													if self.new_user_input:
																			self.past_user_inputs.append(self.new_user_input    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None

							def      A_  (      self				:     Optional[int]							,	a				:     str    )	->Optional[Any]:
													self.generated_responses.append(a    )

							def      A_  (      self				:     int    )	->List[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				:     Any    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					f"""Conversation id: {self.uuid} \n"""
													for is_user, text in self.iter_texts():
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"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							):






							"""simple docstring"""

							def __init__(      self				:     str							,	*a				:     Tuple							,	**a				:     str    )	->Union[str, Any]:
													super().__init__(*a							,	**a    )
													if self.tokenizer.pad_token_id is None:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.tokenizer.eos_token

							def      A_  (      self				:     List[str]							,	a				:     Union[str, Any]=None							,	a				:     Tuple=None							,	a				:     Union[str, Any]=None							,	**a				:     Optional[Any]    )	->str:
													SCREAMING_SNAKE_CASE__     :				Any   =					{}
													SCREAMING_SNAKE_CASE__     :				Dict   =					{}
													SCREAMING_SNAKE_CASE__     :				Dict   =					{}

													if min_length_for_response is not None:
																			SCREAMING_SNAKE_CASE__     :				int   =					min_length_for_response
													if minimum_tokens is not None:
																			SCREAMING_SNAKE_CASE__     :				Any   =					minimum_tokens

													if "max_length" in generate_kwargs:
																			SCREAMING_SNAKE_CASE__     :				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:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				int   =					super().__call__(a							,	num_workers=a							,	**a    )
													if isinstance(a							,	a    ) and len(a    ) == 1:
																			return outputs[0]
													return outputs

							def      A_  (      self				:     Union[str, Any]							,	a				:     Conversation							,	a				:     Optional[int]=32    )	->int:

													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"    ):
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.tokenizer._build_conversation_input_ids(a    )
													else:
																			# If the tokenizer cannot handle conversations, we default to only the old version
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self._legacy_parse_and_tokenize(a    )

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

							def      A_  (      self				:     int							,	a				:     List[str]							,	a				:     Tuple=10							,	**a				:     List[str]    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					generate_kwargs.get("max_length"							,	self.model.config.max_length    )

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

							def      A_  (      self				:     Optional[Any]							,	a				:     str							,	a				:     Union[str, Any]=True    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model_outputs["output_ids"]
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.tokenizer.decode(
													    output_ids[0]							,	skip_special_tokens=a							,	clean_up_tokenization_spaces=a							,	)
													SCREAMING_SNAKE_CASE__     :				int   =					model_outputs["conversation"]
													conversation.mark_processed()
													conversation.append_response(a    )
													return conversation






							def      A_  (      self				:     Dict							,	a				:     Conversation    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.tokenizer.eos_token_id
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[]
													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:
																			SCREAMING_SNAKE_CASE__     :				Any   =					input_ids[-self.tokenizer.model_max_length :]
													return input_ids






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase       :str       =  {
    "configuration_upernet": ["UperNetConfig"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :Union[str, Any]       =  [
				    "UperNetForSemanticSegmentation",
				    "UperNetPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_upernet import UperNetConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
				import sys

				__lowercase       :str       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)