





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


UpperCAmelCase__						:			Union[str, Any]									=logging.get_logger(__name__)

class       __A     :


				def __init__(		self   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_=None   , UpperCAmelCase_=None							):
								if not conversation_id:
												lowerCamelCase	 =uuid.uuida()
								if past_user_inputs is None:
												lowerCamelCase	 =[]
								if generated_responses is None:
												lowerCamelCase	 =[]

								lowerCamelCase	 =conversation_id
								lowerCamelCase	 =past_user_inputs
								lowerCamelCase	 =generated_responses
								lowerCamelCase	 =text


				def __eq__(		self   , UpperCAmelCase_							):
								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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = 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	 =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	 =text


				def _snake_case       (		self							):
								if self.new_user_input:
												self.past_user_inputs.append(self.new_user_input							)
								lowerCamelCase	 =None


				def _snake_case       (		self   , UpperCAmelCase_							):
								self.generated_responses.append(UpperCAmelCase_							)


				def _snake_case       (		self							):
								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							):
								lowerCamelCase	 =f"""Conversation id: {self.uuid} \n"""
								for is_user, text in self.iter_texts():
												lowerCamelCase	 ="""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       __A     (						a    ):


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


				def _snake_case       (		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase	 ={}
								lowerCamelCase	 ={}
								lowerCamelCase	 ={}

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

								if "max_length" in generate_kwargs:
												lowerCamelCase	 =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	 =clean_up_tokenization_spaces

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


				def __call__(		self   , UpperCAmelCase_   , UpperCAmelCase_=0   , **UpperCAmelCase_							):
								lowerCamelCase	 =super().__call__(UpperCAmelCase_   , num_workers=UpperCAmelCase_   , **UpperCAmelCase_							)
								if isinstance(UpperCAmelCase_   , UpperCAmelCase_							) and len(UpperCAmelCase_							) == 1:
												return outputs[0]
								return outputs


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=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"""							):
												lowerCamelCase	 =self.tokenizer._build_conversation_input_ids(UpperCAmelCase_							)
								else:
												# If the tokenizer cannot handle conversations, we default to only the old version
												lowerCamelCase	 =self._legacy_parse_and_tokenize(UpperCAmelCase_							)

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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=10   , **UpperCAmelCase_							):
								lowerCamelCase	 =generate_kwargs.get("""max_length"""   , self.model.config.max_length							)

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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=True							):
								lowerCamelCase	 =model_outputs["""output_ids"""]
								lowerCamelCase	 =self.tokenizer.decode(
								    output_ids[0]   , skip_special_tokens=UpperCAmelCase_   , clean_up_tokenization_spaces=UpperCAmelCase_   , )
								lowerCamelCase	 =model_outputs["""conversation"""]
								conversation.mark_processed()
								conversation.append_response(UpperCAmelCase_							)
								return conversation


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =self.tokenizer.eos_token_id
								lowerCamelCase	 =[]
								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:
												lowerCamelCase	 =input_ids[-self.tokenizer.model_max_length :]
								return input_ids












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__						:			List[Any]									={'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=['''YolosFeatureExtractor''']
							UpperCAmelCase__						:			List[Any]									=['''YolosImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Optional[Any]									=[
							    '''YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''YolosForObjectDetection''',
							    '''YolosModel''',
							    '''YolosPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[Any]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





