



"""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


__snake_case    :		List[str]  =logging.get_logger(__name__)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase=None		,__lowerCamelCase=None			)					->      Tuple:

						"""simple docstring"""






						if not conversation_id:
										lowerCAmelCase__					:    Optional[Any]												=					uuid.uuida()
						if past_user_inputs is None:
										lowerCAmelCase__					:    Dict												=					[]
						if generated_responses is None:
										lowerCAmelCase__					:    int												=					[]

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



		def __eq__(self		,__lowerCamelCase			)					->      Optional[int]:

						"""simple docstring"""






						if not isinstance(__lowerCamelCase		,__lowerCamelCase			):
										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		,__lowerCamelCase		,__lowerCamelCase = False			)					->      List[str]:

						"""simple docstring"""






						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__					:    Dict												=					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__					:    Union[str, Any]												=					text



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						if self.new_user_input:
										self.past_user_inputs.append(self.new_user_input			)
						lowerCAmelCase__					:    List[str]												=					None



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						self.generated_responses.append(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						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]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					f"""Conversation id: {self.uuid} \n"""
						for is_user, text in self.iter_texts():
										lowerCAmelCase__					:    Tuple												=					'user' if is_user else 'bot'
										output += f"""{name} >> {text} \n"""
						return output



@add_end_docstrings(
    SCREAMING_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 	lowerCamelCase__      (							SCREAMING_SNAKE_CASE__):




		'''simple docstring'''



		def __init__(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						super().__init__(*__lowerCamelCase		,**__lowerCamelCase			)
						if self.tokenizer.pad_token_id is None:
										lowerCAmelCase__					:    Dict												=					self.tokenizer.eos_token



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					{}
						lowerCAmelCase__					:    Union[str, Any]												=					{}
						lowerCAmelCase__					:    Dict												=					{}

						if min_length_for_response is not None:
										lowerCAmelCase__					:    int												=					min_length_for_response
						if minimum_tokens is not None:
										lowerCAmelCase__					:    List[str]												=					minimum_tokens

						if "max_length" in generate_kwargs:
										lowerCAmelCase__					:    Optional[int]												=					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__					:    int												=					clean_up_tokenization_spaces

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



		def __call__(self		,__lowerCamelCase		,__lowerCamelCase=0		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					super().__call__(__lowerCamelCase		,num_workers=__lowerCamelCase		,**__lowerCamelCase			)
						if isinstance(__lowerCamelCase		,__lowerCamelCase			) and len(__lowerCamelCase			) == 1:
										return outputs[0]
						return outputs



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=32			)					->      Dict[str, Any]:

						"""simple docstring"""






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

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=10		,**__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=True			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					model_outputs['output_ids']
						lowerCAmelCase__					:    Optional[Any]												=					self.tokenizer.decode(
						    output_ids[0]		,skip_special_tokens=__lowerCamelCase		,clean_up_tokenization_spaces=__lowerCamelCase		,)
						lowerCAmelCase__					:    Union[str, Any]												=					model_outputs['conversation']
						conversation.mark_processed()
						conversation.append_response(__lowerCamelCase			)
						return conversation



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.tokenizer.eos_token_id
						lowerCAmelCase__					:    str												=					[]
						for is_user, text in conversation.iter_texts():
										if eos_token_id is not None:
														input_ids.extend(self.tokenizer.encode(__lowerCamelCase		,add_special_tokens=__lowerCamelCase			) + [eos_token_id]			)
										else:
														input_ids.extend(self.tokenizer.encode(__lowerCamelCase		,add_special_tokens=__lowerCamelCase			)			)

						if len(__lowerCamelCase			) > self.tokenizer.model_max_length:
										lowerCAmelCase__					:    List[str]												=					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


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

							__snake_case    :		str  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

