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							:      Dict      							=logging.get_logger(__name__)



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str = None      ,	SCREAMING_SNAKE_CASE__    :					uuid.UUID = None      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=None      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=None     )    ->				Optional[int]:
         if not conversation_id:
                A						:     Any         =uuid.uuida()
         if past_user_inputs is None:
                A						:     List[Any]         =[]
         if generated_responses is None:
                A						:     int         =[]

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







  def __eq__(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				int:
         if not isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_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 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					bool = False     )    ->				Optional[int]:
         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}".'     )
                       A						:     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:
                A						:     Optional[Any]         =text







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         if self.new_user_input:
                self.past_user_inputs.append(self.new_user_input     )
         A						:     int         =None







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				Dict:
         self.generated_responses.append(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				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    :					Any     )    ->				List[str]:
         A						:     str         =f'Conversation id: {self.uuid} \n'
         for is_user, text in self.iter_texts():
                A						:     Optional[int]         ='user' if is_user else 'bot'
                output += f'{name} >> {text} \n'
         return output







@add_end_docstrings(
    lowerCAmelCase_   ,						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  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=None      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=None      ,	**SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				str:
         A						:     Optional[int]         ={}
         A						:     Any         ={}
         A						:     List[str]         ={}

         if min_length_for_response is not None:
                A						:     List[Any]         =min_length_for_response
         if minimum_tokens is not None:
                A						:     Dict         =minimum_tokens

         if "max_length" in generate_kwargs:
                A						:     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:
                A						:     List[Any]         =clean_up_tokenization_spaces

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







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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Conversation      ,	SCREAMING_SNAKE_CASE__    :					str=32     )    ->				Dict[str, Any]:
         if not isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_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'     ):
                A						:     int         =self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE__     )
         else:
                # If the tokenizer cannot handle conversations, we default to only the old version
                A						:     Union[str, Any]         =self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=10      ,	**SCREAMING_SNAKE_CASE__    :					int     )    ->				Optional[Any]:
         A						:     int         =generate_kwargs.get('max_length'      ,	self.model.config.max_length     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Any=True     )    ->				Tuple:
         A						:     Optional[Any]         =model_outputs['output_ids']
         A						:     Optional[int]         =self.tokenizer.decode(
             output_ids[0]      ,	skip_special_tokens=SCREAMING_SNAKE_CASE__      ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__      ,	)
         A						:     Any         =model_outputs['conversation']
         conversation.mark_processed()
         conversation.append_response(SCREAMING_SNAKE_CASE__     )
         return conversation







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Conversation     )    ->				Dict:
         A						:     Any         =self.tokenizer.eos_token_id
         A						:     Optional[int]         =[]
         for is_user, text in conversation.iter_texts():
                if eos_token_id is not None:
                       input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     ) + [eos_token_id]     )
                else:
                       input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__     )     )

         if len(SCREAMING_SNAKE_CASE__     ) > self.tokenizer.model_max_length:
                A						:     Tuple         =input_ids[-self.tokenizer.model_max_length :]
         return input_ids







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






