




import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


A      = logging.get_logger(__name__)


@dataclass
class     lowercase__  :
  A__=  field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()		)}		)
  A__=  field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}		)
  A__=  field(
      default=128					,							metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }					,							)
  A__=  field(
      default=UpperCAmelCase__					,							metadata={'help': 'Overwrite the cached training and evaluation sets'}		)






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.task_name.lower()




class     lowercase__  (					UpperCAmelCase__		):
  A__=  "train"
  A__=  "dev"
  A__=  "test"




class     lowercase__  (					UpperCAmelCase__		):
  A__=  42
  A__=  42
  A__=  42






  def __init__(					self      :					List[str]		,						_lowercase      :					GlueDataTrainingArguments		,						_lowercase      :					PreTrainedTokenizerBase		,						_lowercase      :					Optional[int] = None		,						_lowercase      :					Union[str, Split] = Split.train		,						_lowercase      :					Optional[str] = None		,						):




         """simple docstring"""







         warnings.warn(
             "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
             "library. You can have a look at this example script for pointers: "
             "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"		,						lowerCamelCase__		,						)
         UpperCAmelCase__										=     args
         UpperCAmelCase__										=     glue_processors[args.task_name]()
         UpperCAmelCase__										=     glue_output_modes[args.task_name]
         if isinstance(lowerCamelCase__		,						lowerCamelCase__ ):
                try:
                       UpperCAmelCase__										=     Split[mode]
                except KeyError:
                       raise KeyError("mode is not a valid split name" )
        # Load data features from cache or dataset file
         UpperCAmelCase__										=     os.path.join(
             cache_dir if cache_dir is not None else args.data_dir		,						F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""		,						)
         UpperCAmelCase__										=     self.processor.get_labels()
         if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
             "RobertaTokenizer",
             "RobertaTokenizerFast",
             "XLMRobertaTokenizer",
             "BartTokenizer",
             "BartTokenizerFast",
         ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                UpperCAmelCase__										=     label_list[2], label_list[1]
         UpperCAmelCase__										=     label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         UpperCAmelCase__										=     cached_features_file + ".lock"
         with FileLock(lowerCamelCase__ ):
                if os.path.exists(lowerCamelCase__ ) and not args.overwrite_cache:
                       UpperCAmelCase__										=     time.time()
                       UpperCAmelCase__										=     torch.load(lowerCamelCase__ )
                       logger.info(
                           F"""Loading features from cached file {cached_features_file} [took %.3f s]"""		,						time.time() - start )
                else:
                       logger.info(F"""Creating features from dataset file at {args.data_dir}""" )

                       if mode == Split.dev:
                              UpperCAmelCase__										=     self.processor.get_dev_examples(args.data_dir )
                       elif mode == Split.test:
                              UpperCAmelCase__										=     self.processor.get_test_examples(args.data_dir )
                       else:
                              UpperCAmelCase__										=     self.processor.get_train_examples(args.data_dir )
                       if limit_length is not None:
                              UpperCAmelCase__										=     examples[:limit_length]
                       UpperCAmelCase__										=     glue_convert_examples_to_features(
                           lowerCamelCase__		,						lowerCamelCase__		,						max_length=args.max_seq_length		,						label_list=lowerCamelCase__		,						output_mode=self.output_mode		,						)
                       UpperCAmelCase__										=     time.time()
                       torch.save(self.features		,						lowerCamelCase__ )
                       # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                       logger.info(
                           F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )






  def __len__(					self      :					Optional[Any] ):




         """simple docstring"""







         return len(self.features )






  def __getitem__(					self      :					List[str]		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         return self.features[i]






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         return self.label_list









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


A      = logging.get_logger(__name__)


class     lowercase__  :






  def __init__(					self      :					List[str]		,						_lowercase      :					str = None		,						_lowercase      :					uuid.UUID = None		,						_lowercase      :					Dict=None		,						_lowercase      :					Any=None ):




         """simple docstring"""







         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      :					Any		,						_lowercase      :					str ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                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 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					str		,						_lowercase      :					bool = False ):




         """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}\".""" )
                       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 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					str ):




         """simple docstring"""







         self.generated_responses.append(_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """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"""







         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(
    __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     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					str		,						*_lowercase      :					Union[str, Any]		,						**_lowercase      :					Any ):




         """simple docstring"""







         super().__init__(*_lowercase		,						**_lowercase )
         if self.tokenizer.pad_token_id is None:
                UpperCAmelCase__										=     self.tokenizer.eos_token






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					int=None		,						_lowercase      :					Any=None		,						_lowercase      :					List[str]=None		,						**_lowercase      :					Dict ):




         """simple docstring"""







         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(_lowercase )
         return preprocess_params, forward_params, postprocess_params






  def __call__(					self      :					Dict		,						_lowercase      :					Union[Conversation, List[Conversation]]		,						_lowercase      :					List[str]=0		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     super().__call__(_lowercase		,						num_workers=_lowercase		,						**_lowercase )
         if isinstance(_lowercase		,						_lowercase ) and len(_lowercase ) == 1:
                return outputs[0]
         return outputs






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Conversation		,						_lowercase      :					List[Any]=32 ):




         """simple docstring"""







         if not isinstance(_lowercase		,						_lowercase ):
                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(_lowercase )
         else:
                # If the tokenizer cannot handle conversations, we default to only the old version
                UpperCAmelCase__										=     self._legacy_parse_and_tokenize(_lowercase )

         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 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					List[Any]		,						_lowercase      :					List[Any]=10		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         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(**_lowercase		,						**_lowercase )
         if self.model.config.is_encoder_decoder:
                UpperCAmelCase__										=     1
         else:
                UpperCAmelCase__										=     n
         return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[Any]=True ):




         """simple docstring"""







         UpperCAmelCase__										=     model_outputs["output_ids"]
         UpperCAmelCase__										=     self.tokenizer.decode(
             output_ids[0]		,						skip_special_tokens=_lowercase		,						clean_up_tokenization_spaces=_lowercase		,						)
         UpperCAmelCase__										=     model_outputs["conversation"]
         conversation.mark_processed()
         conversation.append_response(_lowercase )
         return conversation






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Conversation ):




         """simple docstring"""







         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(_lowercase		,						add_special_tokens=_lowercase ) + [eos_token_id] )
                else:
                       input_ids.extend(self.tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase ) )

         if len(_lowercase ) > self.tokenizer.model_max_length:
                UpperCAmelCase__										=     input_ids[-self.tokenizer.model_max_length :]
         return input_ids





