


'''simple docstring'''







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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      str  ,lowerCamelCase					:      str  ,lowerCamelCase					:      PreTrainedTokenizer  ,lowerCamelCase					:      int  ,lowerCamelCase					:      Optional[int] = None  ,):
       _A					:     Tuple     = {}

       if train_file is not None:
              _A					:     Optional[int]     = [train_file]
       if eval_file is not None:
              _A					:     Optional[int]     = [eval_file]
       if test_file is not None:
              _A					:     Dict     = [test_file]

       _A					:     List[str]     = datasets.load_dataset('csv'  ,data_files=lowerCamelCase       )
       _A					:     Union[str, Any]     = list(ds[list(files.keys()       )[0]].features.keys()       )
       _A					:     Any     = features_name.pop(lowerCamelCase       )
       _A					:     Optional[int]     = list(set(ds[list(files.keys()       )[0]][label_name]       )       )
       _A					:     Tuple     = {label: i for i, label in enumerate(lowerCamelCase       )}
       _A					:     str     = tokenizer.model_input_names
       _A					:     Tuple     = {}

       if len(lowerCamelCase       ) == 1:
              for k in files.keys():
                     _A					:     Optional[Any]     = ds[k].map(
                         lambda lowerCamelCase       : tokenizer.batch_encode_plus(
                             example[features_name[0]]  ,truncation=lowerCamelCase  ,max_length=lowerCamelCase  ,padding='max_length'       )  ,batched=lowerCamelCase  ,)
       elif len(lowerCamelCase       ) == 2:
              for k in files.keys():
                     _A					:     Optional[int]     = ds[k].map(
                         lambda lowerCamelCase       : tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])  ,truncation=lowerCamelCase  ,max_length=lowerCamelCase  ,padding='max_length'  ,)  ,batched=lowerCamelCase  ,)

       def gen_train():
              for ex in transformed_ds[datasets.Split.TRAIN]:
                     _A					:     List[str]     = {k: v for k, v in ex.items() if k in input_names}
                     _A					:     Tuple     = labelaid[ex[label_name]]
                     yield (d, label)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     _A					:     Any     = {k: v for k, v in ex.items() if k in input_names}
                     _A					:     Union[str, Any]     = labelaid[ex[label_name]]
                     yield (d, label)

       def gen_test():
              for ex in transformed_ds[datasets.Split.TEST]:
                     _A					:     Optional[Any]     = {k: v for k, v in ex.items() if k in input_names}
                     _A					:     Any     = labelaid[ex[label_name]]
                     yield (d, label)

       _A					:     Optional[Any]     = (
           tf.data.Dataset.from_generator(
               lowerCamelCase  ,({k: tf.intaa for k in input_names}, tf.intaa)  ,({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,)
           if datasets.Split.TRAIN in transformed_ds
           else None
       )

       if train_ds is not None:
              _A					:     Optional[Any]     = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

       _A					:     Union[str, Any]     = (
           tf.data.Dataset.from_generator(
               lowerCamelCase  ,({k: tf.intaa for k in input_names}, tf.intaa)  ,({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,)
           if datasets.Split.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              _A					:     Optional[int]     = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

       _A					:     Dict     = (
           tf.data.Dataset.from_generator(
               lowerCamelCase  ,({k: tf.intaa for k in input_names}, tf.intaa)  ,({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,)
           if datasets.Split.TEST in transformed_ds
           else None
       )

       if test_ds is not None:
              _A					:     int     = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

       return train_ds, val_ds, test_ds, labelaid


A				:					str    						=			logging.getLogger(__name__)


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(metadata={"help": "Which column contains the label"}	)
    a													=   field(default=a_    , metadata={"help": "The path of the training file"}	)
    a													=   field(default=a_    , metadata={"help": "The path of the development file"}	)
    a													=   field(default=a_    , metadata={"help": "The path of the test file"}	)
    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=a_    , metadata={"help": "Overwrite the cached training and evaluation sets"}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
    a													=   field(
        default=a_    , metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
    a													=   field(
        default=a_    , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
    a													=   field(default=a_    , metadata={"help": "Set this flag to use fast tokenization."}	)
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    a													=   field(
        default=a_    , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    , )






def 				lowerCAmelCase__	(    ):
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       _A					:     List[Any]     = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
       _A  ,			_A  ,			_A					:     Optional[int]     = parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir       )
           and os.listdir(training_args.output_dir       )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
                  ' --overwrite_output_dir to overcome.'       )

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'  ,datefmt='%m/%d/%Y %H:%M:%S'  ,level=logging.INFO  ,)
       logger.info(
           F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, '
           F'16-bits training: {training_args.fpaa}'       )
       logger.info(F'Training/evaluation parameters {training_args}'       )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       _A					:     Optional[int]     = AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,cache_dir=model_args.cache_dir  ,)

       _A  ,			_A  ,			_A  ,			_A					:     Dict     = get_tfds(
           train_file=data_args.train_file  ,eval_file=data_args.dev_file  ,test_file=data_args.test_file  ,tokenizer=lowerCamelCase  ,label_column_id=data_args.label_column_id  ,max_seq_length=data_args.max_seq_length  ,)

       _A					:     List[str]     = AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,num_labels=len(lowerCamelCase       )  ,labelaid=lowerCamelCase  ,idalabel={id: label for label, id in labelaid.items()}  ,finetuning_task='text-classification'  ,cache_dir=model_args.cache_dir  ,)

       with training_args.strategy.scope():
              _A					:     Any     = TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path  ,from_pt=bool('.bin' in model_args.model_name_or_path       )  ,config=lowerCamelCase  ,cache_dir=model_args.cache_dir  ,)

       def compute_metrics(lowerCamelCase					:      EvalPrediction       ) -> Dict:
              _A					:     List[str]     = np.argmax(p.predictions  ,axis=1       )

              return {"acc": (preds == p.label_ids).mean()}

       # Initialize our Trainer
       _A					:     str     = TFTrainer(
           model=lowerCamelCase  ,args=lowerCamelCase  ,train_dataset=lowerCamelCase  ,eval_dataset=lowerCamelCase  ,compute_metrics=lowerCamelCase  ,)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir       )

       # Evaluation
       _A					:     List[str]     = {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'       )
              _A					:     Optional[Any]     = trainer.evaluate()
              _A					:     List[str]     = os.path.join(training_args.output_dir  ,'eval_results.txt'       )

              with open(lowerCamelCase  ,'w'       ) as writer:
                     logger.info('***** Eval results *****'       )

                     for key, value in result.items():
                            logger.info(F'  {key} = {value}'       )
                            writer.write(F'{key} = {value}\n'       )

                     results.update(lowerCamelCase       )

       return results


if __name__ == "__main__":
   main()










'''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


A				:					Any    						=			logging.get_logger(__name__)


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str = None				,							SCREAMING_SNAKE_CASE      :							uuid.UUID = None				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None):
           if not conversation_id:
                  _A					:     str     = uuid.uuida()
           if past_user_inputs is None:
                  _A					:     List[Any]     = []
           if generated_responses is None:
                  _A					:     Union[str, Any]     = []

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


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[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        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							bool = 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}".')
                         _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        A   (  self      :							Optional[int]):
           if self.new_user_input:
                  self.past_user_inputs.append(self.new_user_input)
           _A					:     Optional[Any]     = None


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str):
           self.generated_responses.append(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           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      :							Dict):
           _A					:     Any     = 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(
    a_    , 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			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							str):
           super().__init__(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if self.tokenizer.pad_token_id is None:
                  _A					:     Any     = self.tokenizer.eos_token


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							**SCREAMING_SNAKE_CASE      :							str):
           _A					:     str     = {}
           _A					:     Union[str, Any]     = {}
           _A					:     List[str]     = {}

           if min_length_for_response is not None:
                  _A					:     Optional[Any]     = min_length_for_response
           if minimum_tokens is not None:
                  _A					:     Tuple     = minimum_tokens

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

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


    def __call__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[Conversation, List[Conversation]]				,							SCREAMING_SNAKE_CASE      :							Any=0				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Any     = 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        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Conversation				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=32):
           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					:     Optional[Any]     = self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE)
           else:
                  # If the tokenizer cannot handle conversations, we default to only the old version
                  _A					:     Dict     = self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE)

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


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=10				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     str     = generate_kwargs.get('max_length'				,							self.model.config.max_length)

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


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							List[Any]=True):
           _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        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Conversation):
           _A					:     Optional[Any]     = self.tokenizer.eos_token_id
           _A					:     List[Any]     = []
           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					:     Dict     = input_ids[-self.tokenizer.model_max_length :]
           return input_ids






