




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  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     PreTrainedTokenizer      ,      __UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     Optional[int] = None      ,      ) ->			Any:
      SCREAMING_SNAKE_CASE_     						=      {}

      if train_file is not None:
            SCREAMING_SNAKE_CASE_     						=      [train_file]
      if eval_file is not None:
            SCREAMING_SNAKE_CASE_     						=      [eval_file]
      if test_file is not None:
            SCREAMING_SNAKE_CASE_     						=      [test_file]

      SCREAMING_SNAKE_CASE_     						=      datasets.load_dataset('csv'      ,      data_files=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      list(ds[list(files.keys()							)[0]].features.keys()							)
      SCREAMING_SNAKE_CASE_     						=      features_name.pop(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      list(set(ds[list(files.keys()							)[0]][label_name]							)							)
      SCREAMING_SNAKE_CASE_     						=      {label: i for i, label in enumerate(__UpperCAmelCase							)}
      SCREAMING_SNAKE_CASE_     						=      tokenizer.model_input_names
      SCREAMING_SNAKE_CASE_     						=      {}

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

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

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

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

      SCREAMING_SNAKE_CASE_     						=      (
          tf.data.Dataset.from_generator(
              __UpperCAmelCase      ,      ({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:
            SCREAMING_SNAKE_CASE_     						=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

      SCREAMING_SNAKE_CASE_     						=      (
          tf.data.Dataset.from_generator(
              __UpperCAmelCase      ,      ({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:
            SCREAMING_SNAKE_CASE_     						=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

      SCREAMING_SNAKE_CASE_     						=      (
          tf.data.Dataset.from_generator(
              __UpperCAmelCase      ,      ({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:
            SCREAMING_SNAKE_CASE_     						=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

      return train_ds, val_ds, test_ds, labelaid


lowerCamelCase__			:       Tuple         =							logging.getLogger(__name__)





@dataclass
class 	lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       =   field(metadata={"help": "Which column contains the label"} )
 lowercase_       =   field(default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "The path of the training file"} )
 lowercase_       =   field(default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "The path of the development file"} )
 lowercase_       =   field(default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "The path of the test file"} )
 lowercase_       =   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."
         )
     }	,       )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Overwrite the cached training and evaluation sets"} )





@dataclass
class 	lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       =   field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
 lowercase_       =   field(default=_SCREAMING_SNAKE_CASE	,       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.
 lowercase_       =   field(
     default=_SCREAMING_SNAKE_CASE	,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,       )


def  UpperCAmelCase_							(				) ->			Tuple:
      # 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.
      SCREAMING_SNAKE_CASE_     						=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      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.

      SCREAMING_SNAKE_CASE_     						=      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      ,      )

      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      get_tfds(
          train_file=data_args.train_file      ,      eval_file=data_args.dev_file      ,      test_file=data_args.test_file      ,      tokenizer=__UpperCAmelCase      ,      label_column_id=data_args.label_column_id      ,      max_seq_length=data_args.max_seq_length      ,      )

      SCREAMING_SNAKE_CASE_     						=      AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,      num_labels=len(__UpperCAmelCase							)      ,      labelaid=__UpperCAmelCase      ,      idalabel={id: label for label, id in labelaid.items()}      ,      finetuning_task='text-classification'      ,      cache_dir=model_args.cache_dir      ,      )

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

      def compute_metrics(__UpperCAmelCase						:     EvalPrediction							) -> Dict:
            SCREAMING_SNAKE_CASE_     						=      np.argmax(p.predictions      ,      axis=1							)

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

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_     						=      TFTrainer(
          model=__UpperCAmelCase      ,      args=__UpperCAmelCase      ,      train_dataset=__UpperCAmelCase      ,      eval_dataset=__UpperCAmelCase      ,      compute_metrics=__UpperCAmelCase      ,      )

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

      # Evaluation
      SCREAMING_SNAKE_CASE_     						=      {}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'							)
            SCREAMING_SNAKE_CASE_     						=      trainer.evaluate()
            SCREAMING_SNAKE_CASE_     						=      os.path.join(training_args.output_dir      ,      'eval_results.txt'							)

            with open(__UpperCAmelCase      ,      '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(__UpperCAmelCase							)

      return results


if __name__ == "__main__":
   main()





import cva
import numpy as np





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		int						):

       if k in (0.04, 0.06):
             SCREAMING_SNAKE_CASE_     						=      k
             SCREAMING_SNAKE_CASE_     						=      window_size
       else:
             raise ValueError('invalid k value'						)



 def __str__(							self  :		Tuple						):
       return str(self.k						)




 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      cva.imread(_lowerCAmelCase    ,    0						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      img.shape
       SCREAMING_SNAKE_CASE_     						=      []
       SCREAMING_SNAKE_CASE_     						=      img.copy()
       SCREAMING_SNAKE_CASE_     						=      cva.cvtColor(_lowerCAmelCase    ,    cva.COLOR_GRAY2RGB						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      np.gradient(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      dx**2
       SCREAMING_SNAKE_CASE_     						=      dy**2
       SCREAMING_SNAKE_CASE_     						=      dx * dy
       SCREAMING_SNAKE_CASE_     						=      0.04
       SCREAMING_SNAKE_CASE_     						=      self.window_size // 2
       for y in range(_lowerCAmelCase    ,    h - offset						):
             for x in range(_lowerCAmelCase    ,    w - offset						):
                   SCREAMING_SNAKE_CASE_     						=      ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   SCREAMING_SNAKE_CASE_     						=      (wxx * wyy) - (wxy**2)
                   SCREAMING_SNAKE_CASE_     						=      wxx + wyy
                   SCREAMING_SNAKE_CASE_     						=      det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)    ,    0						)
                         color_img.itemset((y, x, 1)    ,    0						)
                         color_img.itemset((y, x, 2)    ,    255						)
       return color_img, corner_list


if __name__ == "__main__":
   lowerCamelCase__			:       Optional[int]         =							HarrisCorner(0.04, 3)
   lowerCamelCase__    ,							lowerCamelCase__			:       str         =							edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)