


import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

a					  =     logging.getLogger(__name__)







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   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.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}   )
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'Whether to pad all samples to `max_seq_length`. '
					            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
					            'value if set.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
					            'value if set.'
					        )
					    }							,				)







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default=__magic_name__							,				metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default=__magic_name__							,				metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Train language if it is different from the evaluation language.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[bool]						=   field(
					    default=__magic_name__							,				metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'}							,				)
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}							,				)
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default='main'							,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							,				)
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}							,				)



def 			UpperCAmelCase_				(      ):
					# 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.

					lowercase_  							=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
					lowercase_							,					lowercase_							,					lowercase_  							=			parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry("""run_xnli"""						,							UpperCAmelCase__  )

					# Setup logging
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""						,							datefmt="""%m/%d/%Y %H:%M:%S"""						,							handlers=[logging.StreamHandler(sys.stdout  )]						,							)

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					lowercase_  							=			training_args.get_process_log_level()
					logger.setLevel(UpperCAmelCase__  )
					datasets.utils.logging.set_verbosity(UpperCAmelCase__  )
					transformers.utils.logging.set_verbosity(UpperCAmelCase__  )
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
					    + F'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
					logger.info(F'''Training/evaluation parameters {training_args}'''  )

					# Detecting last checkpoint.
					lowercase_  							=			None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										lowercase_  							=			get_last_checkpoint(training_args.output_dir  )
										if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
															raise ValueError(
															    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
															    """Use --overwrite_output_dir to overcome."""  )
										elif last_checkpoint is not None:
															logger.info(
															    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
															    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Set seed before initializing model.
					set_seed(training_args.seed  )

					# In distributed training, the load_dataset function guarantees that only one local process can concurrently
					# download the dataset.
					# Downloading and loading xnli dataset from the hub.
					if training_args.do_train:
										if model_args.train_language is None:
															lowercase_  							=			load_dataset(
															    """xnli"""						,							model_args.language						,							split="""train"""						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
										else:
															lowercase_  							=			load_dataset(
															    """xnli"""						,							model_args.train_language						,							split="""train"""						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
										lowercase_  							=			train_dataset.features["""label"""].names

					if training_args.do_eval:
										lowercase_  							=			load_dataset(
										    """xnli"""						,							model_args.language						,							split="""validation"""						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
										lowercase_  							=			eval_dataset.features["""label"""].names

					if training_args.do_predict:
										lowercase_  							=			load_dataset(
										    """xnli"""						,							model_args.language						,							split="""test"""						,							cache_dir=model_args.cache_dir						,							use_auth_token=True if model_args.use_auth_token else None						,							)
										lowercase_  							=			predict_dataset.features["""label"""].names

					# Labels
					lowercase_  							=			len(UpperCAmelCase__  )

					# Load pretrained model and tokenizer
					# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					lowercase_  							=			AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,							num_labels=UpperCAmelCase__						,							idalabel={str(UpperCAmelCase__  ): label for i, label in enumerate(UpperCAmelCase__  )}						,							labelaid={label: i for i, label in enumerate(UpperCAmelCase__  )}						,							finetuning_task="""xnli"""						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							)
					lowercase_  							=			AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,							do_lower_case=model_args.do_lower_case						,							cache_dir=model_args.cache_dir						,							use_fast=model_args.use_fast_tokenizer						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							)
					lowercase_  							=			AutoModelForSequenceClassification.from_pretrained(
					    model_args.model_name_or_path						,							from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )						,							config=UpperCAmelCase__						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,							)

					# Preprocessing the datasets
					# Padding strategy
					if data_args.pad_to_max_length:
										lowercase_  							=			"""max_length"""
					else:
										# We will pad later, dynamically at batch creation, to the max sequence length in each batch
										lowercase_  							=			False

					def preprocess_function(UpperCAmelCase__  ):
										# Tokenize the texts
										return tokenizer(
										    examples["""premise"""]						,							examples["""hypothesis"""]						,							padding=UpperCAmelCase__						,							max_length=data_args.max_seq_length						,							truncation=UpperCAmelCase__						,							)

					if training_args.do_train:
										if data_args.max_train_samples is not None:
															lowercase_  							=			min(len(UpperCAmelCase__  )						,							data_args.max_train_samples  )
															lowercase_  							=			train_dataset.select(range(UpperCAmelCase__  )  )
										with training_args.main_process_first(desc="""train dataset map pre-processing"""  ):
															lowercase_  							=			train_dataset.map(
															    UpperCAmelCase__						,							batched=UpperCAmelCase__						,							load_from_cache_file=not data_args.overwrite_cache						,							desc="""Running tokenizer on train dataset"""						,							)
										# Log a few random samples from the training set:
										for index in random.sample(range(len(UpperCAmelCase__  )  )						,							3  ):
															logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''  )

					if training_args.do_eval:
										if data_args.max_eval_samples is not None:
															lowercase_  							=			min(len(UpperCAmelCase__  )						,							data_args.max_eval_samples  )
															lowercase_  							=			eval_dataset.select(range(UpperCAmelCase__  )  )
										with training_args.main_process_first(desc="""validation dataset map pre-processing"""  ):
															lowercase_  							=			eval_dataset.map(
															    UpperCAmelCase__						,							batched=UpperCAmelCase__						,							load_from_cache_file=not data_args.overwrite_cache						,							desc="""Running tokenizer on validation dataset"""						,							)

					if training_args.do_predict:
										if data_args.max_predict_samples is not None:
															lowercase_  							=			min(len(UpperCAmelCase__  )						,							data_args.max_predict_samples  )
															lowercase_  							=			predict_dataset.select(range(UpperCAmelCase__  )  )
										with training_args.main_process_first(desc="""prediction dataset map pre-processing"""  ):
															lowercase_  							=			predict_dataset.map(
															    UpperCAmelCase__						,							batched=UpperCAmelCase__						,							load_from_cache_file=not data_args.overwrite_cache						,							desc="""Running tokenizer on prediction dataset"""						,							)

    # Get the metric function
					lowercase_  							=			evaluate.load("""xnli"""  )

					# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
					# predictions and label_ids field) and has to return a dictionary string to float.
					def compute_metrics(UpperCAmelCase__  ):
										lowercase_  							=			p.predictions[0] if isinstance(p.predictions						,							UpperCAmelCase__  ) else p.predictions
										lowercase_  							=			np.argmax(UpperCAmelCase__						,							axis=1  )
										return metric.compute(predictions=UpperCAmelCase__						,							references=p.label_ids  )

					# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
					if data_args.pad_to_max_length:
										lowercase_  							=			default_data_collator
					elif training_args.fpaa:
										lowercase_  							=			DataCollatorWithPadding(UpperCAmelCase__						,							pad_to_multiple_of=8  )
					else:
										lowercase_  							=			None

					# Initialize our Trainer
					lowercase_  							=			Trainer(
					    model=UpperCAmelCase__						,							args=UpperCAmelCase__						,							train_dataset=train_dataset if training_args.do_train else None						,							eval_dataset=eval_dataset if training_args.do_eval else None						,							compute_metrics=UpperCAmelCase__						,							tokenizer=UpperCAmelCase__						,							data_collator=UpperCAmelCase__						,							)

					# Training
					if training_args.do_train:
										lowercase_  							=			None
										if training_args.resume_from_checkpoint is not None:
															lowercase_  							=			training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															lowercase_  							=			last_checkpoint
										lowercase_  							=			trainer.train(resume_from_checkpoint=UpperCAmelCase__  )
										lowercase_  							=			train_result.metrics
										lowercase_  							=			(
										    data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase__  )
										)
										lowercase_  							=			min(UpperCAmelCase__						,							len(UpperCAmelCase__  )  )

										trainer.save_model()  # Saves the tokenizer too for easy upload

										trainer.log_metrics("""train"""						,							UpperCAmelCase__  )
										trainer.save_metrics("""train"""						,							UpperCAmelCase__  )
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										logger.info("""*** Evaluate ***"""  )
										lowercase_  							=			trainer.evaluate(eval_dataset=UpperCAmelCase__  )

										lowercase_  							=			data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCAmelCase__  )
										lowercase_  							=			min(UpperCAmelCase__						,							len(UpperCAmelCase__  )  )

										trainer.log_metrics("""eval"""						,							UpperCAmelCase__  )
										trainer.save_metrics("""eval"""						,							UpperCAmelCase__  )

					# Prediction
					if training_args.do_predict:
										logger.info("""*** Predict ***"""  )
										lowercase_							,					lowercase_							,					lowercase_  							=			trainer.predict(UpperCAmelCase__						,							metric_key_prefix="""predict"""  )

										lowercase_  							=			(
										    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCAmelCase__  )
										)
										lowercase_  							=			min(UpperCAmelCase__						,							len(UpperCAmelCase__  )  )

										trainer.log_metrics("""predict"""						,							UpperCAmelCase__  )
										trainer.save_metrics("""predict"""						,							UpperCAmelCase__  )

										lowercase_  							=			np.argmax(UpperCAmelCase__						,							axis=1  )
										lowercase_  							=			os.path.join(training_args.output_dir						,							"""predictions.txt"""  )
										if trainer.is_world_process_zero():
															with open(UpperCAmelCase__						,							"""w"""  ) as writer:
																				writer.write("""index\tprediction\n"""  )
																				for index, item in enumerate(UpperCAmelCase__  ):
																									lowercase_  							=			label_list[item]
																									writer.write(F'''{index}\t{item}\n'''  )


if __name__ == "__main__":
							main()






import cva
import numpy as np







class        UpperCamelCase__   :

					def __init__(     self       :   List[str]							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






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

					def __str__(     self       :   Optional[int]       ):






										'''simple docstring'''






										return str(self.k       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






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

																				lowercase_  							=			(wxx * wyy) - (wxy**2)
																				lowercase_  							=			wxx + wyy
																				lowercase_  							=			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__":
							a					  =     HarrisCorner(0.04, 3)
							a  ,					a					  =     edge_detect.detect('path_to_image')
							cva.imwrite('detect.png', color_img)


