

"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    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()


__A	:       Dict        =						logging.getLogger(__name__)





@dataclass
class      __UpperCamelCase					:
		SCREAMING_SNAKE_CASE														=       field(
		    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
		SCREAMING_SNAKE_CASE														=       field(
		    default=_A	,						metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
		SCREAMING_SNAKE_CASE														=       field(
		    default="NER"	,						metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}		)
		SCREAMING_SNAKE_CASE														=       field(
		    default=_A	,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
		SCREAMING_SNAKE_CASE														=       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.
		SCREAMING_SNAKE_CASE														=       field(
		    default=_A	,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,						)





@dataclass
class      __UpperCamelCase					:
		SCREAMING_SNAKE_CASE														=       field(
		    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}		)
		SCREAMING_SNAKE_CASE														=       field(
		    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}		)
		SCREAMING_SNAKE_CASE														=       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														=       field(
		    default=_A	,						metadata={"help": "Overwrite the cached training and evaluation sets"}		)

def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					# 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     =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
					A     ,       A     ,       A     =   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." )

					A     =   import_module("tasks" )

					try:
										A     =   getattr(lowercase__     ,				model_args.task_type )
										A     =   token_classification_task_clazz()
					except AttributeError:
										raise ValueError(
										    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
										    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )

					# 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(
					    "n_replicas: %s, distributed training: %s, 16-bits training: %s"     ,				training_args.n_replicas     ,				bool(training_args.n_replicas > 1 )     ,				training_args.fpaa     ,				)
					logger.info("Training/evaluation parameters %s"     ,				lowercase__ )

					# Prepare Token Classification task
					A     =   token_classification_task.get_labels(data_args.labels )
					A     =   dict(enumerate(lowercase__ ) )
					A     =   len(lowercase__ )

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

					A     =   AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,				num_labels=lowercase__     ,				idalabel=lowercase__     ,				labelaid={label: i for i, label in enumerate(lowercase__ )}     ,				cache_dir=model_args.cache_dir     ,				)
					A     =   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     ,				use_fast=model_args.use_fast     ,				)

					with training_args.strategy.scope():
										A     =   TFAutoModelForTokenClassification.from_pretrained(
										    model_args.model_name_or_path     ,				from_pt=bool(".bin" in model_args.model_name_or_path )     ,				config=lowercase__     ,				cache_dir=model_args.cache_dir     ,				)

					# Get datasets
					A     =   (
					    TFTokenClassificationDataset(
					        token_classification_task=lowercase__     ,				data_dir=data_args.data_dir     ,				tokenizer=lowercase__     ,				labels=lowercase__     ,				model_type=config.model_type     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.train     ,				)
					    if training_args.do_train
					    else None
					)
					A     =   (
					    TFTokenClassificationDataset(
					        token_classification_task=lowercase__     ,				data_dir=data_args.data_dir     ,				tokenizer=lowercase__     ,				labels=lowercase__     ,				model_type=config.model_type     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.dev     ,				)
					    if training_args.do_eval
					    else None
					)

					def align_predictions(lowercase__     ,				lowercase__ ) -> Tuple[List[int], List[int]]:
										A     =   np.argmax(lowercase__     ,				axis=2 )
										A     ,       A     =   preds.shape
										A     =   [[] for _ in range(lowercase__ )]
										A     =   [[] for _ in range(lowercase__ )]

										for i in range(lowercase__ ):
															for j in range(lowercase__ ):
																				if label_ids[i, j] != -100:
																									out_label_list[i].append(label_map[label_ids[i][j]] )
																									preds_list[i].append(label_map[preds[i][j]] )

										return preds_list, out_label_list

					def compute_metrics(lowercase__ ) -> Dict:
										A     ,       A     =   align_predictions(p.predictions     ,				p.label_ids )

										return {
										    "precision": precision_score(lowercase__     ,				lowercase__ ),
										    "recall": recall_score(lowercase__     ,				lowercase__ ),
										    "f1": fa_score(lowercase__     ,				lowercase__ ),
										}

					# Initialize our Trainer
					A     =   TFTrainer(
					    model=lowercase__     ,				args=lowercase__     ,				train_dataset=train_dataset.get_dataset() if train_dataset else None     ,				eval_dataset=eval_dataset.get_dataset() if eval_dataset else None     ,				compute_metrics=lowercase__     ,				)

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

					# Evaluation
					A     =   {}
					if training_args.do_eval:
										logger.info("*** Evaluate ***" )

										A     =   trainer.evaluate()
										A     =   os.path.join(training_args.output_dir     ,				"eval_results.txt" )

										with open(lowercase__     ,				"w" ) as writer:
															logger.info("***** Eval results *****" )

															for key, value in result.items():
																				logger.info("  %s = %s"     ,				lowercase__     ,				lowercase__ )
																				writer.write("%s = %s\n" % (key, value) )

															results.update(lowercase__ )

    # Predict
					if training_args.do_predict:
										A     =   TFTokenClassificationDataset(
										    token_classification_task=lowercase__     ,				data_dir=data_args.data_dir     ,				tokenizer=lowercase__     ,				labels=lowercase__     ,				model_type=config.model_type     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.test     ,				)

										A     ,       A     ,       A     =   trainer.predict(test_dataset.get_dataset() )
										A     ,       A     =   align_predictions(lowercase__     ,				lowercase__ )
										A     =   classification_report(lowercase__     ,				lowercase__ )

										logger.info("\n%s"     ,				lowercase__ )

										A     =   os.path.join(training_args.output_dir     ,				"test_results.txt" )

										with open(lowercase__     ,				"w" ) as writer:
															writer.write("%s\n" % report )

										# Save predictions
										A     =   os.path.join(training_args.output_dir     ,				"test_predictions.txt" )

										with open(lowercase__     ,				"w" ) as writer:
															with open(os.path.join(data_args.data_dir     ,				"test.txt" )     ,				"r" ) as f:
																				A     =   0

																				for line in f:
																									if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
																														writer.write(lowercase__ )

																														if not preds_list[example_id]:
																																			example_id += 1
																									elif preds_list[example_id]:
																														A     =   line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"

																														writer.write(lowercase__ )
																									else:
																														logger.warning("Maximum sequence length exceeded: No prediction for '%s'."     ,				line.split()[0] )

					return results


if __name__ == "__main__":
	main()













from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def        _lowerCamelCase(      lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.stack((horizontal_flow, vertical_flow)     ,     2   )

				# Create a grid of all pixel coordinates and subtract the flow to get the
				# target pixels coordinates
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.stack(
				    np.meshgrid(np.arange(0     ,     image.shape[1]   )     ,     np.arange(0     ,     image.shape[0]   )   )     ,     2   )
				SCREAMING_SNAKE_CASE_ :						List[str]    				= np.round(grid - flow   ).astype(np.intaa   )

				# Find the locations outside of the original image
				SCREAMING_SNAKE_CASE_ :						str    				= (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]   ))
				SCREAMING_SNAKE_CASE_ :						Dict    				= 0

				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= image[grid[:, :, 1], grid[:, :, 0]]

				# Set pixels at invalid locations to 0
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 0

				return warped



def        _lowerCamelCase(      lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		SupportsIndex     ,     lowerCAmelCase__					:		float | None = None     ,     ):





				'''simple docstring'''







				if alpha is None:
								SCREAMING_SNAKE_CASE_ :						Any    				= 0.1

				# Initialize flow
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.zeros_like(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Any    				= np.zeros_like(lowerCAmelCase__   )

				# Prepare kernels for the calculation of the derivatives and the average velocity
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= np.array([[-1, 1], [-1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Tuple    				= np.array([[-1, -1], [1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= np.array([[1, 1], [1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Tuple    				= np.array(
				    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]   )

				# Iteratively refine the flow
				for _ in range(lowerCAmelCase__   ):
								SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= warp(lowerCAmelCase__     ,     lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Any    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     -kernel_t   )

								SCREAMING_SNAKE_CASE_ :						List[str]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )

								# This updates the flow as proposed in the paper (Step 12)
								SCREAMING_SNAKE_CASE_ :						Tuple    				= (
								    derivative_x * avg_horizontal_velocity
								    + derivative_y * avg_vertical_velocity
								    + derivative_t
								)
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= update / (alpha**2 + derivative_x**2 + derivative_y**2)

								SCREAMING_SNAKE_CASE_ :						List[str]    				= avg_horizontal_velocity - derivative_x * update
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= avg_vertical_velocity - derivative_y * update

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()