



"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case_				     =       logging.get_logger(__name__)


if is_vision_available():
				import PIL







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							["""pixel_values"""]






						def __init__(					self :List[Any]   ,  lowercase_ :bool = True   ,  lowercase_ :Dict[str, int] = None   ,  lowercase_ :PILImageResampling = PILImageResampling.BICUBIC   ,  lowercase_ :bool = True   ,  lowercase_ :Dict[str, int] = None   ,  lowercase_ :bool = True   ,  lowercase_ :Union[int, float] = 1 / 2_55   ,  lowercase_ :bool = True   ,  lowercase_ :Optional[Union[float, List[float]]] = None   ,  lowercase_ :Optional[Union[float, List[float]]] = None   ,  lowercase_ :bool = True   ,  **lowercase_ :Optional[int]   ,  )				->			None:
												super().__init__(**lowercase_					)
												UpperCAmelCase             = size if size is not None else {'shortest_edge': 2_24}
												UpperCAmelCase             = get_size_dict(lowercase_   ,  default_to_square=lowercase_					)
												UpperCAmelCase             = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												UpperCAmelCase             = get_size_dict(lowercase_   ,  default_to_square=lowercase_   ,  param_name='crop_size'					)

												UpperCAmelCase             = do_resize
												UpperCAmelCase             = size
												UpperCAmelCase             = resample
												UpperCAmelCase             = do_center_crop
												UpperCAmelCase             = crop_size
												UpperCAmelCase             = do_rescale
												UpperCAmelCase             = rescale_factor
												UpperCAmelCase             = do_normalize
												UpperCAmelCase             = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												UpperCAmelCase             = image_std if image_std is not None else OPENAI_CLIP_STD
												UpperCAmelCase             = do_convert_rgb






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :np.ndarray   ,  lowercase_ :Dict[str, int]   ,  lowercase_ :PILImageResampling = PILImageResampling.BICUBIC   ,  lowercase_ :Optional[Union[str, ChannelDimension]] = None   ,  **lowercase_ :Dict   ,  )				->			np.ndarray:
												UpperCAmelCase             = get_size_dict(lowercase_   ,  default_to_square=lowercase_					)
												if "shortest_edge" not in size:
																		raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
												UpperCAmelCase             = get_resize_output_image_size(lowercase_   ,  size=size['shortest_edge']   ,  default_to_square=lowercase_					)
												return resize(lowercase_   ,  size=lowercase_   ,  resample=lowercase_   ,  data_format=lowercase_   ,  **lowercase_					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]   ,  lowercase_ :np.ndarray   ,  lowercase_ :Dict[str, int]   ,  lowercase_ :Optional[Union[str, ChannelDimension]] = None   ,  **lowercase_ :List[str]   ,  )				->			np.ndarray:
												UpperCAmelCase             = get_size_dict(lowercase_					)
												if "height" not in size or "width" not in size:
																		raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""					)
												return center_crop(lowercase_   ,  size=(size['height'], size['width'])   ,  data_format=lowercase_   ,  **lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple   ,  lowercase_ :np.ndarray   ,  lowercase_ :Union[int, float]   ,  lowercase_ :Optional[Union[str, ChannelDimension]] = None   ,  **lowercase_ :str   ,  )				->			Optional[Any]:
												return rescale(lowercase_   ,  scale=lowercase_   ,  data_format=lowercase_   ,  **lowercase_					)






						def 	UpperCAmelCase__     (					self :Any   ,  lowercase_ :np.ndarray   ,  lowercase_ :Union[float, List[float]]   ,  lowercase_ :Union[float, List[float]]   ,  lowercase_ :Optional[Union[str, ChannelDimension]] = None   ,  **lowercase_ :Optional[int]   ,  )				->			np.ndarray:
												return normalize(lowercase_   ,  mean=lowercase_   ,  std=lowercase_   ,  data_format=lowercase_   ,  **lowercase_					)






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :ImageInput   ,  lowercase_ :bool = None   ,  lowercase_ :Dict[str, int] = None   ,  lowercase_ :PILImageResampling = None   ,  lowercase_ :bool = None   ,  lowercase_ :int = None   ,  lowercase_ :bool = None   ,  lowercase_ :float = None   ,  lowercase_ :bool = None   ,  lowercase_ :Optional[Union[float, List[float]]] = None   ,  lowercase_ :Optional[Union[float, List[float]]] = None   ,  lowercase_ :bool = None   ,  lowercase_ :Optional[Union[str, TensorType]] = None   ,  lowercase_ :Optional[ChannelDimension] = ChannelDimension.FIRST   ,  **lowercase_ :List[str]   ,  )				->			PIL.Image.Image:
												UpperCAmelCase             = do_resize if do_resize is not None else self.do_resize
												UpperCAmelCase             = size if size is not None else self.size
												UpperCAmelCase             = get_size_dict(lowercase_   ,  param_name='size'   ,  default_to_square=lowercase_					)
												UpperCAmelCase             = resample if resample is not None else self.resample
												UpperCAmelCase             = do_center_crop if do_center_crop is not None else self.do_center_crop
												UpperCAmelCase             = crop_size if crop_size is not None else self.crop_size
												UpperCAmelCase             = get_size_dict(lowercase_   ,  param_name='crop_size'   ,  default_to_square=lowercase_					)
												UpperCAmelCase             = do_rescale if do_rescale is not None else self.do_rescale
												UpperCAmelCase             = rescale_factor if rescale_factor is not None else self.rescale_factor
												UpperCAmelCase             = do_normalize if do_normalize is not None else self.do_normalize
												UpperCAmelCase             = image_mean if image_mean is not None else self.image_mean
												UpperCAmelCase             = image_std if image_std is not None else self.image_std
												UpperCAmelCase             = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												UpperCAmelCase             = make_list_of_images(lowercase_					)

												if not valid_images(lowercase_					):
																		raise ValueError(
																		    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																		    'torch.Tensor, tf.Tensor or jax.ndarray.'					)

												if do_resize and size is None:
																		raise ValueError('Size must be specified if do_resize is True.'					)

												if do_center_crop and crop_size is None:
																		raise ValueError('Crop size must be specified if do_center_crop is True.'					)

												if do_rescale and rescale_factor is None:
																		raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

												if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

												# PIL RGBA images are converted to RGB
												if do_convert_rgb:
																		UpperCAmelCase             = [convert_to_rgb(lowercase_					) for image in images]

												# All transformations expect numpy arrays.
												UpperCAmelCase             = [to_numpy_array(lowercase_					) for image in images]

												if do_resize:
																		UpperCAmelCase             = [self.resize(image=lowercase_   ,  size=lowercase_   ,  resample=lowercase_					) for image in images]

												if do_center_crop:
																		UpperCAmelCase             = [self.center_crop(image=lowercase_   ,  size=lowercase_					) for image in images]

												if do_rescale:
																		UpperCAmelCase             = [self.rescale(image=lowercase_   ,  scale=lowercase_					) for image in images]

												if do_normalize:
																		UpperCAmelCase             = [self.normalize(image=lowercase_   ,  mean=lowercase_   ,  std=lowercase_					) for image in images]

												UpperCAmelCase             = [to_channel_dimension_format(lowercase_   ,  lowercase_					) for image in images]

												UpperCAmelCase             = {'pixel_values': images}
												return BatchFeature(data=lowercase_   ,  tensor_type=lowercase_					)










import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__								=						logging.getLogger(__name__)


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


@dataclass
class       a							:
		__lowerCAmelCase	:    str											=					field(
		    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}				)
		__lowerCAmelCase	:    Optional[str]											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,							)
		__lowerCAmelCase	:    int											=					field(
		    default=1_28  ,							metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }  ,							)
		__lowerCAmelCase	:    bool											=					field(
		    default=__lowerCamelCase  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      Any     		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				if len(sys.argv      ) == 2 and sys.argv[1].endswith('''.json'''      ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								snake_case__		, snake_case__		, snake_case__	:      str     		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								snake_case__		, snake_case__		, snake_case__	:      List[Any]     		=	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.'''      )

				snake_case__	:      int     		=	import_module('''tasks'''      )
				try:
								snake_case__	:      Optional[int]     		=	getattr(__lowerCAmelCase  ,  model_args.task_type      )
								snake_case__	:      TokenClassificationTask     		=	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 if training_args.local_rank in [-1, 0] else logging.WARN  ,  )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,  training_args.local_rank  ,  training_args.device  ,  training_args.n_gpu  ,  bool(training_args.local_rank != -1      )  ,  training_args.fpaa  ,  )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank      ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('''Training/evaluation parameters %s'''  ,  __lowerCAmelCase      )

				# Set seed
				set_seed(training_args.seed      )

				# Prepare CONLL-2003 task
				snake_case__	:      Optional[int]     		=	token_classification_task.get_labels(data_args.labels      )
				snake_case__	:      Dict[int, str]     		=	dict(enumerate(__lowerCAmelCase      )      )
				snake_case__	:      Optional[Any]     		=	len(__lowerCAmelCase      )

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

				snake_case__	:      List[str]     		=	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,  num_labels=__lowerCAmelCase  ,  idalabel=__lowerCAmelCase  ,  labelaid={label: i for i, label in enumerate(__lowerCAmelCase      )}  ,  cache_dir=model_args.cache_dir  ,  )
				snake_case__	:      List[str]     		=	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  ,  )
				snake_case__	:      Tuple     		=	AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path  ,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )  ,  config=__lowerCAmelCase  ,  cache_dir=model_args.cache_dir  ,  )

				# Get datasets
				snake_case__	:      Dict     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  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
				)
				snake_case__	:      Optional[Any]     		=	(
				    TokenClassificationDataset(
				        token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  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(__lowerCAmelCase  ,  __lowerCAmelCase      ) -> Tuple[List[int], List[int]]:
								snake_case__	:      Any     		=	np.argmax(__lowerCAmelCase  ,  axis=2      )

								snake_case__		, snake_case__	:      List[Any]     		=	preds.shape

								snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
								snake_case__	:      int     		=	[[] for _ in range(__lowerCAmelCase      )]

								for i in range(__lowerCAmelCase      ):
												for j in range(__lowerCAmelCase      ):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				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(__lowerCAmelCase      ) -> Dict:
								snake_case__		, snake_case__	:      List[str]     		=	align_predictions(p.predictions  ,  p.label_ids      )
								return {
								    "accuracy_score": accuracy_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "precision": precision_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "recall": recall_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								    "f1": fa_score(__lowerCAmelCase  ,  __lowerCAmelCase      ),
								}

				# Data collator
				snake_case__	:      Any     		=	DataCollatorWithPadding(__lowerCAmelCase  ,  pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				snake_case__	:      Tuple     		=	Trainer(
				    model=__lowerCAmelCase  ,  args=__lowerCAmelCase  ,  train_dataset=__lowerCAmelCase  ,  eval_dataset=__lowerCAmelCase  ,  compute_metrics=__lowerCAmelCase  ,  data_collator=__lowerCAmelCase  ,  )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
				snake_case__	:      Any     		=	{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''      )

								snake_case__	:      Tuple     		=	trainer.evaluate()

								snake_case__	:      Optional[Any]     		=	os.path.join(training_args.output_dir  ,  '''eval_results.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																logger.info('''***** Eval results *****'''      )
																for key, value in result.items():
																				logger.info('''  %s = %s'''  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
																				writer.write('''%s = %s\n''' % (key, value)      )

												results.update(__lowerCAmelCase      )

    # Predict
				if training_args.do_predict:
								snake_case__	:      Optional[Any]     		=	TokenClassificationDataset(
								    token_classification_task=__lowerCAmelCase  ,  data_dir=data_args.data_dir  ,  tokenizer=__lowerCAmelCase  ,  labels=__lowerCAmelCase  ,  model_type=config.model_type  ,  max_seq_length=data_args.max_seq_length  ,  overwrite_cache=data_args.overwrite_cache  ,  mode=Split.test  ,  )

								snake_case__		, snake_case__		, snake_case__	:      List[str]     		=	trainer.predict(__lowerCAmelCase      )
								snake_case__		, snake_case__	:      int     		=	align_predictions(__lowerCAmelCase  ,  __lowerCAmelCase      )

								snake_case__	:      Optional[int]     		=	os.path.join(training_args.output_dir  ,  '''test_results.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																for key, value in metrics.items():
																				logger.info('''  %s = %s'''  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
																				writer.write('''%s = %s\n''' % (key, value)      )

        # Save predictions
								snake_case__	:      Any     		=	os.path.join(training_args.output_dir  ,  '''test_predictions.txt'''      )
								if trainer.is_world_process_zero():
												with open(__lowerCAmelCase  ,  '''w'''      ) as writer:
																with open(os.path.join(data_args.data_dir  ,  '''test.txt'''      )  ,  '''r'''      ) as f:
																				token_classification_task.write_predictions_to_file(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )

				return results



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Union[str, Any]:


				"""simple docstring"""

				main()


if __name__ == "__main__":
				main()

