






"""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		:	Optional[int]      =		logging.getLogger(__name__)





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





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



def 						lowercase   (    ):
		# 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_			:			int								=       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
		lowercase_	, lowercase_	, lowercase_			:			str								=       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.'''     )

		lowercase_			:			Tuple								=       import_module('''tasks'''     )

		try:
				lowercase_			:			Optional[int]								=       getattr(__snake_case		, model_args.task_type     )
				lowercase_			:			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		, )
		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'''		, __snake_case     )

		# Prepare Token Classification task
		lowercase_			:			Dict								=       token_classification_task.get_labels(data_args.labels     )
		lowercase_			:			Dict[int, str]								=       dict(enumerate(__snake_case     )     )
		lowercase_			:			Dict								=       len(__snake_case     )

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

		lowercase_			:			Dict								=       AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path		, num_labels=__snake_case		, idalabel=__snake_case		, labelaid={label: i for i, label in enumerate(__snake_case     )}		, cache_dir=model_args.cache_dir		, )
		lowercase_			:			Optional[Any]								=       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():
				lowercase_			:			Any								=       TFAutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path		, from_pt=bool('''.bin''' in model_args.model_name_or_path     )		, config=__snake_case		, cache_dir=model_args.cache_dir		, )

		# Get datasets
		lowercase_			:			List[str]								=       (
		    TFTokenClassificationDataset(
		        token_classification_task=__snake_case		, data_dir=data_args.data_dir		, tokenizer=__snake_case		, labels=__snake_case		, 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
		)
		lowercase_			:			List[Any]								=       (
		    TFTokenClassificationDataset(
		        token_classification_task=__snake_case		, data_dir=data_args.data_dir		, tokenizer=__snake_case		, labels=__snake_case		, 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(__snake_case    :			np.ndarray		, __snake_case    :			np.ndarray     ) -> Tuple[List[int], List[int]]:
				lowercase_			:			Union[str, Any]								=       np.argmax(__snake_case		, axis=2     )
				lowercase_	, lowercase_			:			Tuple								=       preds.shape
				lowercase_			:			Optional[Any]								=       [[] for _ in range(__snake_case     )]
				lowercase_			:			Any								=       [[] for _ in range(__snake_case     )]

				for i in range(__snake_case     ):
						for j in range(__snake_case     ):
								if label_ids[i, j] != -1_0_0:
										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(__snake_case    :			EvalPrediction     ) -> Dict:
				lowercase_	, lowercase_			:			Optional[Any]								=       align_predictions(p.predictions		, p.label_ids     )

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

		# Initialize our Trainer
		lowercase_			:			List[Any]								=       TFTrainer(
		    model=__snake_case		, args=__snake_case		, train_dataset=train_dataset.get_dataset() if train_dataset else None		, eval_dataset=eval_dataset.get_dataset() if eval_dataset else None		, compute_metrics=__snake_case		, )

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

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

				lowercase_			:			Dict								=       trainer.evaluate()
				lowercase_			:			Tuple								=       os.path.join(training_args.output_dir		, '''eval_results.txt'''     )

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

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

						results.update(__snake_case     )

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

				lowercase_	, lowercase_	, lowercase_			:			List[str]								=       trainer.predict(test_dataset.get_dataset()     )
				lowercase_	, lowercase_			:			List[Any]								=       align_predictions(__snake_case		, __snake_case     )
				lowercase_			:			Tuple								=       classification_report(__snake_case		, __snake_case     )

				logger.info('''\n%s'''		, __snake_case     )

				lowercase_			:			Optional[int]								=       os.path.join(training_args.output_dir		, '''test_results.txt'''     )

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

				# Save predictions
				lowercase_			:			List[str]								=       os.path.join(training_args.output_dir		, '''test_predictions.txt'''     )

				with open(__snake_case		, '''w'''     ) as writer:
						with open(os.path.join(data_args.data_dir		, '''test.txt'''     )		, '''r'''     ) as f:
								lowercase_			:			int								=       0

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

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

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

		return results


if __name__ == "__main__":
						main()











"""simple docstring"""



import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 						lowercase   (    ):
		lowercase_			:			Tuple								=       set()
		# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
		for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
				lowercase_			:			str								=       getattr(transformers.models.auto		, __snake_case		, __snake_case     )
				if module is None:
						continue
				# all mappings in a single auto modeling file
				lowercase_			:			Tuple								=       [
				    x
				    for x in dir(__snake_case     )
				    if x.endswith('''_MAPPING_NAMES'''     )
				    and (x.startswith('''MODEL_'''     ) or x.startswith('''TF_MODEL_'''     ) or x.startswith('''FLAX_MODEL_'''     ))
				]
				for name in mapping_names:
						lowercase_			:			Tuple								=       getattr(__snake_case		, __snake_case     )
						if mapping is not None:
								for v in mapping.values():
										if isinstance(__snake_case		, (list, tuple)     ):
												model_names.update(__snake_case     )
										elif isinstance(__snake_case		, __snake_case     ):
												model_names.add(__snake_case     )

		return sorted(__snake_case     )



def 						lowercase   (    ):
		# All model names defined in auto mappings
		lowercase_			:			Tuple								=       set(get_all_model_names()     )

		with open('''tests/utils/tiny_model_summary.json'''     ) as fp:
				lowercase_			:			Tuple								=       json.load(__snake_case     )
		lowercase_			:			Union[str, Any]								=       set()
		for model_base_name in tiny_model_info:
				tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']     )

		# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
		lowercase_			:			Union[str, Any]								=       model_names.difference(__snake_case     )
		for model_name in copy.copy(__snake_case     ):
				if not model_name.startswith('''TF'''     ) and F'''TF{model_name}''' in not_on_hub:
						tiny_models_names.remove(__snake_case     )
				elif model_name.startswith('''TF'''     ) and model_name[2:] in not_on_hub:
						tiny_models_names.remove(__snake_case     )

		return sorted(__snake_case     )



def 						lowercase   (    __snake_case    :			List[Any]     ):
		lowercase_			:			List[str]								=       COMPOSITE_MODELS.values()

		# All tiny model base names on Hub
		lowercase_			:			List[Any]								=       get_all_model_names()
		lowercase_			:			Tuple								=       hf_api.list_models(
		    filter=ModelFilter(
		        author='''hf-internal-testing'''		, )     )
		lowercase_			:			Tuple								=       set()
		for x in models:
				lowercase_			:			Tuple								=       x.modelId
				lowercase_	, lowercase_			:			Tuple								=       model.split('''/'''     )
				if not model.startswith('''tiny-random-'''     ):
						continue
				lowercase_			:			List[str]								=       model.replace('''tiny-random-'''		, ''''''     )
				if not model[0].isupper():
						continue
				if model not in model_names and model not in special_models:
						continue
				_models.add(__snake_case     )

		lowercase_			:			Tuple								=       sorted(_models     )
		# All tiny model names on Hub
		lowercase_			:			int								=       {}
		for model in models:
				lowercase_			:			str								=       F'''hf-internal-testing/tiny-random-{model}'''
				lowercase_			:			str								=       model.split('''-'''     )[0]
				try:
						lowercase_			:			Any								=       hf_api.repo_info(__snake_case     )
						lowercase_			:			str								=       {
						    '''tokenizer_classes''': set(),
						    '''processor_classes''': set(),
						    '''model_classes''': set(),
						    '''sha''': repo_info.sha,
						}
				except Exception:
						continue
				try:
						time.sleep(1     )
						lowercase_			:			Any								=       AutoTokenizer.from_pretrained(__snake_case     )
						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			List[str]								=       AutoTokenizer.from_pretrained(__snake_case		, use_fast=__snake_case     )
						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Union[str, Any]								=       AutoImageProcessor.from_pretrained(__snake_case     )
						content["processor_classes"].add(img_p.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Optional[Any]								=       AutoFeatureExtractor.from_pretrained(__snake_case     )
						if not isinstance(__snake_case		, __snake_case     ):
								content["processor_classes"].add(feat_p.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Union[str, Any]								=       getattr(__snake_case		, __snake_case     )
						lowercase_			:			Dict								=       model_class.from_pretrained(__snake_case     )
						content["model_classes"].add(m.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			List[str]								=       getattr(__snake_case		, F'''TF{model}'''     )
						lowercase_			:			List[str]								=       model_class.from_pretrained(__snake_case     )
						content["model_classes"].add(m.__class__.__name__     )
				except Exception:
						pass

				lowercase_			:			Dict								=       sorted(content['''tokenizer_classes''']     )
				lowercase_			:			Any								=       sorted(content['''processor_classes''']     )
				lowercase_			:			Union[str, Any]								=       sorted(content['''model_classes''']     )

				lowercase_			:			str								=       content
				with open(os.path.join(__snake_case		, '''hub_tiny_model_summary.json'''     )		, '''w'''     ) as fp:
						json.dump(__snake_case		, __snake_case		, ensure_ascii=__snake_case		, indent=4     )


if __name__ == "__main__":
						__A		:	Optional[int]      =		argparse.ArgumentParser()
						parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
						__A		:	Optional[int]      =		parser.parse_args()

						# This has to be `spawn` to avoid hanging forever!
						multiprocessing.set_start_method('''spawn''')

						__A		:	List[str]      =		'''tiny_models'''
						__A		:	List[str]      =		True
						__A		:	int      =		None
						__A		:	Dict      =		get_tiny_model_names_from_repo()
						__A		:	Union[str, Any]      =		True
						__A		:	str      =		True
						__A		:	Optional[Any]      =		'''hf-internal-testing'''

						create_tiny_models(
						    output_path,
						    all,
						    model_types,
						    models_to_skip,
						    no_check,
						    upload,
						    organization,
						    token=os.environ.get('''TOKEN''', None),
						    num_workers=args.num_workers,
						)



