
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


_UpperCAmelCase :			Any              =	logging.getLogger(__name__)


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






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






def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					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.
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					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.'			)

		UpperCamelCase          =					import_module('tasks'			)
		try:
				UpperCamelCase          =					getattr(lowercase   ,	model_args.task_type			)
				UpperCamelCase          =					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'   ,	lowercase			)

		# Set seed
		set_seed(training_args.seed			)

		# Prepare CONLL-2003 task
		UpperCamelCase          =					token_classification_task.get_labels(data_args.labels			)
		UpperCamelCase          =					dict(enumerate(lowercase			)			)
		UpperCamelCase          =					len(lowercase			)

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

		UpperCamelCase          =					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   ,	)
		UpperCamelCase          =					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   ,	)
		UpperCamelCase          =					AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path   ,	from_tf=bool('.ckpt' in model_args.model_name_or_path			)   ,	config=lowercase   ,	cache_dir=model_args.cache_dir   ,	)

		# Get datasets
		UpperCamelCase          =					(
		    TokenClassificationDataset(
		        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
		)
		UpperCamelCase          =					(
		    TokenClassificationDataset(
		        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]]:
				UpperCamelCase          =					np.argmax(lowercase   ,	axis=2			)

				UpperCamelCase     ,  UpperCamelCase          =					preds.shape

				UpperCamelCase          =					[[] for _ in range(lowercase			)]
				UpperCamelCase          =					[[] for _ in range(lowercase			)]

				for i in range(lowercase			):
						for j in range(lowercase			):
								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(lowercase			) -> Dict:
				UpperCamelCase     ,  UpperCamelCase          =					align_predictions(p.predictions   ,	p.label_ids			)
				return {
				    "accuracy_score": accuracy_score(lowercase   ,	lowercase			),
				    "precision": precision_score(lowercase   ,	lowercase			),
				    "recall": recall_score(lowercase   ,	lowercase			),
				    "f1": fa_score(lowercase   ,	lowercase			),
				}

		# Data collator
		UpperCamelCase          =					DataCollatorWithPadding(lowercase   ,	pad_to_multiple_of=8			) if training_args.fpaa else None

		# Initialize our Trainer
		UpperCamelCase          =					Trainer(
		    model=lowercase   ,	args=lowercase   ,	train_dataset=lowercase   ,	eval_dataset=lowercase   ,	compute_metrics=lowercase   ,	data_collator=lowercase   ,	)

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

				UpperCamelCase          =					trainer.evaluate()

				UpperCamelCase          =					os.path.join(training_args.output_dir   ,	'eval_results.txt'			)
				if trainer.is_world_process_zero():
						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:
				UpperCamelCase          =					TokenClassificationDataset(
				    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   ,	)

				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					trainer.predict(lowercase			)
				UpperCamelCase     ,  UpperCamelCase          =					align_predictions(lowercase   ,	lowercase			)

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

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

		return results


def 				A	( lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		main()


if __name__ == "__main__":
					main()


import os
import sys
import unittest


_UpperCAmelCase :			str              =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase :			Tuple              =	os.path.join(git_repo_path, "src", "diffusers")


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					find_backend('    if not is_torch_available():'	)
				self.assertEqual(A_    ,				'torch'	)

				# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
				# self.assertEqual(backend_with_underscore, "tensorflow_text")

				UpperCamelCase          =					find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers'	)

				# double_backend_with_underscore = find_backend(
				#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
				# )
				# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

				UpperCamelCase          =					find_backend(
				    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers_and_onnx'	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'    ,				A_	)
				self.assertIn('torch_and_transformers'    ,				A_	)
				self.assertIn('flax_and_transformers'    ,				A_	)
				self.assertIn('torch_and_transformers_and_onnx'    ,				A_	)

				# Likewise, we can't assert on the exact content of a key
				self.assertIn('UNet2DModel'    ,				objects['torch']	)
				self.assertIn('FlaxUNet2DConditionModel'    ,				objects['flax']	)
				self.assertIn('StableDiffusionPipeline'    ,				objects['torch_and_transformers']	)
				self.assertIn('FlaxStableDiffusionPipeline'    ,				objects['flax_and_transformers']	)
				self.assertIn('LMSDiscreteScheduler'    ,				objects['torch_and_scipy']	)
				self.assertIn('OnnxStableDiffusionPipeline'    ,				objects['torch_and_transformers_and_onnx']	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					create_dummy_object('CONSTANT'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				'\nCONSTANT = None\n'	)

				UpperCamelCase          =					create_dummy_object('function'    ,				'\'torch\''	)
				self.assertEqual(
				    A_    ,				'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'	)

				UpperCamelCase          =					'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
				UpperCamelCase          =					create_dummy_object('FakeClass'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
				UpperCamelCase          =					create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
				self.assertEqual(dummy_files['torch']    ,				A_	)
