



"""simple docstring"""
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


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


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__   :       str										= field(
					    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
					lowercase__   :       Optional[str]										= field(
					    default=A__,				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=A__,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
					lowercase__   :       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.
					lowercase__   :       Optional[str]										= field(
					    default=A__,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},				)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					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=A__,				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=A__,				metadata={'help': 'Overwrite the cached training and evaluation sets'}    )



def        lowerCAmelCase_(   )	->       Optional[Any]:
				# 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.

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

				_lowerCamelCase        =				import_module('''tasks'''							)
				try:
								_lowerCamelCase        =				getattr(lowercase_						,   model_args.task_type							)
								_lowerCamelCase        =				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
				_lowerCamelCase        =				token_classification_task.get_labels(data_args.labels							)
				_lowerCamelCase        =				dict(enumerate(lowercase_							)							)
				_lowerCamelCase        =				len(lowercase_							)

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

				_lowerCamelCase        =				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						,   )
				_lowerCamelCase        =				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						,   )
				_lowerCamelCase        =				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
				_lowerCamelCase        =				(
				    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
				)
				_lowerCamelCase        =				(
				    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_     :							np.ndarray						,   lowercase_     :							np.ndarray							) -> Tuple[List[int], List[int]]:
								_lowerCamelCase        =				np.argmax(lowercase_						,   axis=2							)

								_lowerCamelCase  ,     _lowerCamelCase        =				preds.shape

								_lowerCamelCase        =				[[] for _ in range(lowercase_							)]
								_lowerCamelCase        =				[[] 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_     :							EvalPrediction							) -> Dict:
								_lowerCamelCase  ,     _lowerCamelCase        =				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
				_lowerCamelCase        =				DataCollatorWithPadding(lowercase_						,   pad_to_multiple_of=8							) if training_args.fpaa else None

				# Initialize our Trainer
				_lowerCamelCase        =				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
				_lowerCamelCase        =				{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''							)

								_lowerCamelCase        =				trainer.evaluate()

								_lowerCamelCase        =				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:
								_lowerCamelCase        =				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						,   )

								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				trainer.predict(lowercase_							)
								_lowerCamelCase  ,     _lowerCamelCase        =				align_predictions(lowercase_						,   lowercase_							)

								_lowerCamelCase        =				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
								_lowerCamelCase        =				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        lowerCAmelCase_(   lowercase_     :							Dict							)	->       str:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
			main()





"""simple docstring"""
import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax

			from transformers.models.big_bird.modeling_flax_big_bird import (
			    FlaxBigBirdForCausalLM,
			    FlaxBigBirdForMaskedLM,
			    FlaxBigBirdForMultipleChoice,
			    FlaxBigBirdForPreTraining,
			    FlaxBigBirdForQuestionAnswering,
			    FlaxBigBirdForSequenceClassification,
			    FlaxBigBirdForTokenClassification,
			    FlaxBigBirdModel,
			)


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=2				,       lowerCamelCase__=5_6				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=9_9				,       lowerCamelCase__=3_2				,       lowerCamelCase__=2				,       lowerCamelCase__=2				,       lowerCamelCase__=7				,       lowerCamelCase__="gelu_new"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=1_6				,       lowerCamelCase__=2				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=4				,       lowerCamelCase__="block_sparse"				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_attention_mask
									_lowerCamelCase        =				use_token_type_ids
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				max_position_embeddings
									_lowerCamelCase        =				type_vocab_size
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				num_choices

									_lowerCamelCase        =				rescale_embeddings
									_lowerCamelCase        =				attention_type
									_lowerCamelCase        =				use_bias
									_lowerCamelCase        =				block_size
									_lowerCamelCase        =				num_random_blocks

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				None
									if self.use_attention_mask:
													_lowerCamelCase        =				random_attention_mask([self.batch_size, self.seq_length]   )

									_lowerCamelCase        =				None
									if self.use_token_type_ids:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size   )

									_lowerCamelCase        =				BigBirdConfig(
									    vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       is_decoder=lowerCamelCase__				,       initializer_range=self.initializer_range				,       attention_type=self.attention_type				,       block_size=self.block_size				,       num_random_blocks=self.num_random_blocks				,       use_bias=self.use_bias				,       rescale_embeddings=self.rescale_embeddings				,       )

									return config, input_ids, token_type_ids, attention_mask





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				config_and_inputs
									_lowerCamelCase        =				{
									    '''input_ids''': input_ids,
									    '''token_type_ids''': token_type_ids,
									    '''attention_mask''': attention_mask,
									}
									return config, inputs_dict


@require_flax
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= (
					    (
					        FlaxBigBirdForCausalLM,
					        FlaxBigBirdModel,
					        FlaxBigBirdForPreTraining,
					        FlaxBigBirdForMaskedLM,
					        FlaxBigBirdForMultipleChoice,
					        FlaxBigBirdForQuestionAnswering,
					        FlaxBigBirdForSequenceClassification,
					        FlaxBigBirdForTokenClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					lowercase__   :       Any										= False
					lowercase__   :       Optional[int]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				FlaxBigBirdModelTester(self   )

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_from_pretrained_save_pretrained()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_from_pretrained_with_no_automatic_init()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_no_automatic_init()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									super().test_hidden_states_output()

					@slow
					def 			snake_case__      (    self   ):
									for model_class_name in self.all_model_classes:
													_lowerCamelCase        =				model_class_name.from_pretrained('''google/bigbird-roberta-base'''   )
													self.assertIsNotNone(lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									if self.test_attn_probs:
													super().test_attention_outputs()

					@slow
					# copied from `test_modeling_flax_common` because it takes much longer than other models
					def 			snake_case__      (    self   ):
									_lowerCamelCase  ,     _lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																	_lowerCamelCase        =				self._prepare_for_class(lowerCamelCase__				,       lowerCamelCase__   )
																	_lowerCamelCase        =				model_class(lowerCamelCase__   )

																	@jax.jit
																	def model_jitted(lowerCamelCase__				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
																					return model(input_ids=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       **lowerCamelCase__   )

																	with self.subTest('''JIT Enabled'''   ):
																					_lowerCamelCase        =				model_jitted(**lowerCamelCase__   ).to_tuple()

																	with self.subTest('''JIT Disabled'''   ):
																					with jax.disable_jit():
																									_lowerCamelCase        =				model_jitted(**lowerCamelCase__   ).to_tuple()

																	self.assertEqual(len(lowerCamelCase__   )				,       len(lowerCamelCase__   )   )
																	for jitted_output, output in zip(lowerCamelCase__				,       lowerCamelCase__   ):
																					self.assertEqual(jitted_output.shape				,       output.shape   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=1e-5				,       lowerCamelCase__="outputs"				,       lowerCamelCase__=None   ):
									# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
									# an effort was done to return `attention_probs` (yet to be verified).
									if name.startswith('''outputs.attentions'''   ):
													return
									else:
													super().check_pt_flax_outputs(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )
