




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 						=logging.getLogger(__name__)






@dataclass
class  __magic_name__						:
				'''simple docstring'''







				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=__UpperCAmelCase    ,  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=__UpperCAmelCase    ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
				SCREAMING_SNAKE_CASE__					:							bool	      =					field(default=__UpperCAmelCase    ,  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=__UpperCAmelCase    ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,  )






@dataclass
class  __magic_name__						:
				'''simple docstring'''







				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(
				    default=__UpperCAmelCase    ,  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=__UpperCAmelCase    ,  metadata={"help": "Overwrite the cached training and evaluation sets"})






def 			a   ():
							# 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.

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

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

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

							SCREAMING_SNAKE_CASE_   	=    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  ,    )
							SCREAMING_SNAKE_CASE_   	=    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  ,    )
							SCREAMING_SNAKE_CASE_   	=    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
							SCREAMING_SNAKE_CASE_   	=    (
							    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
							)
							SCREAMING_SNAKE_CASE_   	=    (
							    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]]:
														SCREAMING_SNAKE_CASE_   	=    np.argmax(_lowerCAmelCase  ,    axis=2     )

														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    preds.shape

														SCREAMING_SNAKE_CASE_   	=    [[] for _ in range(_lowerCAmelCase     )]
														SCREAMING_SNAKE_CASE_   	=    [[] 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:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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
							SCREAMING_SNAKE_CASE_   	=    DataCollatorWithPadding(_lowerCAmelCase  ,    pad_to_multiple_of=8     ) if training_args.fpaa else None

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

														SCREAMING_SNAKE_CASE_   	=    trainer.evaluate()

														SCREAMING_SNAKE_CASE_   	=    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:
														SCREAMING_SNAKE_CASE_   	=    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  ,    )

														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    trainer.predict(_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    align_predictions(_lowerCAmelCase  ,    _lowerCAmelCase     )

														SCREAMING_SNAKE_CASE_   	=    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
														SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase     ):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
			main()








import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
			import jax






@require_flax
class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					FlaxAutoencoderKL


				@property
				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    4
											SCREAMING_SNAKE_CASE_   	=    3
											SCREAMING_SNAKE_CASE_   	=    (32, 32)

											SCREAMING_SNAKE_CASE_   	=    jax.random.PRNGKey(0		)
											SCREAMING_SNAKE_CASE_   	=    jax.random.uniform(_lowerCamelCase		,					((batch_size, num_channels) + sizes)		)

											return {"sample": image, "prng_key": prng_key}





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    {
											    '''block_out_channels''': [32, 64],
											    '''in_channels''': 3,
											    '''out_channels''': 3,
											    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
											    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
											    '''latent_channels''': 4,
											}
											SCREAMING_SNAKE_CASE_   	=    self.dummy_input
											return init_dict, inputs_dict


