






"""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()


lowercase           =							logging.getLogger(__name__)

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





@dataclass
class     A_				:
				UpperCAmelCase__										=					field(
				    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}	)
				UpperCAmelCase__										=					field(
				    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}	)
				UpperCAmelCase__										=					field(
				    default=1_2_8       ,				metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    }       ,				)
				UpperCAmelCase__										=					field(
				    default=snake_case_       ,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)






def _lowerCAmelCase						(	):

						'''simple docstring'''






						__magic_name__      =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
						__magic_name__   ,     __magic_name__   ,     __magic_name__      =  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." )

						__magic_name__      =  import_module("tasks" )

						try:
												__magic_name__      =  getattr(__lowerCamelCase			,    model_args.task_type )
												__magic_name__      =  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"			,    __lowerCamelCase )

						# Prepare Token Classification task
						__magic_name__      =  token_classification_task.get_labels(data_args.labels )
						__magic_name__      =  dict(enumerate(__lowerCamelCase ) )
						__magic_name__      =  len(__lowerCamelCase )

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

						__magic_name__      =  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			,    )
						__magic_name__      =  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():
												__magic_name__      =  TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path			,    from_pt=bool(".bin" in model_args.model_name_or_path )			,    config=__lowerCamelCase			,    cache_dir=model_args.cache_dir			,    )

						# Get datasets
						__magic_name__      =  (
						    TFTokenClassificationDataset(
						        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
						)
						__magic_name__      =  (
						    TFTokenClassificationDataset(
						        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:np.ndarray			,    __lowerCamelCase:np.ndarray ) -> Tuple[List[int], List[int]]:
												__magic_name__      =  np.argmax(__lowerCamelCase			,    axis=2 )
												__magic_name__   ,     __magic_name__      =  preds.shape
												__magic_name__      =  [[] for _ in range(__lowerCamelCase )]
												__magic_name__      =  [[] for _ in range(__lowerCamelCase )]

												for i in range(__lowerCamelCase ):
																		for j in range(__lowerCamelCase ):
																								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(__lowerCamelCase:EvalPrediction ) -> Dict:
												__magic_name__   ,     __magic_name__      =  align_predictions(p.predictions			,    p.label_ids )

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

						# Initialize our Trainer
						__magic_name__      =  TFTrainer(
						    model=__lowerCamelCase			,    args=__lowerCamelCase			,    train_dataset=train_dataset.get_dataset() if train_dataset else None			,    eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,    compute_metrics=__lowerCamelCase			,    )

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

						# Evaluation
						__magic_name__      =  {}
						if training_args.do_eval:
												logger.info("*** Evaluate ***" )

												__magic_name__      =  trainer.evaluate()
												__magic_name__      =  os.path.join(training_args.output_dir			,    "eval_results.txt" )

												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:
												__magic_name__      =  TFTokenClassificationDataset(
												    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			,    )

												__magic_name__   ,     __magic_name__   ,     __magic_name__      =  trainer.predict(test_dataset.get_dataset() )
												__magic_name__   ,     __magic_name__      =  align_predictions(__lowerCamelCase			,    __lowerCamelCase )
												__magic_name__      =  classification_report(__lowerCamelCase			,    __lowerCamelCase )

												logger.info("\n%s"			,    __lowerCamelCase )

												__magic_name__      =  os.path.join(training_args.output_dir			,    "test_results.txt" )

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

												# Save predictions
												__magic_name__      =  os.path.join(training_args.output_dir			,    "test_predictions.txt" )

												with open(__lowerCamelCase			,    "w" ) as writer:
																		with open(os.path.join(data_args.data_dir			,    "test.txt" )			,    "r" ) as f:
																								__magic_name__      =  0

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

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

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

						return results


if __name__ == "__main__":
			main()











"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase           =							logging.get_logger(__name__)

lowercase           =							{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}

class     A_				(   snake_case_	):







				def __init__(				self				:    List[Any]    ,     __lowerCamelCase				:    str=2_5_6    ,     __lowerCamelCase				:    str=1_0_2_4    ,     __lowerCamelCase				:    Optional[Any]=1_6    ,     __lowerCamelCase				:    Union[str, Any]=1_6    ,     __lowerCamelCase				:    str=4    ,     __lowerCamelCase				:    Union[str, Any]="gelu"    ,     __lowerCamelCase				:    Optional[int]=1e-6    ,     **__lowerCamelCase				:    str    ,     )					->       List[Any]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  image_size // patch_size
										__magic_name__      =  mask_input_channels
										__magic_name__      =  num_point_embeddings
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    List[str]    ,     __lowerCamelCase				:    Optional[Any]=2_5_6    ,     __lowerCamelCase				:    int="relu"    ,     __lowerCamelCase				:    List[str]=2_0_4_8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Optional[Any]=8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Dict=3    ,     __lowerCamelCase				:    Optional[int]=3    ,     __lowerCamelCase				:    Tuple=2_5_6    ,     __lowerCamelCase				:    List[str]=1e-6    ,     **__lowerCamelCase				:    Any    ,     )					->       str:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  hidden_act
										__magic_name__      =  mlp_dim
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  attention_downsample_rate
										__magic_name__      =  num_multimask_outputs
										__magic_name__      =  iou_head_depth
										__magic_name__      =  iou_head_hidden_dim
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    str=7_6_8    ,     __lowerCamelCase				:    Optional[int]=2_5_6    ,     __lowerCamelCase				:    Tuple=1_2    ,     __lowerCamelCase				:    int=1_2    ,     __lowerCamelCase				:    int=3    ,     __lowerCamelCase				:    int=1_0_2_4    ,     __lowerCamelCase				:    Any=1_6    ,     __lowerCamelCase				:    Tuple="gelu"    ,     __lowerCamelCase				:    Dict=1e-06    ,     __lowerCamelCase				:    List[Any]=0.0    ,     __lowerCamelCase				:    Optional[Any]=1e-10    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    List[str]=4.0    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    Optional[Any]=1_4    ,     __lowerCamelCase				:    str=[2, 5, 8, 1_1]    ,     __lowerCamelCase				:    List[Any]=1_2_8    ,     __lowerCamelCase				:    Dict=None    ,     **__lowerCamelCase				:    List[str]    ,     )					->       Optional[int]:
										super().__init__(**__lowerCamelCase						)

										__magic_name__      =  hidden_size
										__magic_name__      =  output_channels
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  num_channels
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps
										__magic_name__      =  attention_dropout
										__magic_name__      =  initializer_range
										__magic_name__      =  qkv_bias
										__magic_name__      =  mlp_ratio
										__magic_name__      =  use_abs_pos
										__magic_name__      =  use_rel_pos
										__magic_name__      =  window_size
										__magic_name__      =  global_attn_indexes
										__magic_name__      =  num_pos_feats
										__magic_name__      =  int(hidden_size * mlp_ratio						) if mlp_dim is None else mlp_dim





class     A_				(   snake_case_	):
				UpperCAmelCase__										=					'''sam'''
				UpperCAmelCase__										=					True







				def __init__(				self				:    Optional[Any]    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Optional[Any]=None    ,     __lowerCamelCase				:    int=0.02    ,     **__lowerCamelCase				:    List[Any]    ,     )					->       List[str]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  vision_config if vision_config is not None else {}
										__magic_name__      =  prompt_encoder_config if prompt_encoder_config is not None else {}
										__magic_name__      =  mask_decoder_config if mask_decoder_config is not None else {}

										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  vision_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  prompt_encoder_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  mask_decoder_config.to_dict()

										__magic_name__      =  SamVisionConfig(**__lowerCamelCase						)
										__magic_name__      =  SamPromptEncoderConfig(**__lowerCamelCase						)
										__magic_name__      =  SamMaskDecoderConfig(**__lowerCamelCase						)
										__magic_name__      =  initializer_range







				def      _snake_case       (				self				:    Optional[int]						)					->       str:
										__magic_name__      =  copy.deepcopy(self.__dict__						)
										__magic_name__      =  self.vision_config.to_dict()
										__magic_name__      =  self.prompt_encoder_config.to_dict()
										__magic_name__      =  self.mask_decoder_config.to_dict()
										__magic_name__      =  self.__class__.model_type
										return output



