





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


_snake_case  :   Tuple						   =  logging.getLogger(__name__)



@dataclass
class     _UpperCAmelCase   :

  """simple docstring"""


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





@dataclass
class     _UpperCAmelCase   :

  """simple docstring"""


  a_      =			field(
      metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}   )
  a_      =			field(
      default=_UpperCamelCase		,   metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}		,   )
  a_      =			field(
      default=1_28		,   metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }		,   )
  a_      =			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.

   __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(lowerCAmelCase_,     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',     lowerCAmelCase_     )

   # Set seed
   set_seed(training_args.seed     )

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

   # 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=lowerCAmelCase_,     idalabel=lowerCAmelCase_,     labelaid={label: i for i, label in enumerate(lowerCAmelCase_     )},     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=lowerCAmelCase_,     cache_dir=model_args.cache_dir,     )

   # Get datasets
   __lowerCAmelCase							     =     (
       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
   )
   __lowerCAmelCase							     =     (
       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_				:      np.ndarray,     lowerCAmelCase_				:      np.ndarray     ) -> Tuple[List[int], List[int]]:
      __lowerCAmelCase							     =     np.argmax(lowerCAmelCase_,     axis=2     )

      __lowerCAmelCase     ,     __lowerCAmelCase							     =     preds.shape

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

   # Initialize our Trainer
   __lowerCAmelCase							     =     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
   __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(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:
      __lowerCAmelCase							     =     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,     )

      __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     trainer.predict(lowerCAmelCase_     )
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     align_predictions(lowerCAmelCase_,     lowerCAmelCase_     )

      __lowerCAmelCase							     =     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
      __lowerCAmelCase							     =     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_				:      List[Any]     ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()








from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		int = 1_6 ,  lowerCAmelCase_			:		int = 8_8 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 3_2 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		str = "geglu" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  )							->						Dict:
     super().__init__()
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     attention_head_dim
     __lowerCAmelCase							     =     num_attention_heads * attention_head_dim

     __lowerCAmelCase							     =     in_channels

     __lowerCAmelCase							     =     torch.nn.GroupNorm(num_groups=lowerCAmelCase_ ,  num_channels=lowerCAmelCase_ ,  eps=1e-6 ,  affine=lowerCAmelCase_					)
     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 3. Define transformers blocks
     __lowerCAmelCase							     =     nn.ModuleList(
         [
             BasicTransformerBlock(
                 lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  dropout=lowerCAmelCase_ ,  cross_attention_dim=lowerCAmelCase_ ,  activation_fn=lowerCAmelCase_ ,  attention_bias=lowerCAmelCase_ ,  double_self_attention=lowerCAmelCase_ ,  norm_elementwise_affine=lowerCAmelCase_ ,  )
             for d in range(lowerCAmelCase_					)
         ]					)

     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[int]=1 ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		bool = True ,  )							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     hidden_states.shape
     __lowerCAmelCase							     =     batch_frames // num_frames

     __lowerCAmelCase							     =     hidden_states

     __lowerCAmelCase							     =     hidden_states[None, :].reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  2 ,  1 ,  3 ,  4					)

     __lowerCAmelCase							     =     self.norm(lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  3 ,  4 ,  2 ,  1					).reshape(batch_size * height * width ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.proj_in(lowerCAmelCase_					)

     # 2. Blocks
     for block in self.transformer_blocks:
        __lowerCAmelCase							     =     block(
            lowerCAmelCase_ ,  encoder_hidden_states=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  cross_attention_kwargs=lowerCAmelCase_ ,  class_labels=lowerCAmelCase_ ,  )

     # 3. Output
     __lowerCAmelCase							     =     self.proj_out(lowerCAmelCase_					)
     __lowerCAmelCase							     =     (
         hidden_states[None, None, :]
         .reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
         .permute(0 ,  3 ,  4 ,  1 ,  2					)
         .contiguous()
     )
     __lowerCAmelCase							     =     hidden_states.reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=lowerCAmelCase_					)

