

'''simple docstring'''




import copy
import os
from typing import Union

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


_a					:			Optional[Any]							=    logging.get_logger(__name__)

_a					:			int							=    {
    'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Dict     	= "altclip_text_model"
  def __init__(  self ,			a_=2_5_0_0_0_2 ,			a_=1_0_2_4 ,			a_=2_4 ,			a_=1_6 ,			a_=4_0_9_6 ,			a_="gelu" ,			a_=0.1 ,			a_=0.1 ,			a_=5_1_4 ,			a_=1 ,			a_=0.02 ,			a_=0.02 ,			a_=1E-05 ,			a_=1 ,			a_=0 ,			a_=2 ,			a_="absolute" ,			a_=True ,			a_=7_6_8 ,			**a_ ,			)				->					Optional[int]:


    """simple docstring"""
    super().__init__(pad_token_id=a_ ,			bos_token_id=a_ ,			eos_token_id=a_ ,			**a_  )

    UpperCAmelCase    				=						vocab_size
    UpperCAmelCase    				=						hidden_size
    UpperCAmelCase    				=						num_hidden_layers
    UpperCAmelCase    				=						num_attention_heads
    UpperCAmelCase    				=						hidden_act
    UpperCAmelCase    				=						intermediate_size
    UpperCAmelCase    				=						hidden_dropout_prob
    UpperCAmelCase    				=						attention_probs_dropout_prob
    UpperCAmelCase    				=						max_position_embeddings
    UpperCAmelCase    				=						type_vocab_size
    UpperCAmelCase    				=						initializer_range
    UpperCAmelCase    				=						initializer_factor
    UpperCAmelCase    				=						layer_norm_eps
    UpperCAmelCase    				=						position_embedding_type
    UpperCAmelCase    				=						use_cache
    UpperCAmelCase    				=						project_dim




class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= "altclip_vision_model"
  def __init__(  self ,			a_=7_6_8 ,			a_=3_0_7_2 ,			a_=5_1_2 ,			a_=1_2 ,			a_=1_2 ,			a_=3 ,			a_=2_2_4 ,			a_=3_2 ,			a_="quick_gelu" ,			a_=1E-5 ,			a_=0.0 ,			a_=0.02 ,			a_=1.0 ,			**a_ ,			)				->					List[Any]:


    """simple docstring"""
    super().__init__(**a_  )

    UpperCAmelCase    				=						hidden_size
    UpperCAmelCase    				=						intermediate_size
    UpperCAmelCase    				=						projection_dim
    UpperCAmelCase    				=						num_hidden_layers
    UpperCAmelCase    				=						num_attention_heads
    UpperCAmelCase    				=						num_channels
    UpperCAmelCase    				=						patch_size
    UpperCAmelCase    				=						image_size
    UpperCAmelCase    				=						initializer_range
    UpperCAmelCase    				=						initializer_factor
    UpperCAmelCase    				=						attention_dropout
    UpperCAmelCase    				=						layer_norm_eps
    UpperCAmelCase    				=						hidden_act





  @classmethod
  def     snake_case_      (  cls ,			a_ ,			**a_  )				->					"PretrainedConfig":


    """simple docstring"""
    cls._set_token_in_kwargs(a_  )

    UpperCAmelCase				,   UpperCAmelCase    				=						cls.get_config_dict(a_ ,			**a_  )

    # get the vision config dict if we are loading from AltCLIPConfig
    if config_dict.get('model_type'  ) == "altclip":
      UpperCAmelCase    				=						config_dict['vision_config']

    if "model_type" in config_dict and hasattr(cls ,			'model_type'  ) and config_dict["model_type"] != cls.model_type:
      logger.warning(
          F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
          F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''  )

    return cls.from_dict(a_ ,			**a_  )




class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= "altclip"
  __lowerCAmelCase       :			Any     	= True
  def __init__(  self ,			a_=None ,			a_=None ,			a_=7_6_8 ,			a_=2.6592 ,			**a_  )				->					int:


    """simple docstring"""
    # If `_config_dict` exist, we use them for the backward compatibility.
    # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
    # of confusion!).
    UpperCAmelCase    				=						kwargs.pop('text_config_dict' ,			a_  )
    UpperCAmelCase    				=						kwargs.pop('vision_config_dict' ,			a_  )

    super().__init__(**a_  )

    # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
    # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
    # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
    if text_config_dict is not None:
      if text_config is None:
        UpperCAmelCase    				=						{}

      # This is the complete result when using `text_config_dict`.
      UpperCAmelCase    				=						AltCLIPTextConfig(**a_  ).to_dict()

      # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
      for key, value in _text_config_dict.items():
        if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
          # If specified in `text_config_dict`
          if key in text_config_dict:
            UpperCAmelCase    				=						(
                F'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
                F'''The value `text_config_dict["{key}"]` will be used instead.'''
            )
          # If inferred from default argument values (just to be super careful)
          else:
            UpperCAmelCase    				=						(
                F'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
                F'''value `text_config["{key}"]` will be overriden.'''
            )
          logger.warning(a_  )

            # Update all values in `text_config` with the ones in `_text_config_dict`.
      text_config.update(_text_config_dict  )

    if vision_config_dict is not None:
      if vision_config is None:
        UpperCAmelCase    				=						{}

      # This is the complete result when using `vision_config_dict`.
      UpperCAmelCase    				=						AltCLIPVisionConfig(**a_  ).to_dict()
      # convert keys to string instead of integer
      if "id2label" in _vision_config_dict:
        UpperCAmelCase    				=						{
            str(a_  ): value for key, value in _vision_config_dict['id2label'].items()
        }

      # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
      for key, value in _vision_config_dict.items():
        if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
          # If specified in `vision_config_dict`
          if key in vision_config_dict:
            UpperCAmelCase    				=						(
                F'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
                F'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
            )
          # If inferred from default argument values (just to be super careful)
          else:
            UpperCAmelCase    				=						(
                F'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
                F'''The value `vision_config["{key}"]` will be overriden.'''
            )
          logger.warning(a_  )

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
      vision_config.update(_vision_config_dict  )

    if text_config is None:
      UpperCAmelCase    				=						{}
      logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.'  )

    if vision_config is None:
      UpperCAmelCase    				=						{}
      logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.'  )

    UpperCAmelCase    				=						AltCLIPTextConfig(**a_  )
    UpperCAmelCase    				=						AltCLIPVisionConfig(**a_  )

    UpperCAmelCase    				=						projection_dim
    UpperCAmelCase    				=						logit_scale_init_value
    UpperCAmelCase    				=						1.0
  @classmethod
  def     snake_case_      (  cls ,			a_ ,			a_ ,			**a_  )				->					Dict:


    """simple docstring"""
    return cls(text_config=text_config.to_dict() ,			vision_config=vision_config.to_dict() ,			**a_  )





  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						copy.deepcopy(self.__dict__  )
    UpperCAmelCase    				=						self.text_config.to_dict()
    UpperCAmelCase    				=						self.vision_config.to_dict()
    UpperCAmelCase    				=						self.__class__.model_type
    return output





'''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


_a					:			List[Any]							=    logging.getLogger(__name__)



@dataclass
class 							lowercase_						:

  '''simple docstring'''





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




@dataclass
class 							lowercase_						:

  '''simple docstring'''





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




def    lowerCamelCase__		(    ):
  # 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.

  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(SCREAMING_SNAKE_CASE							, 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'							, SCREAMING_SNAKE_CASE    )

  # Set seed
  set_seed(training_args.seed    )

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

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

  # Get datasets
  UpperCAmelCase    				=						(
      TokenClassificationDataset(
          token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_SNAKE_CASE							, 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=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_SNAKE_CASE							, 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(SCREAMING_SNAKE_CASE   : np.ndarray							, SCREAMING_SNAKE_CASE   : np.ndarray    ) -> Tuple[List[int], List[int]]:
    UpperCAmelCase    				=						np.argmax(SCREAMING_SNAKE_CASE							, axis=2    )

    UpperCAmelCase				,   UpperCAmelCase    				=						preds.shape

    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]
    UpperCAmelCase    				=						[[] for _ in range(SCREAMING_SNAKE_CASE    )]

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

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

  # Initialize our Trainer
  UpperCAmelCase    				=						Trainer(
      model=SCREAMING_SNAKE_CASE							, args=SCREAMING_SNAKE_CASE							, train_dataset=SCREAMING_SNAKE_CASE							, eval_dataset=SCREAMING_SNAKE_CASE							, compute_metrics=SCREAMING_SNAKE_CASE							, data_collator=SCREAMING_SNAKE_CASE							, )

  # 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(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        logger.info('***** Eval results *****'    )
        for key, value in result.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
          writer.write('%s = %s\n' % (key, value)    )

      results.update(SCREAMING_SNAKE_CASE    )

    # Predict
  if training_args.do_predict:
    UpperCAmelCase    				=						TokenClassificationDataset(
        token_classification_task=SCREAMING_SNAKE_CASE							, data_dir=data_args.data_dir							, tokenizer=SCREAMING_SNAKE_CASE							, labels=SCREAMING_SNAKE_CASE							, 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(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase				,   UpperCAmelCase    				=						align_predictions(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    UpperCAmelCase    				=						os.path.join(training_args.output_dir							, 'test_results.txt'    )
    if trainer.is_world_process_zero():
      with open(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        for key, value in metrics.items():
          logger.info('  %s = %s'							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
          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(SCREAMING_SNAKE_CASE							, 'w'    ) as writer:
        with open(os.path.join(data_args.data_dir							, 'test.txt'    )							, 'r'    ) as f:
          token_classification_task.write_predictions_to_file(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  return results


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict    ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
  main()


