

'''simple docstring'''




import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


_a					:			Any							=    'https://openaipublic.azureedge.net/jukebox/models/'
_a					:			Optional[int]							=    {
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):
  if key.endswith('.model.1.bias'    ) and len(key.split('.'    )    ) > 10:
    UpperCAmelCase    				=						key.replace('.model.1.bias'							, '.conv1d_1.bias'    )
  elif key.endswith('.model.1.weight'    ) and len(key.split('.'    )    ) > 10:
    UpperCAmelCase    				=						key.replace('.model.1.weight'							, '.conv1d_1.weight'    )
  elif key.endswith('.model.3.bias'    ) and len(key.split('.'    )    ) > 10:
    UpperCAmelCase    				=						key.replace('.model.3.bias'							, '.conv1d_2.bias'    )
  elif key.endswith('.model.3.weight'    ) and len(key.split('.'    )    ) > 10:
    UpperCAmelCase    				=						key.replace('.model.3.weight'							, '.conv1d_2.weight'    )

  if "conditioner_blocks.0." in key:
    UpperCAmelCase    				=						key.replace('conditioner_blocks.0'							, 'conditioner_blocks'    )

  if "prime_prior" in key:
    UpperCAmelCase    				=						key.replace('prime_prior'							, 'encoder'    )

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    UpperCAmelCase    				=						key.replace('.emb.'							, '.'    )

  if key.endswith('k'    ):  # replace vqvae.X.k with vqvae.X.codebook
    return key.replace('.k'							, '.codebook'    )
  if "y_emb." in key:
    return key.replace('y_emb.'							, 'metadata_embedding.'    )

  if "x_emb.emb." in key:
    UpperCAmelCase    				=						key.replace('0.x_emb.emb'							, 'embed_tokens'    )

  if "prime_state_ln" in key:
    return key.replace('prime_state_ln'							, 'encoder.final_layer_norm'    )
  if ".ln" in key:
    return key.replace('.ln'							, '.layer_norm'    )
  if "_ln" in key:
    return key.replace('_ln'							, '_layer_norm'    )

  if "prime_state_proj" in key:
    return key.replace('prime_state_proj'							, 'encoder.proj_in'    )
  if "prime_x_out" in key:
    return key.replace('prime_x_out'							, 'encoder.lm_head'    )
  if "prior.x_out" in key:
    return key.replace('x_out'							, 'fc_proj_out'    )
  if "x_emb" in key:
    return key.replace('x_emb'							, 'embed_tokens'    )

  return key


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Any    ):
  UpperCAmelCase    				=						{}
  import re

  UpperCAmelCase    				=						re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'    )
  UpperCAmelCase    				=						re.compile(
      R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'    )
  UpperCAmelCase    				=						re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'    )

  UpperCAmelCase    				=						re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'    )
  UpperCAmelCase    				=						re.compile(
      R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'    )
  UpperCAmelCase    				=						re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'    )

  UpperCAmelCase    				=						re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'    )
  UpperCAmelCase    				=						re.compile(
      R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'    )
  UpperCAmelCase    				=						re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'    )

  for original_key, value in state_dict.items():
    # rename vqvae.encoder keys
    if re_encoder_block_conv_in.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_encoder_block_conv_in.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						int(groups[2]    ) * 2 + int(groups[3]    )
      UpperCAmelCase    				=						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
      UpperCAmelCase    				=						re_encoder_block_conv_in.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						int(groups[2]    ) * 2 + int(groups[3]    )
      UpperCAmelCase    				=						{'1': 1, '3': 2}[groups[-2]]
      UpperCAmelCase    				=						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
      UpperCAmelCase    				=						f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      UpperCAmelCase    				=						prefix + resnet_block
      UpperCAmelCase    				=						re_encoder_block_resnet.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
      UpperCAmelCase    				=						re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    # rename vqvae.decoder keys
    elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						int(groups[2]    ) * 2 + int(groups[3]    ) - 2
      UpperCAmelCase    				=						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
      UpperCAmelCase    				=						re_decoder_block_conv_out.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						int(groups[2]    ) * 2 + int(groups[3]    ) - 2
      UpperCAmelCase    				=						{'1': 1, '3': 2}[groups[-2]]
      UpperCAmelCase    				=						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
      UpperCAmelCase    				=						f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      UpperCAmelCase    				=						prefix + resnet_block
      UpperCAmelCase    				=						re_decoder_block_resnet.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      UpperCAmelCase    				=						re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    # rename prior cond.model to upsampler.upsample_block and resnet
    elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						int(groups[1]    ) * 2 + int(groups[2]    ) - 2
      UpperCAmelCase    				=						f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
      UpperCAmelCase    				=						re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						int(groups[1]    ) * 2 + int(groups[2]    ) - 2
      UpperCAmelCase    				=						{'1': 1, '3': 2}[groups[-2]]
      UpperCAmelCase    				=						f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
      UpperCAmelCase    				=						f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      UpperCAmelCase    				=						prefix + resnet_block
      UpperCAmelCase    				=						re_prior_cond_resnet.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE    ):
      UpperCAmelCase    				=						re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE    )
      UpperCAmelCase    				=						regex_match.groups()
      UpperCAmelCase    				=						f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      UpperCAmelCase    				=						re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

    # keep original key
    else:
      UpperCAmelCase    				=						original_key

    UpperCAmelCase    				=						replace_key(SCREAMING_SNAKE_CASE    )

    if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
      print(f'''failed converting {original_key} to {key}, does not match'''    )

    # handle missmatched shape
    elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
      UpperCAmelCase    				=						model_state_dict[f'''{key_prefix}.{key}''']
      print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''    )
      UpperCAmelCase    				=						original_key

    UpperCAmelCase    				=						original_key
    UpperCAmelCase    				=						value

  return new_dict


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]=None							, SCREAMING_SNAKE_CASE   : Dict=None    ):
  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split('/'    )[-1]}'''    ):
      UpperCAmelCase    				=						requests.get(f'''{PREFIX}{file}'''							, allow_redirects=SCREAMING_SNAKE_CASE    )
      os.makedirs(f'''{pytorch_dump_folder_path}/'''							, exist_ok=SCREAMING_SNAKE_CASE    )
      open(f'''{pytorch_dump_folder_path}/{file.split('/'    )[-1]}'''							, 'wb'    ).write(r.content    )

  UpperCAmelCase    				=						MODEL_MAPPING[model_name.split('/'    )[-1]]

  UpperCAmelCase    				=						JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						JukeboxModel(SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						[]
  UpperCAmelCase    				=						{}
  for i, dict_name in enumerate(SCREAMING_SNAKE_CASE    ):
    UpperCAmelCase    				=						torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split('/'    )[-1]}'''    )['model']

    UpperCAmelCase    				=						{}
    for k in old_dic.keys():
      if k.endswith('.b'    ):
        UpperCAmelCase    				=						old_dic[k]
      elif k.endswith('.w'    ):
        UpperCAmelCase    				=						old_dic[k]
      elif "level_2" not in dict_name and "cond.model." in k:
        UpperCAmelCase    				=						old_dic[k]
      else:
        UpperCAmelCase    				=						old_dic[k]

    UpperCAmelCase    				=						'vqvae' if i == 0 else f'''priors.{3 - i}'''
    UpperCAmelCase    				=						fix_jukebox_keys(SCREAMING_SNAKE_CASE							, model.state_dict()							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
    weight_dict.append(SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						weight_dict.pop(0    )
  model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE    )
  for i in range(len(SCREAMING_SNAKE_CASE    )    ):
    model.priors[i].load_state_dict(weight_dict[2 - i]    )

  Path(SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE    )
  with open(f'''{pytorch_dump_folder_path}/mapping.json'''							, 'w'    ) as txtfile:
    json.dump(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

  print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''    )
  model.save_pretrained(SCREAMING_SNAKE_CASE    )

  return weight_dict


if __name__ == "__main__":
  _a					:			Dict							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='jukebox-5b-lyrics',
      type=str,
      help='Name of the model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='jukebox-5b-lyrics-converted',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  _a					:			Any							=    parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





'''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()


