





import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


lowercase_          =		{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 131_072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
}




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       int:
		return torch.atana(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) / math.pi * 2




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       str:
		lowercase__           =  torch.sin(t * math.pi / 2		) ** 2
		lowercase__           =  (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
			pass






class     SCREAMING_SNAKE_CASE       (nn.Module				):


			def __init__(   self      :    int      ,					a      :    int	)->       Union[str, Any]:





					"""simple docstring"""







					super().__init__()

					lowercase__           =  DiffusionAttnUnetaD(a      ,					n_attn_layers=4	)
					lowercase__           =  deepcopy(self.diffusion	)
					lowercase__           =  torch.quasirandom.SobolEngine(1      ,					scramble=a	)






def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
		lowercase__           =  MODELS_MAP[model_name]['url']
		os.system(F"""wget {url} ./"""		)

		return F"""./{model_name}.ckpt"""


lowercase_          =		{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
lowercase_          =		{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
lowercase_          =		{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
    """8""": """resnets.3""",
    """9""": """attentions.3""",
    """10""": """resnets.4""",
    """11""": """attentions.4""",
    """12""": """resnets.5""",
    """13""": """attentions.5""",
}
lowercase_          =		{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

lowercase_          =		{
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

lowercase_          =		{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Tuple:
		if name.startswith('skip'		):
				return name.replace('skip'		,						RES_CONV_MAP['skip']		)

		# name has to be of format main.{digit}
		if not name.startswith('main.'		):
				raise ValueError(F"""ResConvBlock error with {name}"""		)

		return name.replace(name[:6]		,						RES_CONV_MAP[name[:6]]		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Tuple:
		for key, value in ATTN_MAP.items():
				if name.startswith(_SCREAMING_SNAKE_CASE		) and not isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
						return name.replace(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
				elif name.startswith(_SCREAMING_SNAKE_CASE		):
						return [name.replace(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) for v in value]
		raise ValueError(F"""Attn error with {name}"""		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=13		)    ->       List[str]:
		lowercase__           =  input_string

		if string.split('.'		)[0] == "timestep_embed":
				return string.replace('timestep_embed'		,						'time_proj'		)

		lowercase__           =  0
		if string.startswith('net.3.'		):
				depth += 1
				lowercase__           =  string[6:]
		elif string.startswith('net.'		):
				lowercase__           =  string[4:]

		while string.startswith('main.7.'		):
				depth += 1
				lowercase__           =  string[7:]

		if string.startswith('main.'		):
				lowercase__           =  string[5:]

		# mid block
		if string[:2].isdigit():
				lowercase__           =  string[:2]
				lowercase__           =  string[2:]
		else:
				lowercase__           =  string[0]
				lowercase__           =  string[1:]

		if depth == max_depth:
				lowercase__           =  MID_NUM_TO_LAYER[layer_num]
				lowercase__           =  'mid_block'
		elif depth > 0 and int(_SCREAMING_SNAKE_CASE		) < 7:
				lowercase__           =  DOWN_NUM_TO_LAYER[layer_num]
				lowercase__           =  F"""down_blocks.{depth}"""
		elif depth > 0 and int(_SCREAMING_SNAKE_CASE		) > 7:
				lowercase__           =  UP_NUM_TO_LAYER[layer_num]
				lowercase__           =  F"""up_blocks.{max_depth - depth - 1}"""
		elif depth == 0:
				lowercase__           =  DEPTH_0_TO_LAYER[layer_num]
				lowercase__           =  F"""up_blocks.{max_depth - 1}""" if int(_SCREAMING_SNAKE_CASE		) > 3 else 'down_blocks.0'

		if not string_left.startswith('.'		):
				raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""		)

		lowercase__           =  string_left[1:]

		if "resnets" in new_layer:
				lowercase__           =  convert_resconv_naming(_SCREAMING_SNAKE_CASE		)
		elif "attentions" in new_layer:
				lowercase__           =  convert_attn_naming(_SCREAMING_SNAKE_CASE		)
				lowercase__           =  new_string_left

		if not isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
				lowercase__           =  prefix + '.' + new_layer + '.' + string_left
		else:
				lowercase__           =  [prefix + '.' + new_layer + '.' + s for s in string_left]
		return new_string




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
		lowercase__           =  {}
		for k, v in state_dict.items():
				if k.endswith('kernel'		):
						# up- and downsample layers, don't have trainable weights
						continue

				lowercase__           =  rename(_SCREAMING_SNAKE_CASE		)

				# check if we need to transform from Conv => Linear for attention
				if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
						lowercase__           =  transform_conv_attns(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
				else:
						lowercase__           =  v

		return new_state_dict




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Dict:
		if len(_SCREAMING_SNAKE_CASE		) == 1:
				if len(v.shape		) == 3:
						# weight
						lowercase__           =  v[:, :, 0]
				else:
						# bias
						lowercase__           =  v
		else:
				# qkv matrices
				lowercase__           =  v.shape[0]
				lowercase__           =  trippled_shape // 3
				for i in range(3		):
						if len(v.shape		) == 3:
								lowercase__           =  v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								lowercase__           =  v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
		lowercase__           =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)

		lowercase__           =  args.model_path.split('/'		)[-1].split('.'		)[0]
		if not os.path.isfile(args.model_path		):
				assert (
				    model_name == args.model_path
				), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
				lowercase__           =  download(_SCREAMING_SNAKE_CASE		)

		lowercase__           =  MODELS_MAP[model_name]['sample_rate']
		lowercase__           =  MODELS_MAP[model_name]['sample_size']

		lowercase__           =  Object()
		lowercase__           =  sample_size
		lowercase__           =  sample_rate
		lowercase__           =  0

		lowercase__           =  UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE		,						sample_rate=_SCREAMING_SNAKE_CASE		)
		lowercase__           =  diffusers_model.state_dict()

		lowercase__           =  DiffusionUncond(_SCREAMING_SNAKE_CASE		)
		orig_model.load_state_dict(torch.load(args.model_path		,						map_location=_SCREAMING_SNAKE_CASE		)['state_dict']		)
		lowercase__           =  orig_model.diffusion_ema.eval()
		lowercase__           =  orig_model.state_dict()
		lowercase__           =  rename_orig_weights(_SCREAMING_SNAKE_CASE		)

		lowercase__           =  set(renamed_state_dict.keys()		) - set(diffusers_state_dict.keys()		)
		lowercase__           =  set(diffusers_state_dict.keys()		) - set(renamed_state_dict.keys()		)

		assert len(_SCREAMING_SNAKE_CASE		) == 0, F"""Problem with {renamed_minus_diffusers}"""
		assert all(k.endswith('kernel'		) for k in list(_SCREAMING_SNAKE_CASE		)		), F"""Problem with {diffusers_minus_renamed}"""

		for key, value in renamed_state_dict.items():
				assert (
				    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
				), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
				if key == "time_proj.weight":
						lowercase__           =  value.squeeze()

				lowercase__           =  value

		diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE		)

		lowercase__           =  100
		lowercase__           =  33

		lowercase__           =  IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE		)

		lowercase__           =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
		lowercase__           =  torch.randn([1, 2, config.sample_size]		,						generator=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

		lowercase__           =  torch.linspace(1		,						0		,						steps + 1		,						device=_SCREAMING_SNAKE_CASE		)[:-1]
		lowercase__           =  get_crash_schedule(_SCREAMING_SNAKE_CASE		)

		lowercase__           =  DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE		,						scheduler=_SCREAMING_SNAKE_CASE		)

		lowercase__           =  torch.manual_seed(33		)
		lowercase__           =  pipe(num_inference_steps=_SCREAMING_SNAKE_CASE		,						generator=_SCREAMING_SNAKE_CASE		).audios

		lowercase__           =  sampling.iplms_sample(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						{}		)
		lowercase__           =  generated.clamp(-1		,						1		)

		lowercase__           =  (generated - audio).abs().sum()
		lowercase__           =  (generated - audio).abs().max()

		if args.save:
				pipe.save_pretrained(args.checkpoint_path		)

		print('Diff sum'		,						_SCREAMING_SNAKE_CASE		)
		print('Diff max'		,						_SCREAMING_SNAKE_CASE		)

		assert diff_max < 1E-3, F"""Diff max: {diff_max} is too much :-/"""

		print(F"""Conversion for {model_name} successful!"""		)


if __name__ == "__main__":
						lowercase_          =		argparse.ArgumentParser()

						parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
						parser.add_argument(
						    """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
						)
						parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
						lowercase_          =		parser.parse_args()

						main(args)









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


lowercase_          =		logging.getLogger(__name__)
@dataclass
class     SCREAMING_SNAKE_CASE       :
   _UpperCamelCase   :		str			 =	field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
   _UpperCamelCase   :		Optional[str]			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
   _UpperCamelCase   :		Optional[str]			 =	field(
       default='NER'      ,     metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}				)
   _UpperCamelCase   :		Optional[str]			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
   _UpperCamelCase   :		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.
   _UpperCamelCase   :		Optional[str]			 =	field(
       default=UpperCAmelCase      ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,     )






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






def       __UpperCamelCase  ()    ->       str:
  # 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.

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

  lowercase__           =  import_module('tasks'		)
  try:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						model_args.task_type		)
    lowercase__           =  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
  lowercase__           =  token_classification_task.get_labels(data_args.labels		)
  lowercase__           =  dict(enumerate(_SCREAMING_SNAKE_CASE		)		)
  lowercase__           =  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.

  lowercase__           =  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		,						)
  lowercase__           =  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		,						)
  lowercase__           =  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
  lowercase__           =  (
      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
  )
  lowercase__           =  (
      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		,						_SCREAMING_SNAKE_CASE		) -> Tuple[List[int], List[int]]:
    lowercase__           =  np.argmax(_SCREAMING_SNAKE_CASE		,						axis=2		)

    lowercase__      ,   lowercase__           =  preds.shape

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

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

    lowercase__           =  trainer.evaluate()

    lowercase__           =  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:
    lowercase__           =  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		,						)

    lowercase__      ,   lowercase__      ,   lowercase__           =  trainer.predict(_SCREAMING_SNAKE_CASE		)
    lowercase__      ,   lowercase__           =  align_predictions(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

    lowercase__           =  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
    lowercase__           =  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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()


