'''simple docstring'''
import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowercase__  :      Dict        =					logging.getLogger(__name__)

lowercase__  :      Optional[int]        =					tf.data.AUTOTUNE



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  argparse.ArgumentParser(description='''Train a masked language model on TPU.'''					)
      parser.add_argument(
          '''--pretrained_model_config'''							,    type=_UpperCamelCase							,    default='''roberta-base'''							,    help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!'''							,    )
      parser.add_argument(
          '''--tokenizer'''							,    type=_UpperCamelCase							,    default='''unigram-tokenizer-wikitext'''							,    help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'''							,    )

      parser.add_argument(
          '''--per_replica_batch_size'''							,    type=_UpperCamelCase							,    default=8							,    help='''Batch size per TPU core.'''							,    )

      parser.add_argument(
          '''--no_tpu'''							,    action='''store_true'''							,    help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'''							,    )

      parser.add_argument(
          '''--tpu_name'''							,    type=_UpperCamelCase							,    help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'''							,    default='''local'''							,    )

      parser.add_argument(
          '''--tpu_zone'''							,    type=_UpperCamelCase							,    help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''							,    )

      parser.add_argument(
          '''--gcp_project'''							,    type=_UpperCamelCase							,    help='''Google cloud project name. Only used for non-Colab TPU nodes.'''					)

      parser.add_argument(
          '''--bfloat16'''							,    action='''store_true'''							,    help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'''							,    )

      parser.add_argument(
          '''--train_dataset'''							,    type=_UpperCamelCase							,    help='''Path to training dataset to load. If the path begins with `gs://`'''
          ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''							,    )

      parser.add_argument(
          '''--shuffle_buffer_size'''							,    type=_UpperCamelCase							,    default=2**18							,    help='''Size of the shuffle buffer (in samples)'''							,    )

      parser.add_argument(
          '''--eval_dataset'''							,    type=_UpperCamelCase							,    help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
          ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''							,    )

      parser.add_argument(
          '''--num_epochs'''							,    type=_UpperCamelCase							,    default=1							,    help='''Number of epochs to train for.'''							,    )

      parser.add_argument(
          '''--learning_rate'''							,    type=_UpperCamelCase							,    default=1E-4							,    help='''Learning rate to use for training.'''							,    )

      parser.add_argument(
          '''--weight_decay_rate'''							,    type=_UpperCamelCase							,    default=1E-3							,    help='''Weight decay rate to use for training.'''							,    )

      parser.add_argument(
          '''--max_length'''							,    type=_UpperCamelCase							,    default=512							,    help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''							,    )

      parser.add_argument(
          '''--mlm_probability'''							,    type=_UpperCamelCase							,    default=0.15							,    help='''Fraction of tokens to mask during training.'''							,    )

      parser.add_argument('''--output_dir'''							,    type=_UpperCamelCase							,    required=_UpperCamelCase							,    help='''Path to save model checkpoints to.'''					)
      parser.add_argument('''--hub_model_id'''							,    type=_UpperCamelCase							,    help='''Model ID to upload to on the Hugging Face Hub.'''					)

      UpperCAmelCase_            =  parser.parse_args()
      return args



def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      try:
            if args.tpu_name:
                  UpperCAmelCase_            =  tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name							,    zone=args.tpu_zone							,    project=args.gcp_project					)
            else:
                  UpperCAmelCase_            =  tf.distribute.cluster_resolver.TPUClusterResolver()
      except ValueError:
            raise RuntimeError(
                '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
                '''--gcp_project. When running on a TPU VM, use --tpu_name local.'''					)

      tf.config.experimental_connect_to_cluster(_UpperCamelCase					)
      tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase					)

      return tpu



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  0
      for file in file_list:
            UpperCAmelCase_            =  file.split('''/'''					)[-1]
            UpperCAmelCase_            =  re.search(R'''-\d+-(\d+)\.tfrecord'''							,    _UpperCamelCase					).group(1					)
            UpperCAmelCase_            =  int(_UpperCamelCase					)
            num_samples += sample_count

      return num_samples



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  int=None					):


      '''simple docstring'''


      UpperCAmelCase_            =  count_samples(_UpperCamelCase					)
      UpperCAmelCase_            =  tf.data.Dataset.from_tensor_slices(_UpperCamelCase					)
      if shuffle:
            UpperCAmelCase_            =  dataset.shuffle(len(_UpperCamelCase					)					)
      UpperCAmelCase_            =  tf.data.TFRecordDataset(_UpperCamelCase							,    num_parallel_reads=_UpperCamelCase					)
      # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
      UpperCAmelCase_            =  dataset.apply(tf.data.experimental.assert_cardinality(_UpperCamelCase					)					)
      UpperCAmelCase_            =  dataset.map(_UpperCamelCase							,    num_parallel_calls=_UpperCamelCase					)
      if shuffle:
            assert shuffle_buffer_size is not None
            UpperCAmelCase_            =  dataset.shuffle(args.shuffle_buffer_size					)
      UpperCAmelCase_            =  dataset.batch(_UpperCamelCase							,    drop_remainder=_UpperCamelCase					)
      UpperCAmelCase_            =  dataset.map(_UpperCamelCase							,    num_parallel_calls=_UpperCamelCase					)
      UpperCAmelCase_            =  dataset.prefetch(_UpperCamelCase					)
      return dataset



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not args.no_tpu:
            UpperCAmelCase_            =  initialize_tpu(_UpperCamelCase					)
            UpperCAmelCase_            =  tf.distribute.TPUStrategy(_UpperCamelCase					)
      else:
            UpperCAmelCase_            =  tf.distribute.OneDeviceStrategy(device='''/gpu:0'''					)

      if args.bfloataa:
            tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''					)

      UpperCAmelCase_            =  AutoTokenizer.from_pretrained(args.tokenizer					)
      UpperCAmelCase_            =  AutoConfig.from_pretrained(args.pretrained_model_config					)
      UpperCAmelCase_            =  tokenizer.vocab_size

      UpperCAmelCase_            =  tf.io.gfile.glob(os.path.join(args.train_dataset							,    '''*.tfrecord'''					)					)
      if not training_records:
            raise ValueError(F"""No .tfrecord files found in {args.train_dataset}."""					)
      UpperCAmelCase_            =  tf.io.gfile.glob(os.path.join(args.eval_dataset							,    '''*.tfrecord'''					)					)
      if not eval_records:
            raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}."""					)

      UpperCAmelCase_            =  count_samples(_UpperCamelCase					)

      UpperCAmelCase_            =  num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
      UpperCAmelCase_            =  steps_per_epoch * args.num_epochs

      with strategy.scope():
            UpperCAmelCase_            =  TFAutoModelForMaskedLM.from_config(_UpperCamelCase					)
            model(model.dummy_inputs					)  # Pass some dummy inputs through the model to ensure all the weights are built
            UpperCAmelCase_      ,		UpperCAmelCase_            =  create_optimizer(
                num_train_steps=_UpperCamelCase							,    num_warmup_steps=total_train_steps // 20							,    init_lr=args.learning_rate							,    weight_decay_rate=args.weight_decay_rate							,    )

            # Transformers models compute the right loss for their task by default when labels are passed, and will
            # use this for training unless you specify your own loss function in compile().
            model.compile(optimizer=_UpperCamelCase							,    metrics=['''accuracy''']					)

      def decode_fn(_UpperCamelCase  :  List[str]					):
            UpperCAmelCase_            =  {
                '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa							,    shape=(args.max_length,)					),
                '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa							,    shape=(args.max_length,)					),
            }
            return tf.io.parse_single_example(_UpperCamelCase							,    _UpperCamelCase					)

      # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
      # use their methods in our data pipeline.
      UpperCAmelCase_            =  DataCollatorForLanguageModeling(
          tokenizer=_UpperCamelCase							,    mlm_probability=args.mlm_probability							,    mlm=_UpperCamelCase							,    return_tensors='''tf'''					)

      def mask_with_collator(_UpperCamelCase  :  Optional[Any]					):
            # TF really needs an isin() function
            UpperCAmelCase_            =  (
                ~tf.cast(batch['''attention_mask''']							,    tf.bool					)
                | (batch['''input_ids'''] == tokenizer.cls_token_id)
                | (batch['''input_ids'''] == tokenizer.sep_token_id)
            )
            UpperCAmelCase_      ,		UpperCAmelCase_            =  data_collator.tf_mask_tokens(
                batch['''input_ids''']							,    vocab_size=len(_UpperCamelCase					)							,    mask_token_id=tokenizer.mask_token_id							,    special_tokens_mask=_UpperCamelCase							,    )
            return batch

      UpperCAmelCase_            =  args.per_replica_batch_size * strategy.num_replicas_in_sync

      UpperCAmelCase_            =  prepare_dataset(
          _UpperCamelCase							,    decode_fn=_UpperCamelCase							,    mask_fn=_UpperCamelCase							,    batch_size=_UpperCamelCase							,    shuffle=_UpperCamelCase							,    shuffle_buffer_size=args.shuffle_buffer_size							,    )

      UpperCAmelCase_            =  prepare_dataset(
          _UpperCamelCase							,    decode_fn=_UpperCamelCase							,    mask_fn=_UpperCamelCase							,    batch_size=_UpperCamelCase							,    shuffle=_UpperCamelCase							,    )

      UpperCAmelCase_            =  []
      if args.hub_model_id:
            callbacks.append(
                PushToHubCallback(output_dir=args.output_dir							,    hub_model_id=args.hub_model_id							,    tokenizer=_UpperCamelCase					)					)

      model.fit(
          _UpperCamelCase							,    validation_data=_UpperCamelCase							,    epochs=args.num_epochs							,    callbacks=_UpperCamelCase							,    )

      model.save_pretrained(args.output_dir					)


if __name__ == "__main__":
      lowercase__  :      Optional[Any]        =					parse_args()
      main(args)






'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


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



def  __lowerCamelCase       (      _UpperCamelCase  :  np.ndarray							,    _UpperCamelCase  :  Union[int, Iterable[int]]							,    _UpperCamelCase  :  bool							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      def constraint_to_multiple_of(_UpperCamelCase  :  int							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[int]=0							,    _UpperCamelCase  :  str=None					):
            UpperCAmelCase_            =  round(val / multiple					) * multiple

            if max_val is not None and x > max_val:
                  UpperCAmelCase_            =  math.floor(val / multiple					) * multiple

            if x < min_val:
                  UpperCAmelCase_            =  math.ceil(val / multiple					) * multiple

            return x

      UpperCAmelCase_            =  (output_size, output_size) if isinstance(_UpperCamelCase							,    _UpperCamelCase					) else output_size

      UpperCAmelCase_      ,		UpperCAmelCase_            =  get_image_size(_UpperCamelCase					)
      UpperCAmelCase_      ,		UpperCAmelCase_            =  output_size

      # determine new height and width
      UpperCAmelCase_            =  output_height / input_height
      UpperCAmelCase_            =  output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width					) < abs(1 - scale_height					):
                  # fit width
                  UpperCAmelCase_            =  scale_width
            else:
                  # fit height
                  UpperCAmelCase_            =  scale_height

      UpperCAmelCase_            =  constraint_to_multiple_of(scale_height * input_height							,    multiple=_UpperCamelCase					)
      UpperCAmelCase_            =  constraint_to_multiple_of(scale_width * input_width							,    multiple=_UpperCamelCase					)

      return (new_height, new_width)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	['''pixel_values''']






  def __init__(      self			:       Any  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BILINEAR  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							**UpperCAmelCase__			:       str  ,							)				->None:
        super().__init__(**UpperCAmelCase__  )
        UpperCAmelCase_            =  size if size is not None else {'''height''': 384, '''width''': 384}
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size
        UpperCAmelCase_            =  keep_aspect_ratio
        UpperCAmelCase_            =  ensure_multiple_of
        UpperCAmelCase_            =  resample
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase_            =  image_std if image_std is not None else IMAGENET_STANDARD_STD






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Dict[str, int]  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BICUBIC  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       List[str]  ,							)				->np.ndarray:
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        if "height" not in size or "width" not in size:
              raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
        UpperCAmelCase_            =  get_resize_output_image_size(
            UpperCAmelCase__  ,							output_size=(size['''height'''], size['''width'''])  ,							keep_aspect_ratio=UpperCAmelCase__  ,							multiple=UpperCAmelCase__  ,							)
        return resize(UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[int, float]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       Optional[Any]  ,							)				->Any:
        return rescale(UpperCAmelCase__  ,							scale=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[float, List[float]]  ,							UpperCAmelCase__			:       Union[float, List[float]]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       Optional[int]  ,							)				->np.ndarray:
        return normalize(UpperCAmelCase__  ,							mean=UpperCAmelCase__  ,							std=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       ImageInput  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       int = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       int = None  ,							UpperCAmelCase__			:       PILImageResampling = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       float = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[str, TensorType]] = None  ,							UpperCAmelCase__			:       ChannelDimension = ChannelDimension.FIRST  ,							**UpperCAmelCase__			:       Any  ,							)				->PIL.Image.Image:
        UpperCAmelCase_            =  do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase_            =  size if size is not None else self.size
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        UpperCAmelCase_            =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
        UpperCAmelCase_            =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
        UpperCAmelCase_            =  resample if resample is not None else self.resample
        UpperCAmelCase_            =  do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase_            =  rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase_            =  do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase_            =  image_std if image_std is not None else self.image_std

        UpperCAmelCase_            =  make_list_of_images(UpperCAmelCase__  )

        if not valid_images(UpperCAmelCase__  ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

        if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

        if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

        # All transformations expect numpy arrays.
        UpperCAmelCase_            =  [to_numpy_array(UpperCAmelCase__  ) for image in images]

        if do_resize:
              UpperCAmelCase_            =  [self.resize(image=UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  ) for image in images]

        if do_rescale:
              UpperCAmelCase_            =  [self.rescale(image=UpperCAmelCase__  ,							scale=UpperCAmelCase__  ) for image in images]

        if do_normalize:
              UpperCAmelCase_            =  [self.normalize(image=UpperCAmelCase__  ,							mean=UpperCAmelCase__  ,							std=UpperCAmelCase__  ) for image in images]

        UpperCAmelCase_            =  [to_channel_dimension_format(UpperCAmelCase__  ,							UpperCAmelCase__  ) for image in images]

        UpperCAmelCase_            =  {'''pixel_values''': images}
        return BatchFeature(data=UpperCAmelCase__  ,							tensor_type=UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       List[Tuple] = None  )				->Optional[Any]:
        UpperCAmelCase_            =  outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
              if len(UpperCAmelCase__  ) != len(UpperCAmelCase__  ):
                    raise ValueError(
                        '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''  )

              if is_torch_tensor(UpperCAmelCase__  ):
                    UpperCAmelCase_            =  target_sizes.numpy()

              UpperCAmelCase_            =  []

              for idx in range(len(UpperCAmelCase__  )  ):
                    UpperCAmelCase_            =  torch.nn.functional.interpolate(
                        logits[idx].unsqueeze(dim=0  )  ,							size=target_sizes[idx]  ,							mode='''bilinear'''  ,							align_corners=UpperCAmelCase__  )
                    UpperCAmelCase_            =  resized_logits[0].argmax(dim=0  )
                    semantic_segmentation.append(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  logits.argmax(dim=1  )
              UpperCAmelCase_            =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

        return semantic_segmentation





