'''simple docstring'''




import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase     							=logging.get_logger(__name__)


__UpperCAmelCase     							={
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

__UpperCAmelCase     							={
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

__UpperCAmelCase     							="</w>"
__UpperCAmelCase     							="@@ "






def __lowerCAmelCase (							UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  set()
    __lowerCamelCase		=  word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)				)
        __lowerCamelCase		=  char
    return pairs


# Speech2Text2 has no max input length
__UpperCAmelCase     							={"facebook/s2t-wav2vec2-large-en-de": 1_0_2_4}




class     a__      (   UpperCAmelCase__ ):
     lowerCamelCase  :     Optional[int]							=VOCAB_FILES_NAMES
     lowerCamelCase  :     Optional[int]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :     Any							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :     Dict							=["input_ids", "attention_mask"]






     def __init__( self		:		int  ,				a		:		List[str]  ,				a		:		int="<s>"  ,				a		:		Any="<pad>"  ,				a		:		Union[str, Any]="</s>"  ,				a		:		Optional[Any]="<unk>"  ,				a		:		List[Any]=False  ,				a		:		Tuple=None  ,				**a		:		str  ,				):





         """simple docstring"""



         super().__init__(
             unk_token=a  ,				bos_token=a  ,				eos_token=a  ,				pad_token=a  ,				do_lower_case=a  ,				**a  ,				)

         __lowerCamelCase		=  do_lower_case

         with open(a  ,				encoding='''utf-8'''    ) as vocab_handle:
             __lowerCamelCase		=  json.load(a    )
         __lowerCamelCase		=  {v: k for k, v in self.encoder.items()}

         if merges_file is None:
             logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""    )

             __lowerCamelCase		=  None
             __lowerCamelCase		=  None
         else:
             with open(a  ,				encoding='''utf-8'''    ) as merges_handle:
                 __lowerCamelCase		=  merges_handle.read().split('''\n'''    )[:-1]

             __lowerCamelCase		=  [tuple(merge.split()[:2]    ) for merge in merges]
             __lowerCamelCase		=  dict(zip(a  ,				range(len(a    )    )    )    )
             __lowerCamelCase		=  {}






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         return len(self.decoder    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         return dict(self.encoder  ,				**self.added_tokens_encoder    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		List[str]    ):





         """simple docstring"""



         __lowerCamelCase		=  tuple(token[:-1]    ) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
             return self.cache[token]
         __lowerCamelCase		=  get_pairs(a    )

         if not pairs:
             return token

         while True:
             __lowerCamelCase		=  min(a  ,				key=lambda a    : self.bpe_ranks.get(a  ,				float('''inf'''    )    )    )
             if bigram not in self.bpe_ranks:
                 break
             __lowerCamelCase	,  __lowerCamelCase		=  bigram
             __lowerCamelCase		=  []
             __lowerCamelCase		=  0
             while i < len(a    ):
                 try:
                     __lowerCamelCase		=  word.index(a  ,				a    )
                 except ValueError:
                     new_word.extend(word[i:]    )
                     break
                 else:
                     new_word.extend(word[i:j]    )
                     __lowerCamelCase		=  j

                 if word[i] == first and i < len(a    ) - 1 and word[i + 1] == second:
                     new_word.append(first + second    )
                     i += 2
                 else:
                     new_word.append(word[i]    )
                     i += 1
             __lowerCamelCase		=  tuple(a    )
             __lowerCamelCase		=  new_word
             if len(a    ) == 1:
                 break
             else:
                 __lowerCamelCase		=  get_pairs(a    )
         __lowerCamelCase		=  ''' '''.join(a    )
         if word == "\n  " + BPE_TOKEN_MERGES:
             __lowerCamelCase		=  '''\n''' + BPE_TOKEN_MERGES

         if word.endswith(a    ):
             __lowerCamelCase		=  word.replace(a  ,				''''''    )

         __lowerCamelCase		=  word.replace(''' '''  ,				a    )
         __lowerCamelCase		=  word
         return word






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		Any    ):





         """simple docstring"""



         if self.bpe_ranks is None:
             raise ValueError(
                 '''This tokenizer was instantiated without a `merges.txt` file, so'''
                 ''' that it can only be used for decoding, not for encoding.'''
                 '''Make sure to provide `merges.txt` file at instantiation to enable '''
                 '''encoding.'''    )

         if self.do_lower_case:
             __lowerCamelCase		=  text.lower()

         __lowerCamelCase		=  text.split()

         __lowerCamelCase		=  []
         for token in text:
             if token:
                 split_tokens.extend(list(self.bpe(a    ).split(''' '''    )    )    )

         return split_tokens






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]  ,				a		:		str    ):





         """simple docstring"""



         return self.encoder.get(a  ,				self.encoder.get(self.unk_token    )    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		int    ):





         """simple docstring"""



         __lowerCamelCase		=  self.decoder.get(a  ,				self.unk_token    )
         return result






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]  ,				a		:		List[str]    ):





         """simple docstring"""



         __lowerCamelCase		=  ''' '''.join(a    )

         # make sure @@ tokens are concatenated
         __lowerCamelCase		=  ''''''.join(string.split(a    )    )

         return string






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]  ,				a		:		str  ,				a		:		Optional[str] = None    ):





         """simple docstring"""



         if not os.path.isdir(a    ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
             return
         __lowerCamelCase		=  os.path.join(
             a  ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
         __lowerCamelCase		=  os.path.join(
             a  ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

         with open(a  ,				'''w'''  ,				encoding='''utf-8'''    ) as f:
             f.write(json.dumps(self.encoder  ,				indent=2  ,				sort_keys=a  ,				ensure_ascii=a    ) + '''\n'''    )

         __lowerCamelCase		=  0
         if self.bpe_ranks is None:
             return (vocab_file,)

         with open(a  ,				'''w'''  ,				encoding='''utf-8'''    ) as writer:
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,				key=lambda a    : kv[1]    ):
                 if index != token_index:
                     logger.warning(
                         f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                         ''' Please check that the tokenizer is not corrupted!'''    )
                     __lowerCamelCase		=  token_index
                 writer.write(''' '''.join(a    ) + '''\n'''    )
                 index += 1

         return (vocab_file, merges_file)







'''simple docstring'''







import argparse
import logging
import os
import re

import tensorflow as tf

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


lowerCAmelCase_					:    List[str]      	=			logging.getLogger(__name__)

lowerCAmelCase_					:    List[Any]      	=			tf.data.AUTOTUNE






def  _lowerCamelCase				( )	->	Optional[int]:
				_a								= argparse.ArgumentParser(description="Train a masked language model on TPU."						)
				parser.add_argument(
				    "--pretrained_model_config"    ,							type=lowercase    ,							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=lowercase    ,							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=lowercase    ,							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=lowercase    ,							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=lowercase    ,							help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."    ,							)

				parser.add_argument(
				    "--gcp_project"    ,							type=lowercase    ,							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=lowercase    ,							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=lowercase    ,							default=2**18    ,							help="Size of the shuffle buffer (in samples)"    ,							)

				parser.add_argument(
				    "--eval_dataset"    ,							type=lowercase    ,							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=lowercase    ,							default=1    ,							help="Number of epochs to train for."    ,							)

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

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

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

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

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

				_a								= parser.parse_args()
				return args






def  _lowerCamelCase				( lowercase						:      Union[str, Any]						)	->	Optional[int]:
				try:
								if args.tpu_name:
												_a								= tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name    ,							zone=args.tpu_zone    ,							project=args.gcp_project						)
								else:
												_a								= 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(lowercase						)
				tf.tpu.experimental.initialize_tpu_system(lowercase						)

				return tpu






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	Any:
				_a								= 0
				for file in file_list:
								_a								= file.split("/"						)[-1]
								_a								= re.search(r"-\d+-(\d+)\.tfrecord"    ,							lowercase						).group(1						)
								_a								= int(lowercase						)
								num_samples += sample_count

				return num_samples






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      Tuple    ,							lowercase						:      List[str]    ,							lowercase						:      Any    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]=None						)	->	int:
				_a								= count_samples(lowercase						)
				_a								= tf.data.Dataset.from_tensor_slices(lowercase						)
				if shuffle:
								_a								= dataset.shuffle(len(lowercase						)						)
				_a								= tf.data.TFRecordDataset(lowercase    ,							num_parallel_reads=lowercase						)
				# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
				_a								= dataset.apply(tf.data.experimental.assert_cardinality(lowercase						)						)
				_a								= dataset.map(lowercase    ,							num_parallel_calls=lowercase						)
				if shuffle:
								assert shuffle_buffer_size is not None
								_a								= dataset.shuffle(args.shuffle_buffer_size						)
				_a								= dataset.batch(lowercase    ,							drop_remainder=lowercase						)
				_a								= dataset.map(lowercase    ,							num_parallel_calls=lowercase						)
				_a								= dataset.prefetch(lowercase						)
				return dataset






def  _lowerCamelCase				( lowercase						:      Union[str, Any]						)	->	Dict:
				if not args.no_tpu:
								_a								= initialize_tpu(lowercase						)
								_a								= tf.distribute.TPUStrategy(lowercase						)
				else:
								_a								= tf.distribute.OneDeviceStrategy(device="/gpu:0"						)

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

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

				_a								= 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}.'						)
				_a								= 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}.'						)

				_a								= count_samples(lowercase						)

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

				with strategy.scope():
								_a								= TFAutoModelForMaskedLM.from_config(lowercase						)
								model(model.dummy_inputs						)  # Pass some dummy inputs through the model to ensure all the weights are built
								_a	,					_a								= create_optimizer(
								    num_train_steps=lowercase    ,							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=lowercase    ,							metrics=["accuracy"]						)

				def decode_fn(lowercase						:      int						):
								_a								= {
								    "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(lowercase    ,							lowercase						)

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

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

				_a								= args.per_replica_batch_size * strategy.num_replicas_in_sync

				_a								= prepare_dataset(
				    lowercase    ,							decode_fn=lowercase    ,							mask_fn=lowercase    ,							batch_size=lowercase    ,							shuffle=lowercase    ,							shuffle_buffer_size=args.shuffle_buffer_size    ,							)

				_a								= prepare_dataset(
				    lowercase    ,							decode_fn=lowercase    ,							mask_fn=lowercase    ,							batch_size=lowercase    ,							shuffle=lowercase    ,							)

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

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

				model.save_pretrained(args.output_dir						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Any      	=			parse_args()
					main(args)
