





"""simple docstring"""
import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
       __UpperCamelCase			:		Optional[Any]		    =   True
       __UpperCamelCase			:		Optional[int]		    =   '''p3dn.24xlarge'''
       import smdistributed.dataparallel.tensorflow as sdp
else:
       __UpperCamelCase			:		int		    =   False

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Optional[Any]   ,			_UpperCAmelCase :  int   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  Optional[int]   ,			_UpperCAmelCase :  Dict   ,			_UpperCAmelCase :  Optional[int]=None       ):
 lowerCAmelCase  							=      tqdm(_UpperCAmelCase       )
 for i, batch in enumerate(_UpperCAmelCase       ):
  with tf.GradientTape() as tape:
   lowerCAmelCase							,lowerCAmelCase  							=      batch
   lowerCAmelCase  							=      model(_UpperCAmelCase       )
   lowerCAmelCase  							=      loss(_UpperCAmelCase   ,			outputs.logits       )

  if SDP_ENABLED:
   lowerCAmelCase  							=      sdp.DistributedGradientTape(_UpperCAmelCase   ,			sparse_as_dense=_UpperCAmelCase       )

  lowerCAmelCase  							=      tape.gradient(_UpperCAmelCase   ,			model.trainable_variables       )
  opt.apply_gradients(zip(_UpperCAmelCase   ,			model.trainable_variables       )       )

  pbar.set_description(F'Loss: {loss_value:.4f}'       )

  if SDP_ENABLED and i == 0:
   sdp.broadcast_variables(model.variables   ,			root_rank=0       )
   sdp.broadcast_variables(opt.variables()   ,			root_rank=0       )

  if max_steps and i >= max_steps:
   break

 lowerCAmelCase  							=      {'loss': loss_value.numpy()}
 return train_results

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  List[str]       ):
 # Load dataset
 lowerCAmelCase							,lowerCAmelCase  							=      load_dataset('imdb'   ,			split=['train', 'test']       )

 # Preprocess train dataset
 lowerCAmelCase  							=      train_dataset.map(
     lambda _UpperCAmelCase       : tokenizer(e['text']   ,			truncation=_UpperCAmelCase   ,			padding='max_length'       )   ,			batched=_UpperCAmelCase       )
 train_dataset.set_format(type='tensorflow'   ,			columns=['input_ids', 'attention_mask', 'label']       )

 lowerCAmelCase  							=      {
     x: train_dataset[x].to_tensor(default_value=0   ,			shape=[None, tokenizer.model_max_length]       )
     for x in ['input_ids', 'attention_mask']
 }
 lowerCAmelCase  							=      tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])       )

 # Preprocess test dataset
 lowerCAmelCase  							=      test_dataset.map(
     lambda _UpperCAmelCase       : tokenizer(e['text']   ,			truncation=_UpperCAmelCase   ,			padding='max_length'       )   ,			batched=_UpperCAmelCase       )
 test_dataset.set_format(type='tensorflow'   ,			columns=['input_ids', 'attention_mask', 'label']       )

 lowerCAmelCase  							=      {
     x: test_dataset[x].to_tensor(default_value=0   ,			shape=[None, tokenizer.model_max_length]       )
     for x in ['input_ids', 'attention_mask']
 }
 lowerCAmelCase  							=      tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])       )

 if SDP_ENABLED:
  lowerCAmelCase  							=      tf_train_dataset.shard(sdp.size()   ,			sdp.rank()       )
  lowerCAmelCase  							=      tf_test_dataset.shard(sdp.size()   ,			sdp.rank()       )
 lowerCAmelCase  							=      tf_train_dataset.batch(_UpperCAmelCase   ,			drop_remainder=_UpperCAmelCase       )
 lowerCAmelCase  							=      tf_test_dataset.batch(_UpperCAmelCase   ,			drop_remainder=_UpperCAmelCase       )

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       __UpperCamelCase			:		List[Any]		    =   argparse.ArgumentParser()

       # Hyperparameters sent by the client are passed as command-line arguments to the script.
       parser.add_argument('''--epochs''', type=int, default=3)
       parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
       parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
       parser.add_argument('''--model_name_or_path''', type=str)
       parser.add_argument('''--learning_rate''', type=str, default=5e-5)
       parser.add_argument('''--do_train''', type=bool, default=True)
       parser.add_argument('''--do_eval''', type=bool, default=True)
       parser.add_argument('''--output_dir''', type=str)
       parser.add_argument('''--max_steps''', type=int, default=None)

       # Data, model, and output directories
       parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
       parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
       parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

       __UpperCamelCase    ,__UpperCamelCase			:		str		    =   parser.parse_known_args()

       # Set up logging
       __UpperCamelCase			:		Union[str, Any]		    =   logging.getLogger(__name__)

       logging.basicConfig(
           level=logging.getLevelName('''INFO'''),
           handlers=[logging.StreamHandler(sys.stdout)],
           format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
       )

       if SDP_ENABLED:
              sdp.init()

              __UpperCamelCase			:		Any		    =   tf.config.experimental.list_physical_devices('''GPU''')
              for gpu in gpus:
                     tf.config.experimental.set_memory_growth(gpu, True)
              if gpus:
                     tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
       __UpperCamelCase			:		List[str]		    =   TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       __UpperCamelCase			:		Union[str, Any]		    =   AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       __UpperCamelCase    ,__UpperCamelCase			:		int		    =   get_datasets(
           tokenizer=tokenizer,
           train_batch_size=args.per_device_train_batch_size,
           eval_batch_size=args.per_device_eval_batch_size,
       )

       # fine optimizer and loss
       __UpperCamelCase			:		Tuple		    =   tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
       __UpperCamelCase			:		Union[str, Any]		    =   tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       __UpperCamelCase			:		Dict		    =   [tf.keras.metrics.SparseCategoricalAccuracy()]
       model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

       # Training
       if args.do_train:
              # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
              __UpperCamelCase			:		Any		    =   time.time()
              __UpperCamelCase			:		List[Any]		    =   fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              __UpperCamelCase			:		Tuple		    =   time.time() - start_train_time
              logger.info('''*** Train ***''')
              logger.info(f'''train_runtime = {end_train_time}''')

              __UpperCamelCase			:		Dict		    =   os.path.join(args.output_dir, '''train_results.txt''')

              if not SDP_ENABLED or sdp.rank() == 0:
                     with open(output_eval_file, '''w''') as writer:
                            logger.info('''***** Train results *****''')
                            logger.info(train_results)
                            for key, value in train_results.items():
                                   logger.info(f'''  {key} = {value}''')
                                   writer.write(f'''{key} = {value}\n''')

    # Evaluation
       if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
              __UpperCamelCase			:		Tuple		    =   model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info('''*** Evaluate ***''')

              __UpperCamelCase			:		Union[str, Any]		    =   os.path.join(args.output_dir, '''eval_results.txt''')

              with open(output_eval_file, '''w''') as writer:
                     logger.info('''***** Eval results *****''')
                     logger.info(result)
                     for key, value in result.items():
                            logger.info(f'''  {key} = {value}''')
                            writer.write(f'''{key} = {value}\n''')

    # Save result
       if SDP_ENABLED:
              if sdp.rank() == 0:
                     model.save_pretrained(args.output_dir)
                     tokenizer.save_pretrained(args.output_dir)
       else:
              model.save_pretrained(args.output_dir)
              tokenizer.save_pretrained(args.output_dir)













import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__A          = logging.get_logger(__name__)

__A          = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A          = {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

__A          = {
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    VOCAB_FILES_NAMES
							A_       =    PRETRAINED_VOCAB_FILES_MAP
							A_       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_       =    ["input_ids", "attention_mask"]
							A_       =    LongformerTokenizer

							def __init__(     self:      List[str]						,   __A:      Dict=None						,   __A:      int=None						,   __A:      Tuple=None						,   __A:      Any="replace"						,   __A:      List[str]="<s>"						,   __A:      int="</s>"						,   __A:      Dict="</s>"						,   __A:      Tuple="<s>"						,   __A:      Optional[int]="<unk>"						,   __A:      Optional[int]="<pad>"						,   __A:      List[Any]="<mask>"						,   __A:      Tuple=False						,   __A:      Dict=True						,   **__A:      List[Any]						,   )			-> List[Any]:
												super().__init__(
												    __A						,   __A						,   tokenizer_file=__A						,   errors=__A						,   bos_token=__A						,   eos_token=__A						,   sep_token=__A						,   cls_token=__A						,   unk_token=__A						,   pad_token=__A						,   mask_token=__A						,   add_prefix_space=__A						,   trim_offsets=__A						,   **__A						,   )

												_A									=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
												if pre_tok_state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																	_A									=       getattr(__A						,   pre_tok_state.pop('''type'''      )      )
																	_A									=       add_prefix_space
																	_A									=       pre_tok_class(**__A      )

												_A									=       add_prefix_space

												_A									=       '''post_processor'''
												_A									=       getattr(self.backend_tokenizer						,   __A						,   __A      )
												if tokenizer_component_instance:
																	_A									=       json.loads(tokenizer_component_instance.__getstate__()      )

																	# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																	if "sep" in state:
																						_A									=       tuple(state['''sep''']      )
																	if "cls" in state:
																						_A									=       tuple(state['''cls''']      )

																	_A									=       False

																	if state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																						_A									=       add_prefix_space
																						_A									=       True

																	if state.get('''trim_offsets'''						,   __A      ) != trim_offsets:
																						_A									=       trim_offsets
																						_A									=       True

																	if changes_to_apply:
																						_A									=       getattr(__A						,   state.pop('''type'''      )      )
																						_A									=       component_class(**__A      )
																						setattr(self.backend_tokenizer						,   __A						,   __A      )

							@property
							def 					__A       (     self:      Union[str, Any]      )			-> str:
												if self._mask_token is None:
																	if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.'''      )
																	return None
												return str(self._mask_token      )

							@mask_token.setter
							def 					__A       (     self:      Optional[int]						,   __A:      str      )			-> Union[str, Any]:
												_A									=       AddedToken(__A						,   lstrip=__A						,   rstrip=__A      ) if isinstance(__A						,   __A      ) else value
												_A									=       value

							def 					__A       (     self:      Optional[Any]						,   *__A:      Any						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )
												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      Optional[Any]						,   *__A:      List[str]						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )

												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      List[str]						,   __A:      str						,   __A:      Optional[str] = None      )			-> Tuple[str]:
												_A									=       self._tokenizer.model.save(__A						,   name=__A      )
												return tuple(__A      )

							def 					__A       (     self:      Tuple						,   __A:      Dict						,   __A:      Optional[Any]=None      )			-> Optional[int]:
												_A									=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																	return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



							def 					__A       (     self:      Optional[int]						,   __A:      List[int]						,   __A:      Optional[List[int]] = None      )			-> List[int]:
												_A									=       [self.sep_token_id]
												_A									=       [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep      ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]



