





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__ :			Tuple       	=			True
				UpperCamelCase__ :			Dict       	=			"""p3dn.24xlarge"""
				import smdistributed.dataparallel.tensorflow as sdp
else:
				UpperCamelCase__ :			List[str]       	=			False




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   A=None ):
	UpperCAmelCase_		    =  tqdm(A )
	for i, batch in enumerate(A ):
		with tf.GradientTape() as tape:
			UpperCAmelCase_    ,						UpperCAmelCase_		    =  batch
			UpperCAmelCase_		    =  model(A )
			UpperCAmelCase_		    =  loss(A					,   outputs.logits )

		if SDP_ENABLED:
			UpperCAmelCase_		    =  sdp.DistributedGradientTape(A					,   sparse_as_dense=A )

		UpperCAmelCase_		    =  tape.gradient(A					,   model.trainable_variables )
		opt.apply_gradients(zip(A					,   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

	UpperCAmelCase_		    =  {"""loss""": loss_value.numpy()}
	return train_results




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_    ,						UpperCAmelCase_		    =  load_dataset("""imdb"""					,   split=["""train""", """test"""] )

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

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

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

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

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

	return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
				UpperCamelCase__ :			Union[str, 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__ :			Any       	=			parser.parse_known_args()

				# Set up logging
				UpperCamelCase__ :			int       	=			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__ :			Optional[int]       	=			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
				UpperCamelCase__ :			Union[str, Any]       	=			AutoTokenizer.from_pretrained(args.model_name_or_path)

				# get datasets
				UpperCamelCase__	,      UpperCamelCase__ :			str       	=			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__ :			Any       	=			tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
				UpperCamelCase__ :			Union[str, Any]       	=			tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
				UpperCamelCase__ :			Optional[int]       	=			[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__ :			Union[str, Any]       	=			time.time()
								UpperCamelCase__ :			List[str]       	=			fit(
								    model,
								    loss,
								    optimizer,
								    tf_train_dataset,
								    args.epochs,
								    args.per_device_train_batch_size,
								    max_steps=args.max_steps,
								)
								UpperCamelCase__ :			Optional[Any]       	=			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__ :			Any       	=			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)




"""simple docstring"""






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Optional[int]     					=     GPTNeoXJapaneseTokenizer
   a_					:		Dict     					=     False
   a_					:		Union[str, Any]     					=     {"""do_clean_text""": False, """add_prefix_space""": False}







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      super().setUp()

      a_						   =     [
          "こん",
          "こんに",
          "にちは",
          "ばんは",
          "世界,㔺界",
          "、",
          "。",
          "<BR>",
          "<SP>",
          "<TAB>",
          "<URL>",
          "<EMAIL>",
          "<TEL>",
          "<DATE>",
          "<PRICE>",
          "<BLOCK>",
          "<KIGOU>",
          "<U2000U2BFF>",
          "<|emoji1|>",
          "<unk>",
          "<|startoftext|>",
          "<|endoftext|>",
      ]
      a_						   =     {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
      a_						   =     {"unk_token": "<unk>"}

      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["vocab_file"])
      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["emoji_file"])
      with open(self.vocab_file       ,							"w"       ,							encoding="utf-8") as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
      with open(self.emoji_file       ,							"w") as emoji_writer:
         emoji_writer.write(json.dumps(__UpperCAmelCase))







   def    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->int:
      kwargs.update(self.special_tokens_map)
      return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Dict:
      a_						   =     "こんにちは、世界。 \nこんばんは、㔺界。😀"
      a_						   =     "こんにちは、世界。 \nこんばんは、世界。😀"
      return input_text, output_text







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_   ,			a_						   =     self.get_input_output_texts(__UpperCAmelCase)
      a_						   =     tokenizer.encode(__UpperCAmelCase       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.decode(__UpperCAmelCase       ,							clean_up_tokenization_spaces=__UpperCAmelCase)
      return text, ids







   def    UpperCAmelCase__	( self)   ->Any:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->int:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->List[str]:
      a_						   =     self.get_tokenizer()

      # Testing tokenization
      a_						   =     "こんにちは、世界。　こんばんは、㔺界。"
      a_						   =     ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
      a_						   =     tokenizer.tokenize(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids without special tokens
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids with special tokens
      a_						   =     tokens + [tokenizer.unk_token]
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)







   @slow
   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")

      a_						   =     tokenizer.encode("ありがとう。"       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.encode("どういたしまして。"       ,							add_special_tokens=__UpperCAmelCase)

      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase)
      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase       ,							__UpperCAmelCase)

      assert encoded_sentence == ids_a
      assert encoded_pair == ids_a + ids_a







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Intentionally convert some words to accommodate character fluctuations unique to Japanese
      pass







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      # tokenizer has no padding token
      pass