


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

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


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

SCREAMING_SNAKE_CASE__       			=				{
    """microsoft/biogpt""": 1_0_2_4,
}




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Any  ):







	'''simple docstring'''



	lowercase_			 = set()
	lowercase_			 = word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)  )
		lowercase_			 = char
	return pairs







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__          =				["input_ids", "attention_mask"]




							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase="<unk>"						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="<pad>"						,       **UpperCAmelCase						,       )     ->       str:






								'''simple docstring'''






								super().__init__(
								    bos_token=UpperCAmelCase						,       eos_token=UpperCAmelCase						,       sep_token=UpperCAmelCase						,       unk_token=UpperCAmelCase						,       pad_token=UpperCAmelCase						,       **UpperCAmelCase						,       )

								try:
									import sacremoses
								except ImportError:
									raise ImportError(
									    "You need to install sacremoses to use BioGptTokenizer. "
									    "See https://pypi.org/project/sacremoses/ for installation."  )

								lowercase_			 = "en"
								lowercase_			 = sacremoses
								# cache of sm.MosesTokenizer instance
								lowercase_			 = {}
								lowercase_			 = {}
								with open(UpperCAmelCase						,       encoding="utf-8"  ) as vocab_handle:
									lowercase_			 = json.load(UpperCAmelCase  )
								lowercase_			 = {v: k for k, v in self.encoder.items()}
								with open(UpperCAmelCase						,       encoding="utf-8"  ) as merges_handle:
									lowercase_			 = merges_handle.read().split("\n"  )[:-1]
								lowercase_			 = [tuple(merge.split()[:2]  ) for merge in merges]
								lowercase_			 = dict(zip(UpperCAmelCase						,       range(len(UpperCAmelCase  )  )  )  )
								lowercase_			 = {}




							@property
							def 				A__      (  self  )     ->       Optional[int]:






								'''simple docstring'''






								return len(self.encoder  )




							def 				A__      (  self  )     ->       Optional[int]:






								'''simple docstring'''






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




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       List[Any]:






								'''simple docstring'''






								if lang not in self.cache_moses_tokenizer:
									lowercase_			 = self.sm.MosesTokenizer(lang=UpperCAmelCase  )
									lowercase_			 = moses_tokenizer
								return self.cache_moses_tokenizer[lang].tokenize(
								    UpperCAmelCase						,       aggressive_dash_splits=UpperCAmelCase						,       return_str=UpperCAmelCase						,       escape=UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								if lang not in self.cache_moses_detokenizer:
									lowercase_			 = self.sm.MosesDetokenizer(lang=UpperCAmelCase  )
									lowercase_			 = moses_detokenizer
								return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Optional[Any]:






								'''simple docstring'''






								lowercase_			 = tuple(token[:-1]  ) + (token[-1] + "</w>",)
								if token in self.cache:
									return self.cache[token]
								lowercase_			 = get_pairs(UpperCAmelCase  )

								if not pairs:
									return token + "</w>"

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

										if word[i] == first and i < len(UpperCAmelCase  ) - 1 and word[i + 1] == second:
											new_word.append(first + second  )
											i += 2
										else:
											new_word.append(word[i]  )
											i += 1
									lowercase_			 = tuple(UpperCAmelCase  )
									lowercase_			 = new_word
									if len(UpperCAmelCase  ) == 1:
										break
									else:
										lowercase_			 = get_pairs(UpperCAmelCase  )
								lowercase_			 = " ".join(UpperCAmelCase  )
								if word == "\n  </w>":
									lowercase_			 = "\n</w>"
								lowercase_			 = word
								return word




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase=False  )     ->       Optional[Any]:






								'''simple docstring'''






								if bypass_tokenizer:
									lowercase_			 = text.split()
								else:
									lowercase_			 = self.moses_tokenize(UpperCAmelCase						,       self.lang  )

								lowercase_			 = []
								for token in text:
									if token:
										split_tokens.extend(list(self.bpe(UpperCAmelCase  ).split(" "  )  )  )

								return split_tokens




							def 				A__      (  self						,       UpperCAmelCase  )     ->       List[str]:






								'''simple docstring'''






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




							def 				A__      (  self						,       UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






								return self.decoder.get(UpperCAmelCase						,       self.unk_token  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								lowercase_			 = [t.replace(" "						,       ""  ).replace("</w>"						,       " "  ) for t in tokens]
								lowercase_			 = "".join(UpperCAmelCase  ).split()
								# detokenize
								lowercase_			 = self.moses_detokenize(UpperCAmelCase						,       self.lang  )
								return text




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								lowercase_			 = [self.sep_token_id]
								return sep + token_ids_a + sep + token_ids_a




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None						,       UpperCAmelCase = False  )     ->       List[int]:






								'''simple docstring'''






								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=UpperCAmelCase						,       token_ids_a=UpperCAmelCase						,       already_has_special_tokens=UpperCAmelCase  )
								# no bos used in fairseq
								if token_ids_a is not None:
									return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  ))
								return [1] + ([0] * len(UpperCAmelCase  ))




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								lowercase_			 = [self.sep_token_id]

								# no bos used in fairseq
								if token_ids_a is None:
									return len(token_ids_a + sep  ) * [0]
								return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       Tuple[str]:






								'''simple docstring'''






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

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

								lowercase_			 = 0
								with open(UpperCAmelCase						,       "w"						,       encoding="utf-8"  ) as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,       key=lambda UpperCAmelCase  : kv[1]  ):
										if index != token_index:
											logger.warning(
											    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
											    " Please check that the tokenizer is not corrupted!"  )
											lowercase_			 = token_index
										writer.write(" ".join(UpperCAmelCase  ) + "\n"  )
										index += 1

								return vocab_file, merge_file




							def __getstate__(  self  )     ->       List[str]:






								'''simple docstring'''






								lowercase_			 = self.__dict__.copy()
								lowercase_			 = None
								return state




							def __setstate__(  self						,       UpperCAmelCase  )     ->       Optional[Any]:






								'''simple docstring'''






								lowercase_			 = d

								try:
									import sacremoses
								except ImportError:
									raise ImportError(
									    "You need to install sacremoses to use XLMTokenizer. "
									    "See https://pypi.org/project/sacremoses/ for installation."  )

								lowercase_			 = sacremoses





import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


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

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=1_6)
							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)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

							# Set up logging
							SCREAMING_SNAKE_CASE__       			=				logging.getLogger(__name__)

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

							# Load model and tokenizer
							SCREAMING_SNAKE_CASE__       			=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							SCREAMING_SNAKE_CASE__       			=				AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

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

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

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

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

							# fine optimizer and loss
							SCREAMING_SNAKE_CASE__       			=				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							SCREAMING_SNAKE_CASE__       			=				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							SCREAMING_SNAKE_CASE__       			=				[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_SNAKE_CASE__       			=				time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(f"""train_runtime = {end_train_time}""")
							for key, value in train_results.history.items():
														logger.info(f"""  {key} = {value}""")

