




import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


UpperCAmelCase_		:				int									=     logging.get_logger(__name__)

UpperCAmelCase_		:				List[str]									=     {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

UpperCAmelCase_		:				int									=     {
    "vocab_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
    },
    "emoji_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
    },
}

UpperCAmelCase_		:				int									=     {
    "abeja/gpt-neox-japanese-2.7b": 2048,
}




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				with open(lowerCamelCase	,     """r"""	,     encoding="""utf-8"""   ) as f:
								__magic_name__	:	Dict								=json.loads(f.read()   )

				__magic_name__	:	Dict								=collections.OrderedDict()
				__magic_name__	:	Tuple								=collections.OrderedDict()
				__magic_name__	:	List[Any]								=collections.OrderedDict()
				with open(lowerCamelCase	,     """r"""	,     encoding="""utf-8"""   ) as f:
								__magic_name__	:	Dict								=f.readlines()
				__magic_name__	:	Tuple								=[[t.rstrip("""\n"""   )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""   ).split(""","""   ) for t in token]
				for idx, b in enumerate(lowerCamelCase   ):
								__magic_name__	:	Optional[Any]								=b
								__magic_name__	:	Union[str, Any]								=idx
								for wd in b:
												__magic_name__	:	int								=idx

				return vocab, raw_vocab, ids_to_tokens, emoji




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					VOCAB_FILES_NAMES
						UpperCamelCase     						=					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase     						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase     						=					["""input_ids""", """attention_mask"""]

						def __init__(		self   :str    , __snake_case   :List[Any]    , __snake_case   :Dict    , __snake_case   :Any="<|endoftext|>"    , __snake_case   :str="<|endoftext|>"    , __snake_case   :int="<|startoftext|>"    , __snake_case   :int="<|endoftext|>"    , __snake_case   :Dict=False    , **__snake_case   :Dict    , ):
										'''simple docstring'''

										super().__init__(
										    unk_token=__snake_case    , pad_token=__snake_case    , bos_token=__snake_case    , eos_token=__snake_case    , do_clean_text=__snake_case    , **__snake_case    , )
										if not os.path.isfile(__snake_case ):
														raise ValueError(
														    f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
														    """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" )
										if not os.path.isfile(__snake_case ):
														raise ValueError(
														    f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
														    """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" )
										__magic_name__	:	Tuple								=do_clean_text
										__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	str								=load_vocab_and_emoji(__snake_case    , __snake_case )
										__magic_name__	:	Dict								=SubWordJapaneseTokenizer(
										    vocab=self.vocab    , ids_to_tokens=self.ids_to_tokens    , emoji=self.emoji )

						@property
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										return len(self.raw_vocab )

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :Tuple    , __snake_case   :Tuple ):
										'''simple docstring'''

										return self.subword_tokenizer.tokenize(__snake_case    , clean=self.do_clean_text )

						def     A__						(		self   :Dict    , __snake_case   :Union[str, Any] ):
										'''simple docstring'''

										return self.vocab.get(__snake_case    , self.vocab.get(self.unk_token ) )

						def     A__						(		self   :Optional[int]    , __snake_case   :List[Any] ):
										'''simple docstring'''

										return self.subword_tokenizer.convert_id_to_token(__snake_case )

						def     A__						(		self   :Optional[Any]    , __snake_case   :int ):
										'''simple docstring'''

										__magic_name__	:	str								="""""".join(__snake_case ).strip()
										return out_string

						def     A__						(		self   :Union[str, Any]    , __snake_case   :"Conversation" ):
										'''simple docstring'''

										__magic_name__	:	int								=[]
										for is_user, text in conversation.iter_texts():
														input_ids.extend(self.encode(__snake_case    , add_special_tokens=__snake_case ) + [self.eos_token_id] )

										if len(__snake_case ) > self.model_max_length:
														__magic_name__	:	Union[str, Any]								=input_ids[-self.model_max_length :]
										return input_ids


						def     A__						(		self   :Dict    , __snake_case   :str    , __snake_case   :Optional[str] = None ):
										'''simple docstring'''

										__magic_name__	:	Dict								=0
										if os.path.isdir(__snake_case ):
														__magic_name__	:	Union[str, Any]								=os.path.join(
														    __snake_case    , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
														__magic_name__	:	Dict								=os.path.join(
														    __snake_case    , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""] )
										else:
														__magic_name__	:	str								=(
														    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
														)
														__magic_name__	:	Dict								=(
														    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
														)
										with open(__snake_case    , """w"""    , encoding="""utf-8""" ) as writer:
														for token_index, token in self.ids_to_tokens.items():
																		if index != token_index:
																						logger.warning(
																						    f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																						    """ Please check that the vocabulary is not corrupted!""" )
																						__magic_name__	:	Optional[Any]								=token_index
																		writer.write(""",""".join(__snake_case ) + """\n""" )
																		index += 1
										with open(__snake_case    , """w"""    , encoding="""utf-8""" ) as writer:
														json.dump(self.emoji    , __snake_case )
										return vocab_file, emoji_file




class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Tuple    , __snake_case   :Union[str, Any]    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	Any								=vocab  # same as swe
										__magic_name__	:	List[str]								=ids_to_tokens  # same as bpe
										__magic_name__	:	int								=emoji
										__magic_name__	:	Optional[Any]								=np.max([len(__snake_case ) for w in self.vocab.keys()] )
										__magic_name__	:	Tuple								=re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)""" )
										__magic_name__	:	Dict								=re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*""" )
										__magic_name__	:	int								=re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}""" )
										__magic_name__	:	Any								=re.compile(
										    r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" )
										__magic_name__	:	Any								=re.compile(
										    r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" )
										__magic_name__	:	str								=re.compile(
										    r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*""" )
										__magic_name__	:	Tuple								="""─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
										__magic_name__	:	List[Any]								="""▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
										__magic_name__	:	List[Any]								=str.maketrans({k: """<BLOCK>""" for k in keisen + blocks} )

						def __len__(		self   :List[Any] ):
										'''simple docstring'''

										return len(self.ids_to_tokens )

						def     A__						(		self   :str    , __snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.content_repattera.sub("""<URL>"""    , __snake_case )
										__magic_name__	:	Optional[Any]								=self.content_repattera.sub("""<EMAIL>"""    , __snake_case )
										__magic_name__	:	List[Any]								=self.content_repattera.sub("""<TEL>"""    , __snake_case )
										__magic_name__	:	int								=self.content_repattera.sub("""<DATE>"""    , __snake_case )
										__magic_name__	:	List[Any]								=self.content_repattera.sub("""<DATE>"""    , __snake_case )
										__magic_name__	:	Optional[int]								=self.content_repattera.sub("""<PRICE>"""    , __snake_case )
										__magic_name__	:	int								=content.translate(self.content_transa )
										while "<BLOCK><BLOCK>" in content:
														__magic_name__	:	List[str]								=content.replace("""<BLOCK><BLOCK>"""    , """<BLOCK>""" )
										return content

						def     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :Tuple=False ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=text.replace(""" """    , """<SP>""" )
										__magic_name__	:	str								=text.replace("""　"""    , """<SP>""" )
										__magic_name__	:	Dict								=text.replace("""\r\n"""    , """<BR>""" )
										__magic_name__	:	List[Any]								=text.replace("""\n"""    , """<BR>""" )
										__magic_name__	:	Optional[int]								=text.replace("""\r"""    , """<BR>""" )
										__magic_name__	:	Dict								=text.replace("""\t"""    , """<TAB>""" )
										__magic_name__	:	int								=text.replace("""—"""    , """ー""" )
										__magic_name__	:	Dict								=text.replace("""−"""    , """ー""" )
										for k, v in self.emoji["emoji"].items():
														if k in text:
																		__magic_name__	:	Union[str, Any]								=text.replace(__snake_case    , __snake_case )
										if clean:
														__magic_name__	:	Dict								=self.clean_text(__snake_case )

										def check_simbol(__snake_case   :Tuple ):
														__magic_name__	:	Optional[int]								=x.encode()
														if len(__snake_case ) == 1 and len(__snake_case ) == 2:
																		__magic_name__	:	Dict								=(int(e[0] ) << 8) + int(e[1] )
																		if (
																		    (c >= 0xC_2_A_1 and c <= 0xC_2_B_F)
																		    or (c >= 0xC_7_8_0 and c <= 0xC_7_8_3)
																		    or (c >= 0xC_A_B_9 and c <= 0xC_B_B_F)
																		    or (c >= 0xC_C_8_0 and c <= 0xC_D_A_2)
																		):
																						return True
														return False

										def checkuae(__snake_case   :Dict ):
														__magic_name__	:	Any								=x.encode()
														if len(__snake_case ) == 1 and len(__snake_case ) == 3:
																		__magic_name__	:	Union[str, Any]								=(int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
																		if c >= 0xE_2_8_0_8_0 and c <= 0xE_2_B_0_7_F:
																						return True
														return False

										__magic_name__	:	Optional[int]								=0
										__magic_name__	:	List[str]								=[]
										while pos < len(__snake_case ):
														__magic_name__	:	Optional[int]								=min(len(__snake_case )    , pos + self.maxlen + 1 ) if text[pos] == """<""" else pos + 3
														__magic_name__	:	Optional[Any]								=[]  # (token_id, token, pos)
														for e in range(__snake_case    , __snake_case    , -1 ):
																		__magic_name__	:	Dict								=text[pos:e]
																		if wd in self.vocab:
																						if wd[0] == "<" and len(__snake_case ) > 2:
																										__magic_name__	:	Any								=[(self.vocab[wd], wd, e)]
																										break
																						else:
																										candidates.append((self.vocab[wd], wd, e) )
														if len(__snake_case ) > 0:
																		# the smallest token_id is adopted
																		__magic_name__				, __magic_name__				, __magic_name__	:	Any								=sorted(__snake_case    , key=lambda __snake_case : x[0] )[0]
																		result.append(__snake_case )
																		__magic_name__	:	str								=e
														else:
																		__magic_name__	:	Optional[int]								=pos + 1
																		__magic_name__	:	Tuple								=text[pos:end]
																		if check_simbol(__snake_case ):
																						result.append("""<KIGOU>""" )
																		elif checkuae(__snake_case ):
																						result.append("""<U2000U2BFF>""" )
																		else:
																						for i in wd.encode("""utf-8""" ):
																										result.append("""<|byte%d|>""" % i )
																		__magic_name__	:	Dict								=end
										return result


						def     A__						(		self   :List[str]    , __snake_case   :Union[str, Any]    , __snake_case   :List[Any]="\n" ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=[]
										__magic_name__	:	int								=[]
										__magic_name__	:	Union[str, Any]								=self.ids_to_tokens[index][0]
										if word[:6] == "<|byte" and word[-2:] == "|>":
														byte_tokens.append(int(word[6:-2] ) )
										else:
														if len(__snake_case ) > 0:
																		words.append(bytearray(__snake_case ).decode("""utf-8"""    , errors="""replace""" ) )
																		__magic_name__	:	Optional[int]								=[]
														if word[:7] == "<|emoji" and word[-2:] == "|>":
																		words.append(self.emoji["""emoji_inv"""][word] )
														elif word == "<SP>":
																		words.append(""" """ )
														elif word == "<BR>":
																		words.append(__snake_case )
														elif word == "<TAB>":
																		words.append("""\t""" )
														elif word == "<BLOCK>":
																		words.append("""▀""" )
														elif word == "<KIGOU>":
																		words.append("""ǀ""" )
														elif word == "<U2000U2BFF>":
																		words.append("""‖""" )
														else:
																		words.append(__snake_case )
										if len(__snake_case ) > 0:
														words.append(bytearray(__snake_case ).decode("""utf-8"""    , errors="""replace""" ) )
										__magic_name__	:	Union[str, Any]								="""""".join(__snake_case )
										return text










import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

				if train_file is not None:
								__magic_name__	:	Optional[int]								=[train_file]
				if eval_file is not None:
								__magic_name__	:	Any								=[eval_file]
				if test_file is not None:
								__magic_name__	:	int								=[test_file]

				__magic_name__	:	Any								=datasets.load_dataset("""csv"""	,     data_files=lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=list(ds[list(files.keys()   )[0]].features.keys()   )
				__magic_name__	:	Optional[Any]								=features_name.pop(lowerCamelCase   )
				__magic_name__	:	str								=list(set(ds[list(files.keys()   )[0]][label_name]   )   )
				__magic_name__	:	Union[str, Any]								={label: i for i, label in enumerate(lowerCamelCase   )}
				__magic_name__	:	Dict								=tokenizer.model_input_names
				__magic_name__	:	Any								={}

				if len(lowerCamelCase   ) == 1:
								for k in files.keys():
												__magic_name__	:	Dict								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        example[features_name[0]]	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""   )	,     batched=lowerCamelCase	,     )
				elif len(lowerCamelCase   ) == 2:
								for k in files.keys():
												__magic_name__	:	Optional[Any]								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""	,     )	,     batched=lowerCamelCase	,     )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												__magic_name__	:	Any								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Any								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												__magic_name__	:	Dict								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	str								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												__magic_name__	:	Union[str, Any]								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Optional[int]								=labelaid[ex[label_name]]
												yield (d, label)

				__magic_name__	:	Union[str, Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								__magic_name__	:	Optional[Any]								=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

				__magic_name__	:	Optional[Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								__magic_name__	:	Any								=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

				__magic_name__	:	Any								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								__magic_name__	:	Optional[int]								=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the test file"""}			)
						UpperCamelCase     						=					field(
						    default=128					,						metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }					,						)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """Set this flag to use fast tokenization."""}			)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,						)




def 		lowerCAmelCase_   (	):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, Any]								=parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir   )
				    and os.listdir(training_args.output_dir   )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    """ --overwrite_output_dir to overcome."""   )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,     datefmt="""%m/%d/%Y %H:%M:%S"""	,     level=logging.INFO	,     )
				logger.info(
				    F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1   )}, "
				    F"16-bits training: {training_args.fpaa}"   )
				logger.info(F"Training/evaluation parameters {training_args}"   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__magic_name__	:	Dict								=AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,     cache_dir=model_args.cache_dir	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=get_tfds(
				    train_file=data_args.train_file	,     eval_file=data_args.dev_file	,     test_file=data_args.test_file	,     tokenizer=lowerCamelCase	,     label_column_id=data_args.label_column_id	,     max_seq_length=data_args.max_seq_length	,     )

				__magic_name__	:	Any								=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,     num_labels=len(lowerCamelCase   )	,     labelaid=lowerCamelCase	,     idalabel={id: label for label, id in labelaid.items()}	,     finetuning_task="""text-classification"""	,     cache_dir=model_args.cache_dir	,     )

				with training_args.strategy.scope():
								__magic_name__	:	Any								=TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path	,     from_pt=bool(""".bin""" in model_args.model_name_or_path   )	,     config=lowerCamelCase	,     cache_dir=model_args.cache_dir	,     )

				def compute_metrics(lowerCamelCase   ) -> Dict:
								__magic_name__	:	Tuple								=np.argmax(p.predictions	,     axis=1   )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				__magic_name__	:	int								=TFTrainer(
				    model=lowerCamelCase	,     args=lowerCamelCase	,     train_dataset=lowerCamelCase	,     eval_dataset=lowerCamelCase	,     compute_metrics=lowerCamelCase	,     )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir   )

				# Evaluation
				__magic_name__	:	List[str]								={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""   )
								__magic_name__	:	List[str]								=trainer.evaluate()
								__magic_name__	:	Optional[Any]								=os.path.join(training_args.output_dir	,     """eval_results.txt"""   )

								with open(lowerCamelCase	,     """w"""   ) as writer:
												logger.info("""***** Eval results *****"""   )

												for key, value in result.items():
																logger.info(F"  {key} = {value}"   )
																writer.write(F"{key} = {value}\n"   )

												results.update(lowerCamelCase   )

				return results


if __name__ == "__main__":
							main()




