






'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


a_		:						Tuple             =    logging.get_logger(__name__)


a_		:						Optional[Any]             =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
a_		:						List[str]             =    {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

a_		:						Dict             =    {
    'allenai/led-base-16384': 1_63_84,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= (
			    list(range(ord('!')							,       ord('~') + 1)) + list(range(ord('¡')							,       ord('¬') + 1)) + list(range(ord('®')							,       ord('ÿ') + 1))
			)
			SCREAMING_SNAKE_CASE						= bs[:]
			SCREAMING_SNAKE_CASE						= 0
			for b in range(2**8):
						if b not in bs:
									bs.append(_UpperCAmelCase)
									cs.append(2**8 + n)
									n += 1
			SCREAMING_SNAKE_CASE						= [chr(_UpperCAmelCase) for n in cs]
			return dict(zip(_UpperCAmelCase							,       _UpperCAmelCase))






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= set()
			SCREAMING_SNAKE_CASE						= word[0]
			for char in word[1:]:
						pairs.add((prev_char, char))
						SCREAMING_SNAKE_CASE						= char
			return pairs







class 		_snake_case  (  A__					):
			_lowercase		:    Dict			     =							VOCAB_FILES_NAMES
			_lowercase		:    Any			     =							PRETRAINED_VOCAB_FILES_MAP
			_lowercase		:    Optional[Any]			     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowercase		:    Optional[Any]			     =							['''input_ids''', '''attention_mask''']






			def __init__(       self		,					a		,					a		,					a="replace"		,					a="<s>"		,					a="</s>"		,					a="</s>"		,					a="<s>"		,					a="<unk>"		,					a="<pad>"		,					a="<mask>"		,					a=False		,					**a		,					)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= AddedToken(a		,					lstrip=a		,					rstrip=a) if isinstance(a		,					a) else bos_token
						SCREAMING_SNAKE_CASE						= AddedToken(a		,					lstrip=a		,					rstrip=a) if isinstance(a		,					a) else eos_token
						SCREAMING_SNAKE_CASE						= AddedToken(a		,					lstrip=a		,					rstrip=a) if isinstance(a		,					a) else sep_token
						SCREAMING_SNAKE_CASE						= AddedToken(a		,					lstrip=a		,					rstrip=a) if isinstance(a		,					a) else cls_token
						SCREAMING_SNAKE_CASE						= AddedToken(a		,					lstrip=a		,					rstrip=a) if isinstance(a		,					a) else unk_token
						SCREAMING_SNAKE_CASE						= AddedToken(a		,					lstrip=a		,					rstrip=a) if isinstance(a		,					a) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE						= AddedToken(a		,					lstrip=a		,					rstrip=a) if isinstance(a		,					a) else mask_token

						super().__init__(
						    errors=a		,					bos_token=a		,					eos_token=a		,					unk_token=a		,					sep_token=a		,					cls_token=a		,					pad_token=a		,					mask_token=a		,					add_prefix_space=a		,					**a		,					)

						with open(a		,					encoding='utf-8') as vocab_handle:
									SCREAMING_SNAKE_CASE						= json.load(a)
						SCREAMING_SNAKE_CASE						= {v: k for k, v in self.encoder.items()}
						SCREAMING_SNAKE_CASE						= errors  # how to handle errors in decoding
						SCREAMING_SNAKE_CASE						= bytes_to_unicode()
						SCREAMING_SNAKE_CASE						= {v: k for k, v in self.byte_encoder.items()}
						with open(a		,					encoding='utf-8') as merges_handle:
									SCREAMING_SNAKE_CASE						= merges_handle.read().split('\n')[1:-1]
						SCREAMING_SNAKE_CASE						= [tuple(merge.split()) for merge in bpe_merges]
						SCREAMING_SNAKE_CASE						= dict(zip(a		,					range(len(a))))
						SCREAMING_SNAKE_CASE						= {}
						SCREAMING_SNAKE_CASE						= add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						SCREAMING_SNAKE_CASE						= re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')






			@property
			# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						return len(self.encoder)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						return dict(self.encoder		,					**self.added_tokens_encoder)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Any:
						if token in self.cache:
									return self.cache[token]
						SCREAMING_SNAKE_CASE						= tuple(a)
						SCREAMING_SNAKE_CASE						= get_pairs(a)

						if not pairs:
									return token

						while True:
									SCREAMING_SNAKE_CASE						= min(a		,					key=lambda a: self.bpe_ranks.get(a		,					float('inf')))
									if bigram not in self.bpe_ranks:
												break
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= bigram
									SCREAMING_SNAKE_CASE						= []
									SCREAMING_SNAKE_CASE						= 0
									while i < len(a):
												try:
															SCREAMING_SNAKE_CASE						= word.index(a		,					a)
												except ValueError:
															new_word.extend(word[i:])
															break
												else:
															new_word.extend(word[i:j])
															SCREAMING_SNAKE_CASE						= 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
									SCREAMING_SNAKE_CASE						= tuple(a)
									SCREAMING_SNAKE_CASE						= new_word
									if len(a) == 1:
												break
									else:
												SCREAMING_SNAKE_CASE						= get_pairs(a)
						SCREAMING_SNAKE_CASE						= ' '.join(a)
						SCREAMING_SNAKE_CASE						= word
						return word






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= []
						for token in re.findall(self.pat		,					a):
									SCREAMING_SNAKE_CASE						= ''.join(
									    self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a).split(' '))
						return bpe_tokens






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       str:
						return self.encoder.get(a		,					self.encoder.get(self.unk_token))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       str:
						return self.decoder.get(a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= ''.join(a)
						SCREAMING_SNAKE_CASE						= bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'		,					errors=self.errors)
						return text






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None)      ->       Tuple[str]:
						if not os.path.isdir(a):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
									return
						SCREAMING_SNAKE_CASE						= os.path.join(
						    a		,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
						SCREAMING_SNAKE_CASE						= 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')

						SCREAMING_SNAKE_CASE						= 0
						with open(a		,					'w'		,					encoding='utf-8') as writer:
									writer.write('#version: 0.2\n')
									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 {merge_file}: BPE merge indices are not consecutive.'''
															    ' Please check that the tokenizer is not corrupted!')
															SCREAMING_SNAKE_CASE						= token_index
												writer.write(' '.join(a) + '\n')
												index += 1

						return vocab_file, merge_file






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None)      ->       List[int]:
						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE						= [self.cls_token_id]
						SCREAMING_SNAKE_CASE						= [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = False)      ->       List[int]:
						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=a		,					token_ids_a=a		,					already_has_special_tokens=a)

						if token_ids_a is None:
									return [1] + ([0] * len(a)) + [1]
						return [1] + ([0] * len(a)) + [1, 1] + ([0] * len(a)) + [1]






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None)      ->       List[int]:
						SCREAMING_SNAKE_CASE						= [self.sep_token_id]
						SCREAMING_SNAKE_CASE						= [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]






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=False		,					**a)      ->       Any:
						SCREAMING_SNAKE_CASE						= kwargs.pop('add_prefix_space'		,					self.add_prefix_space)
						if (is_split_into_words or add_prefix_space) and (len(a) > 0 and not text[0].isspace()):
									SCREAMING_SNAKE_CASE						= ' ' + text
						return (text, kwargs)







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = PaddingStrategy.DO_NOT_PAD		,					a = None		,					a = None		,					)      ->       dict:
						SCREAMING_SNAKE_CASE						= super()._pad(
						    encoded_inputs=a		,					max_length=a		,					padding_strategy=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					)

						# Load from model defaults
						if return_attention_mask is None:
									SCREAMING_SNAKE_CASE						= 'attention_mask' in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
									SCREAMING_SNAKE_CASE						= encoded_inputs[self.model_input_names[0]]
									# `global_attention_mask` need to have the same length as other (sequential) inputs.
									SCREAMING_SNAKE_CASE						= len(encoded_inputs['global_attention_mask']) != len(a)

									if needs_to_be_padded:
												SCREAMING_SNAKE_CASE						= len(a) - len(encoded_inputs['global_attention_mask'])

												if self.padding_side == "right":
															# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
															SCREAMING_SNAKE_CASE						= (
															    encoded_inputs['global_attention_mask'] + [-1] * difference
															)
												elif self.padding_side == "left":
															SCREAMING_SNAKE_CASE						= [-1] * difference + encoded_inputs[
															    'global_attention_mask'
															]
												else:
															raise ValueError('Invalid padding strategy:' + str(self.padding_side))

						return encoded_inputs












import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

a_		:						Union[str, Any]             =    logging.getLogger(__name__)

@dataclass
class 		_snake_case  :
			_lowercase		:    Optional[int]			     =							field(
			    default=1_28   ,    metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    }   ,    )



@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    default=A__   ,    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
			_lowercase		:    str			     =							field(
			    default=A__   ,    metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Train language if it is different from the evaluation language.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,    )
			_lowercase		:    Optional[bool]			     =							field(
			    default=A__   ,    metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}   ,    )
			_lowercase		:    str			     =							field(
			    default='''main'''   ,    metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }   ,    )
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}   ,    )






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.

			SCREAMING_SNAKE_CASE						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry('run_xnli'							,       _UpperCAmelCase)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,       datefmt='%m/%d/%Y %H:%M:%S'							,       handlers=[logging.StreamHandler(sys.stdout)]							,       )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			SCREAMING_SNAKE_CASE						= training_args.get_process_log_level()
			logger.setLevel(_UpperCAmelCase)
			datasets.utils.logging.set_verbosity(_UpperCAmelCase)
			transformers.utils.logging.set_verbosity(_UpperCAmelCase)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
			    + F'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
			logger.info(F'''Training/evaluation parameters {training_args}''')

			# Detecting last checkpoint.
			SCREAMING_SNAKE_CASE						= None
			if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_SNAKE_CASE						= get_last_checkpoint(training_args.output_dir)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
									raise ValueError(
									    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
									    'Use --overwrite_output_dir to overcome.')
						elif last_checkpoint is not None:
									logger.info(
									    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.')

    # Set seed before initializing model.
			set_seed(training_args.seed)

			# In distributed training, the load_dataset function guarantees that only one local process can concurrently
			# download the dataset.
			# Downloading and loading xnli dataset from the hub.
			if training_args.do_train:
						if model_args.train_language is None:
									SCREAMING_SNAKE_CASE						= load_dataset(
									    'xnli'							,       model_args.language							,       split='train'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						else:
									SCREAMING_SNAKE_CASE						= load_dataset(
									    'xnli'							,       model_args.train_language							,       split='train'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= train_dataset.features['label'].names

			if training_args.do_eval:
						SCREAMING_SNAKE_CASE						= load_dataset(
						    'xnli'							,       model_args.language							,       split='validation'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= eval_dataset.features['label'].names

			if training_args.do_predict:
						SCREAMING_SNAKE_CASE						= load_dataset(
						    'xnli'							,       model_args.language							,       split='test'							,       cache_dir=model_args.cache_dir							,       use_auth_token=True if model_args.use_auth_token else None							,       )
						SCREAMING_SNAKE_CASE						= predict_dataset.features['label'].names

			# Labels
			SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase)

			# Load pretrained model and tokenizer
			# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			SCREAMING_SNAKE_CASE						= AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       num_labels=_UpperCAmelCase							,       idalabel={str(_UpperCAmelCase): label for i, label in enumerate(_UpperCAmelCase)}							,       labelaid={label: i for i, label in enumerate(_UpperCAmelCase)}							,       finetuning_task='xnli'							,       cache_dir=model_args.cache_dir							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       )
			SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,       do_lower_case=model_args.do_lower_case							,       cache_dir=model_args.cache_dir							,       use_fast=model_args.use_fast_tokenizer							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       )
			SCREAMING_SNAKE_CASE						= AutoModelForSequenceClassification.from_pretrained(
			    model_args.model_name_or_path							,       from_tf=bool('.ckpt' in model_args.model_name_or_path)							,       config=_UpperCAmelCase							,       cache_dir=model_args.cache_dir							,       revision=model_args.model_revision							,       use_auth_token=True if model_args.use_auth_token else None							,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,       )

			# Preprocessing the datasets
			# Padding strategy
			if data_args.pad_to_max_length:
						SCREAMING_SNAKE_CASE						= 'max_length'
			else:
						# We will pad later, dynamically at batch creation, to the max sequence length in each batch
						SCREAMING_SNAKE_CASE						= False

			def preprocess_function(_UpperCAmelCase):
						# Tokenize the texts
						return tokenizer(
						    examples['premise']							,       examples['hypothesis']							,       padding=_UpperCAmelCase							,       max_length=data_args.max_seq_length							,       truncation=_UpperCAmelCase							,       )

			if training_args.do_train:
						if data_args.max_train_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_train_samples)
									SCREAMING_SNAKE_CASE						= train_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='train dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= train_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on train dataset'							,       )
						# Log a few random samples from the training set:
						for index in random.sample(range(len(_UpperCAmelCase))							,       3):
									logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''')

			if training_args.do_eval:
						if data_args.max_eval_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_eval_samples)
									SCREAMING_SNAKE_CASE						= eval_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='validation dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= eval_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on validation dataset'							,       )

			if training_args.do_predict:
						if data_args.max_predict_samples is not None:
									SCREAMING_SNAKE_CASE						= min(len(_UpperCAmelCase)							,       data_args.max_predict_samples)
									SCREAMING_SNAKE_CASE						= predict_dataset.select(range(_UpperCAmelCase))
						with training_args.main_process_first(desc='prediction dataset map pre-processing'):
									SCREAMING_SNAKE_CASE						= predict_dataset.map(
									    _UpperCAmelCase							,       batched=_UpperCAmelCase							,       load_from_cache_file=not data_args.overwrite_cache							,       desc='Running tokenizer on prediction dataset'							,       )

    # Get the metric function
			SCREAMING_SNAKE_CASE						= evaluate.load('xnli')

			# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= p.predictions[0] if isinstance(p.predictions							,       _UpperCAmelCase) else p.predictions
						SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
						return metric.compute(predictions=_UpperCAmelCase							,       references=p.label_ids)

			# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
			if data_args.pad_to_max_length:
						SCREAMING_SNAKE_CASE						= default_data_collator
			elif training_args.fpaa:
						SCREAMING_SNAKE_CASE						= DataCollatorWithPadding(_UpperCAmelCase							,       pad_to_multiple_of=8)
			else:
						SCREAMING_SNAKE_CASE						= None

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE						= Trainer(
			    model=_UpperCAmelCase							,       args=_UpperCAmelCase							,       train_dataset=train_dataset if training_args.do_train else None							,       eval_dataset=eval_dataset if training_args.do_eval else None							,       compute_metrics=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase							,       data_collator=_UpperCAmelCase							,       )

			# Training
			if training_args.do_train:
						SCREAMING_SNAKE_CASE						= None
						if training_args.resume_from_checkpoint is not None:
									SCREAMING_SNAKE_CASE						= training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									SCREAMING_SNAKE_CASE						= last_checkpoint
						SCREAMING_SNAKE_CASE						= trainer.train(resume_from_checkpoint=_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= train_result.metrics
						SCREAMING_SNAKE_CASE						= (
						    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase)
						)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.save_model()  # Saves the tokenizer too for easy upload

						trainer.log_metrics('train'							,       _UpperCAmelCase)
						trainer.save_metrics('train'							,       _UpperCAmelCase)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						logger.info('*** Evaluate ***')
						SCREAMING_SNAKE_CASE						= trainer.evaluate(eval_dataset=_UpperCAmelCase)

						SCREAMING_SNAKE_CASE						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.log_metrics('eval'							,       _UpperCAmelCase)
						trainer.save_metrics('eval'							,       _UpperCAmelCase)

			# Prediction
			if training_args.do_predict:
						logger.info('*** Predict ***')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= trainer.predict(_UpperCAmelCase							,       metric_key_prefix='predict')

						SCREAMING_SNAKE_CASE						= (
						    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCAmelCase)
						)
						SCREAMING_SNAKE_CASE						= min(_UpperCAmelCase							,       len(_UpperCAmelCase))

						trainer.log_metrics('predict'							,       _UpperCAmelCase)
						trainer.save_metrics('predict'							,       _UpperCAmelCase)

						SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
						SCREAMING_SNAKE_CASE						= os.path.join(training_args.output_dir							,       'predictions.txt')
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,       'w') as writer:
												writer.write('index\tprediction\n')
												for index, item in enumerate(_UpperCAmelCase):
															SCREAMING_SNAKE_CASE						= label_list[item]
															writer.write(F'''{index}\t{item}\n''')


if __name__ == "__main__":
				main()




