

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""")

_UpperCAmelCase						:  List[Any]									=			logging.getLogger(__name__)







@dataclass
class 					lowercase							:
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     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.'''
				        )
				    }      ,    )
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     field(
				    default=_UpperCamelCase      ,    metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}     )
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     field(
				    default=_UpperCamelCase      ,    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.'''
				        )
				    }      ,    )
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     field(
				    default=_UpperCamelCase      ,    metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }      ,    )
				__SCREAMING_SNAKE_CASE   :					int			     =     field(
				    default=_UpperCamelCase      ,    metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
				            '''value if set.'''
				        )
				    }      ,    )
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     field(
				    default=_UpperCamelCase      ,    metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
				            '''value if set.'''
				        )
				    }      ,    )







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





def    __lowerCamelCase      (					):







							'''simple docstring'''






							snake_case_     							=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
							snake_case_							,       snake_case_							,       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'  ,				__snake_case )

							# 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()

							snake_case_     							=       training_args.get_process_log_level()
							logger.setLevel(__snake_case )
							datasets.utils.logging.set_verbosity(__snake_case )
							transformers.utils.logging.set_verbosity(__snake_case )
							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.
							snake_case_     							=       None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														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:
																					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:
																					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  ,				)
														snake_case_     							=       train_dataset.features['label'].names

							if training_args.do_eval:
														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  ,				)
														snake_case_     							=       eval_dataset.features['label'].names

							if training_args.do_predict:
														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  ,				)
														snake_case_     							=       predict_dataset.features['label'].names

							# Labels
							snake_case_     							=       len(__snake_case )

							# Load pretrained model and tokenizer
							# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							snake_case_     							=       AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,				num_labels=__snake_case  ,				idalabel={str(__snake_case ): label for i, label in enumerate(__snake_case )}  ,				labelaid={label: i for i, label in enumerate(__snake_case )}  ,				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  ,				)
							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  ,				)
							snake_case_     							=       AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path  ,				from_tf=bool('.ckpt' in model_args.model_name_or_path )  ,				config=__snake_case  ,				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:
														snake_case_     							=       'max_length'
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														snake_case_     							=       False

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

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					snake_case_     							=       min(len(__snake_case )  ,				data_args.max_train_samples )
																					snake_case_     							=       train_dataset.select(range(__snake_case ) )
														with training_args.main_process_first(desc='train dataset map pre-processing' ):
																					snake_case_     							=       train_dataset.map(
																					    __snake_case  ,				batched=__snake_case  ,				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(__snake_case ) )  ,				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:
																					snake_case_     							=       min(len(__snake_case )  ,				data_args.max_eval_samples )
																					snake_case_     							=       eval_dataset.select(range(__snake_case ) )
														with training_args.main_process_first(desc='validation dataset map pre-processing' ):
																					snake_case_     							=       eval_dataset.map(
																					    __snake_case  ,				batched=__snake_case  ,				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:
																					snake_case_     							=       min(len(__snake_case )  ,				data_args.max_predict_samples )
																					snake_case_     							=       predict_dataset.select(range(__snake_case ) )
														with training_args.main_process_first(desc='prediction dataset map pre-processing' ):
																					snake_case_     							=       predict_dataset.map(
																					    __snake_case  ,				batched=__snake_case  ,				load_from_cache_file=not data_args.overwrite_cache  ,				desc='Running tokenizer on prediction dataset'  ,				)

    # Get the metric function
							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__ ):
														snake_case_     							=       p.predictions[0] if isinstance(p.predictions  ,				__snake_case ) else p.predictions
														snake_case_     							=       np.argmax(__snake_case  ,				axis=1 )
														return metric.compute(predictions=__snake_case  ,				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:
														snake_case_     							=       default_data_collator
							elif training_args.fpaa:
														snake_case_     							=       DataCollatorWithPadding(__snake_case  ,				pad_to_multiple_of=8 )
							else:
														snake_case_     							=       None

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

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

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

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

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

														snake_case_     							=       data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__snake_case )
														snake_case_     							=       min(__snake_case  ,				len(__snake_case ) )

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

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

														snake_case_     							=       (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__snake_case )
														)
														snake_case_     							=       min(__snake_case  ,				len(__snake_case ) )

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

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


if __name__ == "__main__":
					main()





'''simple docstring'''






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        _A    		=  [
            'safety_checker/pytorch_model.bin',
            'safety_checker/model.safetensors',
            'vae/diffusion_pytorch_model.bin',
            'vae/diffusion_pytorch_model.safetensors',
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  [
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Union[str, Any]:
        _A    		=  [
            'safety_checker/pytorch_model.bin',
            'safety_checker/model.safetensors',
            'vae/diffusion_pytorch_model.bin',
            'vae/diffusion_pytorch_model.safetensors',
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
            'unet/diffusion_pytorch_model.bin',
            # Removed: 'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Dict:
        _A    		=  [
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
        ]
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        _A    		=  [
            'safety_checker/pytorch_model.bin',
            'safety_checker/model.safetensors',
            'vae/diffusion_pytorch_model.bin',
            'vae/diffusion_pytorch_model.safetensors',
            'text_encoder/pytorch_model.bin',
            # Removed: 'text_encoder/model.safetensors',
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        _A    		=  [
            'safety_checker/pytorch_model.fp16.bin',
            'safety_checker/model.fp16.safetensors',
            'vae/diffusion_pytorch_model.fp16.bin',
            'vae/diffusion_pytorch_model.fp16.safetensors',
            'text_encoder/pytorch_model.fp16.bin',
            'text_encoder/model.fp16.safetensors',
            'unet/diffusion_pytorch_model.fp16.bin',
            'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[str]:
        _A    		=  [
            'unet/diffusion_pytorch_model.fp16.bin',
            'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[str]:
        # pass variant but use the non-variant filenames
        _A    		=  [
            'unet/diffusion_pytorch_model.bin',
            'unet/diffusion_pytorch_model.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  [
            'safety_checker/pytorch_model.fp16.bin',
            'safety_checker/model.fp16.safetensors',
            'vae/diffusion_pytorch_model.fp16.bin',
            'vae/diffusion_pytorch_model.fp16.safetensors',
            'text_encoder/pytorch_model.fp16.bin',
            'text_encoder/model.fp16.safetensors',
            'unet/diffusion_pytorch_model.fp16.bin',
            # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Union[str, Any]:
        _A    		=  [
            'text_encoder/pytorch_model.fp16.bin',
            'text_encoder/model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        # pass variant but use the non-variant filenames
        _A    		=  [
            'text_encoder/pytorch_model.bin',
            'text_encoder/model.safetensors',
        ]
        _A    		=  'fp16'
        self.assertTrue(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        _A    		=  [
            'safety_checker/pytorch_model.fp16.bin',
            'safety_checker/model.fp16.safetensors',
            'vae/diffusion_pytorch_model.fp16.bin',
            'vae/diffusion_pytorch_model.fp16.safetensors',
            'text_encoder/pytorch_model.fp16.bin',
            # 'text_encoder/model.fp16.safetensors',
            'unet/diffusion_pytorch_model.fp16.bin',
            'unet/diffusion_pytorch_model.fp16.safetensors',
        ]
        _A    		=  'fp16'
        self.assertFalse(is_safetensors_compatible(UpperCamelCase__, variant=UpperCamelCase__     )     )



