





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__:      Tuple									=		logging.getLogger(__name__)




@dataclass
class     SCREAMING_SNAKE_CASE__ :
 __SCREAMING_SNAKE_CASE							       =	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.'''
         )
     }				,					)
 __SCREAMING_SNAKE_CASE							       =	field(
     default=UpperCamelCase__				,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}      )
 __SCREAMING_SNAKE_CASE							       =	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							       =	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							       =	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							       =	field(
     default=UpperCamelCase__				,					metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
             '''value if set.'''
         )
     }				,					)




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


def 	UpperCamelCase__(       )->str:
       # 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.

       A__											=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
       A__					,	A__					,	A__											=						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()

       A__											=						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.
       A__											=						None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              A__											=						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:
                     A__											=						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:
                     A__											=						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 ,     )
              A__											=						train_dataset.features['''label'''].names

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

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

       # Labels
       A__											=						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.
       A__											=						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 ,     )
       A__											=						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 ,     )
       A__											=						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:
              A__											=						'''max_length'''
       else:
              # We will pad later, dynamically at batch creation, to the max sequence length in each batch
              A__											=						False

       def preprocess_function(UpperCamelCase__			:       str  ):
              # 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:
                     A__											=						min(len(UpperCamelCase__  ) ,     data_args.max_train_samples  )
                     A__											=						train_dataset.select(range(UpperCamelCase__  )  )
              with training_args.main_process_first(desc='''train dataset map pre-processing'''  ):
                     A__											=						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:
                     A__											=						min(len(UpperCamelCase__  ) ,     data_args.max_eval_samples  )
                     A__											=						eval_dataset.select(range(UpperCamelCase__  )  )
              with training_args.main_process_first(desc='''validation dataset map pre-processing'''  ):
                     A__											=						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:
                     A__											=						min(len(UpperCamelCase__  ) ,     data_args.max_predict_samples  )
                     A__											=						predict_dataset.select(range(UpperCamelCase__  )  )
              with training_args.main_process_first(desc='''prediction dataset map pre-processing'''  ):
                     A__											=						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
       A__											=						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__			:       EvalPrediction  ):
              A__											=						p.predictions[0] if isinstance(p.predictions ,     UpperCamelCase__  ) else p.predictions
              A__											=						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:
              A__											=						default_data_collator
       elif training_args.fpaa:
              A__											=						DataCollatorWithPadding(UpperCamelCase__ ,     pad_to_multiple_of=8  )
       else:
              A__											=						None

       # Initialize our Trainer
       A__											=						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:
              A__											=						None
              if training_args.resume_from_checkpoint is not None:
                     A__											=						training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     A__											=						last_checkpoint
              A__											=						trainer.train(resume_from_checkpoint=UpperCamelCase__  )
              A__											=						train_result.metrics
              A__											=						(
                  data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase__  )
              )
              A__											=						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 ***'''  )
              A__											=						trainer.evaluate(eval_dataset=UpperCamelCase__  )

              A__											=						data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__  )
              A__											=						min(UpperCamelCase__ ,     len(UpperCamelCase__  )  )

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

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

              A__											=						(
                  data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCamelCase__  )
              )
              A__											=						min(UpperCamelCase__ ,     len(UpperCamelCase__  )  )

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

              A__											=						np.argmax(UpperCamelCase__ ,     axis=1  )
              A__											=						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__  ):
                                   A__											=						label_list[item]
                                   writer.write(f"{index}\t{item}\n"  )


if __name__ == "__main__":
       main()












import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2 )
        A__											=						[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        for example in examples:
               A__											=						video_classifier(__lowerCamelCase )

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                   ],)



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__											=						VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10} )
        A__											=						pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4 )

        A__											=						hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						video_classifier(__lowerCamelCase,top_k=2 )
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__											=						video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 				UpperCamelCase			(							self ):
        pass





