



"""simple docstring"""
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 			lowercase	(_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = None ,)       ->    Optional[Any]:




  '''simple docstring'''







  __UpperCamelCase  						=							{}

  if train_file is not None:
    __UpperCamelCase  						=							[train_file]
  if eval_file is not None:
    __UpperCamelCase  						=							[eval_file]
  if test_file is not None:
    __UpperCamelCase  						=							[test_file]

  __UpperCamelCase  						=							datasets.load_dataset("csv" ,data_files=_snake_case			)
  __UpperCamelCase  						=							list(ds[list(files.keys()			)[0]].features.keys()			)
  __UpperCamelCase  						=							features_name.pop(_snake_case			)
  __UpperCamelCase  						=							list(set(ds[list(files.keys()			)[0]][label_name]			)			)
  __UpperCamelCase  						=							{label: i for i, label in enumerate(_snake_case			)}
  __UpperCamelCase  						=							tokenizer.model_input_names
  __UpperCamelCase  						=							{}

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

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

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

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

  __UpperCamelCase  						=							(
      tf.data.Dataset.from_generator(
          _snake_case ,({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:
    __UpperCamelCase  						=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]			)			)			)

  __UpperCamelCase  						=							(
      tf.data.Dataset.from_generator(
          _snake_case ,({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:
    __UpperCamelCase  						=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]			)			)			)

  __UpperCamelCase  						=							(
      tf.data.Dataset.from_generator(
          _snake_case ,({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:
    __UpperCamelCase  						=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]			)			)			)

  return train_ds, val_ds, test_ds, labelaid


_A													=   logging.getLogger(__name__)





@dataclass
class 		__UpperCAmelCase				:







 """simple docstring"""




 _snake_case       :							Optional[Any]      	=	field(metadata={'help': 'Which column contains the label'}					)
 _snake_case       :							Union[str, Any]      	=	field(default=_a  ,       metadata={'help': 'The path of the training file'}					)
 _snake_case       :							str      	=	field(default=_a  ,       metadata={'help': 'The path of the development file'}					)
 _snake_case       :							int      	=	field(default=_a  ,       metadata={'help': 'The path of the test file'}					)
 _snake_case       :							List[str]      	=	field(
     default=1_2_8  ,       metadata={
         'help': (
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )
     }  ,       )
 _snake_case       :							Optional[int]      	=	field(
     default=_a  ,       metadata={'help': 'Overwrite the cached training and evaluation sets'}					)





@dataclass
class 		__UpperCAmelCase				:







 """simple docstring"""




 _snake_case       :							List[str]      	=	field(
     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
 _snake_case       :							List[str]      	=	field(
     default=_a  ,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
 _snake_case       :							Any      	=	field(
     default=_a  ,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
 _snake_case       :							Any      	=	field(default=_a  ,       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.
 _snake_case       :							List[Any]      	=	field(
     default=_a  ,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,       )





def 			lowercase	()       ->    List[Any]:




  '''simple docstring'''







  __UpperCamelCase  						=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase  						=							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.

  __UpperCamelCase  						=							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 ,)

  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase  						=							get_tfds(
      train_file=data_args.train_file ,eval_file=data_args.dev_file ,test_file=data_args.test_file ,tokenizer=_snake_case ,label_column_id=data_args.label_column_id ,max_seq_length=data_args.max_seq_length ,)

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

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

  def compute_metrics(_snake_case			) -> Dict:
    __UpperCamelCase  						=							np.argmax(p.predictions ,axis=1			)

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

  # Initialize our Trainer
  __UpperCamelCase  						=							TFTrainer(
      model=_snake_case ,args=_snake_case ,train_dataset=_snake_case ,eval_dataset=_snake_case ,compute_metrics=_snake_case ,)

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

  # Evaluation
  __UpperCamelCase  						=							{}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"			)
    __UpperCamelCase  						=							trainer.evaluate()
    __UpperCamelCase  						=							os.path.join(training_args.output_dir ,"eval_results.txt"			)

    with open(_snake_case ,"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(_snake_case			)

  return results


if __name__ == "__main__":
 main()





'''simple docstring'''


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor

class      A       (						unittest.TestCase       ):







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=7				,     lowerCAmelCase_			:    Dict=3				,     lowerCAmelCase_			:    List[Any]=18				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    Optional[int]=4_00				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    List[str]=True				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							size if size is not None else {'''height''': 18, '''width''': 18}
														_a      	=							parent
														_a      	=							batch_size
														_a      	=							num_channels
														_a      	=							image_size
														_a      	=							min_resolution
														_a      	=							max_resolution
														_a      	=							do_resize
														_a      	=							size
														_a      	=							do_normalize







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
														            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
														        ]   ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}



@require_torch
@require_vision
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							ImageGPTImageProcessor if is_vision_available() else None







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessingTester(self   )







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''clusters'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_resize'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''size'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_normalize'''   )   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict   )
														self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}   )

														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42   )
														self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														_a      	=							json.loads(image_processor.to_json_string()   )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     obj[key]   )   )
																					else:
																												self.assertEqual(obj[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							os.path.join(lowerCAmelCase_				,     '''image_processor.json'''   )
																					image_processor_first.to_json_file(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_json_file(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_pretrained(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							@unittest.skip('''ImageGPT requires clusters at initialization'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

							_a      	=							Image.open(dataset[4]['''file'''] )
							_a      	=							Image.open(dataset[5]['''file'''] )

							_a      	=							[imagea, imagea]

							return images

@require_vision
@require_torch
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

														_a      	=							prepare_images()

														# test non-batched
														_a      	=							image_processing(images[0]				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (1, 10_24)   )

														_a      	=							[3_06, 1_91, 1_91]
														self.assertEqual(encoding.input_ids[0, :3].tolist()				,     lowerCAmelCase_   )

														# test batched
														_a      	=							image_processing(lowerCAmelCase_				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (2, 10_24)   )

														_a      	=							[3_03, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     lowerCAmelCase_   )

