





import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


lowercase__   :				Optional[List[str]]      =   None
lowercase__   :				List[Any]      =   '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowercase__   :				str      =   [
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]


@dataclass
class 			SCREAMING_SNAKE_CASE   :
     lowerCAmelCase		      =	True
     lowerCAmelCase		      =	None
     # Automatically constructed
     lowerCAmelCase		      =	'''PIL.Image.Image'''
     lowerCAmelCase		      =	pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}							)
     lowerCAmelCase		      =	field(default='''Image''' ,		init=a__ ,		repr=a__							)


     def __call__(						self):

          '''simple docstring'''






          return self.pa_type


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if config.PIL_AVAILABLE:
               import PIL.Image
          else:
               raise ImportError('To support encoding images, please install \'Pillow\'.')

          if isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
               __A						:			Dict								=	np.array(_UpperCAmelCase)

          if isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
               return {"path": value, "bytes": None}
          elif isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
               return {"path": None, "bytes": value}
          elif isinstance(_UpperCAmelCase						,     np.ndarray):
               # convert the image array to PNG/TIFF bytes
               return encode_np_array(_UpperCAmelCase)
          elif isinstance(_UpperCAmelCase						,     PIL.Image.Image):
               # convert the PIL image to bytes (default format is PNG/TIFF)
               return encode_pil_image(_UpperCAmelCase)
          elif value.get('path') is not None and os.path.isfile(value['path']):
               # we set "bytes": None to not duplicate the data if they're already available locally
               return {"bytes": None, "path": value.get('path')}
          elif value.get('bytes') is not None or value.get('path') is not None:
               # store the image bytes, and path is used to infer the image format using the file extension
               return {"bytes": value.get('bytes'), "path": value.get('path')}
          else:
               raise ValueError(
                   F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.')


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=None):

          '''simple docstring'''






          if not self.decode:
               raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.')

          if config.PIL_AVAILABLE:
               import PIL.Image
          else:
               raise ImportError('To support decoding images, please install \'Pillow\'.')

          if token_per_repo_id is None:
               __A						:			List[str]								=	{}

          __A						:			Any								=	value['path'], value['bytes']
          if bytes_ is None:
               if path is None:
                    raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.')
               else:
                    if is_local_path(_UpperCAmelCase):
                         __A						:			List[Any]								=	PIL.Image.open(_UpperCAmelCase)
                    else:
                         __A						:			Optional[int]								=	path.split('::')[-1]
                         try:
                              __A						:			int								=	string_to_dict(_UpperCAmelCase						,     config.HUB_DATASETS_URL)['repo_id']
                              __A						:			Dict								=	token_per_repo_id.get(_UpperCAmelCase)
                         except ValueError:
                              __A						:			Tuple								=	None
                         with xopen(_UpperCAmelCase						,     'rb'						,     use_auth_token=_UpperCAmelCase) as f:
                              __A						:			Optional[Any]								=	BytesIO(f.read())
                         __A						:			Optional[Any]								=	PIL.Image.open(bytes_)
          else:
               __A						:			Union[str, Any]								=	PIL.Image.open(BytesIO(bytes_))
          image.load()  # to avoid "Too many open files" errors
          return image


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          from .features import Value

          return (
              self
              if self.decode
              else {
                  "bytes": Value('binary'),
                  "path": Value('string'),
              }
          )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if pa.types.is_string(storage.type):
               __A						:			List[str]								=	pa.array([None] * len(_UpperCAmelCase)						,     type=pa.binary())
               __A						:			str								=	pa.StructArray.from_arrays([bytes_array, storage]						,     ['bytes', 'path']						,     mask=storage.is_null())
          elif pa.types.is_binary(storage.type):
               __A						:			Union[str, Any]								=	pa.array([None] * len(_UpperCAmelCase)						,     type=pa.string())
               __A						:			Optional[Any]								=	pa.StructArray.from_arrays([storage, path_array]						,     ['bytes', 'path']						,     mask=storage.is_null())
          elif pa.types.is_struct(storage.type):
               if storage.type.get_field_index('bytes') >= 0:
                    __A						:			Optional[int]								=	storage.field('bytes')
               else:
                    __A						:			List[str]								=	pa.array([None] * len(_UpperCAmelCase)						,     type=pa.binary())
               if storage.type.get_field_index('path') >= 0:
                    __A						:			Dict								=	storage.field('path')
               else:
                    __A						:			Dict								=	pa.array([None] * len(_UpperCAmelCase)						,     type=pa.string())
               __A						:			str								=	pa.StructArray.from_arrays([bytes_array, path_array]						,     ['bytes', 'path']						,     mask=storage.is_null())
          elif pa.types.is_list(storage.type):
               __A						:			Tuple								=	pa.array(
                   [encode_np_array(np.array(_UpperCAmelCase))['bytes'] if arr is not None else None for arr in storage.to_pylist()]						,     type=pa.binary()						,     )
               __A						:			Optional[Any]								=	pa.array([None] * len(_UpperCAmelCase)						,     type=pa.string())
               __A						:			Optional[Any]								=	pa.StructArray.from_arrays(
                   [bytes_array, path_array]						,     ['bytes', 'path']						,     mask=bytes_array.is_null())
          return array_cast(_UpperCAmelCase						,     self.pa_type)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          @no_op_if_value_is_null
          def path_to_bytes(_UpperCAmelCase):
               with xopen(_UpperCAmelCase						,     'rb') as f:
                    __A						:			Dict								=	f.read()
               return bytes_

          __A						:			List[Any]								=	pa.array(
              [
                  (path_to_bytes(x['path']) if x['bytes'] is None else x['bytes']) if x is not None else None
                  for x in storage.to_pylist()
              ]						,     type=pa.binary()						,     )
          __A						:			str								=	pa.array(
              [os.path.basename(_UpperCAmelCase) if path is not None else None for path in storage.field('path').to_pylist()]						,     type=pa.string()						,     )
          __A						:			Dict								=	pa.StructArray.from_arrays([bytes_array, path_array]						,     ['bytes', 'path']						,     mask=bytes_array.is_null())
          return array_cast(_UpperCAmelCase						,     self.pa_type)






def 	_lowerCAmelCase      (  )					->					List[str]:
     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'      )

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          __A						:			Tuple								=	list(set(PIL.Image.OPEN.keys()      ) & set(PIL.Image.SAVE.keys()      )      )
     return _IMAGE_COMPRESSION_FORMATS






def 	_lowerCAmelCase      (  __snake_case  :			"PIL.Image.Image"      )					->					bytes:
     __A						:			Union[str, Any]								=	BytesIO()
     if image.format in list_image_compression_formats():
          __A						:			Union[str, Any]								=	image.format
     else:
          __A						:			Dict								=	'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
     image.save(__snake_case			,							format=__snake_case      )
     return buffer.getvalue()






def 	_lowerCAmelCase      (  __snake_case  :			"PIL.Image.Image"      )					->					dict:
     if hasattr(__snake_case			,							'filename'      ) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(__snake_case      )}






def 	_lowerCAmelCase      (  __snake_case  :			np.ndarray      )					->					dict:
     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'      )

     __A						:			Union[str, Any]								=	array.dtype
     __A						:			Union[str, Any]								=	dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
     __A						:			Union[str, Any]								=	dtype.kind
     __A						:			str								=	dtype.itemsize

     __A						:			Optional[Any]								=	None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          __A						:			List[str]								=	np.dtype('|u1'      )
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'      )
          if dtype is not dest_dtype:
               warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''      )
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          __A						:			int								=	dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               __A						:			Optional[Any]								=	dtype_byteorder + dtype_kind + str(__snake_case      )
               __A						:			Optional[int]								=	np.dtype(__snake_case      )
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''      )
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'      )
     __A						:			List[Any]								=	PIL.Image.fromarray(array.astype(__snake_case      )      )
     return {"path": None, "bytes": image_to_bytes(__snake_case      )}







def 	_lowerCAmelCase      (  __snake_case  :			Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]      )					->					List[dict]:
     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'      )

     if objs:
          __A						:			Tuple								=	first_non_null_value(__snake_case      )
          if isinstance(__snake_case			,							__snake_case      ):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(__snake_case			,							np.ndarray      ):
               __A						:			Any								=	no_op_if_value_is_null(__snake_case      )
               return [obj_to_image_dict_func(__snake_case      ) for obj in objs]
          elif isinstance(__snake_case			,							PIL.Image.Image      ):
               __A						:			str								=	no_op_if_value_is_null(__snake_case      )
               return [obj_to_image_dict_func(__snake_case      ) for obj in objs]
          else:
               return objs
     else:
          return objs






'''simple docstring'''




import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)






def 	_lowerCAmelCase      (  )					->					Union[str, Any]:
     __A						:			Optional[int]								=	argparse.ArgumentParser()
     parser.add_argument('--model_ckpt'			,							type=__snake_case			,							default='microsoft/unixcoder-base-nine'      )
     parser.add_argument('--num_epochs'			,							type=__snake_case			,							default=5      )
     parser.add_argument('--batch_size'			,							type=__snake_case			,							default=6      )
     parser.add_argument('--gradient_accumulation_steps'			,							type=__snake_case			,							default=1      )
     parser.add_argument('--freeze'			,							type=__snake_case			,							default=__snake_case      )
     parser.add_argument('--learning_rate'			,							type=__snake_case			,							default=5e-4      )
     parser.add_argument('--seed'			,							type=__snake_case			,							default=0      )
     parser.add_argument('--lr_scheduler_type'			,							type=__snake_case			,							default='cosine'      )
     parser.add_argument('--num_warmup_steps'			,							type=__snake_case			,							default=10      )
     parser.add_argument('--weight_decay'			,							type=__snake_case			,							default=0.01      )
     parser.add_argument('--output_dir'			,							type=__snake_case			,							default='./results'      )
     return parser.parse_args()


lowercase__   :				Tuple      =   load('''accuracy''')






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					Any:
     __A			,__A						:			List[Any]								=	eval_pred
     __A						:			Dict								=	np.argmax(__snake_case			,							axis=1      )
     return metric.compute(predictions=__snake_case			,							references=__snake_case      )


class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A						:			int								=	trainer


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          if control.should_evaluate:
               __A						:			str								=	deepcopy(_UpperCAmelCase)
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,     metric_key_prefix='train')
               return control_copy






def 	_lowerCAmelCase      (  )					->					str:
     __A						:			List[Any]								=	get_args()
     set_seed(args.seed      )

     __A						:			Union[str, Any]								=	load_dataset('codeparrot/codecomplex'			,							split='train'      )
     __A						:			Optional[int]								=	dataset.train_test_split(test_size=0.2      )
     __A						:			Union[str, Any]								=	train_test['test'].train_test_split(test_size=0.5      )
     __A						:			Optional[Any]								=	DatasetDict(
         {
             'train': train_test['train'],
             'test': test_validation['train'],
             'valid': test_validation['test'],
         }      )

     print('Loading tokenizer and model'      )
     __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(args.model_ckpt      )
     __A						:			Tuple								=	tokenizer.eos_token
     __A						:			Dict								=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt			,							num_labels=7      )
     __A						:			Optional[int]								=	model.config.eos_token_id

     if args.freeze:
          for param in model.roberta.parameters():
               __A						:			Optional[Any]								=	False

     __A						:			Dict								=	ClassLabel(num_classes=7			,							names=list(set(train_test_validation['train']['complexity']      )      )      )

     def tokenize(__snake_case  :			Optional[Any]      ):
          __A						:			Optional[Any]								=	tokenizer(example['src']			,							truncation=__snake_case			,							max_length=10_24      )
          __A						:			str								=	labels.straint(example['complexity']      )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     __A						:			str								=	train_test_validation.map(
         __snake_case			,							batched=__snake_case			,							remove_columns=train_test_validation['train'].column_names			,							)
     __A						:			str								=	DataCollatorWithPadding(tokenizer=__snake_case      )

     __A						:			str								=	TrainingArguments(
         output_dir=args.output_dir			,							learning_rate=args.learning_rate			,							lr_scheduler_type=args.lr_scheduler_type			,							evaluation_strategy='epoch'			,							save_strategy='epoch'			,							logging_strategy='epoch'			,							per_device_train_batch_size=args.batch_size			,							per_device_eval_batch_size=args.batch_size			,							num_train_epochs=args.num_epochs			,							gradient_accumulation_steps=args.gradient_accumulation_steps			,							weight_decay=0.01			,							metric_for_best_model='accuracy'			,							run_name='complexity-java'			,							report_to='wandb'			,							)

     __A						:			Tuple								=	Trainer(
         model=__snake_case			,							args=__snake_case			,							train_dataset=tokenized_datasets['train']			,							eval_dataset=tokenized_datasets['valid']			,							tokenizer=__snake_case			,							data_collator=__snake_case			,							compute_metrics=__snake_case			,							)

     print('Training...'      )
     trainer.add_callback(CustomCallback(__snake_case      )      )
     trainer.train()


if __name__ == "__main__":
  main()