





'''simple docstring'''





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


UpperCamelCase__   : Optional[List[str]]					  = None
UpperCamelCase__   : Optional[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
UpperCamelCase__   : Dict					  = [
    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   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     bool			    =       True
					lowerCamelCase     :     Optional[str]			    =       None
					# Automatically constructed
					lowerCamelCase     :     ClassVar[str]			    =       "PIL.Image.Image"
					lowerCamelCase     :     ClassVar[Any]			    =       pa.struct({'bytes': pa.binary(), 'path': pa.string()}  )
					lowerCamelCase     :     str			    =       field(default='Image'						,       init=A_						,       repr=A_  )





					def __call__(						self    :  List[Any]		):






						'''simple docstring'''







						return self.pa_type





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]		):






						'''simple docstring'''







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

						if isinstance(__lowercase ,					__lowercase		):
							UpperCAmelCase_								=     np.array(__lowercase		)

						if isinstance(__lowercase ,					__lowercase		):
							return {"path": value, "bytes": None}
						elif isinstance(__lowercase ,					__lowercase		):
							return {"path": None, "bytes": value}
						elif isinstance(__lowercase ,					np.ndarray		):
							# convert the image array to PNG/TIFF bytes
							return encode_np_array(__lowercase		)
						elif isinstance(__lowercase ,					PIL.Image.Image		):
							# convert the PIL image to bytes (default format is PNG/TIFF)
							return encode_pil_image(__lowercase		)
						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    :  List[str] ,					__lowercase    :  dict ,					__lowercase    :  Dict=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:
							UpperCAmelCase_								=     {}

						UpperCAmelCase_						,	UpperCAmelCase_								=     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(__lowercase		):
									UpperCAmelCase_								=     PIL.Image.open(__lowercase		)
								else:
									UpperCAmelCase_								=     path.split("""::"""		)[-1]
									try:
										UpperCAmelCase_								=     string_to_dict(__lowercase ,					config.HUB_DATASETS_URL		)["""repo_id"""]
										UpperCAmelCase_								=     token_per_repo_id.get(__lowercase		)
									except ValueError:
										UpperCAmelCase_								=     None
									with xopen(__lowercase ,					"""rb""" ,					use_auth_token=__lowercase		) as f:
										UpperCAmelCase_								=     BytesIO(f.read()		)
									UpperCAmelCase_								=     PIL.Image.open(bytes_		)
						else:
							UpperCAmelCase_								=     PIL.Image.open(BytesIO(bytes_		)		)
						image.load()  # to avoid "Too many open files" errors
						return image





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						from .features import Value

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





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Union[pa.StringArray, pa.StructArray, pa.ListArray]		):






						'''simple docstring'''







						if pa.types.is_string(storage.type		):
							UpperCAmelCase_								=     pa.array([None] * len(__lowercase		) ,					type=pa.binary()		)
							UpperCAmelCase_								=     pa.StructArray.from_arrays([bytes_array, storage] ,					["""bytes""", """path"""] ,					mask=storage.is_null()		)
						elif pa.types.is_binary(storage.type		):
							UpperCAmelCase_								=     pa.array([None] * len(__lowercase		) ,					type=pa.string()		)
							UpperCAmelCase_								=     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:
								UpperCAmelCase_								=     storage.field("""bytes"""		)
							else:
								UpperCAmelCase_								=     pa.array([None] * len(__lowercase		) ,					type=pa.binary()		)
							if storage.type.get_field_index("""path"""		) >= 0:
								UpperCAmelCase_								=     storage.field("""path"""		)
							else:
								UpperCAmelCase_								=     pa.array([None] * len(__lowercase		) ,					type=pa.string()		)
							UpperCAmelCase_								=     pa.StructArray.from_arrays([bytes_array, path_array] ,					["""bytes""", """path"""] ,					mask=storage.is_null()		)
						elif pa.types.is_list(storage.type		):
							UpperCAmelCase_								=     pa.array(
							    [encode_np_array(np.array(__lowercase		)		)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] ,					type=pa.binary() ,					)
							UpperCAmelCase_								=     pa.array([None] * len(__lowercase		) ,					type=pa.string()		)
							UpperCAmelCase_								=     pa.StructArray.from_arrays(
							    [bytes_array, path_array] ,					["""bytes""", """path"""] ,					mask=bytes_array.is_null()		)
						return array_cast(__lowercase ,					self.pa_type		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  pa.StructArray		):






						'''simple docstring'''







						@no_op_if_value_is_null
						def path_to_bytes(__lowercase    :  Dict		):
							with xopen(__lowercase ,					"""rb"""		) as f:
								UpperCAmelCase_								=     f.read()
							return bytes_

						UpperCAmelCase_								=     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() ,					)
						UpperCAmelCase_								=     pa.array(
						    [os.path.basename(__lowercase		) if path is not None else None for path in storage.field("""path"""		).to_pylist()] ,					type=pa.string() ,					)
						UpperCAmelCase_								=     pa.StructArray.from_arrays([bytes_array, path_array] ,					["""bytes""", """path"""] ,					mask=bytes_array.is_null()		)
						return array_cast(__lowercase ,					self.pa_type		)




def    A_(			):
	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()
		UpperCAmelCase_								=     list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
	return _IMAGE_COMPRESSION_FORMATS




def    A_(			A   ):
	UpperCAmelCase_								=     BytesIO()
	if image.format in list_image_compression_formats():
		UpperCAmelCase_								=     image.format
	else:
		UpperCAmelCase_								=     """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
	image.save(A				,      format=A   )
	return buffer.getvalue()




def    A_(			A   ):
	if hasattr(A				,      """filename"""   ) and image.filename != "":
		return {"path": image.filename, "bytes": None}
	else:
		return {"path": None, "bytes": image_to_bytes(A   )}




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

	UpperCAmelCase_								=     array.dtype
	UpperCAmelCase_								=     dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
	UpperCAmelCase_								=     dtype.kind
	UpperCAmelCase_								=     dtype.itemsize

	UpperCAmelCase_								=     None

	# Multi-channel array case (only np.dtype("|u1") is allowed)
	if array.shape[2:]:
		UpperCAmelCase_								=     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:
		UpperCAmelCase_								=     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:
			UpperCAmelCase_								=     dtype_byteorder + dtype_kind + str(A   )
			UpperCAmelCase_								=     np.dtype(A   )
			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}"""   )
	UpperCAmelCase_								=     PIL.Image.fromarray(array.astype(A   )   )
	return {"path": None, "bytes": image_to_bytes(A   )}




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

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









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    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser()
	parser.add_argument("""--model_ckpt"""				,      type=A				,      default="""microsoft/unixcoder-base-nine"""   )
	parser.add_argument("""--num_epochs"""				,      type=A				,      default=5   )
	parser.add_argument("""--batch_size"""				,      type=A				,      default=6   )
	parser.add_argument("""--gradient_accumulation_steps"""				,      type=A				,      default=1   )
	parser.add_argument("""--freeze"""				,      type=A				,      default=A   )
	parser.add_argument("""--learning_rate"""				,      type=A				,      default=5E-4   )
	parser.add_argument("""--seed"""				,      type=A				,      default=0   )
	parser.add_argument("""--lr_scheduler_type"""				,      type=A				,      default="""cosine"""   )
	parser.add_argument("""--num_warmup_steps"""				,      type=A				,      default=10   )
	parser.add_argument("""--weight_decay"""				,      type=A				,      default=0.01   )
	parser.add_argument("""--output_dir"""				,      type=A				,      default="""./results"""   )
	return parser.parse_args()


UpperCamelCase__   : Any					  = load("""accuracy""")




def    A_(			A   ):
	UpperCAmelCase_						,	UpperCAmelCase_								=     eval_pred
	UpperCAmelCase_								=     np.argmax(A				,      axis=1   )
	return metric.compute(predictions=A				,      references=A   )







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     trainer





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  Tuple ,					__lowercase    :  Dict ,					**__lowercase    :  Dict		):






						'''simple docstring'''







						if control.should_evaluate:
							UpperCAmelCase_								=     deepcopy(__lowercase		)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,					metric_key_prefix="""train"""		)
							return control_copy




def    A_(			):
	UpperCAmelCase_								=     get_args()
	set_seed(args.seed   )

	UpperCAmelCase_								=     load_dataset("""codeparrot/codecomplex"""				,      split="""train"""   )
	UpperCAmelCase_								=     dataset.train_test_split(test_size=0.2   )
	UpperCAmelCase_								=     train_test["""test"""].train_test_split(test_size=0.5   )
	UpperCAmelCase_								=     DatasetDict(
	    {
	        """train""": train_test["""train"""],
	        """test""": test_validation["""train"""],
	        """valid""": test_validation["""test"""],
	    }   )

	print("""Loading tokenizer and model"""   )
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained(args.model_ckpt   )
	UpperCAmelCase_								=     tokenizer.eos_token
	UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained(args.model_ckpt				,      num_labels=7   )
	UpperCAmelCase_								=     model.config.eos_token_id

	if args.freeze:
		for param in model.roberta.parameters():
			UpperCAmelCase_								=     False

	UpperCAmelCase_								=     ClassLabel(num_classes=7				,      names=list(set(train_test_validation["""train"""]["""complexity"""]   )   )   )

	def tokenize(A   ):
		UpperCAmelCase_								=     tokenizer(example["""src"""]				,      truncation=A				,      max_length=1024   )
		UpperCAmelCase_								=     labels.straint(example["""complexity"""]   )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	UpperCAmelCase_								=     train_test_validation.map(
	    A				,      batched=A				,      remove_columns=train_test_validation["""train"""].column_names				,      )
	UpperCAmelCase_								=     DataCollatorWithPadding(tokenizer=A   )

	UpperCAmelCase_								=     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"""				,      )

	UpperCAmelCase_								=     Trainer(
	    model=A				,      args=A				,      train_dataset=tokenized_datasets["""train"""]				,      eval_dataset=tokenized_datasets["""valid"""]				,      tokenizer=A				,      data_collator=A				,      compute_metrics=A				,      )

	print("""Training..."""   )
	trainer.add_callback(CustomCallback(A   )   )
	trainer.train()


if __name__ == "__main__":
				main()


