





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     f"""{sampling_rate}"""
	UpperCAmelCase_								=     """1"""
	UpperCAmelCase_								=     """f32le"""
	UpperCAmelCase_								=     [
	    """ffmpeg""",
	    """-i""",
	    """pipe:0""",
	    """-ac""",
	    ac,
	    """-ar""",
	    ar,
	    """-f""",
	    format_for_conversion,
	    """-hide_banner""",
	    """-loglevel""",
	    """quiet""",
	    """pipe:1""",
	]

	try:
		with subprocess.Popen(A				,      stdin=subprocess.PIPE				,      stdout=subprocess.PIPE   ) as ffmpeg_process:
			UpperCAmelCase_								=     ffmpeg_process.communicate(A   )
	except FileNotFoundError as error:
		raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""   ) from error
	UpperCAmelCase_								=     output_stream[0]
	UpperCAmelCase_								=     np.frombuffer(A				,      np.floataa   )
	if audio.shape[0] == 0:
		raise ValueError("""Malformed soundfile"""   )
	return audio




def    A_(			A				,      A				,      A = "f32le"				,      ):
	UpperCAmelCase_								=     f"""{sampling_rate}"""
	UpperCAmelCase_								=     """1"""
	if format_for_conversion == "s16le":
		UpperCAmelCase_								=     2
	elif format_for_conversion == "f32le":
		UpperCAmelCase_								=     4
	else:
		raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""   )

	UpperCAmelCase_								=     platform.system()
	if system == "Linux":
		UpperCAmelCase_								=     """alsa"""
		UpperCAmelCase_								=     """default"""
	elif system == "Darwin":
		UpperCAmelCase_								=     """avfoundation"""
		UpperCAmelCase_								=     """:0"""
	elif system == "Windows":
		UpperCAmelCase_								=     """dshow"""
		UpperCAmelCase_								=     """default"""

	UpperCAmelCase_								=     [
	    """ffmpeg""",
	    """-f""",
	    format_,
	    """-i""",
	    input_,
	    """-ac""",
	    ac,
	    """-ar""",
	    ar,
	    """-f""",
	    format_for_conversion,
	    """-fflags""",
	    """nobuffer""",
	    """-hide_banner""",
	    """-loglevel""",
	    """quiet""",
	    """pipe:1""",
	]
	UpperCAmelCase_								=     int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
	UpperCAmelCase_								=     _ffmpeg_stream(A				,      A   )
	for item in iterator:
		yield item




def    A_(			A				,      A				,      A = None				,      A = None				,      A = "f32le"				,      ):
	if stream_chunk_s is not None:
		UpperCAmelCase_								=     stream_chunk_s
	else:
		UpperCAmelCase_								=     chunk_length_s

	UpperCAmelCase_								=     ffmpeg_microphone(A				,      A				,      format_for_conversion=A   )
	if format_for_conversion == "s16le":
		UpperCAmelCase_								=     np.intaa
		UpperCAmelCase_								=     2
	elif format_for_conversion == "f32le":
		UpperCAmelCase_								=     np.floataa
		UpperCAmelCase_								=     4
	else:
		raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""   )

	if stride_length_s is None:
		UpperCAmelCase_								=     chunk_length_s / 6
	UpperCAmelCase_								=     int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
	if isinstance(A				,      (int, float)   ):
		UpperCAmelCase_								=     [stride_length_s, stride_length_s]

	UpperCAmelCase_								=     int(round(sampling_rate * stride_length_s[0]   )   ) * size_of_sample
	UpperCAmelCase_								=     int(round(sampling_rate * stride_length_s[1]   )   ) * size_of_sample
	UpperCAmelCase_								=     datetime.datetime.now()
	UpperCAmelCase_								=     datetime.timedelta(seconds=A   )
	for item in chunk_bytes_iter(A				,      A				,      stride=(stride_left, stride_right)				,      stream=A   ):
		# Put everything back in numpy scale
		UpperCAmelCase_								=     np.frombuffer(item["""raw"""]				,      dtype=A   )
		UpperCAmelCase_								=     (
		    item["""stride"""][0] // size_of_sample,
		    item["""stride"""][1] // size_of_sample,
		)
		UpperCAmelCase_								=     sampling_rate
		audio_time += delta
		if datetime.datetime.now() > audio_time + 10 * delta:
			# We're late !! SKIP
			continue
		yield item




def    A_(			A				,      A				,      A				,      A = False   ):
	UpperCAmelCase_								=     b""""""
	UpperCAmelCase_						,	UpperCAmelCase_								=     stride
	if stride_left + stride_right >= chunk_len:
		raise ValueError(
		    f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""   )
	UpperCAmelCase_								=     0
	for raw in iterator:
		acc += raw
		if stream and len(A   ) < chunk_len:
			UpperCAmelCase_								=     (_stride_left, 0)
			yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
		else:
			while len(A   ) >= chunk_len:
				# We are flushing the accumulator
				UpperCAmelCase_								=     (_stride_left, stride_right)
				UpperCAmelCase_								=     {"""raw""": acc[:chunk_len], """stride""": stride}
				if stream:
					UpperCAmelCase_								=     False
				yield item
				UpperCAmelCase_								=     stride_left
				UpperCAmelCase_								=     acc[chunk_len - stride_left - stride_right :]
    # Last chunk
	if len(A   ) > stride_left:
		UpperCAmelCase_								=     {"""raw""": acc, """stride""": (_stride_left, 0)}
		if stream:
			UpperCAmelCase_								=     False
		yield item




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     2**24  # 16Mo
	try:
		with subprocess.Popen(A				,      stdout=subprocess.PIPE				,      bufsize=A   ) as ffmpeg_process:
			while True:
				UpperCAmelCase_								=     ffmpeg_process.stdout.read(A   )
				if raw == b"":
					break
				yield raw
	except FileNotFoundError as error:
		raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""   ) from error









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()


