
import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

__a							  =						logging.get_logger(__name__)

def    UpperCamelCase_ (	a_  ,				a_		)     ->Tuple:
		A				=f'''{sampling_rate}'''
		A				='''1'''
		A				='''f32le'''
		A				=[
		    '''ffmpeg''',
		    '''-i''',
		    '''pipe:0''',
		    '''-ac''',
		    ac,
		    '''-ar''',
		    ar,
		    '''-f''',
		    format_for_conversion,
		    '''-hide_banner''',
		    '''-loglevel''',
		    '''quiet''',
		    '''pipe:1''',
		]

		try:
				A				=subprocess.Popen(a_  ,				stdin=subprocess.PIPE  ,				stdout=subprocess.PIPE		)
		except FileNotFoundError:
				raise ValueError("ffmpeg was not found but is required to load audio files from filename"		)
		A				=ffmpeg_process.communicate(a_		)
		A				=output_stream[0]

		A				=np.frombuffer(a_  ,				np.floataa		)
		if audio.shape[0] == 0:
				raise ValueError("Malformed soundfile"		)
		return audio






@add_end_docstrings(snake_case_	)
class        UpperCamelCase__(    snake_case_	):

	"""simple docstring"""

	def __init__(      self				:			Optional[int]							,			*snake_case__				:			Tuple							,			**snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			A				=5
			super().__init__(*UpperCAmelCase__							,			**UpperCAmelCase__ )

			if self.framework != "pt":
					raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )

			self.check_model_type(UpperCAmelCase__ )







	def __call__(      self				:			Any							,			snake_case__				:			Dict							,			**snake_case__				:			int							,			):
			"""simple docstring"""





			return super().__call__(UpperCAmelCase__							,			**UpperCAmelCase__ )







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			List[Any]=None							,			**snake_case__				:			Optional[int] ):
			"""simple docstring"""





			A				={}
			if top_k is not None:
					if top_k > self.model.config.num_labels:
							A				=self.model.config.num_labels
					A				=top_k
			return {}, {}, postprocess_params







	def        _a					(      self				:			List[str]							,			snake_case__				:			Optional[int] ):
			"""simple docstring"""





			if isinstance(UpperCAmelCase__							,			UpperCAmelCase__ ):
					if inputs.startswith("http://" ) or inputs.startswith("https://" ):
							# We need to actually check for a real protocol, otherwise it's impossible to use a local file
							# like http_huggingface_co.png
							A				=requests.get(UpperCAmelCase__ ).content
					else:
							with open(UpperCAmelCase__							,			"rb" ) as f:
									A				=f.read()

			if isinstance(UpperCAmelCase__							,			UpperCAmelCase__ ):
					A				=ffmpeg_read(UpperCAmelCase__							,			self.feature_extractor.sampling_rate )

			if isinstance(UpperCAmelCase__							,			UpperCAmelCase__ ):
					# Accepting `"array"` which is the key defined in `datasets` for
					# better integration
					if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
							raise ValueError(
							    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
							    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
							    "containing the sampling_rate associated with that array" )

					A				=inputs.pop("raw"							,			UpperCAmelCase__ )
					if _inputs is None:
							# Remove path which will not be used from `datasets`.
							inputs.pop("path"							,			UpperCAmelCase__ )
							A				=inputs.pop("array"							,			UpperCAmelCase__ )
					A				=inputs.pop("sampling_rate" )
					A				=_inputs
					if in_sampling_rate != self.feature_extractor.sampling_rate:
							import torch

							if is_torchaudio_available():
									from torchaudio import functional as F
							else:
									raise ImportError(
									    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
									    "The torchaudio package can be installed through: `pip install torchaudio`." )

							A				=F.resample(
							    torch.from_numpy(UpperCAmelCase__ )							,			UpperCAmelCase__							,			self.feature_extractor.sampling_rate ).numpy()

			if not isinstance(UpperCAmelCase__							,			np.ndarray ):
					raise ValueError("We expect a numpy ndarray as input" )
			if len(inputs.shape ) != 1:
					raise ValueError("We expect a single channel audio input for AudioClassificationPipeline" )

			A				=self.feature_extractor(
			    UpperCAmelCase__							,			sampling_rate=self.feature_extractor.sampling_rate							,			return_tensors="pt" )
			return processed







	def        _a					(      self				:			Any							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			A				=self.model(**UpperCAmelCase__ )
			return model_outputs







	def        _a					(      self				:			str							,			snake_case__				:			Dict							,			snake_case__				:			str=5 ):
			"""simple docstring"""





			A				=model_outputs.logits[0].softmax(-1 )
			A				=probs.topk(UpperCAmelCase__ )

			A				=scores.tolist()
			A				=ids.tolist()

			A				=[{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(UpperCAmelCase__							,			UpperCAmelCase__ )]

			return labels











'''simple docstring'''






import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
	print('''#################### AVAILABLE DEVICES ####################''')
	print(jax.devices())
	print('''###########################################################''')

	# setup for wandb sweep
	__UpperCamelCase      :   str								=     Args()
	__UpperCamelCase      :   Optional[int]								=     wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
	__UpperCamelCase      :   Dict								=     dict(logger.config)
	del wandb_args["batch_size"]
	__UpperCamelCase      :   int								=     replace(args, **wandb_args)
	__UpperCamelCase      :   str								=     args.base_dir + '''-''' + wandb.run.id
	__UpperCamelCase      :   List[Any]								=     replace(args, base_dir=base_dir)
	print(args)

	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.tr_data_path)['''train''']
	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.val_data_path)['''train''']

	# drop extra batch for now
	__UpperCamelCase      :   int								=     range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
	__UpperCamelCase      :   Optional[int]								=     tr_dataset.shuffle().select(indices)
	__UpperCamelCase      :   str								=     range(len(val_dataset) - len(val_dataset) % args.batch_size)
	__UpperCamelCase      :   int								=     val_dataset.shuffle().select(indices)

	if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
		__UpperCamelCase      :   str								=     tr_dataset.shuffle().select(range(80000))
		__UpperCamelCase      :   List[Any]								=     val_dataset.shuffle().select(range(8000))

	print(tr_dataset)
	print(val_dataset)

	__UpperCamelCase      :   Tuple								=     FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	__UpperCamelCase      :   Optional[Any]								=     BigBirdTokenizerFast.from_pretrained(args.model_id)
	__UpperCamelCase      :   int								=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

	__UpperCamelCase      :   Dict								=     {
	    '''lr''': args.lr,
	    '''init_lr''': args.init_lr,
	    '''warmup_steps''': args.warmup_steps,
	    '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
	    '''weight_decay''': args.weight_decay,
	}
	__UpperCamelCase							,  __UpperCamelCase      :   str								=     build_tx(**tx_args)

	__UpperCamelCase      :   List[str]								=     Trainer(
	    args=args,
	    data_collator=data_collator,
	    model_save_fn=model.save_pretrained,
	    train_step_fn=train_step,
	    val_step_fn=val_step,
	    logger=logger,
	    scheduler_fn=lr,
	)

	__UpperCamelCase      :   List[Any]								=     None
	__UpperCamelCase      :   str								=     trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
	try:
		trainer.train(state, tr_dataset, val_dataset)
	except KeyboardInterrupt:
		print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')

	print('''SAVING WEIGHTS IN `final-weights`''')
	__UpperCamelCase      :   List[Any]								=     jax_utils.unreplicate(state.params)
	model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)
