

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

__snake_case							:     List[Any] 						=	logging.get_logger(__name__)


def 			_UpperCAmelCase							(		a__       ,					a__) ->    List[str]:

			'''simple docstring'''



			a_		:		List[Any]				    =				f'''{sampling_rate}'''
			a_		:		Dict				    =				"""1"""
			a_		:		List[Any]				    =				"""f32le"""
			a_		:		Tuple				    =				[
			    """ffmpeg""",
			    """-i""",
			    """pipe:0""",
			    """-ac""",
			    ac,
			    """-ar""",
			    ar,
			    """-f""",
			    format_for_conversion,
			    """-hide_banner""",
			    """-loglevel""",
			    """quiet""",
			    """pipe:1""",
			]

			try:
						a_		:		List[Any]				    =				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_		:		Dict				    =				ffmpeg_process.communicate(a__)
			a_		:		Any				    =				output_stream[0]

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



@add_end_docstrings(a_    )
class    A__(a_    ):
						"""simple docstring"""






						def __init__(  self       ,     *_lowercase       ,     **_lowercase  )      ->				Any:
									# Default, might be overriden by the model.config.
									a_		:		Optional[Any]				    =				5
									super().__init__(*_lowercase       ,     **_lowercase  )

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

									self.check_model_type(_lowercase  )





						def __call__(  self       ,     _lowercase       ,     **_lowercase       ,     )      ->				List[str]:
									return super().__call__(_lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase=None       ,     **_lowercase  )      ->				int:
									# No parameters on this pipeline right now
									a_		:		int				    =				{}
									if top_k is not None:
												if top_k > self.model.config.num_labels:
															a_		:		Dict				    =				self.model.config.num_labels
												a_		:		Dict				    =				top_k
									return {}, {}, postprocess_params





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				List[Any]:
									if isinstance(_lowercase       ,     _lowercase  ):
												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_		:		str				    =				requests.get(_lowercase  ).content
												else:
															with open(_lowercase       ,     """rb"""  ) as f:
																		a_		:		List[Any]				    =				f.read()

									if isinstance(_lowercase       ,     _lowercase  ):
												a_		:		Union[str, Any]				    =				ffmpeg_read(_lowercase       ,     self.feature_extractor.sampling_rate  )

									if isinstance(_lowercase       ,     _lowercase  ):
												# 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_		:		Tuple				    =				inputs.pop("""raw"""       ,     _lowercase  )
												if _inputs is None:
															# Remove path which will not be used from `datasets`.
															inputs.pop("""path"""       ,     _lowercase  )
															a_		:		Any				    =				inputs.pop("""array"""       ,     _lowercase  )
												a_		:		List[str]				    =				inputs.pop("""sampling_rate"""  )
												a_		:		int				    =				_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_		:		str				    =				F.resample(
															    torch.from_numpy(_lowercase  )       ,     _lowercase       ,     self.feature_extractor.sampling_rate  ).numpy()

									if not isinstance(_lowercase       ,     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_		:		List[str]				    =				self.feature_extractor(
									    _lowercase       ,     sampling_rate=self.feature_extractor.sampling_rate       ,     return_tensors="""pt"""  )
									return processed





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				int:
									a_		:		Union[str, Any]				    =				self.model(**_lowercase  )
									return model_outputs





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase=5  )      ->				Tuple:
									a_		:		int				    =				model_outputs.logits[0].softmax(-1  )
									a_		:		Union[str, Any]				    =				probs.topk(_lowercase  )

									a_		:		Any				    =				scores.tolist()
									a_		:		Any				    =				ids.tolist()

									a_		:		Optional[int]				    =				[{"""score""": score, """label""": self.model.config.idalabel[_id]} for score, _id in zip(_lowercase       ,     _lowercase  )]

									return labels





import sys

from transformers import AutoTokenizer


__snake_case							:     int 						=	sys.argv[1]
__snake_case							:     Dict 						=	sys.argv[2]
__snake_case							:     Dict 						=	int(sys.argv[3])

__snake_case							:     Union[str, Any] 						=	0

__snake_case							:     Any 						=	AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
	for line in f_p:
		__snake_case							:     str 						=	line.rstrip()

		if not line:
			print(line)
			__snake_case							:     Optional[int] 						=	0
			continue

		__snake_case							:     Dict 						=	line.split()[0]

		__snake_case							:     str 						=	len(tokenizer.tokenize(token))

		# Token contains strange control characters like \x96 or \x95
		# Just filter out the complete line
		if current_subwords_len == 0:
			continue

		if (subword_len_counter + current_subwords_len) > max_len:
			print("""""")
			print(line)
			__snake_case							:     Optional[int] 						=	current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)


