

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

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
			from .features import FeatureType


UpperCAmelCase_ ,		UpperCAmelCase_ ,		UpperCAmelCase_							:					int		 =   False, False, False






@dataclass
class      lowerCamelCase_      :
	_lowercase		:   Optional[int]     					=				None
	_lowercase		:   bool     					=				True
	_lowercase		:   bool     					=				True
	_lowercase		:   Optional[str]     					=				None
	# Automatically constructed
	_lowercase		:   ClassVar[str]     					=				"dict"
	_lowercase		:   ClassVar[Any]     					=				pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}	)
	_lowercase		:   str     					=				field(default='''Audio'''						,      init=_lowercase						,      repr=_lowercase	)




	def __call__(     self						:     Dict							):
		return self.pa_type




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Union[str, bytes, dict]							):
		try:
			import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
		except ImportError as err:
			raise ImportError("""To support encoding audio data, please install 'soundfile'."""							) from err
		if isinstance(__A					,      __A							):
			return {"bytes": None, "path": value}
		elif isinstance(__A					,      __A							):
			return {"bytes": value, "path": None}
		elif "array" in value:
			# convert the audio array to wav bytes
			__A :			Tuple		    =			BytesIO()
			sf.write(__A					,      value["""array"""]					,      value["""sampling_rate"""]					,      format="""wav"""							)
			return {"bytes": buffer.getvalue(), "path": None}
		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
			if value["path"].endswith("""pcm"""							):
				# "PCM" only has raw audio bytes
				if value.get("""sampling_rate"""							) is None:
					# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
					raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object"""							)
				if value.get("""bytes"""							):
					# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
					__A :			Tuple		    =			np.frombuffer(value["""bytes"""]					,      dtype=np.intaa							).astype(np.floataa							) / 3_2767
				else:
					__A :			Dict		    =			np.memmap(value["""path"""]					,      dtype="""h"""					,      mode="""r"""							).astype(np.floataa							) / 3_2767

				__A :			Any		    =			BytesIO(bytes()							)
				sf.write(__A					,      __A					,      value["""sampling_rate"""]					,      format="""wav"""							)
				return {"bytes": buffer.getvalue(), "path": None}
			else:
				return {"bytes": None, "path": value.get("""path"""							)}
		elif value.get("""bytes"""							) is not None or value.get("""path"""							) is not None:
			# store the audio bytes, and path is used to infer the audio format using the file extension
			return {"bytes": value.get("""bytes"""							), "path": value.get("""path"""							)}
		else:
			raise ValueError(
			    F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     dict					,      __A						:     Optional[Dict[str, Union[str, bool, None]]] = None							):
		if not self.decode:
			raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""							)

		__A    ,				__A :			List[Any]		    =			(value["""path"""], BytesIO(value["""bytes"""]							)) if value["""bytes"""] is not None else (value["""path"""], None)
		if path is None and file is None:
			raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""							)

		try:
			import librosa
			import soundfile as sf
		except ImportError as err:
			raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'."""							) from err

		__A :			Tuple		    =			xsplitext(__A							)[1][1:].lower() if path is not None else None
		if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
			raise RuntimeError(
			    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
			    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """							)
		elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
			raise RuntimeError(
			    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
			    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """							)

		if file is None:
			__A :			Optional[int]		    =			token_per_repo_id or {}
			__A :			List[str]		    =			path.split("""::"""							)[-1]
			try:
				__A :			Tuple		    =			string_to_dict(__A					,      config.HUB_DATASETS_URL							)["""repo_id"""]
				__A :			int		    =			token_per_repo_id[repo_id]
			except (ValueError, KeyError):
				__A :			Union[str, Any]		    =			None

			with xopen(__A					,      """rb"""					,      use_auth_token=__A							) as f:
				__A    ,				__A :			Optional[Any]		    =			sf.read(__A							)

		else:
			__A    ,				__A :			int		    =			sf.read(__A							)

		__A :			Any		    =			array.T
		if self.mono:
			__A :			int		    =			librosa.to_mono(__A							)
		if self.sampling_rate and self.sampling_rate != sampling_rate:
			__A :			Dict		    =			librosa.resample(__A					,      orig_sr=__A					,      target_sr=self.sampling_rate							)
			__A :			List[str]		    =			self.sampling_rate

		return {"path": path, "array": array, "sampling_rate": sampling_rate}




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		from .features import Value

		if self.decode:
			raise ValueError("""Cannot flatten a decoded Audio feature."""							)
		return {
		    "bytes": Value("""binary"""							),
		    "path": Value("""string"""							),
		}




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     Union[pa.StringArray, pa.StructArray]							):
		if pa.types.is_string(storage.type							):
			__A :			Dict		    =			pa.array([None] * len(__A							)					,      type=pa.binary()							)
			__A :			List[Any]		    =			pa.StructArray.from_arrays([bytes_array, storage]					,      ["""bytes""", """path"""]					,      mask=storage.is_null()							)
		elif pa.types.is_binary(storage.type							):
			__A :			List[Any]		    =			pa.array([None] * len(__A							)					,      type=pa.string()							)
			__A :			Any		    =			pa.StructArray.from_arrays([storage, path_array]					,      ["""bytes""", """path"""]					,      mask=storage.is_null()							)
		elif pa.types.is_struct(storage.type							) and storage.type.get_all_field_indices("""array"""							):
			__A :			Union[str, Any]		    =			pa.array([Audio().encode_example(__A							) if x is not None else None for x in storage.to_pylist()]							)
		elif pa.types.is_struct(storage.type							):
			if storage.type.get_field_index("""bytes"""							) >= 0:
				__A :			Dict		    =			storage.field("""bytes"""							)
			else:
				__A :			Tuple		    =			pa.array([None] * len(__A							)					,      type=pa.binary()							)
			if storage.type.get_field_index("""path"""							) >= 0:
				__A :			List[Any]		    =			storage.field("""path"""							)
			else:
				__A :			List[Any]		    =			pa.array([None] * len(__A							)					,      type=pa.string()							)
			__A :			Tuple		    =			pa.StructArray.from_arrays([bytes_array, path_array]					,      ["""bytes""", """path"""]					,      mask=storage.is_null()							)
		return array_cast(__A					,      self.pa_type							)




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     pa.StructArray							):

		@no_op_if_value_is_null
		def path_to_bytes(__A						:     Dict							):
			with xopen(__A					,      """rb"""							) as f:
				__A :			Optional[Any]		    =			f.read()
			return bytes_

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








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





