




'''simple docstring'''







import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase_     						=				logging.get_logger(__name__)

class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Union[str, Any]						=					['''input_features''']






 def __init__(self       :  str    ,		a       :  Union[str, Any]=80    ,		a       :  Optional[Any]=1_60_00    ,		a       :  Optional[int]=1_60    ,		a       :  str=30    ,		a       :  Dict=4_00    ,		a       :  List[str]=0.0    ,		a       :  Dict=False    ,		**a       :  Tuple    ,		)       ->   List[Any]:






    """simple docstring"""


    super().__init__(
        feature_size=lowerCamelCase_    ,		sampling_rate=lowerCamelCase_    ,		padding_value=lowerCamelCase_    ,		return_attention_mask=lowerCamelCase_    ,		**lowerCamelCase_    ,		)
    A__       =     n_fft
    A__       =     hop_length
    A__       =     chunk_length
    A__       =     chunk_length * sampling_rate
    A__       =     self.n_samples // hop_length
    A__       =     sampling_rate
    A__       =     mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2    ,		num_mel_filters=lowerCamelCase_    ,		min_frequency=0.0    ,		max_frequency=80_00.0    ,		sampling_rate=lowerCamelCase_    ,		norm='slaney'    ,		mel_scale='slaney'    ,		)






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Any    )       ->   np.ndarray:






    """simple docstring"""


    A__       =     spectrogram(
        lowerCamelCase_    ,		window_function(self.n_fft    ,		'hann'    )    ,		frame_length=self.n_fft    ,		hop_length=self.hop_length    ,		power=2.0    ,		mel_filters=self.mel_filters    ,		log_mel='log10'    ,		)
    A__       =     log_spec[:, :-1]
    A__       =     np.maximum(lowerCamelCase_    ,		log_spec.max() - 8.0    )
    A__       =     (log_spec + 4.0) / 4.0
    return log_spec






 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 						_UpperCamelCase (a       :  Optional[int]    ,		a       :  str    ,		a       :  Any = 0.0    )       ->   List[np.ndarray]:






    """simple docstring"""


    if attention_mask is not None:
       A__       =     np.array(lowerCamelCase_    ,		np.intaa    )
       A__       =     []

       for vector, length in zip(lowerCamelCase_    ,		attention_mask.sum(-1    )    ):
          A__       =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
          if length < normed_slice.shape[0]:
             A__       =     padding_value

          normed_input_values.append(lowerCamelCase_    )
    else:
       A__       =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

    return normed_input_values






 def __call__(self       :  Optional[Any]    ,		a       :  Tuple    ,		a       :  Optional[int] = True    ,		a       :  Any = None    ,		a       :  Any = None    ,		a       :  List[str] = None    ,		a       :  List[str] = "max_length"    ,		a       :  List[str] = None    ,		a       :  List[Any] = None    ,		a       :  List[str] = None    ,		**a       :  Any    ,		)       ->   BatchFeature:






    """simple docstring"""


    if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
          raise ValueError(
              f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
              f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
              f""" was sampled with {self.sampling_rate} and not {sampling_rate}."""    )
    else:
       logger.warning(
           'It is strongly recommended to pass the `sampling_rate` argument to this function. '
           'Failing to do so can result in silent errors that might be hard to debug.'    )

    A__       =     isinstance(lowerCamelCase_    ,		np.ndarray    ) and len(raw_speech.shape    ) > 1
    if is_batched_numpy and len(raw_speech.shape    ) > 2:
       raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""    )
    A__       =     is_batched_numpy or (
        isinstance(lowerCamelCase_    ,		(list, tuple)    ) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)    ))
    )

    if is_batched:
       A__       =     [np.asarray([speech]    ,		dtype=np.floataa    ).T for speech in raw_speech]
    elif not is_batched and not isinstance(lowerCamelCase_    ,		np.ndarray    ):
       A__       =     np.asarray(lowerCamelCase_    ,		dtype=np.floataa    )
    elif isinstance(lowerCamelCase_    ,		np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
       A__       =     raw_speech.astype(np.floataa    )

    # always return batch
    if not is_batched:
       A__       =     [np.asarray([raw_speech]    ).T]

    A__       =     BatchFeature({'input_features': raw_speech}    )

    # convert into correct format for padding

    A__       =     self.pad(
        lowerCamelCase_    ,		padding=lowerCamelCase_    ,		max_length=max_length if max_length else self.n_samples    ,		truncation=lowerCamelCase_    ,		pad_to_multiple_of=lowerCamelCase_    ,		return_attention_mask=return_attention_mask or do_normalize    ,		)

    # zero-mean and unit-variance normalization
    if do_normalize:
       A__       =     self.zero_mean_unit_var_norm(
           padded_inputs['input_features']    ,		attention_mask=padded_inputs['attention_mask']    ,		padding_value=self.padding_value    ,		)
       A__       =     np.stack(padded_inputs['input_features']    ,		axis=0    )

    # make sure list is in array format
    A__       =     padded_inputs.get('input_features'    ).transpose(2    ,		0    ,		1    )

    A__       =     [self._np_extract_fbank_features(lowerCamelCase_    ) for waveform in input_features[0]]

    if isinstance(input_features[0]    ,		lowerCamelCase_    ):
       A__       =     [np.asarray(lowerCamelCase_    ,		dtype=np.floataa    ) for feature in input_features]
    else:
       A__       =     input_features

    if return_attention_mask:
       # rescale from sample (48000) to feature (3000)
       A__       =     padded_inputs['attention_mask'][:, :: self.hop_length]

    if return_tensors is not None:
       A__       =     padded_inputs.convert_to_tensors(lowerCamelCase_    )

    return padded_inputs






 def 						_UpperCamelCase (self       :  Dict    )       ->   Dict[str, Any]:






    """simple docstring"""


    A__       =     copy.deepcopy(self.__dict__    )
    A__       =     self.__class__.__name__
    if "mel_filters" in output:
       del output["mel_filters"]
    return output





"""simple docstring"""


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[Any]				):



      """simple docstring"""






      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            A				  =		flax_key_tuple[:-1] + ("""weight""",)
            A				  =		torch.permute(_a     ,   (0, 2, 1)				)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_a				):
            # linear layer
            A				  =		flax_key_tuple[:-1] + ("""weight""",)
            A				  =		flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            A				  =		flax_key_tuple[:-1] + ("""weight""",)

      return flax_key_tuple, flax_tensor



def   _A					(	_a	:						Tuple     ,   _a	:						Any     ,   _a	:						Any				):



      """simple docstring"""






      if "metadata" in layer:
            A				  =		layer.split("""metadata"""				)
            A				  =		"""""".join(split_layer[0]				)[:-1]
            A				  =		[tuple(("""metadata""" + split_layer[1]).split("""/"""				)				)]
      elif "kvstore" in layer:
            A				  =		layer.split("""kvstore"""				)
            A				  =		"""""".join(split_layer[0]				)[:-1]
            A				  =		[tuple(("""kvstore""" + split_layer[1]).split("""/"""				)				)]

      else:
            A				  =		layer.split("""/"""				)
            A				  =		"""/""".join(split_layer[:-1]				)
            A				  =		(split_layer[-1],)

      if "kvstore/path" in layer:
            A				  =		f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
      elif "kvstore/driver" in layer:
            A				  =		"""file"""
      else:
            A				  =		checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		rename_keys(_a				)
      A				  =		{}
      for k, v in current_block.items():
            A				  =		v
      A				  =		new_current_block
      torch.save(_a     ,   _a				)



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						int     ,   _a	:						Optional[int]     ,   _a	:						str = WEIGHTS_NAME				):



      """simple docstring"""






      A				  =		convert_file_size_to_int(_a				)
      A				  =		[]
      A				  =		{}
      A				  =		0
      A				  =		0

      os.makedirs(_a     ,   exist_ok=_a				)
      with gfile.GFile(switch_checkpoint_path + """/checkpoint"""     ,   """rb"""				) as fp:
            A				  =		serialization.msgpack_restore(fp.read()				)["""optimizer"""]["""target"""]
            A				  =		flatten_dict(_a     ,   sep="""/"""				)

      A				  =		{}
      for layer in checkpoint_info.keys():
            A  ,			A  ,			A				  =		get_key_and_tensorstore_dict(
                _a     ,   _a     ,   _a				)
            if curr_real_layer_name in all_layers:
                  A				  =		content
            else:
                  A				  =		{split_layer[-1]: content}

      for key in all_layers.keys():
            # open tensorstore file
            A				  =		ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
            A				  =		torch.tensor(_a				)
            A				  =		raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

            # use the renaming pattern from the small conversion scripts
            A  ,			A				  =		rename_base_flax_keys(tuple(key.split("""/"""				)				)     ,   _a				)
            A				  =		"""/""".join(_a				)

            # If this weight is going to tip up over the maximal size, we split.
            if current_block_size + weight_size > max_shard_size:
                  A				  =		os.path.join(
                      _a     ,   weights_name.replace(""".bin"""     ,   f'-{len(_a				)+1:05d}-of-???.bin'				)				)
                  rename_and_save_block(_a     ,   _a				)
                  sharded_state_dicts.append(current_block.keys()				)
                  del current_block
                  A				  =		{}
                  A				  =		0

            A				  =		raw_weights.to(getattr(_a     ,   _a				)				)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      A				  =		os.path.join(_a     ,   weights_name.replace(""".bin"""     ,   f'-{len(_a				)+1:05d}-of-???.bin'				)				)
      rename_and_save_block(_a     ,   _a				)
      sharded_state_dicts.append(current_block.keys()				)

      # If we only have one shard, we return it
      if len(_a				) == 1:
            return {weights_name: sharded_state_dicts[0]}, None

      # Otherwise, let's build the index
      A				  =		{}
      A				  =		{}
      for idx, shard in enumerate(_a				):
            A				  =		weights_name.replace(
                """.bin"""     ,   f'-{idx+1:05d}-of-{len(_a				):05d}.bin'				)  # len(sharded_state_dicts):05d}
            A				  =		os.path.join(_a     ,   weights_name.replace(""".bin"""     ,   f'-{idx+1:05d}-of-???.bin'				)				)
            os.rename(_a     ,   os.path.join(_a     ,   _a				)				)
            A				  =		shard
            for key in shard:
                  A				  =		shard_file

    # Add the metadata
      A				  =		{"""total_size""": total_size}
      A				  =		{"""metadata""": metadata, """weight_map""": weight_map}

      with open(os.path.join(_a     ,   _a				)     ,   """w"""     ,   encoding="""utf-8"""				) as f:
            A				  =		json.dumps(_a     ,   indent=2     ,   sort_keys=_a				) + """\n"""
            f.write(_a				)

      return metadata, index


if __name__ == "__main__":
  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
      type=str,
      required=False,
      help="Path to a directory containing a folder per layer. Follows the original Google format.",
  )
  parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
  parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
      type=str,
      required=False,
      help="Path to the output pytorch model.",
  )
  UpperCAmelCase													=parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )





def   _A					(	):



      """simple docstring"""






      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      A				  =		SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""				)
      config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""				)
      A				  =		SwitchTransformersForConditionalGeneration.from_pretrained(
          """/home/arthur_huggingface_co/transformers/switch_converted"""     ,   device_map="""auto"""				)

      A				  =		TaTokenizer.from_pretrained("""t5-small"""				)
      A				  =		"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

      A				  =		tokenizer(_a     ,   return_tensors="""pt"""				).input_ids
      A				  =		model.generate(_a     ,   decoder_start_token_id=0				)
      print(tokenizer.decode(out[0]				)				)




