






'''simple docstring'''







from typing import List, Optional, Union

import numpy as np

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


UpperCamelCase     :   Optional[Any]	      =		logging.get_logger(__name__)




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       ['input_features', 'attention_mask']






		def __init__(   self ,_lowerCAmelCase=80 ,_lowerCAmelCase=1_60_00 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=10 ,_lowerCAmelCase=25 ,_lowerCAmelCase="hamming_window" ,_lowerCAmelCase=3_2768.0 ,_lowerCAmelCase=0.97 ,_lowerCAmelCase=1.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
								super().__init__(feature_size=_lowerCAmelCase ,sampling_rate=_lowerCAmelCase ,padding_value=_lowerCAmelCase ,**_lowerCAmelCase							)

								lowerCamelCase__								=	feature_size
								lowerCamelCase__								=	sampling_rate
								lowerCamelCase__								=	padding_value
								lowerCamelCase__								=	hop_length
								lowerCamelCase__								=	win_length
								lowerCamelCase__								=	frame_signal_scale
								lowerCamelCase__								=	preemphasis_coeff
								lowerCamelCase__								=	mel_floor
								lowerCamelCase__								=	normalize_means
								lowerCamelCase__								=	normalize_vars
								lowerCamelCase__								=	win_function
								lowerCamelCase__								=	return_attention_mask

								lowerCamelCase__								=	win_length * sampling_rate // 10_00
								lowerCamelCase__								=	hop_length * sampling_rate // 10_00

								lowerCamelCase__								=	optimal_fft_length(self.sample_size							)
								lowerCamelCase__								=	(self.n_fft // 2) + 1






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if self.win_function == "hamming_window":
														lowerCamelCase__								=	window_function(window_length=self.sample_size ,name=self.win_function ,periodic=_lowerCAmelCase							)
								else:
														lowerCamelCase__								=	window_function(window_length=self.sample_size ,name=self.win_function							)

								lowerCamelCase__								=	mel_filter_bank(
								    num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)

								lowerCamelCase__								=	spectrogram(
								    one_waveform * self.frame_signal_scale ,window=_lowerCAmelCase ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=_lowerCAmelCase ,preemphasis=self.preemphasis_coeff ,mel_filters=_lowerCAmelCase ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
								return msfc_features.T






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								# make sure we normalize float32 arrays
								if self.normalize_means:
														lowerCamelCase__								=	x[:input_length].mean(axis=0							)
														lowerCamelCase__								=	np.subtract(_lowerCAmelCase ,_lowerCAmelCase							)
								if self.normalize_vars:
														lowerCamelCase__								=	x[:input_length].std(axis=0							)
														lowerCamelCase__								=	np.divide(_lowerCAmelCase ,_lowerCAmelCase							)

								if input_length < x.shape[0]:
														lowerCamelCase__								=	padding_value

								# make sure array is in float32
								lowerCamelCase__								=	x.astype(np.floataa							)

								return x






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = None							):
								lowerCamelCase__								=	attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
								return [self._normalize_one(_lowerCAmelCase ,_lowerCAmelCase ,self.padding_value							) for x, n in zip(_lowerCAmelCase ,_lowerCAmelCase							)]






		def __call__(   self ,_lowerCAmelCase ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,**_lowerCAmelCase ,):

								if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																				raise ValueError(
																				    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																				    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																				    F''' {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."""							)

								lowerCamelCase__								=	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}'''							)
								lowerCamelCase__								=	is_batched_numpy or (
								    isinstance(_lowerCAmelCase ,(list, tuple)							) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list)							))
								)

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

								# always return batch
								if not is_batched:
														lowerCamelCase__								=	[raw_speech]

								# extract fbank features
								lowerCamelCase__								=	[self._extract_mfsc_features(_lowerCAmelCase							) for one_waveform in raw_speech]

								# convert into correct format for padding
								lowerCamelCase__								=	BatchFeature({"""input_features""": features}							)

								lowerCamelCase__								=	self.pad(
								    _lowerCAmelCase ,padding=_lowerCAmelCase ,max_length=_lowerCAmelCase ,truncation=_lowerCAmelCase ,pad_to_multiple_of=_lowerCAmelCase ,return_attention_mask=_lowerCAmelCase ,**_lowerCAmelCase ,)
								# make sure list is in array format
								lowerCamelCase__								=	padded_inputs.get("""input_features"""							)
								if isinstance(input_features[0] ,_lowerCAmelCase							):
														lowerCamelCase__								=	[np.asarray(_lowerCAmelCase ,dtype=np.floataa							) for feature in input_features]

								lowerCamelCase__								=	padded_inputs.get("""attention_mask"""							)
								if attention_mask is not None:
														lowerCamelCase__								=	[np.asarray(_lowerCAmelCase ,dtype=np.intaa							) for array in attention_mask]

								if self.normalize_means or self.normalize_vars:
														lowerCamelCase__								=	(
														    np.array(_lowerCAmelCase ,dtype=np.intaa							)
														    if self._get_padding_strategies(_lowerCAmelCase ,max_length=_lowerCAmelCase							) is not PaddingStrategy.DO_NOT_PAD
														    and padding
														    else None
														)
														lowerCamelCase__								=	self.normalize(
														    padded_inputs["""input_features"""] ,attention_mask=_lowerCAmelCase							)

								if return_tensors is not None:
														lowerCamelCase__								=	padded_inputs.convert_to_tensors(_lowerCAmelCase							)

								return padded_inputs






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     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								a__	:							List[str]      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Optional[int]      						= torch.permute(__UpperCamelCase	,				(0, 2, 1) )
				elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ):
								# linear layer
								a__	:							Tuple      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Tuple      						= flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								a__	:							str      						= flax_key_tuple[:-1] + ("weight",)

				return flax_key_tuple, flax_tensor






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				if "metadata" in layer:
								a__	:							Union[str, Any]      						= layer.split("metadata" )
								a__	:							Any      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[int]      						= [tuple(("metadata" + split_layer[1]).split("/" ) )]
				elif "kvstore" in layer:
								a__	:							Optional[int]      						= layer.split("kvstore" )
								a__	:							Union[str, Any]      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[Any]      						= [tuple(("kvstore" + split_layer[1]).split("/" ) )]

				else:
								a__	:							Any      						= layer.split("/" )
								a__	:							Union[str, Any]      						= "/".join(split_layer[:-1] )
								a__	:							Tuple      						= (split_layer[-1],)

				if "kvstore/path" in layer:
								a__	:							Tuple      						= F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
				elif "kvstore/driver" in layer:
								a__	:							int      						= "file"
				else:
								a__	:							List[str]      						= checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							List[Any]      						= rename_keys(__UpperCamelCase )
				a__	:							Tuple      						= {}
				for k, v in current_block.items():
								a__	:							str      						= v
				a__	:							Optional[int]      						= new_current_block
				torch.save(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Any:
				a__	:							Optional[Any]      						= convert_file_size_to_int(__UpperCamelCase )
				a__	:							Tuple      						= []
				a__	:							Dict      						= {}
				a__	:							str      						= 0
				a__	:							Tuple      						= 0

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

				a__	:							Any      						= {}
				for layer in checkpoint_info.keys():
								a__						,   a__						,   a__	:							Optional[Any]      						= get_key_and_tensorstore_dict(
								    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
								if curr_real_layer_name in all_layers:
												a__	:							int      						= content
								else:
												a__	:							List[Any]      						= {split_layer[-1]: content}

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

								# use the renaming pattern from the small conversion scripts
								a__						,   a__	:							int      						= rename_base_flax_keys(tuple(key.split("/" ) )	,				__UpperCamelCase )
								a__	:							Union[str, Any]      						= "/".join(__UpperCamelCase )

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

								a__	:							List[str]      						= raw_weights.to(getattr(__UpperCamelCase	,				__UpperCamelCase ) )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
				rename_and_save_block(__UpperCamelCase	,				__UpperCamelCase )
				sharded_state_dicts.append(current_block.keys() )

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

				# Otherwise, let's build the index
				a__	:							int      						= {}
				a__	:							Tuple      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Tuple      						= weights_name.replace(
								    ".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )  # len(sharded_state_dicts):05d}
								a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								a__	:							List[str]      						= shard
								for key in shard:
												a__	:							Tuple      						= shard_file

    # Add the metadata
				a__	:							int      						= {"total_size": total_size}
				a__	:							List[str]      						= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							List[str]      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      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.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						shard_on_the_fly(
						    args.switch_tax_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.max_shard_size,
						    args.dtype,
						)






def     SCREAMING_SNAKE_CASE(      )	->						int:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				a__	:							Any      						= SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
				config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
				a__	:							List[str]      						= SwitchTransformersForConditionalGeneration.from_pretrained(
				    "/home/arthur_huggingface_co/transformers/switch_converted"	,				device_map="auto" )

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

				a__	:							Optional[int]      						= tokenizer(__UpperCamelCase	,				return_tensors="pt" ).input_ids
				a__	:							Optional[Any]      						= model.generate(__UpperCamelCase	,				decoder_start_token_id=0 )
				print(tokenizer.decode(out[0] ) )


