
import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME


def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				__a										=    [
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''encoder.embed_positions._float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(a__						,    a__  )


def     __lowerCAmelCase			(			a__  )       ->  List[Any]:
				__a							,     __a										=    emb.weight.shape
				__a										=    nn.Linear(a__						,    a__						,    bias=a__  )
				__a										=    emb.weight.data
				return lin_layer


def     __lowerCAmelCase			(			a__						,    a__=None  )       ->  List[Any]:
				__a										=    {}
				for old_key in state_dict.keys():
								__a										=    old_key
								if "moe_layer.experts." in key:
												if expert_idx is not None:
																__a										=    key.replace('''moe_layer.experts.0'''						,    F"""ffn.experts.expert_{expert_idx}"""  )
												else:
																__a										=    key.replace('''moe_layer.experts.'''						,    '''ffn.experts.expert_'''  )
								if "gate" in key:
												__a										=    key.replace('''.moe_layer.gate.wg'''						,    '''.ffn.router.classifier'''  )
								if "fc2" and "experts" not in key:
												__a										=    key.replace('''.fc2.'''						,    '''.ffn.fc2.'''  )
								if "fc1" and "experts" not in key:
												__a										=    key.replace('''.fc1.'''						,    '''.ffn.fc1.'''  )
								if ".encoder_attn." in key:
												__a										=    key.replace('''.encoder_attn.'''						,    '''.cross_attention.'''  )
								if "encoder_attn_layer_norm" in key:
												__a										=    key.replace('''encoder_attn_layer_norm'''						,    '''cross_attention_layer_norm'''  )
								if "final_layer_norm" in key:
												__a										=    key.replace('''final_layer_norm'''						,    '''ff_layer_norm'''  )
								__a										=    state_dict[old_key]
				return new_dict


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__ = WEIGHTS_NAME  )       ->  Any:
				__a										=    []
				__a										=    0
				os.makedirs(a__						,    exist_ok=a__  )

				for expert in range(a__  ):
								__a										=    switch_checkpoint_path + F"""-rank-{expert}.pt"""
								if os.path.isfile(a__  ):
												__a										=    torch.load(a__  )['''model''']
												remove_ignore_keys_(a__  )
												__a										=    rename_fairseq_keys(a__						,    a__  )
												__a										=    os.path.join(
												    a__						,    weights_name.replace('''.bin'''						,    F"""-{len(a__  )+1:05d}-of-???.bin"""  )  )
												torch.save(a__						,    a__  )
												sharded_state_dicts.append(expert_state.keys()  )
												total_size += sum([value.numel() for key, value in expert_state.items()]  ) * dtype_byte_size(
												    expert_state[list(a__  )[0]].dtype  )

    # Add the last block
				__a										=    os.path.join(a__						,    weights_name.replace('''.bin'''						,    F"""-{len(a__  )+1:05d}-of-???.bin"""  )  )
				__a										=    torch.load(switch_checkpoint_path + '''-shared.pt'''  )['''model''']
				remove_ignore_keys_(a__  )
				__a										=    rename_fairseq_keys(a__						,    a__  )
				__a										=    shared_weights['''decoder.embed_tokens.weight''']
				sharded_state_dicts.append(shared_weights.keys()  )

				# If we only have the shared weights (dummy model/experts saved on the same file)
				if len(a__  ) == 1:
								__a										=    os.path.join(a__						,    a__  )
								torch.save(a__						,    a__  )
								return {weights_name: sharded_state_dicts[0]}, None
				else:
								torch.save(a__						,    a__  )
				# Otherwise, let's build the index
				__a										=    {}
				for idx, shard in enumerate(a__  ):
								__a										=    weights_name.replace('''.bin'''						,    F"""-{idx+1:05d}-of-{len(a__  ):05d}.bin"""  )
								__a										=    os.path.join(a__						,    weights_name.replace('''.bin'''						,    F"""-{idx+1:05d}-of-???.bin"""  )  )
								os.rename(a__						,    os.path.join(a__						,    a__  )  )
								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__":
					A     :	List[Any]    = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--nllb_moe_checkpoint_path',
					    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
					    type=str,
					    required=False,
					    help='Path to a directory containing a folder per layer. Follows the original Google format.',
					)
					parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
					    type=str,
					    required=False,
					    help='Path to the output pytorch model.',
					)
					A     :	Optional[Any]    = parser.parse_args()
					A ,   A     :	List[str]    = shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    1_2_8,
					    args.dtype,
					)

					A     :	Dict    = NllbMoeConfig.from_pretrained(
					    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
					)
					config.save_pretrained(args.pytorch_dump_folder_path)
					A     :	Optional[Any]    = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print('Done')
					model.save_pretrained(args.pytorch_dump_folder_path)

from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 - _cos) / 2
				__a										=    1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    (1 + _cos) / 2
				__a										=    -1 - _cos

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    _sin / 2
				__a										=    0
				__a										=    -ba

				__a										=    1 + alpha
				__a										=    -2 * _cos
				__a										=    1 - alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__ = 1 / sqrt(2  )  )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)

				__a										=    1 - alpha
				__a										=    -2 * _cos
				__a										=    1 + alpha

				__a										=    IIRFilter(2  )
				filt.set_coefficients([ba, ba, ba]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)

				__a										=    1 + alpha * big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha * big_a
				__a										=    1 + alpha / big_a
				__a										=    -2 * _cos
				__a										=    1 - alpha / big_a

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (pmc + aaa)
				__a										=    2 * big_a * mpc
				__a										=    big_a * (pmc - aaa)
				__a										=    ppmc + aaa
				__a										=    -2 * pmpc
				__a										=    ppmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt






def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__ = 1 / sqrt(2  )						,    )       ->  IIRFilter:
				__a										=    tau * frequency / samplerate
				__a										=    sin(a__  )
				__a										=    cos(a__  )
				__a										=    _sin / (2 * q_factor)
				__a										=    10 ** (gain_db / 40)
				__a										=    (big_a + 1) - (big_a - 1) * _cos
				__a										=    (big_a + 1) + (big_a - 1) * _cos
				__a										=    (big_a - 1) - (big_a + 1) * _cos
				__a										=    (big_a - 1) + (big_a + 1) * _cos
				__a										=    2 * sqrt(a__  ) * alpha

				__a										=    big_a * (ppmc + aaa)
				__a										=    -2 * big_a * pmpc
				__a										=    big_a * (ppmc - aaa)
				__a										=    pmc + aaa
				__a										=    2 * mpc
				__a										=    pmc - aaa

				__a										=    IIRFilter(2  )
				filt.set_coefficients([aa, aa, aa]						,    [ba, ba, ba]  )
				return filt