


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 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Tuple:
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        _lowercase       :							Tuple      		=		flax_key_tuple[:-1] + ('weight',)
        _lowercase       :							int      		=		torch.permute(lowerCamelCase_							, (0, 2, 1) )
    elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_ ):
        # linear layer
        _lowercase       :							Any      		=		flax_key_tuple[:-1] + ('weight',)
        _lowercase       :							int      		=		flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        _lowercase       :							List[str]      		=		flax_key_tuple[:-1] + ('weight',)

    return flax_key_tuple, flax_tensor



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						List[str]:
    if "metadata" in layer:
        _lowercase       :							Any      		=		layer.split('metadata' )
        _lowercase       :							Union[str, Any]      		=		''.join(split_layer[0] )[:-1]
        _lowercase       :							List[str]      		=		[tuple(('metadata' + split_layer[1]).split('/' ) )]
    elif "kvstore" in layer:
        _lowercase       :							str      		=		layer.split('kvstore' )
        _lowercase       :							Union[str, Any]      		=		''.join(split_layer[0] )[:-1]
        _lowercase       :							Optional[Any]      		=		[tuple(('kvstore' + split_layer[1]).split('/' ) )]

    else:
        _lowercase       :							int      		=		layer.split('/' )
        _lowercase       :							Tuple      		=		'/'.join(split_layer[:-1] )
        _lowercase       :							Optional[Any]      		=		(split_layer[-1],)

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

    return curr_real_layer_name, split_layer, content



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Dict:
    _lowercase       :							Tuple      		=		rename_keys(lowerCamelCase_ )
    _lowercase       :							List[str]      		=		{}
    for k, v in current_block.items():
        _lowercase       :							Dict      		=		v
    _lowercase       :							Any      		=		new_current_block
    torch.save(lowerCamelCase_							, lowerCamelCase_ )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ = WEIGHTS_NAME )    ->						Dict:
    _lowercase       :							Optional[Any]      		=		convert_file_size_to_int(lowerCamelCase_ )
    _lowercase       :							Any      		=		[]
    _lowercase       :							Optional[Any]      		=		{}
    _lowercase       :							Any      		=		0
    _lowercase       :							str      		=		0

    os.makedirs(lowerCamelCase_							, exist_ok=lowerCamelCase_ )
    with gfile.GFile(switch_checkpoint_path + '/checkpoint'							, 'rb' ) as fp:
        _lowercase       :							Union[str, Any]      		=		serialization.msgpack_restore(fp.read() )['optimizer']['target']
        _lowercase       :							Optional[int]      		=		flatten_dict(lowerCamelCase_							, sep='/' )

    _lowercase       :							str      		=		{}
    for layer in checkpoint_info.keys():
        _lowercase		,				_lowercase		,				_lowercase       :							List[Any]      		=		get_key_and_tensorstore_dict(
            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
        if curr_real_layer_name in all_layers:
            _lowercase       :							List[Any]      		=		content
        else:
            _lowercase       :							Dict      		=		{split_layer[-1]: content}

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

        # use the renaming pattern from the small conversion scripts
        _lowercase		,				_lowercase       :							str      		=		rename_base_flax_keys(tuple(key.split('/' ) )							, lowerCamelCase_ )
        _lowercase       :							Any      		=		'/'.join(lowerCamelCase_ )

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

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

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

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

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

    # Add the metadata
    _lowercase       :							Any      		=		{'total_size': total_size}
    _lowercase       :							Tuple      		=		{'metadata': metadata, 'weight_map': weight_map}

    with open(os.path.join(lowerCamelCase_							, lowerCamelCase_ )							, 'w'							, encoding='utf-8' ) as f:
        _lowercase       :							Tuple      		=		json.dumps(lowerCamelCase_							, indent=2							, sort_keys=lowerCamelCase_ ) + '\n'
        f.write(lowerCamelCase_ )

    return metadata, index


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      List[str]												= 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.",
 )
 SCREAMING_SNAKE_CASE       :      Dict												= parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )



def 				UpperCamelCase_(      )    ->						str:
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

    _lowercase       :							Optional[int]      		=		tokenizer(lowerCamelCase_							, return_tensors='pt' ).input_ids
    _lowercase       :							Tuple      		=		model.generate(lowerCamelCase_							, decoder_start_token_id=0 )
    print(tokenizer.decode(out[0] ) )






import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class _lowerCamelCase(						unittest.TestCase   ):
       lowercase_ :	Dict     =			JukeboxTokenizer
       lowercase_ :	Dict     =			{
           """artist""": """Zac Brown Band""",
           """genres""": """Country""",
           """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
       }
       @require_torch
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           import torch

           _lowercase       :							str      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics')
           _lowercase       :							Optional[Any]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
                   76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                   44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                   47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                   76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                   30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                   27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                   45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                   41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                   76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                   76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                   64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                   30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                   27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                   34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                   27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                   41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                   44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                   76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                   32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                   40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                   45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                   31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                   45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                   34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                   31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                   40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                   38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                   76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                   41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                   27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                   46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                   41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                   46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                   41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                   40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                   27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                   76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                   41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                   76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                   27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                   34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                   44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                   40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                   46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                   38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                   40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                   27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                   76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                   76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                   76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                   76, 76]]),
               torch.tensor([[0, 0, 0, 10_69, 11]]),
               torch.tensor([[0, 0, 0, 10_69, 11]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]))




       @require_torch
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           import torch

           _lowercase       :							List[str]      		=		JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics')
           _lowercase       :							List[str]      		=		tokenizer(**self.metas)['input_ids']
           # fmt: off
           _lowercase       :							Optional[int]      		=		[
               torch.tensor([[
                   0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
                   31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                   31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                   40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                   79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                   77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                   27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                   37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                   32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                   77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                   77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                   46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                   77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                   77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                   77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                   77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                   64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                   40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                   40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                   38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                   31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                   41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                   46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                   41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                   31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                   31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                   44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                   31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                   38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                   40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                   27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                   31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                   34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                   31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                   31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                   45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                   31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                   15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                   11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                   45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                   41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                   44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                   46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                   27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                   35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                   77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                   31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                   41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                   77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                   40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                   77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                   27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                   77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                   77, 77, 77, 77, 77, 77]]),
               torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
               torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]))


