


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] ) )






"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str			) ->      list:
		if n_term == "":
				return []
		__a           =			[]
		for temp in range(int(lowerCAmelCase__			)			):
				series.append(f'''1/{temp + 1}''' if series else '''1'''			)
		return series


if __name__ == "__main__":
				lowercase_			     =  input("Enter the last number (nth term) of the Harmonic Series")
				print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
				print(harmonic_series(nth_term))






