'''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  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





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

     return flax_key_tuple, flax_tensor


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Tuple     ,					lowerCamelCase_ :							List[str]       ):



     """simple docstring"""





     if "metadata" in layer:
          UpperCAmelCase_							:       List[str]       =				layer.split('metadata'       )
          UpperCAmelCase_							:       List[Any]       =				''.join(split_layer[0]       )[:-1]
          UpperCAmelCase_							:       Tuple       =				[tuple(('metadata' + split_layer[1]).split('/'       )       )]
     elif "kvstore" in layer:
          UpperCAmelCase_							:       Dict       =				layer.split('kvstore'       )
          UpperCAmelCase_							:       List[Any]       =				''.join(split_layer[0]       )[:-1]
          UpperCAmelCase_							:       Dict       =				[tuple(('kvstore' + split_layer[1]).split('/'       )       )]

     else:
          UpperCAmelCase_							:       List[str]       =				layer.split('/'       )
          UpperCAmelCase_							:       List[Any]       =				'/'.join(split_layer[:-1]       )
          UpperCAmelCase_							:       Any       =				(split_layer[-1],)

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

     return curr_real_layer_name, split_layer, content


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				rename_keys(lowerCamelCase_       )
     UpperCAmelCase_							:       str       =				{}
     for k, v in current_block.items():
          UpperCAmelCase_							:       Optional[Any]       =				v
     UpperCAmelCase_							:       Any       =				new_current_block
     torch.save(lowerCamelCase_     ,					lowerCamelCase_       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							str = WEIGHTS_NAME       ):



     """simple docstring"""





     UpperCAmelCase_							:       Tuple       =				convert_file_size_to_int(lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				[]
     UpperCAmelCase_							:       Union[str, Any]       =				{}
     UpperCAmelCase_							:       List[Any]       =				0
     UpperCAmelCase_							:       Union[str, Any]       =				0

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

     UpperCAmelCase_							:       Union[str, Any]       =				{}
     for layer in checkpoint_info.keys():
          UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				get_key_and_tensorstore_dict(
              lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )
          if curr_real_layer_name in all_layers:
               UpperCAmelCase_							:       Optional[int]       =				content
          else:
               UpperCAmelCase_							:       Optional[int]       =				{split_layer[-1]: content}

     for key in all_layers.keys():
          # open tensorstore file
          UpperCAmelCase_							:       int       =				ts.open(unflatten_dict(all_layers[key]       )       ).result().read().result()
          UpperCAmelCase_							:       Any       =				torch.tensor(lowerCamelCase_       )
          UpperCAmelCase_							:       List[str]       =				raw_weights.numel() * dtype_byte_size(raw_weights.dtype       )

          # use the renaming pattern from the small conversion scripts
          UpperCAmelCase_       ,      UpperCAmelCase_							:       Tuple       =				rename_base_flax_keys(tuple(key.split('/'       )       )     ,					lowerCamelCase_       )
          UpperCAmelCase_							:       str       =				'/'.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:
               UpperCAmelCase_							:       Dict       =				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
               UpperCAmelCase_							:       str       =				{}
               UpperCAmelCase_							:       List[Any]       =				0

          UpperCAmelCase_							:       Union[str, Any]       =				raw_weights.to(getattr(lowerCamelCase_     ,					lowerCamelCase_       )       )
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     UpperCAmelCase_							:       Tuple       =				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
     UpperCAmelCase_							:       Any       =				{}
     UpperCAmelCase_							:       List[Any]       =				{}
     for idx, shard in enumerate(lowerCamelCase_       ):
          UpperCAmelCase_							:       Dict       =				weights_name.replace(
              '.bin'     ,					F'''-{idx+1:05d}-of-{len(lowerCamelCase_       ):05d}.bin'''       )  # len(sharded_state_dicts):05d}
          UpperCAmelCase_							:       Tuple       =				os.path.join(lowerCamelCase_     ,					weights_name.replace('.bin'     ,					F'''-{idx+1:05d}-of-???.bin'''       )       )
          os.rename(lowerCamelCase_     ,					os.path.join(lowerCamelCase_     ,					lowerCamelCase_       )       )
          UpperCAmelCase_							:       Dict       =				shard
          for key in shard:
               UpperCAmelCase_							:       Dict       =				shard_file

    # Add the metadata
     UpperCAmelCase_							:       Optional[Any]       =				{'total_size': total_size}
     UpperCAmelCase_							:       List[str]       =				{'metadata': metadata, 'weight_map': weight_map}

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

     return metadata, index


if __name__ == "__main__":
 snake_case__					:      Union[str, Any]				      =  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.''',
 )
 snake_case__					:      Any				      =  parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )





def  _lowerCamelCase   (				):



     """simple docstring"""





     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

     UpperCAmelCase_							:       Dict       =				TaTokenizer.from_pretrained('t5-small'       )
     UpperCAmelCase_							:       Optional[Any]       =				'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

     UpperCAmelCase_							:       int       =				tokenizer(lowerCamelCase_     ,					return_tensors='pt'       ).input_ids
     UpperCAmelCase_							:       Union[str, Any]       =				model.generate(lowerCamelCase_     ,					decoder_start_token_id=0       )
     print(tokenizer.decode(out[0]       )       )







'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     return int(input_a == input_a == 0       )


def  _lowerCamelCase   (				):



     """simple docstring"""





     print('Truth Table of NOR Gate:'       )
     print('|   Input 1   |  Input 2  | Output |'       )
     print(F'''|      0      |    0      |  {nor_gate(0     ,					0       )}     |'''       )
     print(F'''|      0      |    1      |  {nor_gate(0     ,					1       )}     |'''       )
     print(F'''|      1      |    0      |  {nor_gate(1     ,					0       )}     |'''       )
     print(F'''|      1      |    1      |  {nor_gate(1     ,					1       )}     |'''       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()






