
"""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     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					List[Any] ) ->      Union[str, Any]:






    """simple docstring"""




    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        UpperCAmelCase									=					flax_key_tuple[:-1] + ('weight',)
        UpperCAmelCase									=					torch.permute(lowerCAmelCase ,				(0, 2, 1) )
    elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase ):
        # linear layer
        UpperCAmelCase									=					flax_key_tuple[:-1] + ('weight',)
        UpperCAmelCase									=					flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        UpperCAmelCase									=					flax_key_tuple[:-1] + ('weight',)

    return flax_key_tuple, flax_tensor




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Dict ) ->      Optional[int]:






    """simple docstring"""




    if "metadata" in layer:
        UpperCAmelCase									=					layer.split('metadata' )
        UpperCAmelCase									=					''.join(split_layer[0] )[:-1]
        UpperCAmelCase									=					[tuple(('metadata' + split_layer[1]).split('/' ) )]
    elif "kvstore" in layer:
        UpperCAmelCase									=					layer.split('kvstore' )
        UpperCAmelCase									=					''.join(split_layer[0] )[:-1]
        UpperCAmelCase									=					[tuple(('kvstore' + split_layer[1]).split('/' ) )]

    else:
        UpperCAmelCase									=					layer.split('/' )
        UpperCAmelCase									=					'/'.join(split_layer[:-1] )
        UpperCAmelCase									=					(split_layer[-1],)

    if "kvstore/path" in layer:
        UpperCAmelCase									=					F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
    elif "kvstore/driver" in layer:
        UpperCAmelCase									=					'file'
    else:
        UpperCAmelCase									=					checkpoint_info[layer]

    return curr_real_layer_name, split_layer, content




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					List[str] ) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					rename_keys(lowerCAmelCase )
    UpperCAmelCase									=					{}
    for k, v in current_block.items():
        UpperCAmelCase									=					v
    UpperCAmelCase									=					new_current_block
    torch.save(lowerCAmelCase ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					str = WEIGHTS_NAME ) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					convert_file_size_to_int(lowerCAmelCase )
    UpperCAmelCase									=					[]
    UpperCAmelCase									=					{}
    UpperCAmelCase									=					0
    UpperCAmelCase									=					0

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

    UpperCAmelCase									=					{}
    for layer in checkpoint_info.keys():
        UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					get_key_and_tensorstore_dict(
            lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )
        if curr_real_layer_name in all_layers:
            UpperCAmelCase									=					content
        else:
            UpperCAmelCase									=					{split_layer[-1]: content}

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

        # use the renaming pattern from the small conversion scripts
        UpperCAmelCase			,							UpperCAmelCase									=					rename_base_flax_keys(tuple(key.split('/' ) ) ,				lowerCAmelCase )
        UpperCAmelCase									=					'/'.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									=					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									=					{}
            UpperCAmelCase									=					0

        UpperCAmelCase									=					raw_weights.to(getattr(lowerCAmelCase ,				lowerCAmelCase ) )
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    UpperCAmelCase									=					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									=					{}
    UpperCAmelCase									=					{}
    for idx, shard in enumerate(lowerCAmelCase ):
        UpperCAmelCase									=					weights_name.replace(
            '.bin' ,				F"-{idx+1:05d}-of-{len(lowerCAmelCase ):05d}.bin" )  # len(sharded_state_dicts):05d}
        UpperCAmelCase									=					os.path.join(lowerCAmelCase ,				weights_name.replace('.bin' ,				F"-{idx+1:05d}-of-???.bin" ) )
        os.rename(lowerCAmelCase ,				os.path.join(lowerCAmelCase ,				lowerCAmelCase ) )
        UpperCAmelCase									=					shard
        for key in shard:
            UpperCAmelCase									=					shard_file

    # Add the metadata
    UpperCAmelCase									=					{'total_size': total_size}
    UpperCAmelCase									=					{'metadata': metadata, 'weight_map': weight_map}

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

    return metadata, index


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







def     lowercase__						(					) ->      List[str]:






    """simple docstring"""




    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    UpperCAmelCase									=					SwitchTransformersConfig.from_pretrained('google/switch-base-8' )
    config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted' )
    UpperCAmelCase									=					SwitchTransformersForConditionalGeneration.from_pretrained(
        '/home/arthur_huggingface_co/transformers/switch_converted' ,				device_map='auto' )

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

    UpperCAmelCase									=					tokenizer(lowerCAmelCase ,				return_tensors='pt' ).input_ids
    UpperCAmelCase									=					model.generate(lowerCAmelCase ,				decoder_start_token_id=0 )
    print(tokenizer.decode(out[0] ) )





"""simple docstring"""

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	""
      __SCREAMING_SNAKE_CASE   :							str							      =	(
          None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
      )
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # compression type in fsspec. ex: "gzip"
      __SCREAMING_SNAKE_CASE   :							str							      =	None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




      def __init__(							self   ,       lowercase_ = ""   ,       lowercase_ = None   ,       lowercase_ = None   ,       **lowercase_							)						->					str:
          super().__init__(self   ,       **lowercase_							)
          # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
          UpperCAmelCase									=					fsspec.open(
              lowercase_   ,       mode='rb'   ,       protocol=lowercase_   ,       compression=self.compression   ,       client_kwargs={
                  'requote_redirect_url': False,  # see https://github.com/huggingface/datasets/pull/5459
                  'trust_env': True,  # Enable reading proxy env variables.
                  **(target_options or {}).pop('client_kwargs'   ,       {}							),  # To avoid issues if it was already passed.
              }   ,       **(target_options or {})   ,       )
          UpperCAmelCase									=					os.path.basename(self.file.path.split('::'							)[0]							)
          UpperCAmelCase									=					(
              self.compressed_name[: self.compressed_name.rindex('.'							)]
              if '.' in self.compressed_name
              else self.compressed_name
          )
          UpperCAmelCase									=					None




      @classmethod
      def     a_      (							cls   ,       lowercase_							)						->					Union[str, Any]:
          # compressed file paths are always relative to the archive root
          return super()._strip_protocol(lowercase_							).lstrip('/'							)




      def     a_      (							self							)						->					int:
          if self.dir_cache is None:
              UpperCAmelCase									=					{**self.file.fs.info(self.file.path							), 'name': self.uncompressed_name}
              UpperCAmelCase									=					{f['name']: f}




      def     a_      (							self   ,       lowercase_							)						->					Any:
          return self.file.open().read()




      def     a_      (							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=None   ,       **lowercase_   ,       )						->					Optional[Any]:
          UpperCAmelCase									=					self._strip_protocol(lowercase_							)
          if mode != "rb":
              raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"							)
          return self.file.open()

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"bz2"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	".bz2"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							Any							      =	"gzip"
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	".gz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	"lz4"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".lz4"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"xz"
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	".xz"

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							int							      =	"zstd"
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	".zst"




      def __init__(							self   ,       lowercase_   ,       lowercase_ = "rb"   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = DEFAULT_BLOCK_SIZE   ,       **lowercase_   ,       )						->					str:
          super().__init__(
              fo=lowercase_   ,       mode=lowercase_   ,       target_protocol=lowercase_   ,       target_options=lowercase_   ,       block_size=lowercase_   ,       **lowercase_   ,       )
          # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
          #
          # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
          #     out.close = close
          # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
          #
          # see https://github.com/intake/filesystem_spec/issues/725
          UpperCAmelCase									=					self.file.__enter__
          class      _UpperCAmelCase							:




                def __init__(							self   ,       lowercase_							)						->					List[Any]:
                    UpperCAmelCase									=					file_




                def __enter__(							self							)						->					List[str]:
                    self._file.__enter__()
                    return self




                def __exit__(							self   ,       *lowercase_   ,       **lowercase_							)						->					List[str]:
                    self._file.__exit__(*lowercase_   ,       **lowercase_							)




                def __iter__(							self							)						->					Optional[int]:
                    return iter(self._file							)




                def     a_      (							self							)						->					Tuple:
                    return next(self._file							)




                def __getattr__(							self   ,       lowercase_							)						->					Optional[int]:
                    return getattr(self._file   ,       lowercase_							)

          def fixed_enter(*lowercase_   ,       **lowercase_							):
              return WrappedFile(_enter(*lowercase_   ,       **lowercase_							)							)

          UpperCAmelCase									=					fixed_enter



