

'''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 		UpperCamelCase			(					a						,		a					) ->      Dict:






     '''simple docstring'''

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

     return flax_key_tuple, flax_tensor




def 		UpperCamelCase			(					a						,		a						,		a					) ->      int:






     '''simple docstring'''

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

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

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

     return curr_real_layer_name, split_layer, content




def 		UpperCamelCase			(					a						,		a					) ->      int:






     '''simple docstring'''
     __magic_name__		  =							rename_keys(lowercase_					)
     __magic_name__		  =							{}
     for k, v in current_block.items():
          __magic_name__		  =							v
     __magic_name__		  =							new_current_block
     torch.save(lowercase_						,		lowercase_					)




def 		UpperCamelCase			(					a						,		a						,		a						,		a						,		a = WEIGHTS_NAME					) ->      Any:






     '''simple docstring'''
     __magic_name__		  =							convert_file_size_to_int(lowercase_					)
     __magic_name__		  =							[]
     __magic_name__		  =							{}
     __magic_name__		  =							0
     __magic_name__		  =							0

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

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

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

          # use the renaming pattern from the small conversion scripts
          __magic_name__  ,    __magic_name__		  =							rename_base_flax_keys(tuple(key.split('''/'''					)					)						,		lowercase_					)
          __magic_name__		  =							'''/'''.join(lowercase_					)

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

          __magic_name__		  =							raw_weights.to(getattr(lowercase_						,		lowercase_					)					)
          current_block_size += weight_size
          total_size += weight_size

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

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

     # Otherwise, let's build the index
     __magic_name__		  =							{}
     __magic_name__		  =							{}
     for idx, shard in enumerate(lowercase_					):
          __magic_name__		  =							weights_name.replace(
              '''.bin'''						,		F'''-{idx+1:05d}-of-{len(lowercase_					):05d}.bin'''					)  # len(sharded_state_dicts):05d}
          __magic_name__		  =							os.path.join(lowercase_						,		weights_name.replace('''.bin'''						,		F'''-{idx+1:05d}-of-???.bin'''					)					)
          os.rename(lowercase_						,		os.path.join(lowercase_						,		lowercase_					)					)
          __magic_name__		  =							shard
          for key in shard:
               __magic_name__		  =							shard_file

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

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

     return metadata, index


if __name__ == "__main__":
      _lowerCAmelCase            =		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.",
      )
      _lowerCAmelCase            =		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			(					) ->      Any:






     '''simple docstring'''
     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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












import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


_lowerCamelCase      :						Optional[List[str]]							=  None
_lowerCamelCase      :						int							=  """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowerCamelCase      :						Union[str, Any]							=  [
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]


@dataclass
class 							UpperCamelCase_       :

    '''simple docstring'''






    UpperCAmelCase__				    = True
    UpperCAmelCase__				    = None
    # Automatically constructed
    UpperCAmelCase__				    = "PIL.Image.Image"
    UpperCAmelCase__				    = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}	)
    UpperCAmelCase__				    = field(default='''Image'''						,   init=UpperCAmelCase__						,   repr=UpperCAmelCase__	)


    def __call__(     self :      List[str])		->List[str]:





           '''simple docstring'''

           return self.pa_type


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					UpperCAmelCase__ :      Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"])		->dict:





           '''simple docstring'''

           if config.PIL_AVAILABLE:
                  import PIL.Image
           else:
                  raise ImportError('''To support encoding images, please install \'Pillow\'.''')

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  A__ 					=       np.array(UpperCAmelCase__)

           if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  return {"path": value, "bytes": None}
           elif isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                  return {"path": None, "bytes": value}
           elif isinstance(UpperCAmelCase__   ,					np.ndarray):
                  # convert the image array to PNG/TIFF bytes
                  return encode_np_array(UpperCAmelCase__)
           elif isinstance(UpperCAmelCase__   ,					PIL.Image.Image):
                  # convert the PIL image to bytes (default format is PNG/TIFF)
                  return encode_pil_image(UpperCAmelCase__)
           elif value.get('''path''') is not None and os.path.isfile(value['''path''']):
                  # we set "bytes": None to not duplicate the data if they're already available locally
                  return {"bytes": None, "path": value.get('''path''')}
           elif value.get('''bytes''') is not None or value.get('''path''') is not None:
                  # store the image bytes, and path is used to infer the image format using the file extension
                  return {"bytes": value.get('''bytes'''), "path": value.get('''path''')}
           else:
                  raise ValueError(
                      f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""")


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      dict   ,					UpperCAmelCase__ :      str=None)		->"PIL.Image.Image":





           '''simple docstring'''

           if not self.decode:
                  raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.''')

           if config.PIL_AVAILABLE:
                  import PIL.Image
           else:
                  raise ImportError('''To support decoding images, please install \'Pillow\'.''')

           if token_per_repo_id is None:
                  A__ 					=       {}

           A__					,				A__ 					=       value['''path'''], value['''bytes''']
           if bytes_ is None:
                  if path is None:
                         raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}.""")
                  else:
                         if is_local_path(UpperCAmelCase__):
                                A__ 					=       PIL.Image.open(UpperCAmelCase__)
                         else:
                                A__ 					=       path.split('''::''')[-1]
                                try:
                                       A__ 					=       string_to_dict(UpperCAmelCase__   ,					config.HUB_DATASETS_URL)['''repo_id''']
                                       A__ 					=       token_per_repo_id.get(UpperCAmelCase__)
                                except ValueError:
                                       A__ 					=       None
                                with xopen(UpperCAmelCase__   ,					'''rb'''   ,					use_auth_token=UpperCAmelCase__) as f:
                                       A__ 					=       BytesIO(f.read())
                                A__ 					=       PIL.Image.open(bytes_)
           else:
                  A__ 					=       PIL.Image.open(BytesIO(bytes_))
           image.load()  # to avoid "Too many open files" errors
           return image


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Union["FeatureType", Dict[str, "FeatureType"]]:





           '''simple docstring'''

           from .features import Value

           return (
               self
               if self.decode
               else {
                   "bytes": Value('''binary'''),
                   "path": Value('''string'''),
               }
           )


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Union[pa.StringArray, pa.StructArray, pa.ListArray])		->pa.StructArray:





           '''simple docstring'''

           if pa.types.is_string(storage.type):
                  A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					type=pa.binary())
                  A__ 					=       pa.StructArray.from_arrays([bytes_array, storage]   ,					['''bytes''', '''path''']   ,					mask=storage.is_null())
           elif pa.types.is_binary(storage.type):
                  A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					type=pa.string())
                  A__ 					=       pa.StructArray.from_arrays([storage, path_array]   ,					['''bytes''', '''path''']   ,					mask=storage.is_null())
           elif pa.types.is_struct(storage.type):
                  if storage.type.get_field_index('''bytes''') >= 0:
                         A__ 					=       storage.field('''bytes''')
                  else:
                         A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					type=pa.binary())
                  if storage.type.get_field_index('''path''') >= 0:
                         A__ 					=       storage.field('''path''')
                  else:
                         A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					type=pa.string())
                  A__ 					=       pa.StructArray.from_arrays([bytes_array, path_array]   ,					['''bytes''', '''path''']   ,					mask=storage.is_null())
           elif pa.types.is_list(storage.type):
                  A__ 					=       pa.array(
                      [encode_np_array(np.array(UpperCAmelCase__))['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]   ,					type=pa.binary()   ,					)
                  A__ 					=       pa.array([None] * len(UpperCAmelCase__)   ,					type=pa.string())
                  A__ 					=       pa.StructArray.from_arrays(
                      [bytes_array, path_array]   ,					['''bytes''', '''path''']   ,					mask=bytes_array.is_null())
           return array_cast(UpperCAmelCase__   ,					self.pa_type)





    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any]   ,					UpperCAmelCase__ :      pa.StructArray)		->pa.StructArray:





           '''simple docstring'''

           @no_op_if_value_is_null
           def path_to_bytes(UpperCAmelCase__ :      Dict):
                  with xopen(UpperCAmelCase__   ,					'''rb''') as f:
                         A__ 					=       f.read()
                  return bytes_

           A__ 					=       pa.array(
               [
                   (path_to_bytes(x['''path''']) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                   for x in storage.to_pylist()
               ]   ,					type=pa.binary()   ,					)
           A__ 					=       pa.array(
               [os.path.basename(UpperCAmelCase__) if path is not None else None for path in storage.field('''path''').to_pylist()]   ,					type=pa.string()   ,					)
           A__ 					=       pa.StructArray.from_arrays([bytes_array, path_array]   ,					['''bytes''', '''path''']   ,					mask=bytes_array.is_null())
           return array_cast(UpperCAmelCase__   ,					self.pa_type)







def SCREAMING_SNAKE_CASE							(			)  ->			List[str]:



       """simple docstring"""

       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

       global _IMAGE_COMPRESSION_FORMATS
       if _IMAGE_COMPRESSION_FORMATS is None:
              PIL.Image.init()
              A__ 					=       list(set(PIL.Image.OPEN.keys()		) & set(PIL.Image.SAVE.keys()		)		)
       return _IMAGE_COMPRESSION_FORMATS







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			bytes:



       """simple docstring"""

       A__ 					=       BytesIO()
       if image.format in list_image_compression_formats():
              A__ 					=       image.format
       else:
              A__ 					=       '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
       image.save(lowercase_					,							format=lowercase_		)
       return buffer.getvalue()







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			dict:



       """simple docstring"""

       if hasattr(lowercase_					,							'''filename'''		) and image.filename != "":
              return {"path": image.filename, "bytes": None}
       else:
              return {"path": None, "bytes": image_to_bytes(lowercase_		)}







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			dict:



       """simple docstring"""

       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

       A__ 					=       array.dtype
       A__ 					=       dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
       A__ 					=       dtype.kind
       A__ 					=       dtype.itemsize

       A__ 					=       None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              A__ 					=       np.dtype('''|u1'''		)
              if dtype_kind not in ["u", "i"]:
                     raise TypeError(
                         f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""		)
              if dtype is not dest_dtype:
                     warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""		)
    # Exact match
       elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
              A__ 					=       dtype
       else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
              while dtype_itemsize >= 1:
                     A__ 					=       dtype_byteorder + dtype_kind + str(lowercase_		)
                     A__ 					=       np.dtype(lowercase_		)
                     if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                            warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""		)
                            break
                     else:
                            dtype_itemsize //= 2

       if dest_dtype is None:
              raise TypeError(
                  f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""		)
       A__ 					=       PIL.Image.fromarray(array.astype(lowercase_		)		)
       return {"path": None, "bytes": image_to_bytes(lowercase_		)}







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[dict]:



       """simple docstring"""

       if config.PIL_AVAILABLE:
              import PIL.Image
       else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

       if objs:
              A__					,				A__ 					=       first_non_null_value(lowercase_		)
              if isinstance(lowercase_					,							lowercase_		):
                     return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
              if isinstance(lowercase_					,							np.ndarray		):
                     A__ 					=       no_op_if_value_is_null(lowercase_		)
                     return [obj_to_image_dict_func(lowercase_		) for obj in objs]
              elif isinstance(lowercase_					,							PIL.Image.Image		):
                     A__ 					=       no_op_if_value_is_null(lowercase_		)
                     return [obj_to_image_dict_func(lowercase_		) for obj in objs]
              else:
                     return objs
       else:
              return objs


