




'''simple docstring'''


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_      :  List[Any]				     =  '<' 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_      :  List[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      SCREAMING_SNAKE_CASE    :
      __magic_name__  :       bool   							=					True
      __magic_name__  :       Optional[str]   							=					None
      # Automatically constructed
      __magic_name__  :       ClassVar[str]   							=					"PIL.Image.Image"
      __magic_name__  :       ClassVar[Any]   							=					pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}				)
      __magic_name__  :       str   							=					field(default='''Image'''							,      init=snake_case_							,      repr=snake_case_				)






      def __call__( self   :  Optional[int]       ):


           '''simple docstring'''

           return self.pa_type






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]       ):


           '''simple docstring'''

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

           if isinstance(lowercase__						,						lowercase__       ):
                a_     :							Tuple     						=  np.array(lowercase__       )

           if isinstance(lowercase__						,						lowercase__       ):
                return {"path": value, "bytes": None}
           elif isinstance(lowercase__						,						lowercase__       ):
                return {"path": None, "bytes": value}
           elif isinstance(lowercase__						,						np.ndarray       ):
                # convert the image array to PNG/TIFF bytes
                return encode_np_array(lowercase__       )
           elif isinstance(lowercase__						,						PIL.Image.Image       ):
                # convert the PIL image to bytes (default format is PNG/TIFF)
                return encode_pil_image(lowercase__       )
           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 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  dict						,						lowercase__   :  Optional[Any]=None       ):


           '''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_     :							int     						=  {}

           a_						,     a_     :							int     						=  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(lowercase__       ):
                          a_     :							int     						=  PIL.Image.open(lowercase__       )
                     else:
                          a_     :							int     						=  path.split("""::"""       )[-1]
                          try:
                               a_     :							List[Any]     						=  string_to_dict(lowercase__						,						config.HUB_DATASETS_URL       )["""repo_id"""]
                               a_     :							int     						=  token_per_repo_id.get(lowercase__       )
                          except ValueError:
                               a_     :							List[Any]     						=  None
                          with xopen(lowercase__						,						"""rb"""						,						use_auth_token=lowercase__       ) as f:
                               a_     :							str     						=  BytesIO(f.read()       )
                          a_     :							Union[str, Any]     						=  PIL.Image.open(bytes_       )
           else:
                a_     :							Any     						=  PIL.Image.open(BytesIO(bytes_       )       )
           image.load()  # to avoid "Too many open files" errors
           return image






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           from .features import Value

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






      def 							lowercase_						( self   :  int						,						lowercase__   :  Union[pa.StringArray, pa.StructArray, pa.ListArray]       ):


           '''simple docstring'''

           if pa.types.is_string(storage.type       ):
                a_     :							int     						=  pa.array([None] * len(lowercase__       )						,						type=pa.binary()       )
                a_     :							Optional[int]     						=  pa.StructArray.from_arrays([bytes_array, storage]						,						["""bytes""", """path"""]						,						mask=storage.is_null()       )
           elif pa.types.is_binary(storage.type       ):
                a_     :							Union[str, Any]     						=  pa.array([None] * len(lowercase__       )						,						type=pa.string()       )
                a_     :							Any     						=  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_     :							Dict     						=  storage.field("""bytes"""       )
                else:
                     a_     :							Union[str, Any]     						=  pa.array([None] * len(lowercase__       )						,						type=pa.binary()       )
                if storage.type.get_field_index("""path"""       ) >= 0:
                     a_     :							int     						=  storage.field("""path"""       )
                else:
                     a_     :							Optional[int]     						=  pa.array([None] * len(lowercase__       )						,						type=pa.string()       )
                a_     :							Optional[Any]     						=  pa.StructArray.from_arrays([bytes_array, path_array]						,						["""bytes""", """path"""]						,						mask=storage.is_null()       )
           elif pa.types.is_list(storage.type       ):
                a_     :							List[str]     						=  pa.array(
                    [encode_np_array(np.array(lowercase__       )       )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]						,						type=pa.binary()						,						)
                a_     :							Dict     						=  pa.array([None] * len(lowercase__       )						,						type=pa.string()       )
                a_     :							int     						=  pa.StructArray.from_arrays(
                    [bytes_array, path_array]						,						["""bytes""", """path"""]						,						mask=bytes_array.is_null()       )
           return array_cast(lowercase__						,						self.pa_type       )






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  pa.StructArray       ):


           '''simple docstring'''

           @no_op_if_value_is_null
           def path_to_bytes(lowercase__   :  Optional[int]       ):
                with xopen(lowercase__						,						"""rb"""       ) as f:
                     a_     :							List[Any]     						=  f.read()
                return bytes_

           a_     :							List[str]     						=  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_     :							Tuple     						=  pa.array(
               [os.path.basename(lowercase__       ) if path is not None else None for path in storage.field("""path"""       ).to_pylist()]						,						type=pa.string()						,						)
           a_     :							Optional[Any]     						=  pa.StructArray.from_arrays([bytes_array, path_array]						,						["""bytes""", """path"""]						,						mask=bytes_array.is_null()       )
           return array_cast(lowercase__						,						self.pa_type       )



def  _SCREAMING_SNAKE_CASE     (      ):




     """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_     :							str     						=  list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
     return _IMAGE_COMPRESSION_FORMATS



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					"PIL.Image.Image"				):




     """simple docstring"""


     a_     :							Optional[Any]     						=  BytesIO()
     if image.format in list_image_compression_formats():
          a_     :							Optional[int]     						=  image.format
     else:
          a_     :							List[str]     						=  """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
     image.save(UpperCamelCase__ ,    format=UpperCamelCase__				)
     return buffer.getvalue()



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					"PIL.Image.Image"				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					np.ndarray				):




     """simple docstring"""


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

     a_     :							int     						=  array.dtype
     a_     :							List[str]     						=  dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
     a_     :							Tuple     						=  dtype.kind
     a_     :							Union[str, Any]     						=  dtype.itemsize

     a_     :							Tuple     						=  None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          a_     :							Any     						=  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_     :							Union[str, Any]     						=  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_     :							Optional[Any]     						=  dtype_byteorder + dtype_kind + str(UpperCamelCase__				)
               a_     :							Tuple     						=  np.dtype(UpperCamelCase__				)
               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_     :							int     						=  PIL.Image.fromarray(array.astype(UpperCamelCase__				)				)
     return {"path": None, "bytes": image_to_bytes(UpperCamelCase__				)}



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]				):




     """simple docstring"""


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

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






'''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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					List[str]				):




     """simple docstring"""


     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          a_     :							Optional[int]     						=  flax_key_tuple[:-1] + ("""weight""",)
          a_     :							Optional[int]     						=  torch.permute(UpperCamelCase__ ,    (0, 2, 1)				)
     elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__				):
          # linear layer
          a_     :							Any     						=  flax_key_tuple[:-1] + ("""weight""",)
          a_     :							Optional[int]     						=  flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          a_     :							Any     						=  flax_key_tuple[:-1] + ("""weight""",)

     return flax_key_tuple, flax_tensor



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     if "metadata" in layer:
          a_     :							Optional[Any]     						=  layer.split("""metadata"""				)
          a_     :							List[str]     						=  """""".join(split_layer[0]				)[:-1]
          a_     :							List[str]     						=  [tuple(("""metadata""" + split_layer[1]).split("""/"""				)				)]
     elif "kvstore" in layer:
          a_     :							Union[str, Any]     						=  layer.split("""kvstore"""				)
          a_     :							Tuple     						=  """""".join(split_layer[0]				)[:-1]
          a_     :							Union[str, Any]     						=  [tuple(("""kvstore""" + split_layer[1]).split("""/"""				)				)]

     else:
          a_     :							List[str]     						=  layer.split("""/"""				)
          a_     :							Union[str, Any]     						=  """/""".join(split_layer[:-1]				)
          a_     :							Union[str, Any]     						=  (split_layer[-1],)

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

     return curr_real_layer_name, split_layer, content



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_     :							int     						=  rename_keys(UpperCamelCase__				)
     a_     :							Any     						=  {}
     for k, v in current_block.items():
          a_     :							Union[str, Any]     						=  v
     a_     :							List[str]     						=  new_current_block
     torch.save(UpperCamelCase__ ,    UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					str = WEIGHTS_NAME				):




     """simple docstring"""


     a_     :							str     						=  convert_file_size_to_int(UpperCamelCase__				)
     a_     :							str     						=  []
     a_     :							Optional[int]     						=  {}
     a_     :							Optional[int]     						=  0
     a_     :							Any     						=  0

     os.makedirs(UpperCamelCase__ ,    exist_ok=UpperCamelCase__				)
     with gfile.GFile(switch_checkpoint_path + """/checkpoint""" ,    """rb"""				) as fp:
          a_     :							str     						=  serialization.msgpack_restore(fp.read()				)["""optimizer"""]["""target"""]
          a_     :							List[str]     						=  flatten_dict(UpperCamelCase__ ,    sep="""/"""				)

     a_     :							Dict     						=  {}
     for layer in checkpoint_info.keys():
          a_						,     a_						,     a_     :							List[Any]     						=  get_key_and_tensorstore_dict(
              UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__				)
          if curr_real_layer_name in all_layers:
               a_     :							int     						=  content
          else:
               a_     :							Tuple     						=  {split_layer[-1]: content}

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

          # use the renaming pattern from the small conversion scripts
          a_						,     a_     :							Tuple     						=  rename_base_flax_keys(tuple(key.split("""/"""				)				) ,    UpperCamelCase__				)
          a_     :							Dict     						=  """/""".join(UpperCamelCase__				)

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

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

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

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

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

    # Add the metadata
     a_     :							List[str]     						=  {"""total_size""": total_size}
     a_     :							str     						=  {"""metadata""": metadata, """weight_map""": weight_map}

     with open(os.path.join(UpperCamelCase__ ,    UpperCamelCase__				) ,    """w""" ,    encoding="""utf-8"""				) as f:
          a_     :							Any     						=  json.dumps(UpperCamelCase__ ,    indent=2 ,    sort_keys=UpperCamelCase__				) + """\n"""
          f.write(UpperCamelCase__				)

     return metadata, index


if __name__ == "__main__":
 lowerCAmelCase_      :  List[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.',
 )
 lowerCAmelCase_      :  List[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  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     a_     :							Union[str, Any]     						=  SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""				)
     config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""				)
     a_     :							List[Any]     						=  SwitchTransformersForConditionalGeneration.from_pretrained(
         """/home/arthur_huggingface_co/transformers/switch_converted""" ,    device_map="""auto"""				)

     a_     :							Union[str, Any]     						=  TaTokenizer.from_pretrained("""t5-small"""				)
     a_     :							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>."""

     a_     :							List[Any]     						=  tokenizer(UpperCamelCase__ ,    return_tensors="""pt"""				).input_ids
     a_     :							str     						=  model.generate(UpperCamelCase__ ,    decoder_start_token_id=0				)
     print(tokenizer.decode(out[0]				)				)
