

"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase													=logging.get_logger(__name__)

UpperCAmelCase													={
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   '''vit_msn'''




    def __init__( self ,lowerCamelCase_=7_6_8 ,lowerCamelCase_=1_2 ,lowerCamelCase_=1_2 ,lowerCamelCase_=3_0_7_2 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.0 ,lowerCamelCase_=0.0 ,lowerCamelCase_=0.02 ,lowerCamelCase_=1E-06 ,lowerCamelCase_=2_2_4 ,lowerCamelCase_=1_6 ,lowerCamelCase_=3 ,lowerCamelCase_=True ,**lowerCamelCase_ ,)    ->      Any:
          super().__init__(**lowerCamelCase_    )

          A				  =		hidden_size
          A				  =		num_hidden_layers
          A				  =		num_attention_heads
          A				  =		intermediate_size
          A				  =		hidden_act
          A				  =		hidden_dropout_prob
          A				  =		attention_probs_dropout_prob
          A				  =		initializer_range
          A				  =		layer_norm_eps
          A				  =		image_size
          A				  =		patch_size
          A				  =		num_channels
          A				  =		qkv_bias







"""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   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[Any]				):



      """simple docstring"""






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

      return flax_key_tuple, flax_tensor



def   _A					(	_a	:						Tuple     ,   _a	:						Any     ,   _a	:						Any				):



      """simple docstring"""






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

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

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

      return curr_real_layer_name, split_layer, content



def   _A					(	_a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






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



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						int     ,   _a	:						Optional[int]     ,   _a	:						str = WEIGHTS_NAME				):



      """simple docstring"""






      A				  =		convert_file_size_to_int(_a				)
      A				  =		[]
      A				  =		{}
      A				  =		0
      A				  =		0

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

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

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

            # use the renaming pattern from the small conversion scripts
            A  ,			A				  =		rename_base_flax_keys(tuple(key.split("""/"""				)				)     ,   _a				)
            A				  =		"""/""".join(_a				)

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

            A				  =		raw_weights.to(getattr(_a     ,   _a				)				)
            current_block_size += weight_size
            total_size += weight_size

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

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

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

    # Add the metadata
      A				  =		{"""total_size""": total_size}
      A				  =		{"""metadata""": metadata, """weight_map""": weight_map}

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

      return metadata, index


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





def   _A					(	):



      """simple docstring"""






      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

      A				  =		tokenizer(_a     ,   return_tensors="""pt"""				).input_ids
      A				  =		model.generate(_a     ,   decoder_start_token_id=0				)
      print(tokenizer.decode(out[0]				)				)




