


"""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 	snake_case		(  UpperCamelCase__							:    List[str]      ,					UpperCamelCase__							:    List[str]     ) ->   Optional[Any]:


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

      return flax_key_tuple, flax_tensor




def 	snake_case		(  UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    str      ,					UpperCamelCase__							:    Any     ) ->   str:


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

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

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

      return curr_real_layer_name, split_layer, content




def 	snake_case		(  UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    Any     ) ->   Tuple:
      lowerCamelCase       :       int   =						rename_keys(UpperCamelCase__     )
      lowerCamelCase       :       Any   =						{}
      for k, v in current_block.items():
            lowerCamelCase       :       Union[str, Any]   =						v
      lowerCamelCase       :       List[str]   =						new_current_block
      torch.save(UpperCamelCase__      ,					UpperCamelCase__     )




def 	snake_case		(  UpperCamelCase__							:    List[Any]      ,					UpperCamelCase__							:    Any      ,					UpperCamelCase__							:    Optional[int]      ,					UpperCamelCase__							:    List[str]      ,					UpperCamelCase__							:    str = WEIGHTS_NAME     ) ->   List[str]:
      lowerCamelCase       :       str   =						convert_file_size_to_int(UpperCamelCase__     )
      lowerCamelCase       :       str   =						[]
      lowerCamelCase       :       Optional[int]   =						{}
      lowerCamelCase       :       Optional[int]   =						0
      lowerCamelCase       :       Any   =						0

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

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

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

            # use the renaming pattern from the small conversion scripts
            lowerCamelCase       :       Tuple   =						rename_base_flax_keys(tuple(key.split("""/"""     )     )      ,					UpperCamelCase__     )
            lowerCamelCase       :       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:
                  lowerCamelCase       :       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
                  lowerCamelCase       :       Optional[int]   =						{}
                  lowerCamelCase       :       Union[str, Any]   =						0

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

      # Add the last block
      lowerCamelCase       :       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
      lowerCamelCase       :       Tuple   =						{}
      lowerCamelCase       :       List[Any]   =						{}
      for idx, shard in enumerate(UpperCamelCase__     ):
            lowerCamelCase       :       str   =						weights_name.replace(
                """.bin"""      ,					F'-{idx+1:05d}-of-{len(UpperCamelCase__     ):05d}.bin'     )  # len(sharded_state_dicts):05d}
            lowerCamelCase       :       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__     )     )
            lowerCamelCase       :       str   =						shard
            for key in shard:
                  lowerCamelCase       :       Union[str, Any]   =						shard_file

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

      with open(os.path.join(UpperCamelCase__      ,					UpperCamelCase__     )      ,					"""w"""      ,					encoding="""utf-8"""     ) as f:
            lowerCamelCase       :       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 	snake_case		(  ) ->   List[Any]:
      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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










'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
