





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__		,      A__				)  ->		str:
       if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
              # expert layer
              a__						:	str =				flax_key_tuple[:-1] + ('weight',)
              a__						:	Union[str, Any] =				torch.permute(A__		,      (0, 2, 1)				)
       elif flax_key_tuple[-1] == "kernel" and ".".join(A__				):
              # linear layer
              a__						:	Dict =				flax_key_tuple[:-1] + ('weight',)
              a__						:	Union[str, Any] =				flax_tensor.T
       elif flax_key_tuple[-1] in ["scale", "embedding"]:
              a__						:	List[str] =				flax_key_tuple[:-1] + ('weight',)

       return flax_key_tuple, flax_tensor



def A_	(		A__		,      A__		,      A__				)  ->		Optional[Any]:
       if "metadata" in layer:
              a__						:	Optional[int] =				layer.split('metadata'				)
              a__						:	Union[str, Any] =				''.join(split_layer[0]				)[:-1]
              a__						:	Tuple =				[tuple(('metadata' + split_layer[1]).split('/'				)				)]
       elif "kvstore" in layer:
              a__						:	str =				layer.split('kvstore'				)
              a__						:	Union[str, Any] =				''.join(split_layer[0]				)[:-1]
              a__						:	Any =				[tuple(('kvstore' + split_layer[1]).split('/'				)				)]

       else:
              a__						:	Union[str, Any] =				layer.split('/'				)
              a__						:	Tuple =				'/'.join(split_layer[:-1]				)
              a__						:	Optional[int] =				(split_layer[-1],)

       if "kvstore/path" in layer:
              a__						:	Union[str, Any] =				F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
       elif "kvstore/driver" in layer:
              a__						:	str =				'file'
       else:
              a__						:	Any =				checkpoint_info[layer]

       return curr_real_layer_name, split_layer, content



def A_	(		A__		,      A__				)  ->		List[str]:
       a__						:	List[str] =				rename_keys(A__				)
       a__						:	List[str] =				{}
       for k, v in current_block.items():
              a__						:	List[Any] =				v
       a__						:	Optional[Any] =				new_current_block
       torch.save(A__		,      A__				)



def A_	(		A__		,      A__		,      A__		,      A__		,      A__ = WEIGHTS_NAME				)  ->		str:
       a__						:	Any =				convert_file_size_to_int(A__				)
       a__						:	List[str] =				[]
       a__						:	Any =				{}
       a__						:	str =				0
       a__						:	Optional[int] =				0

       os.makedirs(A__		,      exist_ok=A__				)
       with gfile.GFile(switch_checkpoint_path + '/checkpoint'		,      'rb'				) as fp:
              a__						:	List[str] =				serialization.msgpack_restore(fp.read()				)['optimizer']['target']
              a__						:	Optional[Any] =				flatten_dict(A__		,      sep='/'				)

       a__						:	List[str] =				{}
       for layer in checkpoint_info.keys():
              a__ ,							a__ ,							a__						:	Any =				get_key_and_tensorstore_dict(
                  A__		,      A__		,      A__				)
              if curr_real_layer_name in all_layers:
                     a__						:	Any =				content
              else:
                     a__						:	Optional[Any] =				{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__						:	int =				torch.tensor(A__				)
              a__						:	Optional[Any] =				raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

              # use the renaming pattern from the small conversion scripts
              a__ ,							a__						:	Any =				rename_base_flax_keys(tuple(key.split('/'				)				)		,      A__				)
              a__						:	Optional[Any] =				'/'.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__						:	List[Any] =				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__						:	Optional[int] =				{}
                     a__						:	List[str] =				0

              a__						:	Union[str, Any] =				raw_weights.to(getattr(A__		,      A__				)				)
              current_block_size += weight_size
              total_size += weight_size

       # Add the last block
       a__						:	int =				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__						:	Tuple =				{}
       a__						:	int =				{}
       for idx, shard in enumerate(A__				):
              a__						:	List[str] =				weights_name.replace(
                  '.bin'		,      F'-{idx+1:05d}-of-{len(A__				):05d}.bin'				)  # len(sharded_state_dicts):05d}
              a__						:	str =				os.path.join(A__		,      weights_name.replace('.bin'		,      F'-{idx+1:05d}-of-???.bin'				)				)
              os.rename(A__		,      os.path.join(A__		,      A__				)				)
              a__						:	Tuple =				shard
              for key in shard:
                     a__						:	int =				shard_file

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

       with open(os.path.join(A__		,      A__				)		,      'w'		,      encoding='utf-8'				) as f:
              a__						:	Union[str, Any] =				json.dumps(A__		,      indent=2		,      sort_keys=A__				) + '\n'
              f.write(A__				)

       return metadata, index


if __name__ == "__main__":
    lowercase      :       Optional[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.""",
    )
    lowercase      :       int									=  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_	(		)  ->		int:
       from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

       a__						:	List[Any] =				SwitchTransformersConfig.from_pretrained('google/switch-base-8'				)
       config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'				)
       a__						:	Optional[int] =				SwitchTransformersForConditionalGeneration.from_pretrained(
           '/home/arthur_huggingface_co/transformers/switch_converted'		,      device_map='auto'				)

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

       a__						:	str =				tokenizer(A__		,      return_tensors='pt'				).input_ids
       a__						:	Any =				model.generate(A__		,      decoder_start_token_id=0				)
       print(tokenizer.decode(out[0]				)				)









import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

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

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


