


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

   return flax_key_tuple, flax_tensor
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):
   if "metadata" in layer:
      UpperCAmelCase							=     layer.split("""metadata"""   )
      UpperCAmelCase							=     """""".join(split_layer[0]   )[:-1]
      UpperCAmelCase							=     [tuple(("""metadata""" + split_layer[1]).split("""/"""   )   )]
   elif "kvstore" in layer:
      UpperCAmelCase							=     layer.split("""kvstore"""   )
      UpperCAmelCase							=     """""".join(split_layer[0]   )[:-1]
      UpperCAmelCase							=     [tuple(("""kvstore""" + split_layer[1]).split("""/"""   )   )]

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

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

   return curr_real_layer_name, split_layer, content
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   ):
   UpperCAmelCase							=     rename_keys(lowerCAmelCase_   )
   UpperCAmelCase							=     {}
   for k, v in current_block.items():
      UpperCAmelCase							=     v
   UpperCAmelCase							=     new_current_block
   torch.save(lowerCAmelCase_  ,						lowerCAmelCase_   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = WEIGHTS_NAME   ):
   UpperCAmelCase							=     convert_file_size_to_int(lowerCAmelCase_   )
   UpperCAmelCase							=     []
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0

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

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

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

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

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

      UpperCAmelCase							=     raw_weights.to(getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )   )
      current_block_size += weight_size
      total_size += weight_size

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

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

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

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

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

   return metadata, index


if __name__ == "__main__":
 __a     =			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.""",
 )
 __a     =			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				(						):
   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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







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


__a     =			logging.get_logger(__name__)

__a     =			{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''ctrl'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }





      def __init__(		self						:					str					,       __lowerCamelCase						:					Optional[int]=2_4_6_5_3_4					,       __lowerCamelCase						:					Union[str, Any]=2_5_6					,       __lowerCamelCase						:					int=1_2_8_0					,       __lowerCamelCase						:					Optional[Any]=8_1_9_2					,       __lowerCamelCase						:					List[str]=4_8					,       __lowerCamelCase						:					Dict=1_6					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=1e-6					,       __lowerCamelCase						:					str=0.02					,       __lowerCamelCase						:					Union[str, Any]=True					,       **__lowerCamelCase						:					List[str]					,       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     n_positions
         UpperCAmelCase							=     n_embd
         UpperCAmelCase							=     n_layer
         UpperCAmelCase							=     n_head
         UpperCAmelCase							=     dff
         UpperCAmelCase							=     resid_pdrop
         UpperCAmelCase							=     embd_pdrop
         UpperCAmelCase							=     layer_norm_epsilon
         UpperCAmelCase							=     initializer_range

         UpperCAmelCase							=     use_cache

         super().__init__(**__lowerCamelCase       )



