

"""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__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> List[Any]:



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

      return flax_key_tuple, flax_tensor




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:



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

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

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

      return curr_real_layer_name, split_layer, content




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> int:
      UpperCAmelCase__			:							List[Any]							=    rename_keys(__A    )
      UpperCAmelCase__			:							Tuple							=    {}
      for k, v in current_block.items():
            UpperCAmelCase__			:							Optional[Any]							=    v
      UpperCAmelCase__			:							Tuple							=    new_current_block
      torch.save(__A			,	__A    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase = WEIGHTS_NAME    )	-> List[str]:
      UpperCAmelCase__			:							Union[str, Any]							=    convert_file_size_to_int(__A    )
      UpperCAmelCase__			:							Tuple							=    []
      UpperCAmelCase__			:							int							=    {}
      UpperCAmelCase__			:							List[str]							=    0
      UpperCAmelCase__			:							List[Any]							=    0

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

      UpperCAmelCase__			:							Optional[int]							=    {}
      for layer in checkpoint_info.keys():
            UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							int							=    get_key_and_tensorstore_dict(
                __A			,	__A			,	__A    )
            if curr_real_layer_name in all_layers:
                  UpperCAmelCase__			:							Optional[Any]							=    content
            else:
                  UpperCAmelCase__			:							Dict							=    {split_layer[-1]: content}

      for key in all_layers.keys():
            # open tensorstore file
            UpperCAmelCase__			:							List[Any]							=    ts.open(unflatten_dict(all_layers[key]    )    ).result().read().result()
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor(__A    )
            UpperCAmelCase__			:							int							=    raw_weights.numel() * dtype_byte_size(raw_weights.dtype    )

            # use the renaming pattern from the small conversion scripts
            UpperCAmelCase__					,		UpperCAmelCase__			:							Union[str, Any]							=    rename_base_flax_keys(tuple(key.split("""/"""    )    )			,	__A    )
            UpperCAmelCase__			:							List[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:
                  UpperCAmelCase__			:							List[str]							=    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
                  UpperCAmelCase__			:							Any							=    {}
                  UpperCAmelCase__			:							Union[str, Any]							=    0

            UpperCAmelCase__			:							Dict							=    raw_weights.to(getattr(__A			,	__A    )    )
            current_block_size += weight_size
            total_size += weight_size

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

    # Add the metadata
      UpperCAmelCase__			:							Optional[int]							=    {"""total_size""": total_size}
      UpperCAmelCase__			:							Tuple							=    {"""metadata""": metadata, """weight_map""": weight_map}

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

      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 					a__						(						)	-> Optional[Any]:
      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

      UpperCAmelCase__			:							Dict							=    tokenizer(__A			,	return_tensors="""pt"""    ).input_ids
      UpperCAmelCase__			:							Optional[int]							=    model.generate(__A			,	decoder_start_token_id=0    )
      print(tokenizer.decode(out[0]    )    )






'''simple docstring'''





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowercase_									=    {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    list(s_dict.keys())
     for key in keys:
          _a           =    r'''.*/layers_(\d+)'''
          _a           =    key
          if re.match(__A    ,      __A):
               _a           =    re.sub(r'''layers_(\d+)'''    ,      r'''block/\1/layer'''    ,      __A)

          _a           =    r'''(encoder|decoder)\/'''

          if re.match(__A    ,      __A):
               _a           =    re.match(__A    ,      __A).groups()
               if groups[0] == "encoder":
                    _a           =    re.sub(r'''/mlp/'''    ,      r'''/1/mlp/'''    ,      __A)
                    _a           =    re.sub(r'''/pre_mlp_layer_norm/'''    ,      r'''/1/layer_norm/'''    ,      __A)

               elif groups[0] == "decoder":
                    _a           =    re.sub(r'''/mlp/'''    ,      r'''/2/mlp/'''    ,      __A)
                    _a           =    re.sub(r'''/pre_mlp_layer_norm/'''    ,      r'''/2/layer_norm/'''    ,      __A)

        # 2. Convert other classic mappings
          for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
               if old_key in new_key:
                    _a           =    new_key.replace(__A    ,      __A)

          print(F'''{key} -> {new_key}''')
          _a           =    s_dict.pop(__A)

     if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          _a           =    s_dict[
              '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
          ].T
     if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          _a           =    s_dict[
              '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
          ].T

     # 3. Take extra care of the EXPERTS layer
     for key in list(s_dict.keys()):
          if "expert" in key:
               _a           =    s_dict[key].shape[0]
               _a           =    s_dict[key]
               for idx in range(__A):
                    _a           =    expert_weihts[idx]
                    print(F'''{key} -> {key.replace('expert/'    ,      'nested fstring')}''')

               s_dict.pop(__A)

     return s_dict


lowercase_									=    {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     import regex as re

     with open(__A    ,      '''r''') as f:
          _a           =    f.read()

     _a           =    re.findall(r'''(.*) = ([0-9.]*)'''    ,      __A)
     _a           =    {}
     for param, value in regex_match:
          if param in GIN_TO_CONFIG_MAPPING and value != "":
               _a           =    float(__A) if '''.''' in value else int(__A)

     _a           =    re.findall(r'''(.*activations) = \(\'(.*)\',\)'''    ,      __A)[0]
     _a           =    str(activation[1])

     _a           =    num_experts
     _a           =    SwitchTransformersConfig(**__A)
     return config




def lowerCAmelCase    (__A    ,      __A    ,      __A=None    ,      __A="./"    ,      __A=8):



     """simple docstring"""



     print(F'''Loading flax weights from : {flax_checkpoint_path}''')
     _a           =    checkpoints.load_tax_checkpoint(__A)

     if gin_file is not None:
          _a           =    convert_gin_to_config(__A    ,      __A)
     else:
          _a           =    SwitchTransformersConfig.from_pretrained(__A)

     _a           =    SwitchTransformersForConditionalGeneration(__A)

     _a           =    flax_params['''target''']
     _a           =    flatten_dict(__A    ,      sep='''/''')
     _a           =    rename_keys(__A)
     _a           =    unflatten_dict(__A    ,      sep='''/''')

     # Load the flax params in the PT model
     load_flax_weights_in_pytorch_model(__A    ,      __A)

     print(F'''Save PyTorch model to {pytorch_dump_path}''')
     pt_model.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_									=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
          " model architecture. If not provided, a `gin_file` has to be provided."
      ),
  )
  parser.add_argument(
      "--gin_file",
      default=None,
      type=str,
      required=False,
      help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
  )
  parser.add_argument(
      "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
  )
  parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
  lowercase_									=    parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )



