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

   return flax_key_tuple, flax_tensor







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if "metadata" in layer:
      UpperCAmelCase_					:					List[Any]			     =    layer.split("metadata"  )
      UpperCAmelCase_					:					List[str]			     =    "".join(split_layer[0]  )[:-1]
      UpperCAmelCase_					:					Dict			     =    [tuple(("metadata" + split_layer[1]).split("/"  )  )]
   elif "kvstore" in layer:
      UpperCAmelCase_					:					int			     =    layer.split("kvstore"  )
      UpperCAmelCase_					:					str			     =    "".join(split_layer[0]  )[:-1]
      UpperCAmelCase_					:					str			     =    [tuple(("kvstore" + split_layer[1]).split("/"  )  )]

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

   if "kvstore/path" in layer:
      UpperCAmelCase_					:					Any			     =    f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
   elif "kvstore/driver" in layer:
      UpperCAmelCase_					:					Dict			     =    "file"
   else:
      UpperCAmelCase_					:					List[str]			     =    checkpoint_info[layer]

   return curr_real_layer_name, split_layer, content







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    rename_keys(__lowerCamelCase  )
   UpperCAmelCase_					:					Dict			     =    {}
   for k, v in current_block.items():
      UpperCAmelCase_					:					Tuple			     =    v
   UpperCAmelCase_					:					Any			     =    new_current_block
   torch.save(__lowerCamelCase,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = WEIGHTS_NAME  ):
   UpperCAmelCase_					:					Optional[Any]			     =    convert_file_size_to_int(__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    []
   UpperCAmelCase_					:					List[Any]			     =    {}
   UpperCAmelCase_					:					List[Any]			     =    0
   UpperCAmelCase_					:					List[Any]			     =    0

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

   UpperCAmelCase_					:					List[str]			     =    {}
   for layer in checkpoint_info.keys():
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    get_key_and_tensorstore_dict(
          __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
      if curr_real_layer_name in all_layers:
         UpperCAmelCase_					:					Union[str, Any]			     =    content
      else:
         UpperCAmelCase_					:					Tuple			     =    {split_layer[-1]: content}

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

      # use the renaming pattern from the small conversion scripts
      UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    rename_base_flax_keys(tuple(key.split("/"  )  ),		__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[int]			     =    "/".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_					:					Tuple			     =    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_					:					List[Any]			     =    {}
         UpperCAmelCase_					:					Dict			     =    0

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

   # Add the last block
   UpperCAmelCase_					:					Optional[Any]			     =    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_					:					List[str]			     =    {}
   UpperCAmelCase_					:					Any			     =    {}
   for idx, shard in enumerate(__lowerCamelCase  ):
      UpperCAmelCase_					:					List[str]			     =    weights_name.replace(
          ".bin",		f"""-{idx+1:05d}-of-{len(__lowerCamelCase  ):05d}.bin"""  )  # len(sharded_state_dicts):05d}
      UpperCAmelCase_					:					Optional[int]			     =    os.path.join(__lowerCamelCase,		weights_name.replace(".bin",		f"""-{idx+1:05d}-of-???.bin"""  )  )
      os.rename(__lowerCamelCase,		os.path.join(__lowerCamelCase,		__lowerCamelCase  )  )
      UpperCAmelCase_					:					Union[str, Any]			     =    shard
      for key in shard:
         UpperCAmelCase_					:					int			     =    shard_file

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

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

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

   UpperCAmelCase_					:					Union[str, Any]			     =    TaTokenizer.from_pretrained("t5-small"  )
   UpperCAmelCase_					:					Union[str, Any]			     =    "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

   UpperCAmelCase_					:					Tuple			     =    tokenizer(__lowerCamelCase,		return_tensors="pt"  ).input_ids
   UpperCAmelCase_					:					List[Any]			     =    model.generate(__lowerCamelCase,		decoder_start_token_id=0  )
   print(tokenizer.decode(out[0]  )  )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_     							= {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= ['ConvBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ConvBertForMaskedLM',
      'ConvBertForMultipleChoice',
      'ConvBertForQuestionAnswering',
      'ConvBertForSequenceClassification',
      'ConvBertForTokenClassification',
      'ConvBertLayer',
      'ConvBertModel',
      'ConvBertPreTrainedModel',
      'load_tf_weights_in_convbert',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFConvBertForMaskedLM',
      'TFConvBertForMultipleChoice',
      'TFConvBertForQuestionAnswering',
      'TFConvBertForSequenceClassification',
      'TFConvBertForTokenClassification',
      'TFConvBertLayer',
      'TFConvBertModel',
      'TFConvBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
  from .tokenization_convbert import ConvBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_convbert_fast import ConvBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convbert import (
        CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvBertForMaskedLM,
        ConvBertForMultipleChoice,
        ConvBertForQuestionAnswering,
        ConvBertForSequenceClassification,
        ConvBertForTokenClassification,
        ConvBertLayer,
        ConvBertModel,
        ConvBertPreTrainedModel,
        load_tf_weights_in_convbert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convbert import (
        TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertLayer,
        TFConvBertModel,
        TFConvBertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

