





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

  return flax_key_tuple, flax_tensor







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

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

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

  return curr_real_layer_name, split_layer, content







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







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = WEIGHTS_NAME)       ->   Tuple:
  __snake_case:						Tuple			  =						convert_file_size_to_int(SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]			  =						[]
  __snake_case:						Union[str, Any]			  =						{}
  __snake_case:						Any			  =						0
  __snake_case:						Tuple			  =						0

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

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

  for key in all_layers.keys():
    # open tensorstore file
    __snake_case:						Optional[Any]			  =						ts.open(unflatten_dict(all_layers[key])).result().read().result()
    __snake_case:						Optional[int]			  =						torch.tensor(SCREAMING_SNAKE_CASE__)
    __snake_case:						Union[str, Any]			  =						raw_weights.numel() * dtype_byte_size(raw_weights.dtype)

    # use the renaming pattern from the small conversion scripts
    __snake_case  ,       __snake_case:						Optional[int]			  =						rename_base_flax_keys(tuple(key.split("""/""")) , SCREAMING_SNAKE_CASE__)
    __snake_case:						Optional[int]			  =						"""/""".join(SCREAMING_SNAKE_CASE__)

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

    __snake_case:						List[Any]			  =						raw_weights.to(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__))
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  __snake_case:						Union[str, Any]			  =						os.path.join(SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{len(SCREAMING_SNAKE_CASE__)+1:05d}-of-???.bin'''))
  rename_and_save_block(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
  sharded_state_dicts.append(current_block.keys())

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

  # Otherwise, let's build the index
  __snake_case:						Optional[int]			  =						{}
  __snake_case:						int			  =						{}
  for idx, shard in enumerate(SCREAMING_SNAKE_CASE__):
    __snake_case:						List[Any]			  =						weights_name.replace(
        """.bin""" , F'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE__):05d}.bin''')  # len(sharded_state_dicts):05d}
    __snake_case:						str			  =						os.path.join(SCREAMING_SNAKE_CASE__ , weights_name.replace(""".bin""" , F'''-{idx+1:05d}-of-???.bin'''))
    os.rename(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__))
    __snake_case:						Any			  =						shard
    for key in shard:
      __snake_case:						Optional[Any]			  =						shard_file

    # Add the metadata
  __snake_case:						Optional[Any]			  =						{"""total_size""": total_size}
  __snake_case:						Dict			  =						{"""metadata""": metadata, """weight_map""": weight_map}

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

  return metadata, index


if __name__ == "__main__":
      __UpperCAmelCase	:       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.",
      )
      __UpperCAmelCase	:       str    	=							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

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

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

  __snake_case:						List[Any]			  =						tokenizer(SCREAMING_SNAKE_CASE__ , return_tensors="""pt""").input_ids
  __snake_case:						int			  =						model.generate(SCREAMING_SNAKE_CASE__ , decoder_start_token_id=0)
  print(tokenizer.decode(out[0]))













from typing import TYPE_CHECKING

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


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






