
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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

      '''simple docstring'''





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

      return flax_key_tuple, flax_tensor


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):

      '''simple docstring'''





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

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

      if "kvstore/path" in layer:
            UpperCamelCase__    :   Dict					=				f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
      elif "kvstore/driver" in layer:
            UpperCamelCase__    :   Optional[int]					=				"""file"""
      else:
            UpperCamelCase__    :   str					=				checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

      '''simple docstring'''





      UpperCamelCase__    :   int					=				rename_keys(lowercase_		)
      UpperCamelCase__    :   Any					=				{}
      for k, v in current_block.items():
            UpperCamelCase__    :   Dict					=				v
      UpperCamelCase__    :   str					=				new_current_block
      torch.save(lowercase_ ,		lowercase_		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = WEIGHTS_NAME		):

      '''simple docstring'''





      UpperCamelCase__    :   Optional[int]					=				convert_file_size_to_int(lowercase_		)
      UpperCamelCase__    :   List[Any]					=				[]
      UpperCamelCase__    :   int					=				{}
      UpperCamelCase__    :   List[str]					=				0
      UpperCamelCase__    :   Any					=				0

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

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

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

            # use the renaming pattern from the small conversion scripts
            UpperCamelCase__    :   Any					=				rename_base_flax_keys(tuple(key.split('''/'''		)		) ,		lowercase_		)
            UpperCamelCase__    :   Any					=				"""/""".join(lowercase_		)

            # 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[Any]					=				os.path.join(
                      lowercase_ ,		weights_name.replace('''.bin''' ,		f'''-{len(lowercase_		)+1:05d}-of-???.bin'''		)		)
                  rename_and_save_block(lowercase_ ,		lowercase_		)
                  sharded_state_dicts.append(current_block.keys()		)
                  del current_block
                  UpperCamelCase__    :   Any					=				{}
                  UpperCamelCase__    :   str					=				0

            UpperCamelCase__    :   List[str]					=				raw_weights.to(getattr(lowercase_ ,		lowercase_		)		)
            current_block_size += weight_size
            total_size += weight_size

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

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

      # Otherwise, let's build the index
      UpperCamelCase__    :   str					=				{}
      UpperCamelCase__    :   Any					=				{}
      for idx, shard in enumerate(lowercase_		):
            UpperCamelCase__    :   Any					=				weights_name.replace(
                '''.bin''' ,		f'''-{idx+1:05d}-of-{len(lowercase_		):05d}.bin'''		)  # len(sharded_state_dicts):05d}
            UpperCamelCase__    :   Dict					=				os.path.join(lowercase_ ,		weights_name.replace('''.bin''' ,		f'''-{idx+1:05d}-of-???.bin'''		)		)
            os.rename(lowercase_ ,		os.path.join(lowercase_ ,		lowercase_		)		)
            UpperCamelCase__    :   str					=				shard
            for key in shard:
                  UpperCamelCase__    :   Any					=				shard_file

    # Add the metadata
      UpperCamelCase__    :   Tuple					=				{"""total_size""": total_size}
      UpperCamelCase__    :   Union[str, Any]					=				{"""metadata""": metadata, """weight_map""": weight_map}

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

      return metadata, index


if __name__ == "__main__":
    lowerCamelCase      =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.",
    )
    lowerCamelCase      =parser.parse_args()
    shard_on_the_fly(
        args.switch_tax_checkpoint_path,
        args.pytorch_dump_folder_path,
        args.max_shard_size,
        args.dtype,
    )


def 							SCREAMING_SNAKE_CASE_      (			):

      '''simple docstring'''





      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      UpperCamelCase__    :   str					=				SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''		)
      config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''		)
      UpperCamelCase__    :   str					=				SwitchTransformersForConditionalGeneration.from_pretrained(
          '''/home/arthur_huggingface_co/transformers/switch_converted''' ,		device_map='''auto'''		)

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

      UpperCamelCase__    :   Union[str, Any]					=				tokenizer(lowercase_ ,		return_tensors='''pt'''		).input_ids
      UpperCamelCase__    :   Tuple					=				model.generate(lowercase_ ,		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,
)


lowerCamelCase      ={
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =["BlenderbotTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BlenderbotForCausalLM",
        "BlenderbotForConditionalGeneration",
        "BlenderbotModel",
        "BlenderbotPreTrainedModel",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TFBlenderbotForConditionalGeneration",
        "TFBlenderbotModel",
        "TFBlenderbotPreTrainedModel",
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "FlaxBlenderbotForConditionalGeneration",
        "FlaxBlenderbotModel",
        "FlaxBlenderbotPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_blenderbot import (
        BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BlenderbotConfig,
        BlenderbotOnnxConfig,
    )
    from .tokenization_blenderbot import BlenderbotTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blenderbot import (
            BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlenderbotForCausalLM,
            BlenderbotForConditionalGeneration,
            BlenderbotModel,
            BlenderbotPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blenderbot import (
            TFBlenderbotForConditionalGeneration,
            TFBlenderbotModel,
            TFBlenderbotPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_blenderbot import (
            FlaxBlenderbotForConditionalGeneration,
            FlaxBlenderbotModel,
            FlaxBlenderbotPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


