






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    (			__lowercase :      List[Any]      ,					__lowercase :      Union[str, Any]     )    ->   str:





    """simple docstring"""

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

    return flax_key_tuple, flax_tensor



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]      ,					__lowercase :      str      ,					__lowercase :      Dict     )    ->   Union[str, Any]:





    """simple docstring"""

    if "metadata" in layer:
        __A       =						layer.split("""metadata"""     )
        __A       =						"""""".join(split_layer[0]     )[:-1]
        __A       =						[tuple(("""metadata""" + split_layer[1]).split("""/"""     )     )]
    elif "kvstore" in layer:
        __A       =						layer.split("""kvstore"""     )
        __A       =						"""""".join(split_layer[0]     )[:-1]
        __A       =						[tuple(("""kvstore""" + split_layer[1]).split("""/"""     )     )]

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

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

    return curr_real_layer_name, split_layer, content



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Dict     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						rename_keys(__lowercase     )
    __A       =						{}
    for k, v in current_block.items():
        __A       =						v
    __A       =						new_current_block
    torch.save(__lowercase      ,					__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]      ,					__lowercase :      Tuple      ,					__lowercase :      Dict      ,					__lowercase :      Dict      ,					__lowercase :      str = WEIGHTS_NAME     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						convert_file_size_to_int(__lowercase     )
    __A       =						[]
    __A       =						{}
    __A       =						0
    __A       =						0

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

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

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

        # use the renaming pattern from the small conversion scripts
        __A     ,						__A       =						rename_base_flax_keys(tuple(key.split("""/"""     )     )      ,					__lowercase     )
        __A       =						"""/""".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:
            __A       =						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
            __A       =						{}
            __A       =						0

        __A       =						raw_weights.to(getattr(__lowercase      ,					__lowercase     )     )
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    __A       =						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
    __A       =						{}
    __A       =						{}
    for idx, shard in enumerate(__lowercase     ):
        __A       =						weights_name.replace(
            """.bin"""      ,					f"-{idx+1:05d}-of-{len(__lowercase     ):05d}.bin"     )  # len(sharded_state_dicts):05d}
        __A       =						os.path.join(__lowercase      ,					weights_name.replace(""".bin"""      ,					f"-{idx+1:05d}-of-???.bin"     )     )
        os.rename(__lowercase      ,					os.path.join(__lowercase      ,					__lowercase     )     )
        __A       =						shard
        for key in shard:
            __A       =						shard_file

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

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

    return metadata, index


if __name__ == "__main__":
  __a   :							Union[str, 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.",
  )
  __a   :							List[Any]						= 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    (			)    ->   Optional[Any]:





    """simple docstring"""

    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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












import argparse
import os
import re

import packaging.version


__a   :							Tuple						= "examples/"
__a   :							Any						= {
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__a   :							Tuple						= {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__a   :							List[str]						= "README.md"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      List[str]      ,					__lowercase :      Optional[Any]     )    ->   List[Any]:





    """simple docstring"""

    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        __A       =						f.read()
    __A     ,						__A       =						REPLACE_PATTERNS[pattern]
    __A       =						replace.replace("""VERSION"""      ,					__lowercase     )
    __A       =						re_pattern.sub(__lowercase      ,					__lowercase     )
    with open(__lowercase      ,					"""w"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        f.write(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   Dict:





    """simple docstring"""

    for folder, directories, fnames in os.walk(__lowercase     ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("""research_projects"""     )
        if "legacy" in directories:
            directories.remove("""legacy"""     )
        for fname in fnames:
            if fname.endswith(""".py"""     ):
                update_version_in_file(os.path.join(__lowercase      ,					__lowercase     )      ,					__lowercase      ,					pattern="""examples"""     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Optional[int]=False     )    ->   Dict:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(__lowercase      ,					__lowercase      ,					__lowercase     )
    if not patch:
        update_version_in_examples(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Union[str, Any]:





    """simple docstring"""

    __A       =						"""🤗 Transformers currently provides the following architectures"""
    __A       =						"""1. Want to contribute a new model?"""
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        __A       =						f.readlines()

    # Find the start of the list.
    __A       =						0
    while not lines[start_index].startswith(_start_prompt     ):
        start_index += 1
    start_index += 1

    __A       =						start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt     ):
        if lines[index].startswith("""1."""     ):
            __A       =						lines[index].replace(
                """https://huggingface.co/docs/transformers/main/model_doc"""      ,					"""https://huggingface.co/docs/transformers/model_doc"""      ,					)
        index += 1

    with open(__lowercase      ,					"""w"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        f.writelines(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]      ,					"""r"""     ) as f:
        __A       =						f.read()
    __A       =						REPLACE_PATTERNS["""init"""][0].search(__lowercase     ).groups()[0]
    return packaging.version.parse(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str=False     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""     )
    if default_version.is_devrelease:
        __A       =						default_version.base_version
    elif patch:
        __A       =						f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
    else:
        __A       =						f"{default_version.major}.{default_version.minor + 1}.0"

    # Now let's ask nicely if that's the right one.
    __A       =						input(f"Which version are you releasing? [{default_version}]"     )
    if len(__lowercase     ) == 0:
        __A       =						default_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase      ,					patch=__lowercase     )
    if not patch:
        print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
        clean_main_ref_in_model_list()



def       _SCREAMING_SNAKE_CASE    (			)    ->   List[str]:





    """simple docstring"""

    __A       =						get_version()
    __A       =						f"{current_version.major}.{current_version.minor + 1}.0.dev0"
    __A       =						current_version.base_version

    # Check with the user we got that right.
    __A       =						input(f"Which version are we developing now? [{dev_version}]"     )
    if len(__lowercase     ) == 0:
        __A       =						dev_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase     )
    print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
    clean_main_ref_in_model_list()


if __name__ == "__main__":
  __a   :							str						= argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  __a   :							Union[str, Any]						= parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()




