
'''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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




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

   return flax_key_tuple, flax_tensor
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




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

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

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

   return curr_real_layer_name, split_layer, content
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				rename_keys(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				{}
   for k, v in current_block.items():
      _SCREAMING_SNAKE_CASE      =				v
   _SCREAMING_SNAKE_CASE      =				new_current_block
   torch.save(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				convert_file_size_to_int(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				[]
   _SCREAMING_SNAKE_CASE      =				{}
   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				0

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

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

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

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

      _SCREAMING_SNAKE_CASE      =				raw_weights.to(getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)						)
      current_block_size += weight_size
      total_size += weight_size

   # Add the last block
   _SCREAMING_SNAKE_CASE      =				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
   _SCREAMING_SNAKE_CASE      =				{}
   _SCREAMING_SNAKE_CASE      =				{}
   for idx, shard in enumerate(SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				weights_name.replace(
          """.bin"""    ,      F"-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_						):05d}.bin"						)  # len(sharded_state_dicts):05d}
      _SCREAMING_SNAKE_CASE      =				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_						)						)
      _SCREAMING_SNAKE_CASE      =				shard
      for key in shard:
         _SCREAMING_SNAKE_CASE      =				shard_file

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

   with open(os.path.join(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)    ,      """w"""    ,      encoding="""utf-8"""						) as f:
      _SCREAMING_SNAKE_CASE      =				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__      :		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__      :		Optional[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    lowerCAmelCase_	(				)     ->     Tuple:




   """simple docstring"""
   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__      :		str       							=  {
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		List[Any]       							=  [
         "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CanineForMultipleChoice",
         "CanineForQuestionAnswering",
         "CanineForSequenceClassification",
         "CanineForTokenClassification",
         "CanineLayer",
         "CanineModel",
         "CaninePreTrainedModel",
         "load_tf_weights_in_canine",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

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






