




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_					,							lowercase_		)  ->			Dict:



       """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_					,							lowercase_					,							lowercase_		)  ->			int:



       """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_					,							lowercase_		)  ->			int:



       """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_					,							lowercase_					,							lowercase_					,							lowercase_					,							lowercase_ = WEIGHTS_NAME		)  ->			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__":
  _lowerCamelCase      :						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.""",
  )
  _lowerCamelCase      :						int							=  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							(			)  ->			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]		)		)







from typing import TYPE_CHECKING

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


_lowercase    	=		{
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2TokenizerFast"]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2FeatureExtractor"]
   _lowercase    	=		["LayoutLMv2ImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMv2ForQuestionAnswering",
       "LayoutLMv2ForSequenceClassification",
       "LayoutLMv2ForTokenClassification",
       "LayoutLMv2Layer",
       "LayoutLMv2Model",
       "LayoutLMv2PreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

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