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   :	Union[str, Any]			,					__UpperCamelCase   :	Union[str, Any]     )							-> str:







  """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(__UpperCamelCase			,					(0, 2, 1)     )
  elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase     ):
    # 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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, Any]:







  """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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Dict     )							-> List[Any]:







  """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	str = WEIGHTS_NAME     )							-> List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				convert_file_size_to_int(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				{}
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0

  os.makedirs(__UpperCamelCase			,					exist_ok=__UpperCamelCase     )
  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(__UpperCamelCase			,					sep="""/"""     )

  SCREAMING_SNAKE_CASE__      							=				{}
  for layer in checkpoint_info.keys():
    SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				get_key_and_tensorstore_dict(
        __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     )
    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(__UpperCamelCase     )
    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("""/"""     )     )			,					__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__      							=				"""/""".join(__UpperCamelCase     )

    # 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(
          __UpperCamelCase			,					weights_name.replace(""".bin"""			,					f"""-{len(__UpperCamelCase     )+1:05d}-of-???.bin"""     )     )
      rename_and_save_block(__UpperCamelCase			,					__UpperCamelCase     )
      sharded_state_dicts.append(current_block.keys()     )
      del current_block
      SCREAMING_SNAKE_CASE__      							=				{}
      SCREAMING_SNAKE_CASE__      							=				0

    SCREAMING_SNAKE_CASE__      							=				raw_weights.to(getattr(__UpperCamelCase			,					__UpperCamelCase     )     )
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					weights_name.replace(""".bin"""			,					f"""-{len(__UpperCamelCase     )+1:05d}-of-???.bin"""     )     )
  rename_and_save_block(__UpperCamelCase			,					__UpperCamelCase     )
  sharded_state_dicts.append(current_block.keys()     )

  # If we only have one shard, we return it
  if len(__UpperCamelCase     ) == 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(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				weights_name.replace(
        """.bin"""			,					f"""-{idx+1:05d}-of-{len(__UpperCamelCase     ):05d}.bin"""     )  # len(sharded_state_dicts):05d}
    SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					weights_name.replace(""".bin"""			,					f"""-{idx+1:05d}-of-???.bin"""     )     )
    os.rename(__UpperCamelCase			,					os.path.join(__UpperCamelCase			,					__UpperCamelCase     )     )
    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(__UpperCamelCase			,					__UpperCamelCase     )			,					"""w"""			,					encoding="""utf-8"""     ) as f:
    SCREAMING_SNAKE_CASE__      							=				json.dumps(__UpperCamelCase			,					indent=2			,					sort_keys=__UpperCamelCase     ) + """\n"""
    f.write(__UpperCamelCase     )

  return metadata, index


if __name__ == "__main__":
   __lowerCamelCase      :			int            =		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      :			Dict            =		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  ( )							-> int:







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

from typing import TYPE_CHECKING

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


__lowerCamelCase      :			Optional[Any]							       =		{
    '''configuration_efficientformer''': [
        '''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientFormerConfig''',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Dict							       =		['''EfficientFormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''EfficientFormerForImageClassification''',
       '''EfficientFormerForImageClassificationWithTeacher''',
       '''EfficientFormerModel''',
       '''EfficientFormerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFEfficientFormerForImageClassification''',
       '''TFEfficientFormerForImageClassificationWithTeacher''',
       '''TFEfficientFormerModel''',
       '''TFEfficientFormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			List[Any]							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
