


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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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

  return flax_key_tuple, flax_tensor






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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

  else:
    lowerCamelCase :					Optional[Any]    =				layer.split("/"	)
    lowerCamelCase :					List[Any]    =				"/".join(split_layer[:-1]	)
    lowerCamelCase :					Optional[Any]    =				(split_layer[-1],)

  if "kvstore/path" in layer:
    lowerCamelCase :					Any    =				f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
  elif "kvstore/driver" in layer:
    lowerCamelCase :					int    =				"file"
  else:
    lowerCamelCase :					List[str]    =				checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				rename_keys(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					int    =				{}
  for k, v in current_block.items():
    lowerCamelCase :					Optional[Any]    =				v
  lowerCamelCase :					Optional[Any]    =				new_current_block
  torch.save(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)






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


  '''simple docstring'''

  lowerCamelCase :					Dict    =				convert_file_size_to_int(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Tuple    =				[]
  lowerCamelCase :					Optional[int]    =				{}
  lowerCamelCase :					Optional[Any]    =				0
  lowerCamelCase :					Any    =				0

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

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

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

    # use the renaming pattern from the small conversion scripts
    lowerCamelCase    ,	lowerCamelCase :					List[Any]    =				rename_base_flax_keys(tuple(key.split("/"	)	)      ,						SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					Optional[int]    =				"/".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:
      lowerCamelCase :					str    =				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
      lowerCamelCase :					str    =				{}
      lowerCamelCase :					List[str]    =				0

    lowerCamelCase :					Optional[Any]    =				raw_weights.to(getattr(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	)
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  lowerCamelCase :					str    =				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
  lowerCamelCase :					List[Any]    =				{}
  lowerCamelCase :					str    =				{}
  for idx, shard in enumerate(SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					List[Any]    =				weights_name.replace(
        ".bin"      ,						f"""-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_	):05d}.bin"""	)  # len(sharded_state_dicts):05d}
    lowerCamelCase :					List[Any]    =				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_	)	)
    lowerCamelCase :					List[Any]    =				shard
    for key in shard:
      lowerCamelCase :					List[Any]    =				shard_file

    # Add the metadata
  lowerCamelCase :					Tuple    =				{"total_size": total_size}
  lowerCamelCase :					Dict    =				{"metadata": metadata, "weight_map": weight_map}

  with open(os.path.join(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						"w"      ,						encoding="utf-8"	) as f:
    lowerCamelCase :					Optional[Any]    =				json.dumps(SCREAMING_SNAKE_CASE_      ,						indent=2      ,						sort_keys=SCREAMING_SNAKE_CASE_	) + "\n"
    f.write(SCREAMING_SNAKE_CASE_	)

  return metadata, index


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







def 		lowercase_(  ):


  '''simple docstring'''

  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  lowerCamelCase :					List[Any]    =				SwitchTransformersConfig.from_pretrained("google/switch-base-8"	)
  config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"	)
  lowerCamelCase :					List[str]    =				SwitchTransformersForConditionalGeneration.from_pretrained(
      "/home/arthur_huggingface_co/transformers/switch_converted"      ,						device_map="auto"	)

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

  lowerCamelCase :					str    =				tokenizer(SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	).input_ids
  lowerCamelCase :					Optional[int]    =				model.generate(SCREAMING_SNAKE_CASE_      ,						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_torch_available,
)


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

   _snake_case             =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



