






"""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 						lowercase    (				__snake_case				: Any		,					__snake_case				: Dict	):
		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				lowercase_ : List[str]							       =   flax_key_tuple[:-1] + ('''weight''',)
				lowercase_ : List[str]							       =   torch.permute(__snake_case		,					(0, 2, 1)	)
		elif flax_key_tuple[-1] == "kernel" and ".".join(__snake_case	):
				# linear layer
				lowercase_ : List[str]							       =   flax_key_tuple[:-1] + ('''weight''',)
				lowercase_ : Optional[Any]							       =   flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				lowercase_ : Union[str, Any]							       =   flax_key_tuple[:-1] + ('''weight''',)

		return flax_key_tuple, flax_tensor



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Dict	):
		if "metadata" in layer:
				lowercase_ : int							       =   layer.split('''metadata'''	)
				lowercase_ : Union[str, Any]							       =   ''''''.join(split_layer[0]	)[:-1]
				lowercase_ : str							       =   [tuple(('''metadata''' + split_layer[1]).split('''/'''	)	)]
		elif "kvstore" in layer:
				lowercase_ : Union[str, Any]							       =   layer.split('''kvstore'''	)
				lowercase_ : List[str]							       =   ''''''.join(split_layer[0]	)[:-1]
				lowercase_ : Any							       =   [tuple(('''kvstore''' + split_layer[1]).split('''/'''	)	)]

		else:
				lowercase_ : Union[str, Any]							       =   layer.split('''/'''	)
				lowercase_ : Tuple							       =   '''/'''.join(split_layer[:-1]	)
				lowercase_ : List[Any]							       =   (split_layer[-1],)

		if "kvstore/path" in layer:
				lowercase_ : int							       =   F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
		elif "kvstore/driver" in layer:
				lowercase_ : Optional[Any]							       =   '''file'''
		else:
				lowercase_ : Optional[Any]							       =   checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: Optional[Any]	):
		lowercase_ : str							       =   rename_keys(__snake_case	)
		lowercase_ : int							       =   {}
		for k, v in current_block.items():
				lowercase_ : Tuple							       =   v
		lowercase_ : List[Any]							       =   new_current_block
		torch.save(__snake_case		,					__snake_case	)



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Any		,					__snake_case				: List[str]		,					__snake_case				: Dict		,					__snake_case				: str = WEIGHTS_NAME	):
		lowercase_ : Union[str, Any]							       =   convert_file_size_to_int(__snake_case	)
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Tuple							       =   {}
		lowercase_ : str							       =   0
		lowercase_ : int							       =   0

		os.makedirs(__snake_case		,					exist_ok=__snake_case	)
		with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''		,					'''rb'''	) as fp:
				lowercase_ : int							       =   serialization.msgpack_restore(fp.read()	)['''optimizer''']['''target''']
				lowercase_ : Tuple							       =   flatten_dict(__snake_case		,					sep='''/'''	)

		lowercase_ : List[Any]							       =   {}
		for layer in checkpoint_info.keys():
				lowercase_       ,					lowercase_       ,					lowercase_ : Tuple							       =   get_key_and_tensorstore_dict(
				    __snake_case		,					__snake_case		,					__snake_case	)
				if curr_real_layer_name in all_layers:
						lowercase_ : Tuple							       =   content
				else:
						lowercase_ : Optional[int]							       =   {split_layer[-1]: content}

		for key in all_layers.keys():
				# open tensorstore file
				lowercase_ : Optional[Any]							       =   ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
				lowercase_ : str							       =   torch.tensor(__snake_case	)
				lowercase_ : Tuple							       =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype	)

				# use the renaming pattern from the small conversion scripts
				lowercase_       ,					lowercase_ : Dict							       =   rename_base_flax_keys(tuple(key.split('''/'''	)	)		,					__snake_case	)
				lowercase_ : Union[str, Any]							       =   '''/'''.join(__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:
						lowercase_ : int							       =   os.path.join(
						    __snake_case		,					weights_name.replace('''.bin'''		,					F'''-{len(__snake_case	)+1:05d}-of-???.bin'''	)	)
						rename_and_save_block(__snake_case		,					__snake_case	)
						sharded_state_dicts.append(current_block.keys()	)
						del current_block
						lowercase_ : Dict							       =   {}
						lowercase_ : Optional[int]							       =   0

				lowercase_ : int							       =   raw_weights.to(getattr(__snake_case		,					__snake_case	)	)
				current_block_size += weight_size
				total_size += weight_size

		# Add the last block
		lowercase_ : List[Any]							       =   os.path.join(__snake_case		,					weights_name.replace('''.bin'''		,					F'''-{len(__snake_case	)+1:05d}-of-???.bin'''	)	)
		rename_and_save_block(__snake_case		,					__snake_case	)
		sharded_state_dicts.append(current_block.keys()	)

		# If we only have one shard, we return it
		if len(__snake_case	) == 1:
				return {weights_name: sharded_state_dicts[0]}, None

		# Otherwise, let's build the index
		lowercase_ : Any							       =   {}
		lowercase_ : Dict							       =   {}
		for idx, shard in enumerate(__snake_case	):
				lowercase_ : Union[str, Any]							       =   weights_name.replace(
				    '''.bin'''		,					F'''-{idx+1:05d}-of-{len(__snake_case	):05d}.bin'''	)  # len(sharded_state_dicts):05d}
				lowercase_ : Tuple							       =   os.path.join(__snake_case		,					weights_name.replace('''.bin'''		,					F'''-{idx+1:05d}-of-???.bin'''	)	)
				os.rename(__snake_case		,					os.path.join(__snake_case		,					__snake_case	)	)
				lowercase_ : List[str]							       =   shard
				for key in shard:
						lowercase_ : Union[str, Any]							       =   shard_file

    # Add the metadata
		lowercase_ : int							       =   {'''total_size''': total_size}
		lowercase_ : Tuple							       =   {'''metadata''': metadata, '''weight_map''': weight_map}

		with open(os.path.join(__snake_case		,					__snake_case	)		,					'''w'''		,					encoding='''utf-8'''	) as f:
				lowercase_ : Dict							       =   json.dumps(__snake_case		,					indent=2		,					sort_keys=__snake_case	) + '''\n'''
				f.write(__snake_case	)

		return metadata, index


if __name__ == "__main__":
						__A		:       List[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		:       str  				=		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    (				):
		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

		lowercase_ : Union[str, Any]							       =   TaTokenizer.from_pretrained('''t5-small'''	)
		lowercase_ : List[Any]							       =   '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

		lowercase_ : List[str]							       =   tokenizer(__snake_case		,					return_tensors='''pt'''	).input_ids
		lowercase_ : List[Any]							       =   model.generate(__snake_case		,					decoder_start_token_id=0	)
		print(tokenizer.decode(out[0]	)	)







'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__    					=      {
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''VivitImageProcessor''']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VivitModel''',
      '''VivitPreTrainedModel''',
      '''VivitForVideoClassification''',
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

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