






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,	snake_case			):







				"""simple docstring"""






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

				return flax_key_tuple, flax_tensor

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






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

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

				if "kvstore/path" in layer:
								__magic_name__		:Optional[Any]   =   f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
				elif "kvstore/driver" in layer:
								__magic_name__		:Optional[Any]   =   '''file'''
				else:
								__magic_name__		:Union[str, Any]   =   checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   rename_keys(snake_case			)
				__magic_name__		:List[str]   =   {}
				for k, v in current_block.items():
								__magic_name__		:Union[str, Any]   =   v
				__magic_name__		:List[str]   =   new_current_block
				torch.save(snake_case,	snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case = WEIGHTS_NAME			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   convert_file_size_to_int(snake_case			)
				__magic_name__		:Union[str, Any]   =   []
				__magic_name__		:Optional[Any]   =   {}
				__magic_name__		:Optional[int]   =   0
				__magic_name__		:Optional[int]   =   0

				os.makedirs(snake_case,	exist_ok=snake_case			)
				with gfile.GFile(switch_checkpoint_path + '''/checkpoint''',	'''rb'''			) as fp:
								__magic_name__		:List[Any]   =   serialization.msgpack_restore(fp.read()			)['''optimizer''']['''target''']
								__magic_name__		:List[Any]   =   flatten_dict(snake_case,	sep='''/'''			)

				__magic_name__		:Any   =   {}
				for layer in checkpoint_info.keys():
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Optional[Any]   =   get_key_and_tensorstore_dict(
								    snake_case,	snake_case,	snake_case			)
								if curr_real_layer_name in all_layers:
												__magic_name__		:str   =   content
								else:
												__magic_name__		:Union[str, Any]   =   {split_layer[-1]: content}

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

								# use the renaming pattern from the small conversion scripts
								__magic_name__  ,					__magic_name__		:Optional[Any]   =   rename_base_flax_keys(tuple(key.split('''/'''			)			),	snake_case			)
								__magic_name__		:Optional[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:
												__magic_name__		:Union[str, 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()			)
												del current_block
												__magic_name__		:Union[str, Any]   =   {}
												__magic_name__		:List[str]   =   0

								__magic_name__		:int   =   raw_weights.to(getattr(snake_case,	snake_case			)			)
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				__magic_name__		: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()			)

				# 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
				__magic_name__		:Union[str, Any]   =   {}
				__magic_name__		:Union[str, Any]   =   {}
				for idx, shard in enumerate(snake_case			):
								__magic_name__		:Union[str, Any]   =   weights_name.replace(
								    '''.bin''',	f'''-{idx+1:05d}-of-{len(snake_case			):05d}.bin'''			)  # len(sharded_state_dicts):05d}
								__magic_name__		:Dict   =   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			)			)
								__magic_name__		:str   =   shard
								for key in shard:
												__magic_name__		:List[str]   =   shard_file

    # Add the metadata
				__magic_name__		:List[Any]   =   {'''total_size''': total_size}
				__magic_name__		:int   =   {'''metadata''': metadata, '''weight_map''': weight_map}

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

				return metadata, index


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      str				=		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.""",
					)
					SCREAMING_SNAKE_CASE__   :      Optional[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   __lowercase       (			):







				"""simple docstring"""






				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

				__magic_name__		:int   =   TaTokenizer.from_pretrained('''t5-small'''			)
				__magic_name__		: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>.'''

				__magic_name__		:Optional[Any]   =   tokenizer(snake_case,	return_tensors='''pt'''			).input_ids
				__magic_name__		:Any   =   model.generate(snake_case,	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


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """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

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






