


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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple						) ->   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(__A							,					(0, 2, 1)						)
	elif flax_key_tuple[-1] == "kernel" and ".".join(__A						):
		# 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   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Dict						) ->   Optional[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   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple						) ->   Union[str, Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int] = WEIGHTS_NAME						) ->   str:





	"""simple docstring"""







	A								=				convert_file_size_to_int(__A						)
	A								=				[]
	A								=				{}
	A								=				0
	A								=				0

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

	A								=				{}
	for layer in checkpoint_info.keys():
		A								=				get_key_and_tensorstore_dict(
		    __A							,					__A							,					__A						)
		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(__A						)
		A								=				raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

		# use the renaming pattern from the small conversion scripts
		A								=				rename_base_flax_keys(tuple(key.split("""/"""						)						)							,					__A						)
		A								=				"""/""".join(__A						)

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

		A								=				raw_weights.to(getattr(__A							,					__A						)						)
		current_block_size += weight_size
		total_size += weight_size

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

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

	# Otherwise, let's build the index
	A								=				{}
	A								=				{}
	for idx, shard in enumerate(__A						):
		A								=				weights_name.replace(
		    """.bin"""							,					f'-{idx+1:05d}-of-{len(__A						):05d}.bin'						)  # len(sharded_state_dicts):05d}
		A								=				os.path.join(__A							,					weights_name.replace(""".bin"""							,					f'-{idx+1:05d}-of-???.bin'						)						)
		os.rename(__A							,					os.path.join(__A							,					__A						)						)
		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(__A							,					__A						)							,					"""w"""							,					encoding="""utf-8"""						) as f:
		A								=				json.dumps(__A							,					indent=2							,					sort_keys=__A						) + """\n"""
		f.write(__A						)

	return metadata, index


if __name__ == "__main__":
	_lowercase       :      Dict	=	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.",
	)
	_lowercase       :      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   _lowerCAmelCase				(   ) ->   Tuple:





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







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_lowercase       :      int	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'upernet'

				def __init__(						self				,   a__=None				,   a__=512				,   a__=0.02				,   a__=[1, 2, 3, 6]				,   a__=True				,   a__=0.4				,   a__=384				,   a__=256				,   a__=1				,   a__=False				,   a__=255				,   **a__				,   )	->				Tuple:
					super().__init__(**a__   )

					if backbone_config is None:
						logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
						A								=				CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   )
					elif isinstance(a__				,   a__   ):
						A								=				backbone_config.get("""model_type"""   )
						A								=				CONFIG_MAPPING[backbone_model_type]
						A								=				config_class.from_dict(a__   )

					A								=				backbone_config
					A								=				hidden_size
					A								=				initializer_range
					A								=				pool_scales
					A								=				use_auxiliary_head
					A								=				auxiliary_loss_weight
					A								=				auxiliary_in_channels
					A								=				auxiliary_channels
					A								=				auxiliary_num_convs
					A								=				auxiliary_concat_input
					A								=				loss_ignore_index

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				copy.deepcopy(self.__dict__   )
					A								=				self.backbone_config.to_dict()
					A								=				self.__class__.model_type
					return output



