






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     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								lowercase__  					=     flax_key_tuple[:-1] + ('''weight''',)
								lowercase__  					=     torch.permute(lowerCamelCase_						,  (0, 2, 1)							)
				elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_							):
								# linear layer
								lowercase__  					=     flax_key_tuple[:-1] + ('''weight''',)
								lowercase__  					=     flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								lowercase__  					=     flax_key_tuple[:-1] + ('''weight''',)

				return flax_key_tuple, flax_tensor






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				if "metadata" in layer:
								lowercase__  					=     layer.split('''metadata'''							)
								lowercase__  					=     ''''''.join(split_layer[0]							)[:-1]
								lowercase__  					=     [tuple(('''metadata''' + split_layer[1]).split('''/'''							)							)]
				elif "kvstore" in layer:
								lowercase__  					=     layer.split('''kvstore'''							)
								lowercase__  					=     ''''''.join(split_layer[0]							)[:-1]
								lowercase__  					=     [tuple(('''kvstore''' + split_layer[1]).split('''/'''							)							)]

				else:
								lowercase__  					=     layer.split('''/'''							)
								lowercase__  					=     '''/'''.join(split_layer[:-1]							)
								lowercase__  					=     (split_layer[-1],)

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

				return curr_real_layer_name, split_layer, content






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     rename_keys(lowerCamelCase_							)
				lowercase__  					=     {}
				for k, v in current_block.items():
								lowercase__  					=     v
				lowercase__  					=     new_current_block
				torch.save(lowerCamelCase_						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_ = WEIGHTS_NAME							):






				'''simple docstring'''
				lowercase__  					=     convert_file_size_to_int(lowerCamelCase_							)
				lowercase__  					=     []
				lowercase__  					=     {}
				lowercase__  					=     0
				lowercase__  					=     0

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

				lowercase__  					=     {}
				for layer in checkpoint_info.keys():
								lowercase__					,		lowercase__					,		lowercase__  					=     get_key_and_tensorstore_dict(
								    lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)
								if curr_real_layer_name in all_layers:
												lowercase__  					=     content
								else:
												lowercase__  					=     {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								lowercase__  					=     ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
								lowercase__  					=     torch.tensor(lowerCamelCase_							)
								lowercase__  					=     raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

								# use the renaming pattern from the small conversion scripts
								lowercase__					,		lowercase__  					=     rename_base_flax_keys(tuple(key.split('''/'''							)							)						,  lowerCamelCase_							)
								lowercase__  					=     '''/'''.join(lowerCamelCase_							)

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												lowercase__  					=     os.path.join(
												    lowerCamelCase_						,  weights_name.replace('''.bin'''						,  F"""-{len(lowerCamelCase_							)+1:05d}-of-???.bin"""							)							)
												rename_and_save_block(lowerCamelCase_						,  lowerCamelCase_							)
												sharded_state_dicts.append(current_block.keys()							)
												del current_block
												lowercase__  					=     {}
												lowercase__  					=     0

								lowercase__  					=     raw_weights.to(getattr(lowerCamelCase_						,  lowerCamelCase_							)							)
								current_block_size += weight_size
								total_size += weight_size

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

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

				# Otherwise, let's build the index
				lowercase__  					=     {}
				lowercase__  					=     {}
				for idx, shard in enumerate(lowerCamelCase_							):
								lowercase__  					=     weights_name.replace(
								    '''.bin'''						,  F"""-{idx+1:05d}-of-{len(lowerCamelCase_							):05d}.bin"""							)  # len(sharded_state_dicts):05d}
								lowercase__  					=     os.path.join(lowerCamelCase_						,  weights_name.replace('''.bin'''						,  F"""-{idx+1:05d}-of-???.bin"""							)							)
								os.rename(lowerCamelCase_						,  os.path.join(lowerCamelCase_						,  lowerCamelCase_							)							)
								lowercase__  					=     shard
								for key in shard:
												lowercase__  					=     shard_file

    # Add the metadata
				lowercase__  					=     {'''total_size''': total_size}
				lowercase__  					=     {'''metadata''': metadata, '''weight_map''': weight_map}

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

				return metadata, index


if __name__ == "__main__":
							A__      :     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.',
							)
							A__      :     Optional[Any]       =					parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)






def     a     (		):






				'''simple docstring'''
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				lowercase__  					=     SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''							)
				config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''							)
				lowercase__  					=     SwitchTransformersForConditionalGeneration.from_pretrained(
				    '''/home/arthur_huggingface_co/transformers/switch_converted'''						,  device_map='''auto'''							)

				lowercase__  					=     TaTokenizer.from_pretrained('''t5-small'''							)
				lowercase__  					=     '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

				lowercase__  					=     tokenizer(lowerCamelCase_						,  return_tensors='''pt'''							).input_ids
				lowercase__  					=     model.generate(lowerCamelCase_						,  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


A__      :     Tuple       =					logging.get_logger(__name__)
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			"""upernet"""




				def __init__(	self		:       Dict,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       str=512,    lowerCamelCase		:       Optional[Any]=0.02,    lowerCamelCase		:       Optional[Any]=[1, 2, 3, 6],    lowerCamelCase		:       Optional[int]=True,    lowerCamelCase		:       Tuple=0.4,    lowerCamelCase		:       Optional[int]=384,    lowerCamelCase		:       Optional[int]=256,    lowerCamelCase		:       Dict=1,    lowerCamelCase		:       str=False,    lowerCamelCase		:       List[str]=255,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase    )

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

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




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     copy.deepcopy(self.__dict__    )
								lowercase__  					=     self.backbone_config.to_dict()
								lowercase__  					=     self.__class__.model_type
								return output




