





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

				return flax_key_tuple, flax_tensor

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

				else:
								_snake_case :			Tuple  						=   layer.split('/' )
								_snake_case :			Any  						=   '/'.join(split_layer[:-1] )
								_snake_case :			Dict  						=   (split_layer[-1],)

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

				return curr_real_layer_name, split_layer, content

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   rename_keys(__lowerCAmelCase )
				_snake_case :			Optional[Any]  						=   {}
				for k, v in current_block.items():
								_snake_case :			int  						=   v
				_snake_case :			Tuple  						=   new_current_block
				torch.save(__lowerCAmelCase	,			__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = WEIGHTS_NAME ):
				_snake_case :			Dict  						=   convert_file_size_to_int(__lowerCAmelCase )
				_snake_case :			Dict  						=   []
				_snake_case :			Dict  						=   {}
				_snake_case :			Optional[int]  						=   0
				_snake_case :			Dict  						=   0

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

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

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

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

								_snake_case :			Union[str, Any]  						=   raw_weights.to(getattr(__lowerCAmelCase	,			__lowerCAmelCase ) )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				_snake_case :			Tuple  						=   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
				_snake_case :			List[str]  						=   {}
				_snake_case :			List[Any]  						=   {}
				for idx, shard in enumerate(__lowerCAmelCase ):
								_snake_case :			List[Any]  						=   weights_name.replace(
								    '.bin'	,			F'''-{idx+1:05d}-of-{len(__lowerCAmelCase ):05d}.bin''' )  # len(sharded_state_dicts):05d}
								_snake_case :			Dict  						=   os.path.join(__lowerCAmelCase	,			weights_name.replace('.bin'	,			F'''-{idx+1:05d}-of-???.bin''' ) )
								os.rename(__lowerCAmelCase	,			os.path.join(__lowerCAmelCase	,			__lowerCAmelCase ) )
								_snake_case :			Any  						=   shard
								for key in shard:
												_snake_case :			int  						=   shard_file

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

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

				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_		:				List[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       A__(		):
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

				_snake_case :			List[str]  						=   tokenizer(__lowerCAmelCase	,			return_tensors='pt' ).input_ids
				_snake_case :			Dict  						=   model.generate(__lowerCAmelCase	,			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


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''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

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






