



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						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str						):







		'''simple docstring'''





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

		return flax_key_tuple, flax_tensor

def        a						(      _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





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

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

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

		return curr_real_layer_name, split_layer, content

def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str = WEIGHTS_NAME						):







		'''simple docstring'''





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

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

		__UpperCAmelCase    :     Tuple													=	{}
		for layer in checkpoint_info.keys():
				__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	get_key_and_tensorstore_dict(
				    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
				if curr_real_layer_name in all_layers:
						__UpperCAmelCase    :     Dict													=	content
				else:
						__UpperCAmelCase    :     Any													=	{split_layer[-1]: content}

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

				# use the renaming pattern from the small conversion scripts
				__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	rename_base_flax_keys(tuple(key.split('''/'''						)						) ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Tuple													=	'''/'''.join(_UpperCAmelCase						)

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

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

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

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

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

    # Add the metadata
		__UpperCAmelCase    :     int													=	{'''total_size''': total_size}
		__UpperCAmelCase    :     str													=	{'''metadata''': metadata, '''weight_map''': weight_map}

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

		return metadata, index


if __name__ == "__main__":
				__A   			=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   			=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

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

		__UpperCAmelCase    :     Optional[Any]													=	TaTokenizer.from_pretrained('''t5-small'''						)
		__UpperCAmelCase    :     Tuple													=	'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

		__UpperCAmelCase    :     Tuple													=	tokenizer(_UpperCAmelCase ,   return_tensors='''pt'''						).input_ids
		__UpperCAmelCase    :     List[Any]													=	model.generate(_UpperCAmelCase ,   decoder_start_token_id=0						)
		print(tokenizer.decode(out[0]						)						)










import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       None




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	self.feature_extraction_class(**self.feat_extract_dict )
					__UpperCAmelCase    :     Optional[int]													=	json.loads(feat_extract.to_json_string() )
					for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]       ,       a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(a_       ,       '''feat_extract.json''' )
							feat_extract_first.to_json_file(a_ )
							__UpperCAmelCase    :     Any													=	self.feature_extraction_class.from_json_file(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     List[str]													=	feat_extract_first.save_pretrained(a_ )[0]
							check_json_file_has_correct_format(a_ )
							__UpperCAmelCase    :     Optional[Any]													=	self.feature_extraction_class.from_pretrained(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.feature_extraction_class()
					self.assertIsNotNone(a_ )





