

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   __UpperCAmelCase					(      __a :     List[str]						,__a :     Dict      )     ->	List[str]:

					"""simple docstring"""

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

					return flax_key_tuple, flax_tensor



def   __UpperCAmelCase					(      __a :     int						,__a :     List[str]						,__a :     List[str]      )     ->	Optional[int]:

					"""simple docstring"""

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

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

					if "kvstore/path" in layer:
										_a      :       Tuple   =						F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
					elif "kvstore/driver" in layer:
										_a      :       str   =						'''file'''
					else:
										_a      :       Optional[Any]   =						checkpoint_info[layer]

					return curr_real_layer_name, split_layer, content



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       int   =						rename_keys(__a      )
					_a      :       Any   =						{}
					for k, v in current_block.items():
										_a      :       Dict   =						v
					_a      :       int   =						new_current_block
					torch.save(__a						,__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     int						,__a :     str						,__a :     int						,__a :     str = WEIGHTS_NAME      )     ->	Any:

					"""simple docstring"""

					_a      :       Optional[Any]   =						convert_file_size_to_int(__a      )
					_a      :       List[str]   =						[]
					_a      :       List[Any]   =						{}
					_a      :       int   =						0
					_a      :       List[Any]   =						0

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

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

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

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

										_a      :       Dict   =						raw_weights.to(getattr(__a						,__a      )      )
										current_block_size += weight_size
										total_size += weight_size

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

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

					with open(os.path.join(__a						,__a      )						,'''w'''						,encoding='''utf-8'''      ) as f:
										_a      :       Optional[int]   =						json.dumps(__a						,indent=2						,sort_keys=__a      ) + '''\n'''
										f.write(__a      )

					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   __UpperCAmelCase					(      )     ->	Dict:

					"""simple docstring"""

					from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

					_a      :       str   =						tokenizer(__a						,return_tensors='''pt'''      ).input_ids
					_a      :       Any   =						model.generate(__a						,decoder_start_token_id=0      )
					print(tokenizer.decode(out[0]      )      )





import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






  raise RuntimeError("""CUDA out of memory.""" )



class    _snake_case (						nn.Module				):


 def __init__(       self):


   '''simple docstring'''




   super().__init__()
   lowercase__	: Optional[Any]     		=   nn.Linear(3       ,	4)
   lowercase__	: Union[str, Any]     		=   nn.BatchNormad(4)
   lowercase__	: str     		=   nn.Linear(4       ,	5)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_)))



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowercase__		,     lowercase__	: int     		=   mock_training_loop_function("""hello""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])
   self.assertListEqual([bs, arga]       ,	[8, """hello"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=0)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     if batch_size > 0:
       raise_fake_out_of_memory()
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function(1_28       ,	"""hello"""       ,	"""world""")
     self.assertIn("""Batch size was passed into `f`"""       ,	cm.exception.args[0])
     self.assertIn("""`f(arg1='hello', arg2='world')"""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     raise ValueError("""Oops, we had an error!""")

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""Oops, we had an error!"""       ,	cm.exception.args[0])



 @require_cuda
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch.cuda.memory_allocated()
   lowercase__	: str     		=   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   release_memory(SCREAMING_SNAKE_CASE_)
   self.assertEqual(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)






