


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   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Optional[int]	)  ->    Dict:
		"""simple docstring"""




		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				lowercase__	     =						flax_key_tuple[:-1] + ("""weight""",)
				lowercase__	     =						torch.permute(__magic_name__       ,       (0, 2, 1)	)
		elif flax_key_tuple[-1] == "kernel" and ".".join(__magic_name__	):
				# 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 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Dict       ,       __magic_name__	:				str	)  ->    Any:
		"""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 					UpperCamelCase   (      __magic_name__	:				int       ,       __magic_name__	:				Dict	)  ->    List[str]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				Tuple       ,       __magic_name__	:				str       ,       __magic_name__	:				List[Any]       ,       __magic_name__	:				str = WEIGHTS_NAME	)  ->    Any:
		"""simple docstring"""




		lowercase__	     =						convert_file_size_to_int(__magic_name__	)
		lowercase__	     =						[]
		lowercase__	     =						{}
		lowercase__	     =						0
		lowercase__	     =						0

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

		lowercase__	     =						{}
		for layer in checkpoint_info.keys():
				lowercase__     ,      lowercase__     ,      lowercase__	     =						get_key_and_tensorstore_dict(
				    __magic_name__       ,       __magic_name__       ,       __magic_name__	)
				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(__magic_name__	)
				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("""/"""	)	)       ,       __magic_name__	)
				lowercase__	     =						"""/""".join(__magic_name__	)

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

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

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

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

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

		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			:						Tuple        =  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   (      )  ->    Optional[int]:
		"""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(__magic_name__       ,       return_tensors="""pt"""	).input_ids
		lowercase__	     =						model.generate(__magic_name__       ,       decoder_start_token_id=0	)
		print(tokenizer.decode(out[0]	)	)




__lowercase				      =						[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						{
    0: """Sunday""",
    1: """Monday""",
    2: """Tuesday""",
    3: """Wednesday""",
    4: """Thursday""",
    5: """Friday""",
    6: """Saturday""",
}







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      assert len(str(SCREAMING_SNAKE_CASE   )   ) > 2, "year should be in YYYY format"
      assert 1 <= month <= 12, "month should be between 1 to 12"
      assert 1 <= day <= 31, "day should be between 1 to 31"

      # Doomsday algorithm:
      A_							       =		year // 100
      A_							       =		(5 * (century % 4) + 2) % 7
      A_							       =		year % 100
      A_							       =		centurian % 12
      A_							       =		(
          (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
      ) % 7
      A_							       =		(
          DOOMSDAY_NOT_LEAP[month - 1]
          if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
          else DOOMSDAY_LEAP[month - 1]
      )
      A_							       =		(dooms_day + day - day_anchor) % 7
      return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
      import doctest

      doctest.testmod()





