


'''simple docstring'''



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					(						UpperCamelCase__       ,					UpperCamelCase__    ):
			if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
						# expert layer
						UpperCAmelCase__     :    List[str]         =   flax_key_tuple[:-1] + ("""weight""",)
						UpperCAmelCase__     :    Tuple         =   torch.permute(_UpperCAmelCase       ,					(0, 2, 1)    )
			elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase    ):
						# linear layer
						UpperCAmelCase__     :    Dict         =   flax_key_tuple[:-1] + ("""weight""",)
						UpperCAmelCase__     :    Union[str, Any]         =   flax_tensor.T
			elif flax_key_tuple[-1] in ["scale", "embedding"]:
						UpperCAmelCase__     :    str         =   flax_key_tuple[:-1] + ("""weight""",)

			return flax_key_tuple, flax_tensor







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

			else:
						UpperCAmelCase__     :    Optional[int]         =   layer.split("""/"""    )
						UpperCAmelCase__     :    Tuple         =   """/""".join(split_layer[:-1]    )
						UpperCAmelCase__     :    List[Any]         =   (split_layer[-1],)

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

			return curr_real_layer_name, split_layer, content







def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__    ):
			UpperCAmelCase__     :    int         =   rename_keys(_UpperCAmelCase    )
			UpperCAmelCase__     :    Optional[Any]         =   {}
			for k, v in current_block.items():
						UpperCAmelCase__     :    Dict         =   v
			UpperCAmelCase__     :    int         =   new_current_block
			torch.save(_UpperCAmelCase       ,					_UpperCAmelCase    )







def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__ = WEIGHTS_NAME    ):
			UpperCAmelCase__     :    Optional[Any]         =   convert_file_size_to_int(_UpperCAmelCase    )
			UpperCAmelCase__     :    Dict         =   []
			UpperCAmelCase__     :    Any         =   {}
			UpperCAmelCase__     :    Optional[Any]         =   0
			UpperCAmelCase__     :    Optional[int]         =   0

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

			UpperCAmelCase__     :    Optional[Any]         =   {}
			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__     :    List[Any]         =   content
						else:
									UpperCAmelCase__     :    Optional[Any]         =   {split_layer[-1]: content}

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

						# use the renaming pattern from the small conversion scripts
						UpperCAmelCase__      ,				UpperCAmelCase__     :    Optional[int]         =   rename_base_flax_keys(tuple(key.split("""/"""    )    )       ,					_UpperCAmelCase    )
						UpperCAmelCase__     :    int         =   """/""".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__     :    List[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()    )
									del current_block
									UpperCAmelCase__     :    int         =   {}
									UpperCAmelCase__     :    List[str]         =   0

						UpperCAmelCase__     :    str         =   raw_weights.to(getattr(_UpperCAmelCase       ,					_UpperCAmelCase    )    )
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			UpperCAmelCase__     :    Optional[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()    )

			# 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__     :    Any         =   {}
			UpperCAmelCase__     :    Optional[int]         =   {}
			for idx, shard in enumerate(_UpperCAmelCase    ):
						UpperCAmelCase__     :    int         =   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__     :    Any         =   shard
						for key in shard:
									UpperCAmelCase__     :    Union[str, Any]         =   shard_file

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

			with open(os.path.join(_UpperCAmelCase       ,					_UpperCAmelCase    )       ,					"""w"""       ,					encoding="""utf-8"""    ) as f:
						UpperCAmelCase__     :    List[Any]         =   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 			_UpperCamelCase					(						):
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

			UpperCAmelCase__     :    Tuple         =   SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""    )
			config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""    )
			UpperCAmelCase__     :    List[str]         =   SwitchTransformersForConditionalGeneration.from_pretrained(
			    """/home/arthur_huggingface_co/transformers/switch_converted"""       ,					device_map="""auto"""    )

			UpperCAmelCase__     :    Optional[Any]         =   TaTokenizer.from_pretrained("""t5-small"""    )
			UpperCAmelCase__     :    Union[str, Any]         =   """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

			UpperCAmelCase__     :    Optional[Any]         =   tokenizer(_UpperCAmelCase       ,					return_tensors="""pt"""    ).input_ids
			UpperCAmelCase__     :    List[Any]         =   model.generate(_UpperCAmelCase       ,					decoder_start_token_id=0    )
			print(tokenizer.decode(out[0]    )    )




from collections import defaultdict





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				first_str.lower().strip()
    __a						       =				second_str.lower().strip()

    # Remove whitespace
    __a						       =				first_str.replace(''' '''   , ''''''				)
    __a						       =				second_str.replace(''' '''   , ''''''				)

    # Strings of different lengths are not anagrams
    if len(_UpperCAmelCase				) != len(_UpperCAmelCase				):
        return False

    # Default values for count should be 0
    __a						       =				defaultdict(_UpperCAmelCase				)

    # For each character in input strings,
    # increment count in the corresponding
    for i in range(len(_UpperCAmelCase				)				):
        count[first_str[i]] += 1
        count[second_str[i]] -= 1

    return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case						:Any      		=     input('''Enter the first string ''').strip()
 __snake_case						:int      		=     input('''Enter the second string ''').strip()

 __snake_case						:int      		=     check_anagrams(input_a, input_b)
 print(f'{input_a} and {input_b} are {"" if status else "not "}anagrams.')





