"""simple docstring"""





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	str="attention"      ) ->      List[str]:



       '''simple docstring'''

       __lowerCamelCase					:		Union[str, Any]		   =     params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
       __lowerCamelCase					:		Any		   =     params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
       __lowerCamelCase					:		Union[str, Any]		   =     params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
       __lowerCamelCase					:		int		   =     params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
       return k, o, q, v


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Any]=False      ) ->      Union[str, Any]:



       '''simple docstring'''

       if split_mlp_wi:
              __lowerCamelCase					:		List[str]		   =     params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
              __lowerCamelCase					:		Optional[int]		   =     params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
              __lowerCamelCase					:		Any		   =     (wi_a, wi_a)
       else:
              __lowerCamelCase					:		Optional[Any]		   =     params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

       __lowerCamelCase					:		Optional[Any]		   =     params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
       return wi, wo


def    lowercase_   (   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	Tuple      ) ->      List[str]:



       '''simple docstring'''

       return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]


def    lowercase_   (   _lowerCamelCase:	dict						,   *, _lowerCamelCase:	int						,   _lowerCamelCase:	bool      ) ->      Union[str, Any]:



       '''simple docstring'''

       __lowerCamelCase					:		List[str]		   =     traverse_util.flatten_dict(variables["target"]      )
       __lowerCamelCase					:		Union[str, Any]		   =     {"/".join(_lowerCamelCase      ): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       __lowerCamelCase					:		str		   =     "encoder/layers_0/mlp/wi_0/kernel" in old
       print("Split MLP:"						,   _lowerCamelCase      )

       __lowerCamelCase					:		Optional[int]		   =     collections.OrderedDict()

       # Shared embeddings.
       __lowerCamelCase					:		Union[str, Any]		   =     old["token_embedder/embedding"]

       # Encoder.
       for i in range(_lowerCamelCase      ):
              # Block i, layer 0 (Self Attention).
              __lowerCamelCase					:		Any		   =     tax_layer_norm_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "encoder"						,   "pre_attention_layer_norm"      )
              __lowerCamelCase					:		Tuple		   =     tax_attention_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "encoder"						,   "attention"      )
              __lowerCamelCase					:		Tuple		   =     layer_norm
              __lowerCamelCase					:		List[Any]		   =     k.T
              __lowerCamelCase					:		Union[str, Any]		   =     o.T
              __lowerCamelCase					:		Optional[int]		   =     q.T
              __lowerCamelCase					:		Any		   =     v.T

              # Block i, layer 1 (MLP).
              __lowerCamelCase					:		int		   =     tax_layer_norm_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "encoder"						,   "pre_mlp_layer_norm"      )
              __lowerCamelCase					:		str		   =     tax_mlp_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "encoder"						,   _lowerCamelCase      )
              __lowerCamelCase					:		Union[str, Any]		   =     layer_norm
              if split_mlp_wi:
                     __lowerCamelCase					:		Union[str, Any]		   =     wi[0].T
                     __lowerCamelCase					:		Tuple		   =     wi[1].T
              else:
                     __lowerCamelCase					:		Dict		   =     wi.T
              __lowerCamelCase					:		List[Any]		   =     wo.T

       __lowerCamelCase					:		List[Any]		   =     old[
           "encoder/relpos_bias/rel_embedding"
       ].T
       __lowerCamelCase					:		Dict		   =     old["encoder/encoder_norm/scale"]

       if not is_encoder_only:
              # Decoder.
              for i in range(_lowerCamelCase      ):
                     # Block i, layer 0 (Self Attention).
                     __lowerCamelCase					:		Any		   =     tax_layer_norm_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   "pre_self_attention_layer_norm"      )
                     __lowerCamelCase					:		Union[str, Any]		   =     tax_attention_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   "self_attention"      )
                     __lowerCamelCase					:		Union[str, Any]		   =     layer_norm
                     __lowerCamelCase					:		Optional[Any]		   =     k.T
                     __lowerCamelCase					:		int		   =     o.T
                     __lowerCamelCase					:		List[str]		   =     q.T
                     __lowerCamelCase					:		Tuple		   =     v.T

                     # Block i, layer 1 (Cross Attention).
                     __lowerCamelCase					:		Optional[int]		   =     tax_layer_norm_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   "pre_cross_attention_layer_norm"      )
                     __lowerCamelCase					:		Union[str, Any]		   =     tax_attention_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   "encoder_decoder_attention"      )
                     __lowerCamelCase					:		Optional[int]		   =     layer_norm
                     __lowerCamelCase					:		str		   =     k.T
                     __lowerCamelCase					:		Any		   =     o.T
                     __lowerCamelCase					:		Optional[int]		   =     q.T
                     __lowerCamelCase					:		Union[str, Any]		   =     v.T

                     # Block i, layer 2 (MLP).
                     __lowerCamelCase					:		List[Any]		   =     tax_layer_norm_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   "pre_mlp_layer_norm"      )
                     __lowerCamelCase					:		Dict		   =     tax_mlp_lookup(_lowerCamelCase						,   _lowerCamelCase						,   "decoder"						,   _lowerCamelCase      )
                     __lowerCamelCase					:		Union[str, Any]		   =     layer_norm
                     if split_mlp_wi:
                            __lowerCamelCase					:		Optional[Any]		   =     wi[0].T
                            __lowerCamelCase					:		str		   =     wi[1].T
                     else:
                            __lowerCamelCase					:		Any		   =     wi.T
                     __lowerCamelCase					:		Dict		   =     wo.T

              __lowerCamelCase					:		Optional[int]		   =     old["decoder/decoder_norm/scale"]
              __lowerCamelCase					:		Optional[int]		   =     old[
                  "decoder/relpos_bias/rel_embedding"
              ].T

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     __lowerCamelCase					:		int		   =     old["decoder/logits_dense/kernel"].T

       return new


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	bool      ) ->      List[Any]:



       '''simple docstring'''

       __lowerCamelCase					:		Union[str, Any]		   =     collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              __lowerCamelCase					:		Tuple		   =     state_dict["shared.weight"]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     __lowerCamelCase					:		List[Any]		   =     state_dict["shared.weight"]

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print("Using shared word embeddings as lm_head."      )
                     __lowerCamelCase					:		Optional[Any]		   =     state_dict["shared.weight"]

       return state_dict


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[str]      ) ->      int:



       '''simple docstring'''

       __lowerCamelCase					:		List[str]		   =     checkpoints.load_tax_checkpoint(_lowerCamelCase      )
       __lowerCamelCase					:		int		   =     convert_tax_to_pytorch(_lowerCamelCase						,   num_layers=config.num_layers						,   is_encoder_only=_lowerCamelCase      )
       __lowerCamelCase					:		Any		   =     make_state_dict(_lowerCamelCase						,   _lowerCamelCase      )
       model.load_state_dict(_lowerCamelCase						,   strict=_lowerCamelCase      )


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	bool = False      ) ->      Any:



       '''simple docstring'''

       __lowerCamelCase					:		int		   =     TaConfig.from_json_file(_lowerCamelCase      )
       print(F"""Building PyTorch model from configuration: {config}"""      )
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              __lowerCamelCase					:		Union[str, Any]		   =     TaEncoderModel(_lowerCamelCase      )
       else:
              __lowerCamelCase					:		Optional[Any]		   =     TaForConditionalGeneration(_lowerCamelCase      )

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

       # Save pytorch-model
       print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
       model.save_pretrained(_lowerCamelCase      )

       # Verify that we can load the checkpoint.
       model.from_pretrained(_lowerCamelCase      )
       print("Done"      )


if __name__ == "__main__":
    __A  =				argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
    )
    __A  =				parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )
"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     int(_lowerCamelCase      )
							except (TypeError, ValueError):
														raise TypeError("Parameter n must be int or castable to int."      )
							if n <= 0:
														raise ValueError("Parameter n must be greater than or equal to one."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


if __name__ == "__main__":
				print(F"""{solution() = }""")