


'''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 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[str]    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	Tuple="attention"  )  ->			Union[str, Any]:
       _a    : Optional[Any]     					=params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
       _a    : str     					=params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
       _a    : Tuple     					=params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
       _a    : Dict     					=params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
       return k, o, q, v


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	int    ,_UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	Dict=False  )  ->			Optional[int]:
       if split_mlp_wi:
              _a    : Any     					=params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
              _a    : List[str]     					=params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
              _a    : List[Any]     					=(wi_a, wi_a)
       else:
              _a    : Optional[Any]     					=params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

       _a    : str     					=params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
       return wi, wo


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	int    ,_UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Dict  )  ->			Tuple:
       return params[F"{prefix}/layers_{i}/{layer_name}/scale"]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	dict    ,*, _UpperCAmelCase	:	int    ,_UpperCAmelCase	:	bool  )  ->			Optional[int]:
       _a    : Union[str, Any]     					=traverse_util.flatten_dict(variables["""target"""]  )
       _a    : Tuple     					={"""/""".join(_UpperCAmelCase  ): v for k, v in old.items()}

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

       _a    : List[Any]     					=collections.OrderedDict()

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

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

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

       _a    : List[str]     					=old[
           """encoder/relpos_bias/rel_embedding"""
       ].T
       _a    : List[str]     					=old["""encoder/encoder_norm/scale"""]

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

                     # Block i, layer 1 (Cross Attention).
                     _a    : str     					=tax_layer_norm_lookup(_UpperCAmelCase    ,_UpperCAmelCase    ,"""decoder"""    ,"""pre_cross_attention_layer_norm"""  )
                     _a					,    _a					,    _a					,    _a    : List[str]     					=tax_attention_lookup(_UpperCAmelCase    ,_UpperCAmelCase    ,"""decoder"""    ,"""encoder_decoder_attention"""  )
                     _a    : int     					=layer_norm
                     _a    : int     					=k.T
                     _a    : Any     					=o.T
                     _a    : Tuple     					=q.T
                     _a    : Tuple     					=v.T

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

              _a    : Optional[int]     					=old["""decoder/decoder_norm/scale"""]
              _a    : Any     					=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:
                     _a    : Dict     					=old["""decoder/logits_dense/kernel"""].T

       return new


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	bool  )  ->			Optional[Any]:
       _a    : Tuple     					=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:
              _a    : Union[str, Any]     					=state_dict["""shared.weight"""]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     _a    : Union[str, 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."""  )
                     _a    : List[str]     					=state_dict["""shared.weight"""]

       return state_dict


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	Optional[Any]  )  ->			Dict:
       _a    : Dict     					=checkpoints.load_tax_checkpoint(_UpperCAmelCase  )
       _a    : Optional[Any]     					=convert_tax_to_pytorch(_UpperCAmelCase    ,num_layers=config.num_layers    ,is_encoder_only=_UpperCAmelCase  )
       _a    : str     					=make_state_dict(_UpperCAmelCase    ,_UpperCAmelCase  )
       model.load_state_dict(_UpperCAmelCase    ,strict=_UpperCAmelCase  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Union[str, Any]    ,_UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	bool = False  )  ->			List[str]:
       _a    : Tuple     					=TaConfig.from_json_file(_UpperCAmelCase  )
       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:
              _a    : int     					=TaEncoderModel(_UpperCAmelCase  )
       else:
              _a    : Optional[int]     					=TaForConditionalGeneration(_UpperCAmelCase  )

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

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

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


if __name__ == "__main__":
   A__:    Dict					    =       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__:    Optional[Any]					    =       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'''







from math import isqrt


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			bool:
       return all(number % divisor != 0 for divisor in range(2    ,isqrt(_UpperCAmelCase  ) + 1  )  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10**6  )  ->			int:
       _a    : List[Any]     					=0
       _a    : str     					=1
       _a    : Optional[Any]     					=7
       while prime_candidate < max_prime:
              primes_count += is_prime(_UpperCAmelCase  )

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


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


