




'''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 		snake_case_							(_a			:							Any					,     _a			:							str					,     _a			:							Union[str, Any]					,     _a			:							Optional[int]="attention"  ):
    UpperCAmelCase     =	params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
    UpperCAmelCase     =	params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
    UpperCAmelCase     =	params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
    UpperCAmelCase     =	params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
    return k, o, q, v





def 		snake_case_							(_a			:							int					,     _a			:							List[str]					,     _a			:							List[str]					,     _a			:							Tuple=False  ):
    if split_mlp_wi:
        UpperCAmelCase     =	params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
        UpperCAmelCase     =	params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
        UpperCAmelCase     =	(wi_a, wi_a)
    else:
        UpperCAmelCase     =	params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

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





def 		snake_case_							(_a			:							List[str]					,     _a			:							List[str]					,     _a			:							Dict					,     _a			:							Optional[int]  ):
    return params[F"{prefix}/layers_{i}/{layer_name}/scale"]





def 		snake_case_							(_a			:							dict					,     *, _a			:							int					,     _a			:							bool  ):
    UpperCAmelCase     =	traverse_util.flatten_dict(variables['''target''']  )
    UpperCAmelCase     =	{'''/'''.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
    UpperCAmelCase     =	'''encoder/layers_0/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''					,     lowerCAmelCase__  )

    UpperCAmelCase     =	collections.OrderedDict()

    # Shared embeddings.
    UpperCAmelCase     =	old['''token_embedder/embedding''']

    # Encoder.
    for i in range(lowerCAmelCase__  ):
        # Block i, layer 0 (Self Attention).
        UpperCAmelCase     =	tax_layer_norm_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''encoder'''					,     '''pre_attention_layer_norm'''  )
        UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	tax_attention_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''encoder'''					,     '''attention'''  )
        UpperCAmelCase     =	layer_norm
        UpperCAmelCase     =	k.T
        UpperCAmelCase     =	o.T
        UpperCAmelCase     =	q.T
        UpperCAmelCase     =	v.T

        # Block i, layer 1 (MLP).
        UpperCAmelCase     =	tax_layer_norm_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''encoder'''					,     '''pre_mlp_layer_norm'''  )
        UpperCAmelCase ,  UpperCAmelCase     =	tax_mlp_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''encoder'''					,     lowerCAmelCase__  )
        UpperCAmelCase     =	layer_norm
        if split_mlp_wi:
            UpperCAmelCase     =	wi[0].T
            UpperCAmelCase     =	wi[1].T
        else:
            UpperCAmelCase     =	wi.T
        UpperCAmelCase     =	wo.T

    UpperCAmelCase     =	old[
        '''encoder/relpos_bias/rel_embedding'''
    ].T
    UpperCAmelCase     =	old['''encoder/encoder_norm/scale''']

    if not is_encoder_only:
        # Decoder.
        for i in range(lowerCAmelCase__  ):
            # Block i, layer 0 (Self Attention).
            UpperCAmelCase     =	tax_layer_norm_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''decoder'''					,     '''pre_self_attention_layer_norm'''  )
            UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	tax_attention_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''decoder'''					,     '''self_attention'''  )
            UpperCAmelCase     =	layer_norm
            UpperCAmelCase     =	k.T
            UpperCAmelCase     =	o.T
            UpperCAmelCase     =	q.T
            UpperCAmelCase     =	v.T

            # Block i, layer 1 (Cross Attention).
            UpperCAmelCase     =	tax_layer_norm_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''decoder'''					,     '''pre_cross_attention_layer_norm'''  )
            UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	tax_attention_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''decoder'''					,     '''encoder_decoder_attention'''  )
            UpperCAmelCase     =	layer_norm
            UpperCAmelCase     =	k.T
            UpperCAmelCase     =	o.T
            UpperCAmelCase     =	q.T
            UpperCAmelCase     =	v.T

            # Block i, layer 2 (MLP).
            UpperCAmelCase     =	tax_layer_norm_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''decoder'''					,     '''pre_mlp_layer_norm'''  )
            UpperCAmelCase ,  UpperCAmelCase     =	tax_mlp_lookup(lowerCAmelCase__					,     lowerCAmelCase__					,     '''decoder'''					,     lowerCAmelCase__  )
            UpperCAmelCase     =	layer_norm
            if split_mlp_wi:
                UpperCAmelCase     =	wi[0].T
                UpperCAmelCase     =	wi[1].T
            else:
                UpperCAmelCase     =	wi.T
            UpperCAmelCase     =	wo.T

        UpperCAmelCase     =	old['''decoder/decoder_norm/scale''']
        UpperCAmelCase     =	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:
            UpperCAmelCase     =	old['''decoder/logits_dense/kernel'''].T

    return new





def 		snake_case_							(_a			:							Any					,     _a			:							bool  ):
    UpperCAmelCase     =	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:
        UpperCAmelCase     =	state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            UpperCAmelCase     =	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.'''  )
            UpperCAmelCase     =	state_dict['''shared.weight''']

    return state_dict





def 		snake_case_							(_a			:							Tuple					,     _a			:							Union[str, Any]					,     _a			:							Any					,     _a			:							List[Any]  ):
    UpperCAmelCase     =	checkpoints.load_tax_checkpoint(lowerCAmelCase__  )
    UpperCAmelCase     =	convert_tax_to_pytorch(lowerCAmelCase__					,     num_layers=config.num_layers					,     is_encoder_only=lowerCAmelCase__  )
    UpperCAmelCase     =	make_state_dict(lowerCAmelCase__					,     lowerCAmelCase__  )
    model.load_state_dict(lowerCAmelCase__					,     strict=lowerCAmelCase__  )





def 		snake_case_							(_a			:							int					,     _a			:							Union[str, Any]					,     _a			:							Dict					,     _a			:							bool = False  ):
    UpperCAmelCase     =	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:
        UpperCAmelCase     =	TaEncoderModel(lowerCAmelCase__  )
    else:
        UpperCAmelCase     =	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 		snake_case_							():
    for n in range(1					,     1_0_0_0_0_0_0  ):
        yield n * (n + 1) // 2





def 		snake_case_							(_a			:							Union[str, Any]  ):
    UpperCAmelCase     =	1
    UpperCAmelCase     =	2
    while i * i <= n:
        UpperCAmelCase     =	0
        while n % i == 0:
            n //= i
            multiplicity += 1
        divisors_count *= multiplicity + 1
        i += 1
    if n > 1:
        divisors_count *= 2
    return divisors_count





def 		snake_case_							():
    return next(i for i in triangle_number_generator() if count_divisors(_a  ) > 5_0_0  )


if __name__ == "__main__":
   print(solution())




