




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 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: str="attention"      ):
     """simple docstring"""

     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
     lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
     return k, o, q, v



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Dict=False      ):
     """simple docstring"""

     if split_mlp_wi:
          lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
          lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
          lowerCAmelCase__          =			(wi_a, wi_a)
     else:
          lowerCAmelCase__          =			params[F'{prefix}/layers_{i}/mlp/wi/kernel']

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



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: dict       ,    *, lowerCAmelCase_					: int       ,    lowerCAmelCase_					: bool      ):
     """simple docstring"""

     lowerCAmelCase__          =			traverse_util.flatten_dict(variables["target"]      )
     lowerCAmelCase__          =			{"/".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__          =			"encoder/layers_0/mlp/wi_0/kernel" in old
     print("Split MLP:"       ,    lowerCAmelCase_      )

     lowerCAmelCase__          =			collections.OrderedDict()

     # Shared embeddings.
     lowerCAmelCase__          =			old["token_embedder/embedding"]

     # Encoder.
     for i in range(lowerCAmelCase_      ):
          # Block i, layer 0 (Self Attention).
          lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    "pre_attention_layer_norm"      )
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_attention_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    "attention"      )
          lowerCAmelCase__          =			layer_norm
          lowerCAmelCase__          =			k.T
          lowerCAmelCase__          =			o.T
          lowerCAmelCase__          =			q.T
          lowerCAmelCase__          =			v.T

          # Block i, layer 1 (MLP).
          lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    "pre_mlp_layer_norm"      )
          lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_mlp_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "encoder"       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			layer_norm
          if split_mlp_wi:
               lowerCAmelCase__          =			wi[0].T
               lowerCAmelCase__          =			wi[1].T
          else:
               lowerCAmelCase__          =			wi.T
          lowerCAmelCase__          =			wo.T

     lowerCAmelCase__          =			old[
         "encoder/relpos_bias/rel_embedding"
     ].T
     lowerCAmelCase__          =			old["encoder/encoder_norm/scale"]

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

               # Block i, layer 1 (Cross Attention).
               lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "pre_cross_attention_layer_norm"      )
               lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_attention_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "encoder_decoder_attention"      )
               lowerCAmelCase__          =			layer_norm
               lowerCAmelCase__          =			k.T
               lowerCAmelCase__          =			o.T
               lowerCAmelCase__          =			q.T
               lowerCAmelCase__          =			v.T

               # Block i, layer 2 (MLP).
               lowerCAmelCase__          =			tax_layer_norm_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    "pre_mlp_layer_norm"      )
               lowerCAmelCase__     ,		lowerCAmelCase__          =			tax_mlp_lookup(lowerCAmelCase_       ,    lowerCAmelCase_       ,    "decoder"       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			layer_norm
               if split_mlp_wi:
                    lowerCAmelCase__          =			wi[0].T
                    lowerCAmelCase__          =			wi[1].T
               else:
                    lowerCAmelCase__          =			wi.T
               lowerCAmelCase__          =			wo.T

          lowerCAmelCase__          =			old["decoder/decoder_norm/scale"]
          lowerCAmelCase__          =			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__          =			old["decoder/logits_dense/kernel"].T

     return new



def 							_A       (						lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: bool      ):
     """simple docstring"""

     lowerCAmelCase__          =			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__          =			state_dict["shared.weight"]

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               lowerCAmelCase__          =			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__          =			state_dict["shared.weight"]

     return state_dict



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: int       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			checkpoints.load_tax_checkpoint(lowerCAmelCase_      )
     lowerCAmelCase__          =			convert_tax_to_pytorch(lowerCAmelCase_       ,    num_layers=config.num_layers       ,    is_encoder_only=lowerCAmelCase_      )
     lowerCAmelCase__          =			make_state_dict(lowerCAmelCase_       ,    lowerCAmelCase_      )
     model.load_state_dict(lowerCAmelCase_       ,    strict=lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     lowerCAmelCase__          =			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__          =			TaEncoderModel(lowerCAmelCase_      )
     else:
          lowerCAmelCase__          =			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__":
     UpperCamelCase								=					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
     )
     UpperCamelCase								=					parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )






from statistics import mean, stdev



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			min(lowerCAmelCase_      )
     lowerCAmelCase__          =			max(lowerCAmelCase_      )
     # normalize data
     return [round((x - x_min) / (x_max - x_min)       ,    lowerCAmelCase_      ) for x in data]






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int = 3      ):
     """simple docstring"""

     lowerCAmelCase__          =			mean(lowerCAmelCase_      )
     lowerCAmelCase__          =			stdev(lowerCAmelCase_      )
     # standardize data
     return [round((x - mu) / (sigma)       ,    lowerCAmelCase_      ) for x in data]
