





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






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

     lowerCAmelCase_					:       List[Any]    =		params[F'{prefix}/layers_{i}/mlp/wo/kernel']
     return wi, wo






def 							_lowerCAmelCase (  _a   :					Any					,   _a   :					int					,   _a   :					Tuple					,   _a   :					Any			)	->			Optional[int]:
     return params[F'{prefix}/layers_{i}/{layer_name}/scale']






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

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

     lowerCAmelCase_					:       Any    =		collections.OrderedDict()

     # Shared embeddings.
     lowerCAmelCase_					:       Any    =		old["""token_embedder/embedding"""]

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

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

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

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

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

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

          lowerCAmelCase_					:       Union[str, Any]    =		old["""decoder/decoder_norm/scale"""]
          lowerCAmelCase_					:       Tuple    =		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_					:       Any    =		old["""decoder/logits_dense/kernel"""].T

     return new






def 							_lowerCAmelCase (  _a   :					Dict					,   _a   :					bool			)	->			Tuple:
     lowerCAmelCase_					:       List[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_					:       int    =		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 							_lowerCAmelCase (  _a   :					List[str]					,   _a   :					Union[str, Any]					,   _a   :					Optional[int]					,   _a   :					List[Any]			)	->			str:
     lowerCAmelCase_					:       str    =		checkpoints.load_tax_checkpoint(_a			)
     lowerCAmelCase_					:       int    =		convert_tax_to_pytorch(_a					,   num_layers=config.num_layers					,   is_encoder_only=_a			)
     lowerCAmelCase_					:       Union[str, Any]    =		make_state_dict(_a					,   _a			)
     model.load_state_dict(_a					,   strict=_a			)






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					str					,   _a   :					int					,   _a   :					bool = False			)	->			List[str]:
     lowerCAmelCase_					:       Union[str, Any]    =		TaConfig.from_json_file(_a			)
     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_					:       Tuple    =		TaEncoderModel(_a			)
     else:
          lowerCAmelCase_					:       Tuple    =		TaForConditionalGeneration(_a			)

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

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

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


if __name__ == "__main__":
      UpperCAmelCase_					:   Tuple 							=						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_					:   int 							=						parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )







from collections import namedtuple

UpperCAmelCase_					:   Union[str, Any] 							=						namedtuple("""from_to""", """from_ to""")

UpperCAmelCase_					:   int 							=						{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.0_01, 10_00),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.0_04_54, 2_64.1_72),
    """cubicyard""": from_to(0.7_64_55, 1.3_07_95),
    """cubicfoot""": from_to(0.0_28, 35.31_47),
    """cup""": from_to(0.0_00_23_65_88, 42_26.75),
}






def 							_lowerCAmelCase (  _a   :					float					,   _a   :					str					,   _a   :					str			)	->			float:
     if from_type not in METRIC_CONVERSION:
          raise ValueError(
              F'Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'
              + """, """.join(_a			)			)
     if to_type not in METRIC_CONVERSION:
          raise ValueError(
              F'Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'
              + """, """.join(_a			)			)
     return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
      import doctest

      doctest.testmod()
