




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase="attention" ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
     UpperCAmelCase_     :				Dict  =     k_tmp.reshape(k_tmp.shape[0]					,		k_tmp.shape[1] * k_tmp.shape[2] )
     UpperCAmelCase_     :				Tuple  =     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
     UpperCAmelCase_     :				Union[str, Any]  =     o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]					,		o_tmp.shape[2] )
     UpperCAmelCase_     :				Optional[Any]  =     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
     UpperCAmelCase_     :				List[Any]  =     q_tmp.reshape(q_tmp.shape[0]					,		q_tmp.shape[1] * q_tmp.shape[2] )
     UpperCAmelCase_     :				str  =     np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
     UpperCAmelCase_     :				Optional[Any]  =     v_tmp.reshape(v_tmp.shape[0]					,		v_tmp.shape[1] * v_tmp.shape[2] )
     return k, o, q, v







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=False ):

     '''simple docstring'''


     if split_mlp_wi:
          UpperCAmelCase_     :				Any  =     params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
          UpperCAmelCase_     :				str  =     params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
          UpperCAmelCase_     :				int  =     (wi_a, wi_a)
     else:
          UpperCAmelCase_     :				str  =     params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

     UpperCAmelCase_     :				Optional[int]  =     params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
     return wi, wo







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]







def lowerCamelCase__  (     _lowercase					,		*, _lowercase					,		_lowercase					,		_lowercase = False ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     traverse_util.flatten_dict(variables['''target'''] )
     UpperCAmelCase_     :				Optional[Any]  =     {'''/'''.join(_lowercase ): v for k, v in old.items()}

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

     UpperCAmelCase_     :				int  =     collections.OrderedDict()

     # Shared embeddings.
     UpperCAmelCase_     :				Optional[Any]  =     old['''token_embedder/embedding''']

     # Encoder.
     for i in range(_lowercase ):
          # Block i, layer 0 (Self Attention).
          UpperCAmelCase_     :				str  =     tax_layer_norm_lookup(_lowercase					,		_lowercase					,		'''encoder'''					,		'''pre_attention_layer_norm''' )
          UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     tax_attention_lookup(_lowercase					,		_lowercase					,		'''encoder'''					,		'''attention''' )
          UpperCAmelCase_     :				Optional[Any]  =     layer_norm
          UpperCAmelCase_     :				str  =     k.T
          UpperCAmelCase_     :				Tuple  =     o.T
          UpperCAmelCase_     :				Union[str, Any]  =     q.T
          UpperCAmelCase_     :				Dict  =     v.T

          # Block i, layer 1 (MLP).
          UpperCAmelCase_     :				str  =     tax_layer_norm_lookup(_lowercase					,		_lowercase					,		'''encoder'''					,		'''pre_mlp_layer_norm''' )
          UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     tax_mlp_lookup(_lowercase					,		_lowercase					,		'''encoder'''					,		_lowercase )
          UpperCAmelCase_     :				List[Any]  =     layer_norm
          if split_mlp_wi:
               UpperCAmelCase_     :				int  =     wi[0].T
               UpperCAmelCase_     :				str  =     wi[1].T
          else:
               UpperCAmelCase_     :				Optional[int]  =     wi.T
          UpperCAmelCase_     :				List[Any]  =     wo.T
          if scalable_attention:
               # convert the rel_embedding of each layer
               UpperCAmelCase_     :				List[str]  =     tax_relpos_bias_lookup(
                   _lowercase					,		_lowercase					,		'''encoder''' ).T

     UpperCAmelCase_     :				Optional[int]  =     old['''encoder/encoder_norm/scale''']

     if not scalable_attention:
          UpperCAmelCase_     :				str  =     tax_relpos_bias_lookup(
              _lowercase					,		0					,		'''encoder''' ).T
          UpperCAmelCase_     :				Optional[int]  =     tax_relpos_bias_lookup(
              _lowercase					,		0					,		'''decoder''' ).T

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

               # Block i, layer 1 (Cross Attention).
               UpperCAmelCase_     :				Union[str, Any]  =     tax_layer_norm_lookup(_lowercase					,		_lowercase					,		'''decoder'''					,		'''pre_cross_attention_layer_norm''' )
               UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     tax_attention_lookup(_lowercase					,		_lowercase					,		'''decoder'''					,		'''encoder_decoder_attention''' )
               UpperCAmelCase_     :				Tuple  =     layer_norm
               UpperCAmelCase_     :				int  =     k.T
               UpperCAmelCase_     :				Optional[Any]  =     o.T
               UpperCAmelCase_     :				Tuple  =     q.T
               UpperCAmelCase_     :				Union[str, Any]  =     v.T

               # Block i, layer 2 (MLP).
               UpperCAmelCase_     :				Optional[int]  =     tax_layer_norm_lookup(_lowercase					,		_lowercase					,		'''decoder'''					,		'''pre_mlp_layer_norm''' )
               UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     tax_mlp_lookup(_lowercase					,		_lowercase					,		'''decoder'''					,		_lowercase )
               UpperCAmelCase_     :				List[Any]  =     layer_norm
               if split_mlp_wi:
                    UpperCAmelCase_     :				int  =     wi[0].T
                    UpperCAmelCase_     :				Any  =     wi[1].T
               else:
                    UpperCAmelCase_     :				Any  =     wi.T
               UpperCAmelCase_     :				Dict  =     wo.T

               if scalable_attention:
                    # convert the rel_embedding of each layer
                    UpperCAmelCase_     :				List[str]  =     tax_relpos_bias_lookup(_lowercase					,		_lowercase					,		'''decoder''' ).T

          UpperCAmelCase_     :				List[str]  =     old['''decoder/decoder_norm/scale''']

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               UpperCAmelCase_     :				Optional[Any]  =     old['''decoder/logits_dense/kernel'''].T

     return new







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     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_     :				Tuple  =     state_dict['''shared.weight''']

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

     return state_dict







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				int  =     checkpoints.load_tax_checkpoint(_lowercase )
     UpperCAmelCase_     :				List[Any]  =     convert_tax_to_pytorch(
         _lowercase					,		num_layers=config.num_layers					,		is_encoder_only=_lowercase					,		scalable_attention=_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     make_state_dict(_lowercase					,		_lowercase )
     model.load_state_dict(_lowercase					,		strict=_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase = False					,		_lowercase = False					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     MTaConfig.from_json_file(_lowercase )
     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_     :				Any  =     UMTaEncoderModel(_lowercase )
     else:
          UpperCAmelCase_     :				List[Any]  =     UMTaForConditionalGeneration(_lowercase )

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

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

     # Verify that we can load the checkpoint.
     model.from_pretrained(_lowercase )
     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
     )
     parser.add_argument(
         '--scalable_attention',
         action='store_true',
         help='Whether the model uses scaled attention (umt5 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,
         args.scalable_attention,
     )





__a							     =					'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Union[str, Any]  =     f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(_lowercase )

     UpperCAmelCase_     :				Any  =     ''''''.join(bin(_lowercase )[2:].zfill(8 ) for byte in data )

     UpperCAmelCase_     :				Any  =     len(_lowercase ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          UpperCAmelCase_     :				Union[str, Any]  =     B'''=''' * ((6 - len(_lowercase ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(_lowercase ) % 6)
     else:
          UpperCAmelCase_     :				int  =     B''''''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]					,		2 )]
             for index in range(0					,		len(_lowercase )					,		6 ) ).encode()
         + padding
     )







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ) and not isinstance(_lowercase					,		_lowercase ):
          UpperCAmelCase_     :				Tuple  =     (
              '''argument should be a bytes-like object or ASCII string, '''
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(_lowercase )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(_lowercase					,		_lowercase ):
          try:
               UpperCAmelCase_     :				Any  =     encoded_data.decode('''utf-8''' )
          except UnicodeDecodeError:
               raise ValueError('''base64 encoded data should only contain ASCII characters''' )

     UpperCAmelCase_     :				str  =     encoded_data.count('''=''' )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(_lowercase ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          UpperCAmelCase_     :				List[Any]  =     encoded_data[:-padding]

          UpperCAmelCase_     :				List[Any]  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
     else:
          UpperCAmelCase_     :				Tuple  =     ''''''.join(
              bin(B64_CHARSET.index(_lowercase ) )[2:].zfill(6 ) for char in encoded_data )

     UpperCAmelCase_     :				str  =     [
         int(binary_stream[index : index + 8]					,		2 )
         for index in range(0					,		len(_lowercase )					,		8 )
     ]

     return bytes(_lowercase )


if __name__ == "__main__":
     import doctest

     doctest.testmod()