




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__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase="attention" ):

     '''simple docstring'''


     UpperCAmelCase_     :				Tuple  =     params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
     UpperCAmelCase_     :				Any  =     params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
     UpperCAmelCase_     :				Optional[int]  =     params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
     UpperCAmelCase_     :				Tuple  =     params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
     return k, o, q, v







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

     '''simple docstring'''


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

     UpperCAmelCase_     :				List[str]  =     params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
     return wi, wo







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

     '''simple docstring'''


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







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

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     traverse_util.flatten_dict(variables['''target'''] )
     UpperCAmelCase_     :				Tuple  =     {'''/'''.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_     :				Any  =     '''encoder/layers_0/mlp/wi_0/kernel''' in old
     print('''Split MLP:'''					,		_lowercase )

     UpperCAmelCase_     :				int  =     collections.OrderedDict()

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

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

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

     UpperCAmelCase_     :				int  =     old[
         '''encoder/relpos_bias/rel_embedding'''
     ].T
     UpperCAmelCase_     :				int  =     old['''encoder/encoder_norm/scale''']

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

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

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

          UpperCAmelCase_     :				Any  =     old['''decoder/decoder_norm/scale''']
          UpperCAmelCase_     :				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:
               UpperCAmelCase_     :				Union[str, 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_     :				List[str]  =     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_     :				str  =     state_dict['''shared.weight''']

     return state_dict







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

     '''simple docstring'''


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







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

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     TaConfig.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_     :				List[str]  =     TaEncoderModel(_lowercase )
     else:
          UpperCAmelCase_     :				Optional[Any]  =     TaForConditionalGeneration(_lowercase )

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(_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
     )
     __a							     =					parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     while a != 0:
          UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     b % a, a
     return b







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     if gcd(_lowercase					,		_lowercase ) != 1:
          UpperCAmelCase_     :				int  =     f'''mod inverse of {a!r} and {m!r} does not exist'''
          raise ValueError(_lowercase )
     UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     1, 0, a
     UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     0, 1, m
     while va != 0:
          UpperCAmelCase_     :				List[Any]  =     ua // va
          UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m