"""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     lowercase__( __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  str="attention"   ):
     lowercase_			:       Tuple									=			params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
     lowercase_			:       Any									=			params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
     lowercase_			:       str									=			params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
     lowercase_			:       Dict									=			params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
     return k, o, q, v



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

     lowercase_			:       Optional[int]									=			params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
     return wi, wo



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']



def     lowercase__( __SCREAMING_SNAKE_CASE							:  dict  ,    *, __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  bool   ):
     lowercase_			:       Tuple									=			traverse_util.flatten_dict(variables['target']   )
     lowercase_			:       Tuple									=			{'/'.join(__SCREAMING_SNAKE_CASE   ): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     lowercase_			:       Optional[Any]									=			'encoder/layers_0/mlp/wi_0/kernel' in old
     print('Split MLP:'  ,    __SCREAMING_SNAKE_CASE   )

     lowercase_			:       Dict									=			collections.OrderedDict()

     # Shared embeddings.
     lowercase_			:       Tuple									=			old['token_embedder/embedding']

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

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

     lowercase_			:       List[str]									=			old[
         'encoder/relpos_bias/rel_embedding'
     ].T
     lowercase_			:       Tuple									=			old['encoder/encoder_norm/scale']

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

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

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

          lowercase_			:       Optional[int]									=			old['decoder/decoder_norm/scale']
          lowercase_			:       int									=			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:
               lowercase_			:       List[Any]									=			old['decoder/logits_dense/kernel'].T

     return new



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  bool   ):
     lowercase_			:       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:
          lowercase_			:       Any									=			state_dict['shared.weight']

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

     return state_dict



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Dict   ):
     lowercase_			:       Optional[int]									=			checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			convert_tax_to_pytorch(__SCREAMING_SNAKE_CASE  ,    num_layers=config.num_layers  ,    is_encoder_only=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[str]									=			make_state_dict(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     model.load_state_dict(__SCREAMING_SNAKE_CASE  ,    strict=__SCREAMING_SNAKE_CASE   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  bool = False   ):
     lowercase_			:       Optional[Any]									=			TaConfig.from_json_file(__SCREAMING_SNAKE_CASE   )
     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:
          lowercase_			:       Dict									=			TaEncoderModel(__SCREAMING_SNAKE_CASE   )
     else:
          lowercase_			:       List[str]									=			TaForConditionalGeneration(__SCREAMING_SNAKE_CASE   )

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

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

     # Verify that we can load the checkpoint.
     model.from_pretrained(__SCREAMING_SNAKE_CASE   )
     print('Done'   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=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
       )
       __SCREAMING_SNAKE_CASE								=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"""







import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class  UpperCamelCase	(    nn.Module						):
   def __init__(    self    )			->   Any:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       Dict									=			nn.Linear(3  ,4    )
        lowercase_			:       Union[str, Any]									=			nn.BatchNormad(4    )
        lowercase_			:       Union[str, Any]									=			nn.Linear(4  ,5    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase    )    )    )







class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       int									=			ModelForTest()
        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,model.state_dict()    )
             lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'index.json'    )
             self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
             # TODO: add tests on what is inside the index

             for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                  lowercase_			:       int									=			os.path.join(__UpperCamelCase  ,f'''{key}.dat'''    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  # TODO: add tests on the fact weights are properly loaded
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[str]									=			[torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
             lowercase_			:       Tuple									=			torch.randn(2  ,3  ,dtype=__UpperCamelCase    )
             with TemporaryDirectory() as tmp_dir:
                  lowercase_			:       List[str]									=			offload_weight(__UpperCamelCase  ,'weight'  ,__UpperCamelCase  ,{}    )
                  lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'weight.dat'    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  self.assertDictEqual(__UpperCamelCase  ,{'weight': {'shape': [2, 3], 'dtype': str(__UpperCamelCase    ).split('.'    )[1]}}    )

                  lowercase_			:       Optional[Any]									=			load_offloaded_weight(__UpperCamelCase  ,index['weight']    )
                  self.assertTrue(torch.equal(__UpperCamelCase  ,__UpperCamelCase    )    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			ModelForTest()
        lowercase_			:       Optional[int]									=			model.state_dict()
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'linear2' not in k}
        lowercase_			:       Optional[Any]									=			{k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' in k}
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       int									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             # Duplicates are removed
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )







   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			{'a.1': 0, 'a.10': 1, 'a.2': 2}
        lowercase_			:       int									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1': 0, 'a.2': 2}    )

        lowercase_			:       Union[str, Any]									=			{'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        lowercase_			:       Optional[int]									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1.a': 0, 'a.2.a': 2}    )

