
'''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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_="attention"			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=False			):







 """simple docstring"""


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

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			*, lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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

 lowerCAmelCase__     :   List[str]     		=	collections.OrderedDict()

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

 # Encoder.
 for i in range(__A			):
  # Block i, layer 0 (Self Attention).
  lowerCAmelCase__     :   Optional[int]     		=	tax_layer_norm_lookup(__A      ,			__A      ,			"encoder"      ,			"pre_attention_layer_norm"			)
  lowerCAmelCase__     :   Tuple     		=	tax_attention_lookup(__A      ,			__A      ,			"encoder"      ,			"attention"			)
  lowerCAmelCase__     :   Dict     		=	layer_norm
  lowerCAmelCase__     :   List[str]     		=	k.T
  lowerCAmelCase__     :   List[Any]     		=	o.T
  lowerCAmelCase__     :   Any     		=	q.T
  lowerCAmelCase__     :   Optional[int]     		=	v.T

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

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

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

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

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

  lowerCAmelCase__     :   Tuple     		=	old['''decoder/decoder_norm/scale''']
  lowerCAmelCase__     :   str     		=	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__     :   Optional[Any]     		=	old['''decoder/logits_dense/kernel'''].T

 return new


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	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__     :   List[str]     		=	state_dict['''shared.weight''']

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

 return state_dict


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	checkpoints.load_tax_checkpoint(__A			)
 lowerCAmelCase__     :   Dict     		=	convert_tax_to_pytorch(__A      ,			num_layers=config.num_layers      ,			is_encoder_only=__A			)
 lowerCAmelCase__     :   Dict     		=	make_state_dict(__A      ,			__A			)
 model.load_state_dict(__A      ,			strict=__A			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_ = False			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	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__     :   Union[str, Any]     		=	TaEncoderModel(__A			)
 else:
  lowerCAmelCase__     :   int     		=	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__":
     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
     )
     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'''



from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def 					A__   (			self      :		Optional[int]		)	->	List[str]:


       '''simple docstring'''

       return [
           {"col_1": 3, "col_2": "a"},
           {"col_1": 2, "col_2": "b"},
           {"col_1": 1, "col_2": "c"},
           {"col_1": 0, "col_2": "d"},
       ]






      def 					A__   (			self      :		Any		)	->	List[str]:


       '''simple docstring'''

       lowercase       : List[str]						={'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
       return Dataset.from_dict(UpperCAmelCase		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Tuple						=self._create_example_records()
       lowercase       : Tuple						=Dataset.from_list(UpperCAmelCase		)
       self.assertListEqual(dset.column_names  ,   ['''col_1''', '''col_2''']		)
       for i, r in enumerate(UpperCAmelCase		):
        self.assertDictEqual(UpperCAmelCase  ,   example_records[i]		)






      def 					A__   (			self      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=self._create_example_records()
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       lowercase       : Tuple						=Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}		)
       self.assertEqual(dset.info  ,   dset_from_dict.info		)






      def 					A__   (			self      :		str		)	->	List[Any]:  # checks what happens with missing columns


       '''simple docstring'''

       lowercase       : Tuple						=[{'''col_1''': 1}, {'''col_2''': '''x'''}]
       lowercase       : Optional[Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertDictEqual(dset[0]  ,   {'''col_1''': 1}		)
       self.assertDictEqual(dset[1]  ,   {'''col_1''': None}		)  # NB: first record is used for columns






      def 					A__   (			self      :		List[Any]		)	->	int:  # checks if the type can be inferred from the second record


       '''simple docstring'''

       lowercase       : List[Any]						=[{'''col_1''': []}, {'''col_1''': [1, 2]}]
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertEqual(dset.info.features['''col_1''']  ,   Sequence(Value('''int64'''		)		)		)






      def 					A__   (			self      :		int		)	->	str:


       '''simple docstring'''

       lowercase       : List[str]						=Dataset.from_list([]		)
       self.assertEqual(len(UpperCAmelCase		)  ,   0		)
       self.assertListEqual(dset.column_names  ,   []		)



