



"""simple docstring"""






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 	snake_case_  (				A_ :		Dict,    A_ :		List[str],    A_ :		Tuple						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		Dict,    A_ :		Dict,    A_ :		List[Any],    A_ :		Dict="attention"						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		int,    A_ :		List[Any],    A_ :		Any,    A_ :		Tuple=False						):


       '''simple docstring'''





       if split_mlp_wi:
              _lowerCamelCase						:    Tuple         =			params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
              _lowerCamelCase						:    Tuple         =			params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
              _lowerCamelCase						:    Any         =			(wi_a, wi_a)
       else:
              _lowerCamelCase						:    Dict         =			params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

       _lowerCamelCase						:    Dict         =			params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
       return wi, wo






def 	snake_case_  (				A_ :		List[str],    A_ :		int,    A_ :		Union[str, Any],    A_ :		Any						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		dict,    *, A_ :		int,    A_ :		bool,    A_ :		bool = False						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			traverse_util.flatten_dict(variables['''target''']						)
       _lowerCamelCase						:    Union[str, Any]         =			{'''/'''.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						:    List[Any]         =			'''encoder/encoder/mlp/wi_0/kernel''' in old
       print('''Split MLP:''',    A_						)

       _lowerCamelCase						:    int         =			collections.OrderedDict()

       # Shared embeddings.
       _lowerCamelCase						:    Optional[int]         =			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      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			tax_attention_lookup(A_,    A_,    '''encoder''',    '''attention'''						)
              _lowerCamelCase						:    List[str]         =			layer_norm
              _lowerCamelCase						:    Optional[Any]         =			k.T
              _lowerCamelCase						:    List[Any]         =			o.T
              _lowerCamelCase						:    List[Any]         =			q.T
              _lowerCamelCase						:    Optional[Any]         =			v.T

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

       _lowerCamelCase						:    int         =			old['''encoder/encoder_norm/scale''']

       if not scalable_attention:
              _lowerCamelCase						:    Any         =			tax_relpos_bias_lookup(
                  A_,    0,    '''encoder'''						).T
              _lowerCamelCase						:    Optional[int]         =			tax_relpos_bias_lookup(
                  A_,    0,    '''decoder'''						).T

       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      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			tax_attention_lookup(A_,    A_,    '''decoder''',    '''self_attention'''						)
                     _lowerCamelCase						:    Dict         =			layer_norm
                     _lowerCamelCase						:    Optional[Any]         =			k.T
                     _lowerCamelCase						:    Any         =			o.T
                     _lowerCamelCase						:    Optional[int]         =			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      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Optional[int]         =			tax_attention_lookup(A_,    A_,    '''decoder''',    '''encoder_decoder_attention'''						)
                     _lowerCamelCase						:    Any         =			layer_norm
                     _lowerCamelCase						:    List[Any]         =			k.T
                     _lowerCamelCase						:    str         =			o.T
                     _lowerCamelCase						:    str         =			q.T
                     _lowerCamelCase						:    Union[str, Any]         =			v.T

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

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            _lowerCamelCase						:    str         =			tax_relpos_bias_lookup(A_,    A_,    '''decoder'''						).T

              _lowerCamelCase						:    Union[str, Any]         =			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:
                     _lowerCamelCase						:    Optional[int]         =			old['''decoder/logits_dense/kernel'''].T

       return new






def 	snake_case_  (				A_ :		str,    A_ :		bool						):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			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						:    Any         =			state_dict['''shared.weight''']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     _lowerCamelCase						:    List[str]         =			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						:    Dict         =			state_dict['''shared.weight''']

       return state_dict






def 	snake_case_  (				A_ :		List[Any],    A_ :		Optional[Any],    A_ :		Union[str, Any],    A_ :		List[str],    A_ :		Dict						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			checkpoints.load_tax_checkpoint(A_						)
       _lowerCamelCase						:    Optional[int]         =			convert_tax_to_pytorch(
           A_,    num_layers=config.num_layers,    is_encoder_only=A_,    scalable_attention=A_						)
       _lowerCamelCase						:    Dict         =			make_state_dict(A_,    A_						)
       model.load_state_dict(A_,    strict=A_						)






def 	snake_case_  (				A_ :		Tuple,    A_ :		int,    A_ :		Dict,    A_ :		bool = False,    A_ :		bool = False,    ):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			MTaConfig.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						:    List[Any]         =			UMTaEncoderModel(A_						)
       else:
              _lowerCamelCase						:    Dict         =			UMTaForConditionalGeneration(A_						)

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(A_,    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__":
   lowerCAmelCase__      =    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,
   )
   lowerCAmelCase__      =    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,
   )







"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       def wrapper(*A_ :		int,    **A_ :		List[str]						):
              _lowerCamelCase						:    Tuple         =			timeit.default_timer()
              _lowerCamelCase						:    Optional[Any]         =			func(*A_,    **A_						)
              _lowerCamelCase						:    Tuple         =			timeit.default_timer() - starttime
              return delta

       _lowerCamelCase						:    Optional[Any]         =			func.__name__

       return wrapper






def 	snake_case_  (				A_ :		dict,    A_ :		List[str]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			[]
       _lowerCamelCase						:    Optional[int]         =			seq_shapes or {}
       for i in range(A_						):
              _lowerCamelCase						:    Any         =			{}
              for col_id, (k, v) in enumerate(features.items()						):
                     if isinstance(A_,    _ArrayXD						):
                            _lowerCamelCase						:    str         =			np.random.rand(*v.shape						).astype(v.dtype						)
                     elif isinstance(A_,    datasets.Value						):
                            if v.dtype == "string":
                                   _lowerCamelCase						:    List[str]         =			'''The small grey turtle was surprisingly fast when challenged.'''
                            else:
                                   _lowerCamelCase						:    int         =			np.random.randint(10,    size=1						).astype(v.dtype						).item()
                     elif isinstance(A_,    datasets.Sequence						):
                            while isinstance(A_,    datasets.Sequence						):
                                   _lowerCamelCase						:    Union[str, Any]         =			v.feature
                            _lowerCamelCase						:    Dict         =			seq_shapes[k]
                            _lowerCamelCase						:    int         =			np.random.rand(*A_						).astype(v.dtype						)
                     _lowerCamelCase						:    Optional[Any]         =			data

              dummy_data.append((i, example)						)

       return dummy_data







def 	snake_case_  (				A_ :		List[str],    A_ :		Optional[int],    A_ :		Optional[int]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			generate_examples(A_,    num_examples=A_,    seq_shapes=A_						)

       with ArrowWriter(features=A_,    path=A_						) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase						:    Optional[Any]         =			features.encode_example(A_						)
                     writer.write(A_						)

              _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			writer.finalize()

       if not num_final_examples == num_examples:
              raise ValueError(
                  F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''						)

       _lowerCamelCase						:    List[Any]         =			datasets.Dataset.from_file(filename=A_,    info=datasets.DatasetInfo(features=A_						)						)

       return dataset


