


"""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 lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: str="attention" ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: int				,   UpperCamelCase__	: List[Any]=False ):






 """simple docstring"""







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

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


def lowerCAmelCase_      (				UpperCamelCase__	: Any				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: Tuple				,   UpperCamelCase__	: Tuple ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: dict				,   *, UpperCamelCase__	: int				,   UpperCamelCase__	: bool				,   UpperCamelCase__	: bool = False ):






 """simple docstring"""







 __lowercase									=	traverse_util.flatten_dict(variables["""target"""] )
 __lowercase									=	{"""/""".join(UpperCamelCase__ ): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 __lowercase									=	"""encoder/encoder/mlp/wi_0/kernel""" in old
 print("""Split MLP:"""				,   UpperCamelCase__ )

 __lowercase									=	collections.OrderedDict()

 # Shared embeddings.
 __lowercase									=	old["""token_embedder/embedding"""]

 # Encoder.
 for i in range(UpperCamelCase__ ):
  # Block i, layer 0 (Self Attention).
  __lowercase									=	tax_layer_norm_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """encoder"""				,   """pre_attention_layer_norm""" )
  __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	tax_attention_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """encoder"""				,   """attention""" )
  __lowercase									=	layer_norm
  __lowercase									=	k.T
  __lowercase									=	o.T
  __lowercase									=	q.T
  __lowercase									=	v.T

  # Block i, layer 1 (MLP).
  __lowercase									=	tax_layer_norm_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """encoder"""				,   """pre_mlp_layer_norm""" )
  __lowercase							,		__lowercase									=	tax_mlp_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """encoder"""				,   UpperCamelCase__ )
  __lowercase									=	layer_norm
  if split_mlp_wi:
   __lowercase									=	wi[0].T
   __lowercase									=	wi[1].T
  else:
   __lowercase									=	wi.T
  __lowercase									=	wo.T
  if scalable_attention:
   # convert the rel_embedding of each layer
   __lowercase									=	tax_relpos_bias_lookup(
       UpperCamelCase__				,   UpperCamelCase__				,   """encoder""" ).T

 __lowercase									=	old["""encoder/encoder_norm/scale"""]

 if not scalable_attention:
  __lowercase									=	tax_relpos_bias_lookup(
      UpperCamelCase__				,   0				,   """encoder""" ).T
  __lowercase									=	tax_relpos_bias_lookup(
      UpperCamelCase__				,   0				,   """decoder""" ).T

 if not is_encoder_only:
  # Decoder.
  for i in range(UpperCamelCase__ ):
   # Block i, layer 0 (Self Attention).
   __lowercase									=	tax_layer_norm_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """decoder"""				,   """pre_self_attention_layer_norm""" )
   __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	tax_attention_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """decoder"""				,   """self_attention""" )
   __lowercase									=	layer_norm
   __lowercase									=	k.T
   __lowercase									=	o.T
   __lowercase									=	q.T
   __lowercase									=	v.T

   # Block i, layer 1 (Cross Attention).
   __lowercase									=	tax_layer_norm_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """decoder"""				,   """pre_cross_attention_layer_norm""" )
   __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	tax_attention_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """decoder"""				,   """encoder_decoder_attention""" )
   __lowercase									=	layer_norm
   __lowercase									=	k.T
   __lowercase									=	o.T
   __lowercase									=	q.T
   __lowercase									=	v.T

   # Block i, layer 2 (MLP).
   __lowercase									=	tax_layer_norm_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """decoder"""				,   """pre_mlp_layer_norm""" )
   __lowercase							,		__lowercase									=	tax_mlp_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """decoder"""				,   UpperCamelCase__ )
   __lowercase									=	layer_norm
   if split_mlp_wi:
    __lowercase									=	wi[0].T
    __lowercase									=	wi[1].T
   else:
    __lowercase									=	wi.T
   __lowercase									=	wo.T

   if scalable_attention:
    # convert the rel_embedding of each layer
    __lowercase									=	tax_relpos_bias_lookup(UpperCamelCase__				,   UpperCamelCase__				,   """decoder""" ).T

  __lowercase									=	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:
   __lowercase									=	old["""decoder/logits_dense/kernel"""].T

 return new


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: bool ):






 """simple docstring"""







 __lowercase									=	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									=	state_dict["""shared.weight"""]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   __lowercase									=	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									=	state_dict["""shared.weight"""]

 return state_dict


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: Tuple				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	checkpoints.load_tax_checkpoint(UpperCamelCase__ )
 __lowercase									=	convert_tax_to_pytorch(
     UpperCamelCase__				,   num_layers=config.num_layers				,   is_encoder_only=UpperCamelCase__				,   scalable_attention=UpperCamelCase__ )
 __lowercase									=	make_state_dict(UpperCamelCase__				,   UpperCamelCase__ )
 model.load_state_dict(UpperCamelCase__				,   strict=UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: bool = False				,   UpperCamelCase__	: bool = False				,   ):






 """simple docstring"""







 __lowercase									=	MTaConfig.from_json_file(UpperCamelCase__ )
 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									=	UMTaEncoderModel(UpperCamelCase__ )
 else:
  __lowercase									=	UMTaForConditionalGeneration(UpperCamelCase__ )

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

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

 # Verify that we can load the checkpoint.
 model.from_pretrained(UpperCamelCase__ )
 print("""Done""" )


if __name__ == "__main__":
       UpperCAmelCase__  							=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,
       )
       UpperCAmelCase__  							=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"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 assert x is not None
 assert y is not None

 __lowercase									=	len(UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

 # declaring the array for storing the dp values
 __lowercase									=	[[0] * (n + 1) for _ in range(m + 1 )]  # noqa: E741

 for i in range(1				,   m + 1 ):
  for j in range(1				,   n + 1 ):
   __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

   __lowercase									=	max(l[i - 1][j]				,   l[i][j - 1]				,   l[i - 1][j - 1] + match )

 __lowercase									=	""""""
 __lowercase							,		__lowercase									=	m, n
 while i > 0 and j > 0:
  __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

  if l[i][j] == l[i - 1][j - 1] + match:
   if match == 1:
    __lowercase									=	x[i - 1] + seq
   i -= 1
   j -= 1
  elif l[i][j] == l[i - 1][j]:
   i -= 1
  else:
   j -= 1

 return l[m][n], seq


if __name__ == "__main__":
       UpperCAmelCase__  							="AGGTAB"
       UpperCAmelCase__  							="GXTXAYB"
       UpperCAmelCase__  							=4
       UpperCAmelCase__  							="GTAB"

       UpperCAmelCase__							,      UpperCAmelCase__  							=longest_common_subsequence(a, b)
       print("len =", ln, ", sub-sequence =", subseq)
       import doctest

       doctest.testmod()
