





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_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:Tuple="attention"				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
     A_          =       params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
     A_          =       params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
     A_          =       params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
     return k, o, q, v

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Optional[int]=False				)	->				str:
     '''simple docstring'''


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

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]  ,						_UpperCAmelCase							:int  ,						_UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:str				)	->				Tuple:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:dict  ,						*, _UpperCAmelCase							:int  ,						_UpperCAmelCase							:bool				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       traverse_util.flatten_dict(variables['''target''']				)
     A_          =       {'''/'''.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
     A_          =       '''encoder/layers_0/mlp/wi_0/kernel''' in old
     print('''Split MLP:'''  ,						_UpperCAmelCase				)

     A_          =       collections.OrderedDict()

     # Shared embeddings.
     A_          =       old['''token_embedder/embedding''']

     # Encoder.
     for i in range(_UpperCAmelCase				):
          # Block i, layer 0 (Self Attention).
          A_          =       tax_layer_norm_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''encoder'''  ,						'''pre_attention_layer_norm'''				)
          A_   ,    A_   ,    A_   ,    A_          =       tax_attention_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''encoder'''  ,						'''attention'''				)
          A_          =       layer_norm
          A_          =       k.T
          A_          =       o.T
          A_          =       q.T
          A_          =       v.T

          # Block i, layer 1 (MLP).
          A_          =       tax_layer_norm_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''encoder'''  ,						'''pre_mlp_layer_norm'''				)
          A_   ,    A_          =       tax_mlp_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''encoder'''  ,						_UpperCAmelCase				)
          A_          =       layer_norm
          if split_mlp_wi:
               A_          =       wi[0].T
               A_          =       wi[1].T
          else:
               A_          =       wi.T
          A_          =       wo.T

     A_          =       old[
         '''encoder/relpos_bias/rel_embedding'''
     ].T
     A_          =       old['''encoder/encoder_norm/scale''']

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

               # Block i, layer 1 (Cross Attention).
               A_          =       tax_layer_norm_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''decoder'''  ,						'''pre_cross_attention_layer_norm'''				)
               A_   ,    A_   ,    A_   ,    A_          =       tax_attention_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''decoder'''  ,						'''encoder_decoder_attention'''				)
               A_          =       layer_norm
               A_          =       k.T
               A_          =       o.T
               A_          =       q.T
               A_          =       v.T

               # Block i, layer 2 (MLP).
               A_          =       tax_layer_norm_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''decoder'''  ,						'''pre_mlp_layer_norm'''				)
               A_   ,    A_          =       tax_mlp_lookup(_UpperCAmelCase  ,						_UpperCAmelCase  ,						'''decoder'''  ,						_UpperCAmelCase				)
               A_          =       layer_norm
               if split_mlp_wi:
                    A_          =       wi[0].T
                    A_          =       wi[1].T
               else:
                    A_          =       wi.T
               A_          =       wo.T

          A_          =       old['''decoder/decoder_norm/scale''']
          A_          =       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:
               A_          =       old['''decoder/logits_dense/kernel'''].T

     return new

def      UpperCAmelCase_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:bool				)	->				List[str]:
     '''simple docstring'''


     A_          =       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:
          A_          =       state_dict['''shared.weight''']

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

     return state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Optional[Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				int:
     '''simple docstring'''


     A_          =       checkpoints.load_tax_checkpoint(_UpperCAmelCase				)
     A_          =       convert_tax_to_pytorch(_UpperCAmelCase  ,						num_layers=config.num_layers  ,						is_encoder_only=_UpperCAmelCase				)
     A_          =       make_state_dict(_UpperCAmelCase  ,						_UpperCAmelCase				)
     model.load_state_dict(_UpperCAmelCase  ,						strict=_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[Any]  ,						_UpperCAmelCase							:Optional[Any]  ,						_UpperCAmelCase							:Dict  ,						_UpperCAmelCase							:bool = False				)	->				Tuple:
     '''simple docstring'''


     A_          =       TaConfig.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:
          A_          =       TaEncoderModel(_UpperCAmelCase				)
     else:
          A_          =       TaForConditionalGeneration(_UpperCAmelCase				)

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(_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__":
      a__      :     Optional[int]         =		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__      :     Any         =		parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )










import logging
import os
import threading
import time


try:
      import warnings
except ImportError:
      a__      :     Optional[Any]         =		None

try:
      import msvcrt
except ImportError:
      a__      :     List[str]         =		None

try:
      import fcntl
except ImportError:
      a__      :     Optional[int]         =		None


# Backward compatibility
# ------------------------------------------------
try:
      TimeoutError
except NameError:
      a__      :     List[str]         =		OSError


# Data
# ------------------------------------------------
a__      :     Union[str, Any]         =		[
    'Timeout',
    'BaseFileLock',
    'WindowsFileLock',
    'UnixFileLock',
    'SoftFileLock',
    'FileLock',
]

a__      :     str         =		'3.0.12'


a__      :     Optional[int]         =		None

def      UpperCAmelCase_    (    )	->				Tuple:
     '''simple docstring'''


     global _logger
     A_          =       _logger or logging.getLogger(__name__				)
     return _logger





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  def __init__(							self ,__snake_case ):



       """simple docstring"""





       A_          =       lock_file
       return None




  def __str__(							self ):



       """simple docstring"""





       A_          =       f'The file lock \'{self.lock_file}\' could not be acquired.'
       return temp





class UpperCAmelCase_				:


  def __init__(							self ,__snake_case ):



       """simple docstring"""





       A_          =       lock
       return None


  def __enter__(							self ):



       """simple docstring"""





       return self.lock




  def __exit__(							self ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       self.lock.release()
       return None





class UpperCAmelCase_				:


  def __init__(							self ,__snake_case ,__snake_case=-1 ,__snake_case=None ):



       """simple docstring"""





       A_          =       max_filename_length if max_filename_length is not None else 2_5_5
       # Hash the filename if it's too long
       A_          =       self.hash_filename_if_too_long(__snake_case ,__snake_case )
       # The path to the lock file.
       A_          =       lock_file

       # The file descriptor for the *_lock_file* as it is returned by the
       # os.open() function.
       # This file lock is only NOT None, if the object currently holds the
       # lock.
       A_          =       None

       # The default timeout value.
       A_          =       timeout

       # We use this lock primarily for the lock counter.
       A_          =       threading.Lock()

       # The lock counter is used for implementing the nested locking
       # mechanism. Whenever the lock is acquired, the counter is increased and
       # the lock is only released, when this value is 0 again.
       A_          =       0
       return None


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return self._lock_file


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return self._timeout


  @timeout.setter
  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       float(__snake_case )
       return None


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       raise NotImplementedError()


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       raise NotImplementedError()


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return self._lock_file_fd is not None


  def 						__UpperCAmelCase     (							self ,__snake_case=None ,__snake_case=0.05 ):



       """simple docstring"""





       if timeout is None:
            A_          =       self.timeout

       # Increment the number right at the beginning.
       # We can still undo it, if something fails.
       with self._thread_lock:
            self._lock_counter += 1

       A_          =       id(self )
       A_          =       self._lock_file
       A_          =       time.time()
       try:
            while True:
                 with self._thread_lock:
                      if not self.is_locked:
                           logger().debug(f'Attempting to acquire lock {lock_id} on {lock_filename}' )
                           self._acquire()

                 if self.is_locked:
                      logger().debug(f'Lock {lock_id} acquired on {lock_filename}' )
                      break
                 elif timeout >= 0 and time.time() - start_time > timeout:
                      logger().debug(f'Timeout on acquiring lock {lock_id} on {lock_filename}' )
                      raise Timeout(self._lock_file )
                 else:
                      logger().debug(
                          f'Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...' )
                      time.sleep(__snake_case )
       except:  # noqa
            # Something did go wrong, so decrement the counter.
            with self._thread_lock:
                 A_          =       max(0 ,self._lock_counter - 1 )

            raise
       return _Acquire_ReturnProxy(lock=self )


  def 						__UpperCAmelCase     (							self ,__snake_case=False ):



       """simple docstring"""





       with self._thread_lock:
            if self.is_locked:
                 self._lock_counter -= 1

                 if self._lock_counter == 0 or force:
                      A_          =       id(self )
                      A_          =       self._lock_file

                      logger().debug(f'Attempting to release lock {lock_id} on {lock_filename}' )
                      self._release()
                      A_          =       0
                      logger().debug(f'Lock {lock_id} released on {lock_filename}' )

       return None


  def __enter__(							self ):



       """simple docstring"""





       self.acquire()
       return self


  def __exit__(							self ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       self.release()
       return None


  def __del__(							self ):



       """simple docstring"""





       self.release(force=__snake_case )
       return None




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       os.path.basename(__snake_case )
       if len(__snake_case ) > max_length and max_length > 0:
            A_          =       os.path.dirname(__snake_case )
            A_          =       str(hash(__snake_case ) )
            A_          =       filename[: max_length - len(__snake_case ) - 8] + '''...''' + hashed_filename + '''.lock'''
            return os.path.join(__snake_case ,__snake_case )
       else:
            return path





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  def __init__(							self ,__snake_case ,__snake_case=-1 ,__snake_case=None ):



       """simple docstring"""





       from .file_utils import relative_to_absolute_path

       super().__init__(__snake_case ,timeout=__snake_case ,max_filename_length=__snake_case )
       A_          =       '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       os.O_RDWR | os.O_CREAT | os.O_TRUNC

       try:
            A_          =       os.open(self._lock_file ,__snake_case )
       except OSError:
            pass
       else:
            try:
                 msvcrt.locking(__snake_case ,msvcrt.LK_NBLCK ,1 )
            except OSError:
                 os.close(__snake_case )
            else:
                 A_          =       fd
       return None




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self._lock_file_fd
       A_          =       None
       msvcrt.locking(__snake_case ,msvcrt.LK_UNLCK ,1 )
       os.close(__snake_case )

       try:
            os.remove(self._lock_file )
       # Probably another instance of the application
       # that acquired the file lock.
       except OSError:
            pass
       return None





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  def __init__(							self ,__snake_case ,__snake_case=-1 ,__snake_case=None ):



       """simple docstring"""





       A_          =       os.statvfs(os.path.dirname(__snake_case ) ).f_namemax
       super().__init__(__snake_case ,timeout=__snake_case ,max_filename_length=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       os.O_RDWR | os.O_CREAT | os.O_TRUNC
       A_          =       os.open(self._lock_file ,__snake_case )

       try:
            fcntl.flock(__snake_case ,fcntl.LOCK_EX | fcntl.LOCK_NB )
       except OSError:
            os.close(__snake_case )
       else:
            A_          =       fd
       return None




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self._lock_file_fd
       A_          =       None
       fcntl.flock(__snake_case ,fcntl.LOCK_UN )
       os.close(__snake_case )
       return None





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
       try:
            A_          =       os.open(self._lock_file ,__snake_case )
       except OSError:
            pass
       else:
            A_          =       fd
       return None




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       os.close(self._lock_file_fd )
       A_          =       None

       try:
            os.remove(self._lock_file )
       # The file is already deleted and that's what we want.
       except OSError:
            pass
       return None




a__      :     Union[str, Any]         =		None

if msvcrt:
      a__      :     Dict         =		WindowsFileLock
elif fcntl:
      a__      :     Dict         =		UnixFileLock
else:
      a__      :     Optional[int]         =		SoftFileLock

      if warnings is not None:
            warnings.warn('only soft file lock is available')



