


import logging
import os
import threading
import time


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

try:
   import msvcrt
except ImportError:
   SCREAMING_SNAKE_CASE       :		int         =				None

try:
   import fcntl
except ImportError:
   SCREAMING_SNAKE_CASE       :		int         =				None


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


# Data
# ------------------------------------------------
SCREAMING_SNAKE_CASE       :		List[str]         =				[
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

SCREAMING_SNAKE_CASE       :		Any         =				"3.0.12"


SCREAMING_SNAKE_CASE       :		Tuple         =				None




def     UpperCamelCase    (      )					->							List[str]:
     '''simple docstring'''





     global _logger
     lowercase_       :List[Any]     =       _logger or logging.getLogger(__name__		)
     return _logger






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_		):
        lowercase_       :Optional[int]     =       lock_file
        return None




   def __str__(     self		):
        lowercase_       :Any     =       f"The file lock '{self.lock_file}' could not be acquired."
        return temp






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_		):
        lowercase_       :Dict     =       lock
        return None
   def __enter__(     self		):
        return self.lock




   def __exit__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        self.lock.release()
        return None






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=-1   ,							UpperCamelCase_=None		):
        lowercase_       :int     =       max_filename_length if max_filename_length is not None else 255
        # Hash the filename if it's too long
        lowercase_       :str     =       self.hash_filename_if_too_long(UpperCamelCase_   ,							UpperCamelCase_		)
        # The path to the lock file.
        lowercase_       :List[Any]     =       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.
        lowercase_       :List[str]     =       None

        # The default timeout value.
        lowercase_       :List[str]     =       timeout

        # We use this lock primarily for the lock counter.
        lowercase_       :str     =       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.
        lowercase_       :Dict     =       0
        return None
   @property
   def 		UpperCamelCase  (     self		):
        return self._lock_file
   @property
   def 		UpperCamelCase  (     self		):
        return self._timeout
   @timeout.setter
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Optional[Any]     =       float(UpperCamelCase_		)
        return None
   def 		UpperCamelCase  (     self		):
        raise NotImplementedError()
   def 		UpperCamelCase  (     self		):
        raise NotImplementedError()
   @property
   def 		UpperCamelCase  (     self		):
        return self._lock_file_fd is not None
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_=None   ,							UpperCamelCase_=0.05		):
        # Use the default timeout, if no timeout is provided.
        if timeout is None:
             lowercase_       :str     =       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

        lowercase_       :Optional[Any]     =       id(self		)
        lowercase_       :str     =       self._lock_file
        lowercase_       :int     =       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(UpperCamelCase_		)
        except:  # noqa
             # Something did go wrong, so decrement the counter.
             with self._thread_lock:
                  lowercase_       :Optional[Any]     =       max(0   ,							self._lock_counter - 1		)

             raise
        return _Acquire_ReturnProxy(lock=self		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_=False		):
        with self._thread_lock:
             if self.is_locked:
                  self._lock_counter -= 1

                  if self._lock_counter == 0 or force:
                       lowercase_       :List[Any]     =       id(self		)
                       lowercase_       :str     =       self._lock_file

                       logger().debug(f"Attempting to release lock {lock_id} on {lock_filename}"		)
                       self._release()
                       lowercase_       :Any     =       0
                       logger().debug(f"Lock {lock_id} released on {lock_filename}"		)

        return None
   def __enter__(     self		):
        self.acquire()
        return self
   def __exit__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        self.release()
        return None
   def __del__(     self		):
        self.release(force=UpperCamelCase_		)
        return None




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Optional[int]     =       os.path.basename(UpperCamelCase_		)
        if len(UpperCamelCase_		) > max_length and max_length > 0:
             lowercase_       :Union[str, Any]     =       os.path.dirname(UpperCamelCase_		)
             lowercase_       :List[str]     =       str(hash(UpperCamelCase_		)		)
             lowercase_       :Dict     =       filename[: max_length - len(UpperCamelCase_		) - 8] + '''...''' + hashed_filename + '''.lock'''
             return os.path.join(UpperCamelCase_   ,							UpperCamelCase_		)
        else:
             return path






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=-1   ,							UpperCamelCase_=None		):
        from .file_utils import relative_to_absolute_path

        super().__init__(UpperCamelCase_   ,							timeout=UpperCamelCase_   ,							max_filename_length=UpperCamelCase_		)
        lowercase_       :Dict     =       '''\\\\?\\''' + relative_to_absolute_path(self.lock_file		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       os.O_RDWR | os.O_CREAT | os.O_TRUNC

        try:
             lowercase_       :str     =       os.open(self._lock_file   ,							UpperCamelCase_		)
        except OSError:
             pass
        else:
             try:
                  msvcrt.locking(UpperCamelCase_   ,							msvcrt.LK_NBLCK   ,							1		)
             except OSError:
                  os.close(UpperCamelCase_		)
             else:
                  lowercase_       :int     =       fd
        return None




   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       self._lock_file_fd
        lowercase_       :List[str]     =       None
        msvcrt.locking(UpperCamelCase_   ,							msvcrt.LK_UNLCK   ,							1		)
        os.close(UpperCamelCase_		)

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






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=-1   ,							UpperCamelCase_=None		):
        lowercase_       :Dict     =       os.statvfs(os.path.dirname(UpperCamelCase_		)		).f_namemax
        super().__init__(UpperCamelCase_   ,							timeout=UpperCamelCase_   ,							max_filename_length=UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       os.O_RDWR | os.O_CREAT | os.O_TRUNC
        lowercase_       :Tuple     =       os.open(self._lock_file   ,							UpperCamelCase_		)

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




   def 		UpperCamelCase  (     self		):
        # Do not remove the lockfile:
        #
        #   https://github.com/benediktschmitt/py-filelock/issues/31
        #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
        lowercase_       :List[str]     =       self._lock_file_fd
        lowercase_       :Optional[Any]     =       None
        fcntl.flock(UpperCamelCase_   ,							fcntl.LOCK_UN		)
        os.close(UpperCamelCase_		)
        return None






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
             lowercase_       :Tuple     =       os.open(self._lock_file   ,							UpperCamelCase_		)
        except OSError:
             pass
        else:
             lowercase_       :List[str]     =       fd
        return None




   def 		UpperCamelCase  (     self		):
        os.close(self._lock_file_fd		)
        lowercase_       :Optional[Any]     =       None

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

if msvcrt:
   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				WindowsFileLock
elif fcntl:
   SCREAMING_SNAKE_CASE       :		Tuple         =				UnixFileLock
else:
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				SoftFileLock

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





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							Dict:
     '''simple docstring'''





     assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
     lowercase_       :str     =       nn.Parameter(_a		)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
          lowercase_       :List[str]     =       nn.Parameter(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       np.asarray(weights[0]		)
     lowercase_       :str     =       np.asarray(weights[1]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[2]		)

     set_param(
         torch_layer.self_attention.query_key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       np.asarray(weights[0]		)
     lowercase_       :Tuple     =       np.asarray(weights[1]		)
     lowercase_       :List[Any]     =       np.asarray(weights[2]		)
     lowercase_       :Optional[int]     =       np.asarray(weights[3]		)

     set_param(
         torch_layer.self_attention.query       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       weights[0][0][0]
     lowercase_       :Optional[Any]     =       np.asarray(layer_norm_a[0]		)
     lowercase_       :Any     =       np.asarray(layer_norm_a[1]		)
     set_param(
         torch_block.attention.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # lsh weights + output
     lowercase_       :int     =       weights[0][1]
     if len(_a		) < 4:
          set_layer_weights_in_torch_lsh(_a       ,		torch_block.attention       ,		_a		)
     else:
          set_layer_weights_in_torch_local(_a       ,		torch_block.attention       ,		_a		)

     # intermediate weighs
     lowercase_       :Optional[int]     =       weights[2][0][1][2]

     # Chunked Feed Forward
     if len(_a		) == 4:
          lowercase_       :Tuple     =       intermediate_weights[2]

     # layernorm 2
     lowercase_       :int     =       np.asarray(intermediate_weights[0][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[0][1]		)
     set_param(
         torch_block.feed_forward.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # intermediate dense
     lowercase_       :Any     =       np.asarray(intermediate_weights[1][0]		)
     lowercase_       :int     =       np.asarray(intermediate_weights[1][1]		)
     set_param(
         torch_block.feed_forward.dense.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)

     # intermediate out
     lowercase_       :Optional[int]     =       np.asarray(intermediate_weights[4][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[4][1]		)
     set_param(
         torch_block.feed_forward.output.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       torch_model.reformer

     # word embeds
     lowercase_       :int     =       np.asarray(weights[1]		)
     set_param(
         torch_model_reformer.embeddings.word_embeddings       ,		torch.tensor(_a		)       ,		)

     if isinstance(weights[3]       ,		_a		):
          lowercase_       :Union[str, Any]     =       torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights		)		):
               lowercase_       :Tuple     =       np.asarray(weights[3][emb_idx][0]		)
               assert (
                   position_embeddings.weights[emb_idx].shape == emb_weights.shape
               ), f"{position_embeddings[emb_idx]} emb does not match"
               lowercase_       :Optional[int]     =       nn.Parameter(torch.tensor(_a		)		)

     lowercase_       :Any     =       weights[5]
     assert len(torch_model_reformer.encoder.layers		) * 4 == len(
         _a		), "HF and trax model do not have the same number of layers"
     for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers		):
          lowercase_       :int     =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(_a       ,		_a       ,		_a		)

     # output layer norm
     lowercase_       :Optional[Any]     =       np.asarray(weights[7][0]		)
     lowercase_       :int     =       np.asarray(weights[7][1]		)
     set_param(
         torch_model_reformer.encoder.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # output embeddings
     lowercase_       :Optional[int]     =       np.asarray(weights[9][0]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[9][1]		)
     set_param(
         torch_model.lm_head.decoder       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       ReformerConfig.from_json_file(_a		)
     print(f"Building PyTorch model from configuration: {config}"		)
     lowercase_       :List[str]     =       ReformerModelWithLMHead(_a		)

     with open(_a       ,		'''rb'''		) as f:
          lowercase_       :Dict     =       pickle.load(_a		)['''weights''']

     set_model_weights_in_torch(_a       ,		_a       ,		config.hidden_size		)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"		)
     torch.save(model.state_dict()       ,		_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained Reformer model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE       :		Dict         =				parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

