
'''simple docstring'''







import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.framework.errors_impl import ResourceExhaustedError

  from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
  import pyanvml.pyanvml as nvml

__lowerCAmelCase         =    logging.get_logger(__name__)





def 					UpperCAmelCase_		(__a      :    bool ,	__a      :    bool ):






   """simple docstring"""

   def run_func(__a      :    int ):
      @wraps(__a )
      def run_in_eager_mode(*__a      :    Optional[Any] ,	**__a      :    Optional[int] ):
         return func(*__a ,	**__a )

      @wraps(__a )
      @tf.function(experimental_compile=__a )
      def run_in_graph_mode(*__a      :    Any ,	**__a      :    Optional[int] ):
         return func(*__a ,	**__a )

      if do_eager_mode is True:
         if use_xla is not False:
            raise ValueError(
                'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.' )
         return run_in_eager_mode
      else:
         return run_in_graph_mode

   return run_func





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    int ,	__a      :    int ):






   """simple docstring"""

   _a				:	List[str]			=   random.Random()
   _a				:	Any			=   [rng.randint(0 ,	vocab_size - 1 ) for i in range(batch_size * sequence_length )]
   return tf.constant(__a ,	shape=(batch_size, sequence_length) ,	dtype=tf.intaa )







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				TensorFlowBenchmarkArguments
  __UpperCAmelCase				:				PretrainedConfig
  __UpperCAmelCase				:				str   =					"TensorFlow"


  @property
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     return tf.__version__


  def   __lowercase		(		self      :     int							,_a      :     str							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     _a				:	Dict			=   self.args.strategy
     if strategy is None:
        raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )
     _a				:	int			=   self._prepare_inference_func(_a							,_a							,_a       )
     return self._measure_speed(_inference       )


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     str							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     _a				:	int			=   self.args.strategy
     if strategy is None:
        raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )
     _a				:	Any			=   self._prepare_train_func(_a							,_a							,_a       )
     return self._measure_speed(_train       )


  def   __lowercase		(		self      :     int							,_a      :     str							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if self.args.is_gpu:
        tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,_a       )
     _a				:	Optional[Any]			=   self.args.strategy
     if strategy is None:
        raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )
     _a				:	Optional[int]			=   self._prepare_inference_func(_a							,_a							,_a       )
     return self._measure_memory(_inference       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     str							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if self.args.is_gpu:
        tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,_a       )
     _a				:	Optional[Any]			=   self.args.strategy
     if strategy is None:
        raise ValueError('A device strategy has to be initialized before using TensorFlow.'       )

     _a				:	Optional[int]			=   self._prepare_train_func(_a							,_a							,_a       )
     return self._measure_memory(_train       )


  def   __lowercase		(		self      :     Any							,_a      :     str							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.config_dict[model_name]

     if self.args.fpaa:
        raise NotImplementedError('Mixed precision is currently not supported.'       )

     _a				:	Optional[Any]			=   (
         hasattr(_a							,'architectures'       )
         and isinstance(config.architectures							,_a       )
         and len(config.architectures       ) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
           _a				:	str			=   'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
           _a				:	Dict			=   __import__('transformers'							,fromlist=[model_class]       )
           _a				:	str			=   getattr(_a							,_a       )
           _a				:	Optional[Any]			=   model_cls(_a       )
        except ImportError:
           raise ImportError(
               F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
               ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'       )
     else:
        _a				:	Any			=   TF_MODEL_MAPPING[config.__class__](_a       )

     # encoder-decoder has vocab size saved differently
     _a				:	Optional[Any]			=   config.vocab_size if hasattr(_a							,'vocab_size'       ) else config.encoder.vocab_size
     _a				:	Union[str, Any]			=   random_input_ids(_a							,_a							,_a       )

     @run_with_tf_optimizations(self.args.eager_mode							,self.args.use_xla       )
     def encoder_decoder_forward():
        return model(_a							,decoder_input_ids=_a							,training=_a       )

     @run_with_tf_optimizations(self.args.eager_mode							,self.args.use_xla       )
     def encoder_forward():
        return model(_a							,training=_a       )

     _a				:	Optional[Any]			=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

     return _inference


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     str							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     _a				:	str			=   self.config_dict[model_name]

     if self.args.eager_mode is not False:
        raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'       )

     if self.args.fpaa:
        raise NotImplementedError('Mixed precision is currently not supported.'       )

     _a				:	Optional[Any]			=   (
         hasattr(_a							,'architectures'       )
         and isinstance(config.architectures							,_a       )
         and len(config.architectures       ) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
           _a				:	str			=   'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
           _a				:	Union[str, Any]			=   __import__('transformers'							,fromlist=[model_class]       )
           _a				:	Union[str, Any]			=   getattr(_a							,_a       )
           _a				:	List[str]			=   model_cls(_a       )
        except ImportError:
           raise ImportError(
               F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
               ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'       )
     else:
        _a				:	Any			=   TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_a       )

     # encoder-decoder has vocab size saved differently
     _a				:	Optional[Any]			=   config.vocab_size if hasattr(_a							,'vocab_size'       ) else config.encoder.vocab_size
     _a				:	List[str]			=   random_input_ids(_a							,_a							,_a       )

     @run_with_tf_optimizations(self.args.eager_mode							,self.args.use_xla       )
     def encoder_decoder_train():
        _a				:	List[Any]			=   model(_a							,decoder_input_ids=_a							,labels=_a							,training=_a       )[0]
        _a				:	int			=   tf.gradients(_a							,model.trainable_variables       )
        return gradients

     @run_with_tf_optimizations(self.args.eager_mode							,self.args.use_xla       )
     def encoder_train():
        _a				:	List[str]			=   model(_a							,labels=_a							,training=_a       )[0]
        _a				:	Union[str, Any]			=   tf.gradients(_a							,model.trainable_variables       )
        return gradients

     _a				:	str			=   encoder_decoder_train if config.is_encoder_decoder else encoder_train

     return _train


  def   __lowercase		(		self      :     List[Any]							,_a      :     int       ):


     '''simple docstring'''






     with self.args.strategy.scope():
        try:
           if self.args.is_tpu or self.args.use_xla:
              # run additional 10 times to stabilize compilation for tpu
              logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'       )
              timeit.repeat(_a							,repeat=1							,number=5       )

           # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
           _a				:	Optional[int]			=   timeit.repeat(
               _a							,repeat=self.args.repeat							,number=10							,)

           return min(_a       ) / 10.0
        except ResourceExhaustedError as e:
           self.print_fn(F"""Doesn't fit on GPU. {e}"""       )


  def   __lowercase		(		self      :     Tuple							,_a      :     Callable[[], None]       ):


     '''simple docstring'''






     logger.info(
         'Note that TensorFlow allocates more memory than '
         'it might need to speed up computation. '
         'The memory reported here corresponds to the memory '
         'reported by `nvidia-smi`, which can vary depending '
         'on total available memory on the GPU that is used.'       )
     with self.args.strategy.scope():
        try:
           if self.args.trace_memory_line_by_line:
              if not self.args.eager_mode:
                 raise ValueError(
                     '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
                     ' consumption line by line.'       )
              _a				:	Any			=   start_memory_tracing('transformers'       )

           if self.args.is_tpu:
              # tpu
              raise NotImplementedError(
                  'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
                  ' with `args.memory=False`'       )
           elif self.args.is_gpu:
              # gpu
              if not is_pyanvml_available():
                 logger.warning(
                     'py3nvml not installed, we won\'t log GPU memory usage. '
                     'Install py3nvml (pip install py3nvml) to log information about GPU.'       )
                 _a				:	str			=   'N/A'
              else:
                 logger.info(
                     'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
                     ' running on the same GPU.'       )
                 # init nvml
                 nvml.nvmlInit()
                 func()
                 _a				:	Tuple			=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
                 _a				:	Tuple			=   nvml.nvmlDeviceGetMemoryInfo(_a       )
                 _a				:	Any			=   meminfo.used
                 _a				:	Dict			=   Memory(_a       )
                 # shutdown nvml
                 nvml.nvmlShutdown()
           else:
              # cpu
              if self.args.trace_memory_line_by_line:
                 logger.info(
                     'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
                     ' TensorFlow.'       )
                 _a				:	Optional[Any]			=   None
              else:
                 _a				:	Any			=   measure_peak_memory_cpu(_a       )
                 _a				:	int			=   Memory(_a       ) if isinstance(_a							,_a       ) else memory_bytes
           if self.args.trace_memory_line_by_line:
              _a				:	Any			=   stop_memory_tracing(_a       )
              if memory is None:
                 _a				:	Optional[Any]			=   summary.total
           else:
              _a				:	Tuple			=   None

           return memory, summary
        except ResourceExhaustedError as e:
           self.print_fn(F"""Doesn't fit on GPU. {e}"""       )
           return "N/A", None





'''simple docstring'''







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      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   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      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	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      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

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

   # lsh weights + output
   _a				:	List[str]			=   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
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

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

   # intermediate dense
   _a				:	Any			=   np.asarray(intermediate_weights[1][0] )
   _a				:	Any			=   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
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   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      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

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

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   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"""
         _a				:	Any			=   nn.Parameter(torch.tensor(__a ) )

   _a				:	List[str]			=   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 ):
      _a				:	Tuple			=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__a ,	__a ,	__a )

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

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   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      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

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

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   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__":
  __lowerCAmelCase         =    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."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



