



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

a__   :					Optional[Any] 						=      logging.get_logger(__name__)

def 	_lowerCAmelCase (    A__    ,	A__						):
      def run_func(A__						):
            @wraps(A__						)
            def run_in_eager_mode(*A__    ,	**A__						):
                  return func(*A__    ,	**A__						)

            @wraps(A__						)
            @tf.function(experimental_compile=A__						)
            def run_in_graph_mode(*A__    ,	**A__						):
                  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 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	random.Random()
      lowercase__  =	[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__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	TensorFlowBenchmarkArguments
   A     :	PretrainedConfig
   A     :	str												=     "TensorFlow"


   @property
   def       UpperCAmelCase		(    self	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         return tf.__version__


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int)						->					float:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int)						->					float:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int)						->					[Memory, Optional[MemorySummary]]:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int)						->					[Memory, Optional[MemorySummary]]:




         """simple docstring"""

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

         lowercase__  =	self._prepare_train_func(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         return self._measure_memory(_train)


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int)						->					Callable[[], None]:




         """simple docstring"""

         lowercase__  =	self.config_dict[model_name]

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

         lowercase__  =	(
             hasattr(lowerCAmelCase      ,					'architectures')
             and isinstance(config.architectures      ,					lowerCAmelCase)
             and len(config.architectures) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                     lowercase__  =	'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                     lowercase__  =	__import__('transformers'      ,					fromlist=[model_class])
                     lowercase__  =	getattr(lowerCAmelCase      ,					lowerCAmelCase)
                     lowercase__  =	model_cls(lowerCAmelCase)
               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:
               lowercase__  =	TF_MODEL_MAPPING[config.__class__](lowerCAmelCase)

         # encoder-decoder has vocab size saved differently
         lowercase__  =	config.vocab_size if hasattr(lowerCAmelCase      ,					'vocab_size') else config.encoder.vocab_size
         lowercase__  =	random_input_ids(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

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

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

         lowercase__  =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

         return _inference


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int)						->					Callable[[], None]:




         """simple docstring"""

         lowercase__  =	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.')

         lowercase__  =	(
             hasattr(lowerCAmelCase      ,					'architectures')
             and isinstance(config.architectures      ,					lowerCAmelCase)
             and len(config.architectures) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                     lowercase__  =	'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                     lowercase__  =	__import__('transformers'      ,					fromlist=[model_class])
                     lowercase__  =	getattr(lowerCAmelCase      ,					lowerCAmelCase)
                     lowercase__  =	model_cls(lowerCAmelCase)
               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:
               lowercase__  =	TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase)

         # encoder-decoder has vocab size saved differently
         lowercase__  =	config.vocab_size if hasattr(lowerCAmelCase      ,					'vocab_size') else config.encoder.vocab_size
         lowercase__  =	random_input_ids(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

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

         @run_with_tf_optimizations(self.args.eager_mode      ,					self.args.use_xla)
         def encoder_train():
               lowercase__  =	model(lowerCAmelCase      ,					labels=lowerCAmelCase      ,					training=lowerCAmelCase)[0]
               lowercase__  =	tf.gradients(lowerCAmelCase      ,					model.trainable_variables)
               return gradients

         lowercase__  =	encoder_decoder_train if config.is_encoder_decoder else encoder_train

         return _train


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Any)						->					float:




         """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(lowerCAmelCase      ,					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
                     lowercase__  =	timeit.repeat(
                         lowerCAmelCase      ,					repeat=self.args.repeat      ,					number=10      ,					)

                     return min(lowerCAmelCase) / 10.0
               except ResourceExhaustedError as e:
                     self.print_fn(f'''Doesn\'t fit on GPU. {e}''')



   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Callable[[], None])						->					[Memory, MemorySummary]:




         """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.')
                           lowercase__  =	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.')
                                 lowercase__  =	'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()
                                 lowercase__  =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                                 lowercase__  =	nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase)
                                 lowercase__  =	meminfo.used
                                 lowercase__  =	Memory(lowerCAmelCase)
                                 # 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.')
                                 lowercase__  =	None
                           else:
                                 lowercase__  =	measure_peak_memory_cpu(lowerCAmelCase)
                                 lowercase__  =	Memory(lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else memory_bytes
                     if self.args.trace_memory_line_by_line:
                           lowercase__  =	stop_memory_tracing(lowerCAmelCase)
                           if memory is None:
                                 lowercase__  =	summary.total
                     else:
                           lowercase__  =	None

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





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a__   :					Optional[Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def __init__(    self	:      Optional[Any]      ,					*lowerCAmelCase	:      Optional[Any]      ,					**lowerCAmelCase	:      List[Any])						->					None:




         """simple docstring"""

         warnings.warn(
             'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use GLPNImageProcessor instead.'      ,					lowerCAmelCase      ,					)
         super().__init__(*lowerCAmelCase      ,					**lowerCAmelCase)
