


import gc
import threading
import time

import psutil
import torch



class A__     :



  """simple docstring"""



  def __init__( self			:  int       ):
        a__	:  Optional[int]     =   psutil.Process()
        a__	:  Union[str, Any]     =   False

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  str     =   -1

        while True:
              a__	:  Dict     =   max(self.process.memory_info().rss				,				self.cpu_memory_peak       )

              # can't sleep or will not catch the peak right (this comment is here on purpose)
              if not self.peak_monitoring:
                    break

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  int     =   True
        a__	:  Tuple     =   threading.Thread(target=self.peak_monitor       )
        a__	:  Dict     =   True
        self.thread.start()

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Dict     =   False
        self.thread.join()
        return self.cpu_memory_peak




UpperCamelCase					:       List[str]			    =     PeakCPUMemory()



def UpperCamelCase_				(				)	->   int:
      # Time
      a__	:  List[Any]     =   {"time": time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      a__	:  Union[str, Any]     =   psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()      ):
            a__	:  Dict     =   torch.cuda.memory_allocated(__a      )
      torch.cuda.reset_peak_memory_stats()

      return measures



def UpperCamelCase_				(				__a      )	->   Any:
      # Time
      a__	:  Union[str, Any]     =   {"time": time.time() - start_measures["time"]}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      a__	:  Any     =   (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
      a__	:  Optional[int]     =   (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()      ):
            a__	:  Optional[Any]     =   (torch.cuda.memory_allocated(__a      ) - start_measures[str(__a      )]) / 2**20
            a__	:  int     =   (torch.cuda.max_memory_allocated(__a      ) - start_measures[str(__a      )]) / 2**20

      return measures



def UpperCamelCase_				(				__a			,				__a      )	->   Optional[Any]:
      print(f'''{description}:'''      )
      print(f'''- Time: {measures['time']:.2f}s'''      )
      for i in range(torch.cuda.device_count()      ):
            print(f'''- GPU {i} allocated: {measures[str(__a      )]:.2f}MiB'''      )
            a__	:  Dict     =   measures[f'''{i}-peak''']
            print(f'''- GPU {i} peak: {peak:.2f}MiB'''      )
      print(f'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''      )
      print(f'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''      )





'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       dtype=tf.floataa			,       )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,       expected_slice.numpy()			,       atol=1e-4 ) )



