






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  MvpTokenizer
  _lowerCamelCase    =  MvpTokenizerFast
  _lowerCamelCase    =  True
  _lowerCamelCase    =  filter_roberta_detectors


  def  lowerCamelCase       (      self		:			Tuple							)						->	Dict:


         """simple docstring"""
         super().setUp()
         _UpperCAmelCase							=    [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         _UpperCAmelCase							=    dict(zip(lowerCamelCase       ,				range(len(lowerCamelCase							)							)							)							)
         _UpperCAmelCase							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         _UpperCAmelCase							=    {"""unk_token""": """<unk>"""}

         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""vocab_file"""]							)
         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""merges_file"""]							)
         with open(self.vocab_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                fp.write(json.dumps(lowerCamelCase							) + """\n"""							)
         with open(self.merges_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                fp.write("""\n""".join(lowerCamelCase							)							)


  def  lowerCamelCase       (      self		:			Dict       ,				**lowerCamelCase		:			int							)						->	int:


         """simple docstring"""
         kwargs.update(self.special_tokens_map							)
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			str       ,				**lowerCamelCase		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         kwargs.update(self.special_tokens_map							)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Tuple							)						->	Tuple:


         """simple docstring"""
         return "lower newer", "lower newer"


  @cached_property
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""							)


  @cached_property
  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[Any]:


         """simple docstring"""
         return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""							)


  @require_torch
  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         _UpperCAmelCase							=    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _UpperCAmelCase							=    tokenizer(lowerCamelCase       ,				max_length=len(lowerCamelCase							)       ,				padding=lowerCamelCase       ,				return_tensors="""pt"""							)
                self.assertIsInstance(lowerCamelCase       ,				lowerCamelCase							)

                self.assertEqual((2, 9)       ,				batch.input_ids.shape							)
                self.assertEqual((2, 9)       ,				batch.attention_mask.shape							)
                _UpperCAmelCase							=    batch.input_ids.tolist()[0]
                self.assertListEqual(lowerCamelCase       ,				lowerCamelCase							)
                # Test that special tokens are reset


  @require_torch
  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _UpperCAmelCase							=    tokenizer(lowerCamelCase       ,				padding=lowerCamelCase       ,				return_tensors="""pt"""							)
                # check if input_ids are returned and no labels
                self.assertIn("""input_ids"""       ,				lowerCamelCase							)
                self.assertIn("""attention_mask"""       ,				lowerCamelCase							)
                self.assertNotIn("""labels"""       ,				lowerCamelCase							)
                self.assertNotIn("""decoder_attention_mask"""       ,				lowerCamelCase							)


  @require_torch
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    [
             """Summary of the text.""",
             """Another summary.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _UpperCAmelCase							=    tokenizer(text_target=lowerCamelCase       ,				max_length=32       ,				padding="""max_length"""       ,				return_tensors="""pt"""							)
                self.assertEqual(32       ,				targets["""input_ids"""].shape[1]							)


  @require_torch
  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _UpperCAmelCase							=    tokenizer(
                    ["""I am a small frog""" * 1024, """I am a small frog"""]       ,				padding=lowerCamelCase       ,				truncation=lowerCamelCase       ,				return_tensors="""pt"""							)
                self.assertIsInstance(lowerCamelCase       ,				lowerCamelCase							)
                self.assertEqual(batch.input_ids.shape       ,				(2, 1024)							)


  @require_torch
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    ["""A long paragraph for summarization."""]
         _UpperCAmelCase							=    [
             """Summary of the text.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _UpperCAmelCase							=    tokenizer(lowerCamelCase       ,				text_target=lowerCamelCase       ,				return_tensors="""pt"""							)
                _UpperCAmelCase							=    inputs["""input_ids"""]
                _UpperCAmelCase							=    inputs["""labels"""]
                self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
                self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
                self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
                self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Tuple:


         """simple docstring"""
         pass





  def  lowerCamelCase       (      self		:			List[str]							)						->	Any:


         """simple docstring"""
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                       _UpperCAmelCase							=    self.rust_tokenizer_class.from_pretrained(lowerCamelCase       ,				**lowerCamelCase							)
                       _UpperCAmelCase							=    self.tokenizer_class.from_pretrained(lowerCamelCase       ,				**lowerCamelCase							)
                       _UpperCAmelCase							=    """A, <mask> AllenNLP sentence."""
                       _UpperCAmelCase							=    tokenizer_r.encode_plus(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				return_token_type_ids=lowerCamelCase							)
                       _UpperCAmelCase							=    tokenizer_p.encode_plus(lowerCamelCase       ,				add_special_tokens=lowerCamelCase       ,				return_token_type_ids=lowerCamelCase							)

                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r["""token_type_ids"""]							)       ,				sum(tokens_p["""token_type_ids"""]							)							)

                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)       ,				sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)       ,				)

                       _UpperCAmelCase							=    tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
                       _UpperCAmelCase							=    tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

                       # Rust correctly handles the space before the mask while python doesnt
                       self.assertSequenceEqual(tokens_p["""input_ids"""]       ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]       ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)

                       self.assertSequenceEqual(
                           lowerCamelCase       ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
                       self.assertSequenceEqual(
                           lowerCamelCase       ,				["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)







import gc
import threading
import time

import psutil
import torch
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    psutil.Process()
         _UpperCAmelCase							=    False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    -1

         while True:
                _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    threading.Thread(target=self.peak_monitor							)
         _UpperCAmelCase							=    True
         self.thread.start()





  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    False
         self.thread.join()
         return self.cpu_memory_peak



__a:     Union[str, Any]   					=					PeakCPUMemory()




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time()}

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

       # CPU mem
       _UpperCAmelCase							=    psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    torch.cuda.memory_allocated(__snake_case )
       torch.cuda.reset_peak_memory_stats()

       return measures




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time() - start_measures["""time"""]}

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

       # CPU mem
       _UpperCAmelCase							=    (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
       _UpperCAmelCase							=    (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    (torch.cuda.memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
              _UpperCAmelCase							=    (torch.cuda.max_memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0

       return measures





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str:
       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(__snake_case )]:.2f}MiB""" )
              _UpperCAmelCase							=    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""" )