



"""simple docstring"""
import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    SCREAMING_SNAKE_CASE__						(      __UpperCAmelCase   ,    unittest.TestCase				):
   __lowerCAmelCase						:     List[Any]		=		GPTaTokenizer
   __lowerCAmelCase						:     List[str]		=		GPTaTokenizerFast
   __lowerCAmelCase						:     Tuple		=		True
   __lowerCAmelCase						:     int		=		{'add_prefix_space': True}
   __lowerCAmelCase						:     Any		=		False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase			:      Any			=			[
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
        """<|endoftext|>""",
    ]
    UpperCAmelCase			:      int			=			dict(zip(lowerCAmelCase_   ,  range(len(lowerCAmelCase_		)		)		)		)
    UpperCAmelCase			:      Any			=			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    UpperCAmelCase			:      str			=			{"""unk_token""": """<unk>"""}

    UpperCAmelCase			:      Dict			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
    UpperCAmelCase			:      Tuple			=			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 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    kwargs.update(self.special_tokens_map		)
    return GPTaTokenizer.from_pretrained(self.tmpdirname   ,  **lowerCAmelCase_		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    kwargs.update(self.special_tokens_map		)
    return GPTaTokenizerFast.from_pretrained(self.tmpdirname   ,  **lowerCAmelCase_		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			"""lower newer"""
    UpperCAmelCase			:      str			=			"""lower newer"""
    return input_text, output_text

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			GPTaTokenizer(self.vocab_file   ,  self.merges_file   ,  **self.special_tokens_map		)
    UpperCAmelCase			:      List[Any]			=			"""lower newer"""
    UpperCAmelCase			:      str			=			["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
    UpperCAmelCase			:      int			=			tokenizer.tokenize(lowerCAmelCase_   ,  add_prefix_space=lowerCAmelCase_		)
    self.assertListEqual(lowerCAmelCase_   ,  lowerCAmelCase_		)

    UpperCAmelCase			:      Union[str, Any]			=			tokens + [tokenizer.unk_token]
    UpperCAmelCase			:      Tuple			=			[14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)   ,  lowerCAmelCase_		)

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





    '''simple docstring'''




    if not self.test_rust_tokenizer:
     return

    UpperCAmelCase			:      Any			=			self.get_tokenizer()
    UpperCAmelCase			:      Union[str, Any]			=			self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_		)

    UpperCAmelCase			:      Any			=			"""lower newer"""

    # Testing tokenization
    UpperCAmelCase			:      List[Any]			=			tokenizer.tokenize(lowerCAmelCase_   ,  add_prefix_space=lowerCAmelCase_		)
    UpperCAmelCase			:      Optional[int]			=			rust_tokenizer.tokenize(lowerCAmelCase_		)
    self.assertListEqual(lowerCAmelCase_   ,  lowerCAmelCase_		)

    # Testing conversion to ids without special tokens
    UpperCAmelCase			:      Union[str, Any]			=			tokenizer.encode(lowerCAmelCase_   ,  add_special_tokens=lowerCAmelCase_   ,  add_prefix_space=lowerCAmelCase_		)
    UpperCAmelCase			:      Any			=			rust_tokenizer.encode(lowerCAmelCase_   ,  add_special_tokens=lowerCAmelCase_		)
    self.assertListEqual(lowerCAmelCase_   ,  lowerCAmelCase_		)

    # Testing conversion to ids with special tokens
    UpperCAmelCase			:      List[str]			=			self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_		)
    UpperCAmelCase			:      Any			=			tokenizer.encode(lowerCAmelCase_   ,  add_prefix_space=lowerCAmelCase_		)
    UpperCAmelCase			:      str			=			rust_tokenizer.encode(lowerCAmelCase_		)
    self.assertListEqual(lowerCAmelCase_   ,  lowerCAmelCase_		)

    # Testing the unknown token
    UpperCAmelCase			:      List[str]			=			tokens + [rust_tokenizer.unk_token]
    UpperCAmelCase			:      List[Any]			=			[14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)   ,  lowerCAmelCase_		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    pass

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE=15		)						->   Union[str, Any]:





    '''simple docstring'''




    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
      UpperCAmelCase			:      Optional[int]			=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_   ,  **lowerCAmelCase_		)

      # Simple input
      UpperCAmelCase			:      Optional[Any]			=			"""This is a simple input"""
      UpperCAmelCase			:      Dict			=			["""This is a simple input 1""", """This is a simple input 2"""]
      UpperCAmelCase			:      List[str]			=			("""This is a simple input""", """This is a pair""")
      UpperCAmelCase			:      str			=			[
          ("""This is a simple input 1""", """This is a simple input 2"""),
          ("""This is a simple pair 1""", """This is a simple pair 2"""),
      ]

      # Simple input tests
      self.assertRaises(lowerCAmelCase_   ,  tokenizer_r.encode   ,  lowerCAmelCase_   ,  max_length=lowerCAmelCase_   ,  padding="""max_length"""		)

      # Simple input
      self.assertRaises(lowerCAmelCase_   ,  tokenizer_r.encode_plus   ,  lowerCAmelCase_   ,  max_length=lowerCAmelCase_   ,  padding="""max_length"""		)

      # Simple input
      self.assertRaises(
          lowerCAmelCase_   ,  tokenizer_r.batch_encode_plus   ,  lowerCAmelCase_   ,  max_length=lowerCAmelCase_   ,  padding="""max_length"""   ,  )

      # Pair input
      self.assertRaises(lowerCAmelCase_   ,  tokenizer_r.encode   ,  lowerCAmelCase_   ,  max_length=lowerCAmelCase_   ,  padding="""max_length"""		)

      # Pair input
      self.assertRaises(lowerCAmelCase_   ,  tokenizer_r.encode_plus   ,  lowerCAmelCase_   ,  max_length=lowerCAmelCase_   ,  padding="""max_length"""		)

      # Pair input
      self.assertRaises(
          lowerCAmelCase_   ,  tokenizer_r.batch_encode_plus   ,  lowerCAmelCase_   ,  max_length=lowerCAmelCase_   ,  padding="""max_length"""   ,  )

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,  pad_token="""<pad>"""		)

    # Simple input
    UpperCAmelCase			:      Tuple			=			"""This is a simple input"""
    UpperCAmelCase			:      Tuple			=			["""This is a simple input looooooooong""", """This is a simple input"""]
    UpperCAmelCase			:      Any			=			("""This is a simple input""", """This is a pair""")
    UpperCAmelCase			:      Tuple			=			[
        ("""This is a simple input loooooong""", """This is a simple input"""),
        ("""This is a simple pair loooooong""", """This is a simple pair"""),
    ]

    UpperCAmelCase			:      Optional[Any]			=			tokenizer.pad_token_id

    UpperCAmelCase			:      str			=			tokenizer(lowerCAmelCase_   ,  padding="""max_length"""   ,  max_length=30   ,  return_tensors="""np"""		)
    UpperCAmelCase			:      Union[str, Any]			=			tokenizer(lowerCAmelCase_   ,  padding=lowerCAmelCase_   ,  truncate=lowerCAmelCase_   ,  return_tensors="""np"""		)
    UpperCAmelCase			:      Any			=			tokenizer(*lowerCAmelCase_   ,  padding="""max_length"""   ,  max_length=60   ,  return_tensors="""np"""		)
    UpperCAmelCase			:      List[str]			=			tokenizer(lowerCAmelCase_   ,  padding=lowerCAmelCase_   ,  truncate=lowerCAmelCase_   ,  return_tensors="""np"""		)

    # s
    # test single string max_length padding
    self.assertEqual(out_s["""input_ids"""].shape[-1]   ,  30		)
    self.assertTrue(pad_token_id in out_s["""input_ids"""]		)
    self.assertTrue(0 in out_s["""attention_mask"""]		)

    # s2
    # test automatic padding
    self.assertEqual(out_sa["""input_ids"""].shape[-1]   ,  33		)
    # long slice doesn't have padding
    self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]		)
    self.assertFalse(0 in out_sa["""attention_mask"""][0]		)
    # short slice does have padding
    self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]		)
    self.assertTrue(0 in out_sa["""attention_mask"""][1]		)

    # p
    # test single pair max_length padding
    self.assertEqual(out_p["""input_ids"""].shape[-1]   ,  60		)
    self.assertTrue(pad_token_id in out_p["""input_ids"""]		)
    self.assertTrue(0 in out_p["""attention_mask"""]		)

    # p2
    # test automatic padding pair
    self.assertEqual(out_pa["""input_ids"""].shape[-1]   ,  52		)
    # long slice pair doesn't have padding
    self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]		)
    self.assertFalse(0 in out_pa["""attention_mask"""][0]		)
    # short slice pair does have padding
    self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]		)
    self.assertTrue(0 in out_pa["""attention_mask"""][1]		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""$$$"""
    UpperCAmelCase			:      Optional[int]			=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,  bos_token=lowerCAmelCase_   ,  add_bos_token=lowerCAmelCase_		)

    UpperCAmelCase			:      Union[str, Any]			=			"""This is a simple input"""
    UpperCAmelCase			:      List[Any]			=			["""This is a simple input 1""", """This is a simple input 2"""]

    UpperCAmelCase			:      str			=			tokenizer.bos_token_id

    UpperCAmelCase			:      str			=			tokenizer(lowerCAmelCase_		)
    UpperCAmelCase			:      Union[str, Any]			=			tokenizer(lowerCAmelCase_		)

    self.assertEqual(out_s.input_ids[0]   ,  lowerCAmelCase_		)
    self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids		)		)

    UpperCAmelCase			:      Optional[int]			=			tokenizer.decode(out_s.input_ids		)
    UpperCAmelCase			:      Tuple			=			tokenizer.batch_decode(out_sa.input_ids		)

    self.assertEqual(decode_s.split()[0]   ,  lowerCAmelCase_		)
    self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa		)		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    pass




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			[self.get_tokenizer(do_lower_case=lowerCAmelCase_   ,  add_bos_token=lowerCAmelCase_		)]
    for tokenizer in tokenizers:
     with self.subTest(F"{tokenizer.__class__.__name__}"		):
      UpperCAmelCase			:      int			=			"""Encode this."""
      UpperCAmelCase			:      Optional[int]			=			"""This one too please."""
      UpperCAmelCase			:      Any			=			tokenizer.encode(lowerCAmelCase_   ,  add_special_tokens=lowerCAmelCase_		)
      encoded_sequence += tokenizer.encode(lowerCAmelCase_   ,  add_special_tokens=lowerCAmelCase_		)
      UpperCAmelCase			:      Any			=			tokenizer.encode_plus(
          lowerCAmelCase_   ,  lowerCAmelCase_   ,  add_special_tokens=lowerCAmelCase_   ,  return_special_tokens_mask=lowerCAmelCase_   ,  )
      UpperCAmelCase			:      Tuple			=			encoded_sequence_dict["""input_ids"""]
      UpperCAmelCase			:      str			=			encoded_sequence_dict["""special_tokens_mask"""]
      self.assertEqual(len(lowerCAmelCase_		)   ,  len(lowerCAmelCase_		)		)

      UpperCAmelCase			:      Optional[int]			=			[
          (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCAmelCase_		)
      ]
      UpperCAmelCase			:      int			=			[x for x in filtered_sequence if x is not None]
      self.assertEqual(lowerCAmelCase_   ,  lowerCAmelCase_		)



@require_tokenizers
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			AutoTokenizer.from_pretrained("""facebook/opt-350m"""   ,  from_slow=lowerCAmelCase_		)
    UpperCAmelCase			:      List[str]			=			"""A photo of a cat"""

    UpperCAmelCase			:      Optional[int]			=			tokenizer.encode(
        lowerCAmelCase_   ,  )
    self.assertEqual(lowerCAmelCase_   ,  [2, 250, 1345, 9, 10, 4758]		)
    tokenizer.save_pretrained("""test_opt"""		)

    UpperCAmelCase			:      Any			=			AutoTokenizer.from_pretrained("""./test_opt"""		)
    UpperCAmelCase			:      Dict			=			tokenizer.encode(
        lowerCAmelCase_   ,  )
    self.assertEqual(lowerCAmelCase_   ,  [2, 250, 1345, 9, 10, 4758]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			AutoTokenizer.from_pretrained("""facebook/opt-350m"""   ,  use_slow=lowerCAmelCase_		)
    UpperCAmelCase			:      int			=			"""A photo of a cat"""

    UpperCAmelCase			:      Dict			=			tokenizer.encode(
        lowerCAmelCase_   ,  )
    # Same as above
    self.assertEqual(lowerCAmelCase_   ,  [2, 250, 1345, 9, 10, 4758]		)




   @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			AutoTokenizer.from_pretrained("""facebook/opt-350m"""   ,  from_slow=lowerCAmelCase_		)

    UpperCAmelCase			:      List[str]			=			"""bos"""
    UpperCAmelCase			:      Any			=			tokenizer.get_vocab()["""bos"""]

    UpperCAmelCase			:      Tuple			=			"""A photo of a cat"""
    UpperCAmelCase			:      List[Any]			=			tokenizer.encode(
        lowerCAmelCase_   ,  )
    # We changed the bos token
    self.assertEqual(lowerCAmelCase_   ,  [31957, 250, 1345, 9, 10, 4758]		)
    tokenizer.save_pretrained("""./tok"""		)
    UpperCAmelCase			:      int			=			AutoTokenizer.from_pretrained("""./tok"""		)
    self.assertTrue(tokenizer.is_fast		)
    UpperCAmelCase			:      List[Any]			=			tokenizer.encode(
        lowerCAmelCase_   ,  )
    self.assertEqual(lowerCAmelCase_   ,  [31957, 250, 1345, 9, 10, 4758]		)





"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
      from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    for model_result in results.values():
     for batch_size, sequence_length in zip(model_result["""bs"""]   ,  model_result["""ss"""]		):
      UpperCAmelCase			:      List[str]			=			model_result["""result"""][batch_size][sequence_length]
      self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Tuple			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sgugger/tiny-distilbert-classification"""
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  only_pretrain_model=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  torchscript=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   @unittest.skipIf(torch_device == """cpu"""   ,  """Cant do half precision"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[int]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  fpaa=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    # set architectures equal to `None`
    UpperCAmelCase			:      str			=			None
    UpperCAmelCase			:      Tuple			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      Dict			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   @unittest.skipIf(torch_device == """cpu"""   ,  """Can't do half precision"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      int			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  fpaa=_SCREAMING_SNAKE_CASE   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      str			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      int			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Tuple			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      str			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      str			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result		)
    self.check_results_dict_not_empty(results.memory_inference_result		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      List[str]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      Optional[int]			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

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





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			"""sshleifer/tinier_bart"""
    UpperCAmelCase			:      Optional[Any]			=			AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      int			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE   ,  configs=[config]		)
    UpperCAmelCase			:      str			=			benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result		)
    self.check_results_dict_not_empty(results.memory_train_result		)

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





    '''simple docstring'''




    UpperCAmelCase			:      str			=			"""sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
     UpperCAmelCase			:      Any			=			PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  save_to_csv=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_time.csv"""		)   ,  train_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_mem.csv"""		)   ,  inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_mem.csv"""		)   ,  train_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_time.csv"""		)   ,  env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE   ,  """env.csv"""		)   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
     UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
     benchmark.run()
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_time.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_time.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """inf_mem.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """train_mem.csv"""		)		).exists()		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """env.csv"""		)		).exists()		)




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





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE		):
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """sequential"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """cumulative"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """current"""		)		)
     self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,  """total"""		)		)

    with tempfile.TemporaryDirectory() as tmp_dir:
     UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  log_filename=os.path.join(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)   ,  log_print=_SCREAMING_SNAKE_CASE   ,  trace_memory_line_by_line=_SCREAMING_SNAKE_CASE   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
     UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			benchmark.run()
     _check_summary_is_not_empty(result.inference_summary		)
     _check_summary_is_not_empty(result.train_summary		)
     self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)		).exists()		)
