

'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf

 from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					List[str]	)						->     Any:
      for model_result in results.values():
       for batch_size, sequence_length in zip(model_result['bs']					,      model_result['ss']	):
        UpperCAmelCase	:     Optional[int]						 =			model_result['result'][batch_size][sequence_length]
        self.assertIsNotNone(lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Union[str, Any]:
      UpperCAmelCase	:     Any						 =			'sshleifer/tiny-gpt2'
      UpperCAmelCase	:     Any						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      eager_mode=lowercase_					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     int						 =			TensorFlowBenchmark(lowercase_	)
      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        UpperCAmelCase_	(		self			:					str	)						->     str:
      UpperCAmelCase	:     Union[str, Any]						 =			'sgugger/tiny-distilbert-classification'
      UpperCAmelCase	:     Union[str, Any]						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      multi_process=lowercase_					,      only_pretrain_model=lowercase_					,      )
      UpperCAmelCase	:     List[str]						 =			TensorFlowBenchmark(lowercase_	)
      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	)


     def        UpperCAmelCase_	(		self			:					Any	)						->     str:
      UpperCAmelCase	:     List[Any]						 =			'sshleifer/tiny-gpt2'
      UpperCAmelCase	:     List[Any]						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     List[str]						 =			TensorFlowBenchmark(lowercase_	)
      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        UpperCAmelCase_	(		self			:					int	)						->     int:
      UpperCAmelCase	:     Optional[int]						 =			'sshleifer/tiny-gpt2'
      UpperCAmelCase	:     Optional[int]						 =			AutoConfig.from_pretrained(lowercase_	)
      UpperCAmelCase	:     Tuple						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      eager_mode=lowercase_					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     Optional[Any]						 =			TensorFlowBenchmark(lowercase_					,      [config]	)
      UpperCAmelCase	:     Optional[int]						 =			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result	)
      self.check_results_dict_not_empty(results.memory_inference_result	)


     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     int						 =			'sshleifer/tiny-gpt2'
      UpperCAmelCase	:     str						 =			AutoConfig.from_pretrained(lowercase_	)
      UpperCAmelCase	:     Dict						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     Optional[int]						 =			TensorFlowBenchmark(lowercase_					,      [config]	)
      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	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
      UpperCAmelCase	:     List[Any]						 =			'sshleifer/tiny-gpt2'
      UpperCAmelCase	:     List[Any]						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     Optional[Any]						 =			TensorFlowBenchmark(lowercase_	)
      UpperCAmelCase	:     Dict						 =			benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result	)
      self.check_results_dict_not_empty(results.memory_train_result	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Any:
      UpperCAmelCase	:     Union[str, Any]						 =			'sshleifer/tiny-gpt2'
      UpperCAmelCase	:     Any						 =			AutoConfig.from_pretrained(lowercase_	)
      UpperCAmelCase	:     Any						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     Union[str, Any]						 =			TensorFlowBenchmark(lowercase_					,      [config]	)
      UpperCAmelCase	:     int						 =			benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result	)
      self.check_results_dict_not_empty(results.memory_train_result	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[str]:
      UpperCAmelCase	:     List[str]						 =			'patrickvonplaten/t5-tiny-random'
      UpperCAmelCase	:     int						 =			AutoConfig.from_pretrained(lowercase_	)
      UpperCAmelCase	:     Any						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     Tuple						 =			TensorFlowBenchmark(lowercase_					,      configs=[config]	)
      UpperCAmelCase	:     Tuple						 =			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result	)
      self.check_results_dict_not_empty(results.memory_inference_result	)


     @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'	)	) == 0					,      'Cannot do xla on CPU.'	)
     def        UpperCAmelCase_	(		self			:					Dict	)						->     Dict:
      UpperCAmelCase	:     Dict						 =			'sshleifer/tiny-gpt2'
      UpperCAmelCase	:     Dict						 =			TensorFlowBenchmarkArguments(
          models=[MODEL_ID]					,      training=lowercase_					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      use_xla=lowercase_					,      multi_process=lowercase_					,      )
      UpperCAmelCase	:     List[str]						 =			TensorFlowBenchmark(lowercase_	)
      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        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      UpperCAmelCase	:     Optional[Any]						 =			'sshleifer/tiny-gpt2'
      with tempfile.TemporaryDirectory() as tmp_dir:
       UpperCAmelCase	:     Optional[Any]						 =			TensorFlowBenchmarkArguments(
           models=[MODEL_ID]					,      inference=lowercase_					,      save_to_csv=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      inference_time_csv_file=os.path.join(lowercase_					,      'inf_time.csv'	)					,      inference_memory_csv_file=os.path.join(lowercase_					,      'inf_mem.csv'	)					,      env_info_csv_file=os.path.join(lowercase_					,      'env.csv'	)					,      multi_process=lowercase_					,      )
       UpperCAmelCase	:     Any						 =			TensorFlowBenchmark(lowercase_	)
       benchmark.run()
       self.assertTrue(Path(os.path.join(lowercase_					,      'inf_time.csv'	)	).exists()	)
       self.assertTrue(Path(os.path.join(lowercase_					,      'inf_mem.csv'	)	).exists()	)
       self.assertTrue(Path(os.path.join(lowercase_					,      'env.csv'	)	).exists()	)




     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
      UpperCAmelCase	:     int						 =			'sshleifer/tiny-gpt2'

      def _check_summary_is_not_empty(lowercase_			:					str	):
       self.assertTrue(hasattr(lowercase_					,      'sequential'	)	)
       self.assertTrue(hasattr(lowercase_					,      'cumulative'	)	)
       self.assertTrue(hasattr(lowercase_					,      'current'	)	)
       self.assertTrue(hasattr(lowercase_					,      'total'	)	)

      with tempfile.TemporaryDirectory() as tmp_dir:
       UpperCAmelCase	:     Dict						 =			TensorFlowBenchmarkArguments(
           models=[MODEL_ID]					,      inference=lowercase_					,      sequence_lengths=[8]					,      batch_sizes=[1]					,      log_filename=os.path.join(lowercase_					,      'log.txt'	)					,      log_print=lowercase_					,      trace_memory_line_by_line=lowercase_					,      eager_mode=lowercase_					,      multi_process=lowercase_					,      )
       UpperCAmelCase	:     Any						 =			TensorFlowBenchmark(lowercase_	)
       UpperCAmelCase	:     str						 =			benchmark.run()
       _check_summary_is_not_empty(result.inference_summary	)
       self.assertTrue(Path(os.path.join(lowercase_					,      'log.txt'	)	).exists()	)







'''simple docstring'''

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowercase__         =      TypeVar("KEY")
lowercase__         =      TypeVar("VAL")




@dataclass(frozen=_snake_case	,       slots=_snake_case							)
class 	A_			(    Generic[KEY, VAL]							):






     '''simple docstring'''



     UpperCAmelCase_						:     KEY
     UpperCAmelCase_						:     VAL




class 	A_			(    _Item							):






     '''simple docstring'''



     def __init__(		self			:					Any	)						->     None:
      super().__init__(lowercase_					,      lowercase_	)




     def __bool__(		self			:					List[str]	)						->     bool:
      return False



lowercase__         =      _DeletedItem()




class 	A_			(    MutableMapping[KEY, VAL]							):






     '''simple docstring'''



     def __init__(		self			:					Optional[int]					,      lowercase_			:					int = 8					,      lowercase_			:					float = 0.75	)						->     None:
      UpperCAmelCase	:     Dict						 =			initial_block_size
      UpperCAmelCase	:     list[_Item | None]						 =			[None] * initial_block_size
      assert 0.0 < capacity_factor < 1.0
      UpperCAmelCase	:     Any						 =			capacity_factor
      UpperCAmelCase	:     Union[str, Any]						 =			0


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					KEY	)						->     int:
      return hash(lowercase_	) % len(self._buckets	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int	)						->     int:
      return (ind + 1) % len(self._buckets	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     bool:
      UpperCAmelCase	:     List[Any]						 =			self._buckets[ind]
      if not stored:
       UpperCAmelCase	:     Dict						 =			_Item(lowercase_					,      lowercase_	)
       self._len += 1
       return True
      elif stored.key == key:
       UpperCAmelCase	:     Dict						 =			_Item(lowercase_					,      lowercase_	)
       return True
      else:
       return False


     def        UpperCAmelCase_	(		self			:					Any	)						->     bool:
      UpperCAmelCase	:     List[str]						 =			len(self._buckets	) * self._capacity_factor
      return len(self	) >= int(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     bool:
      if len(self._buckets	) <= self._initial_block_size:
       return False
      UpperCAmelCase	:     Optional[int]						 =			len(self._buckets	) * self._capacity_factor / 2
      return len(self	) < limit


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int	)						->     None:
      UpperCAmelCase	:     int						 =			self._buckets
      UpperCAmelCase	:     List[str]						 =			[None] * new_size
      UpperCAmelCase	:     Dict						 =			0
      for item in old_buckets:
       if item:
        self._add_item(item.key					,      item.val	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     None:
      self._resize(len(self._buckets	) * 2	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     None:
      self._resize(len(self._buckets	) // 2	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					KEY	)						->     Iterator[int]:
      UpperCAmelCase	:     Dict						 =			self._get_bucket_index(lowercase_	)
      for _ in range(len(self._buckets	)	):
       yield ind
       UpperCAmelCase	:     Union[str, Any]						 =			self._get_next_ind(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     None:
      for ind in self._iterate_buckets(lowercase_	):
       if self._try_set(lowercase_					,      lowercase_					,      lowercase_	):
        break


     def __setitem__(		self			:					Union[str, Any]					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     None:
      if self._is_full():
       self._size_up()

      self._add_item(lowercase_					,      lowercase_	)


     def __delitem__(		self			:					Tuple					,      lowercase_			:					KEY	)						->     None:
      for ind in self._iterate_buckets(lowercase_	):
       UpperCAmelCase	:     int						 =			self._buckets[ind]
       if item is None:
        raise KeyError(lowercase_	)
       if item is _deleted:
        continue
       if item.key == key:
        UpperCAmelCase	:     Union[str, Any]						 =			_deleted
        self._len -= 1
        break
      if self._is_sparse():
       self._size_down()


     def __getitem__(		self			:					Dict					,      lowercase_			:					KEY	)						->     VAL:
      for ind in self._iterate_buckets(lowercase_	):
       UpperCAmelCase	:     List[str]						 =			self._buckets[ind]
       if item is None:
        break
       if item is _deleted:
        continue
       if item.key == key:
        return item.val
      raise KeyError(lowercase_	)


     def __len__(		self			:					Optional[Any]	)						->     int:
      return self._len


     def __iter__(		self			:					int	)						->     Iterator[KEY]:
      yield from (item.key for item in self._buckets if item)




     def __repr__(		self			:					Optional[Any]	)						->     str:
      UpperCAmelCase	:     int						 =			' ,'.join(
          f"""{item.key}: {item.val}""" for item in self._buckets if item	)
      return f"""HashMap({val_string})"""




