



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    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           for model_result in results.values():
                 for batch_size, sequence_length in zip(model_result["bs"]				,			model_result["ss"]    ):
                       lowerCamelCase								=		model_result["result"][batch_size][sequence_length]
                       self.assertIsNotNone(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			eager_mode=__a				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result    )
           self.check_results_dict_not_empty(results.memory_inference_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sgugger/tiny-distilbert-classification"
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=__a				,			only_pretrain_model=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result    )
           self.check_results_dict_not_empty(results.memory_inference_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result    )
           self.check_results_dict_not_empty(results.memory_inference_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           lowerCamelCase								=		AutoConfig.from_pretrained(__a    )
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			eager_mode=__a				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a				,			[config]    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result    )
           self.check_results_dict_not_empty(results.memory_inference_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           lowerCamelCase								=		AutoConfig.from_pretrained(__a    )
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a				,			[config]    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result    )
           self.check_results_dict_not_empty(results.memory_inference_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result    )
           self.check_results_dict_not_empty(results.memory_train_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           lowerCamelCase								=		AutoConfig.from_pretrained(__a    )
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a				,			[config]    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result    )
           self.check_results_dict_not_empty(results.memory_train_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"patrickvonplaten/t5-tiny-random"
           lowerCamelCase								=		AutoConfig.from_pretrained(__a    )
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a				,			configs=[config]    )
           lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           lowerCamelCase								=		TensorFlowBenchmarkArguments(
               models=[MODEL_ID]				,			training=__a				,			inference=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			use_xla=__a				,			multi_process=__a				,			)
           lowerCamelCase								=		TensorFlowBenchmark(__a    )
           lowerCamelCase								=		benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result    )
           self.check_results_dict_not_empty(results.memory_inference_result    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"
           with tempfile.TemporaryDirectory() as tmp_dir:
                 lowerCamelCase								=		TensorFlowBenchmarkArguments(
                     models=[MODEL_ID]				,			inference=__a				,			save_to_csv=__a				,			sequence_lengths=[8]				,			batch_sizes=[1]				,			inference_time_csv_file=os.path.join(__a				,			"inf_time.csv"    )				,			inference_memory_csv_file=os.path.join(__a				,			"inf_mem.csv"    )				,			env_info_csv_file=os.path.join(__a				,			"env.csv"    )				,			multi_process=__a				,			)
                 lowerCamelCase								=		TensorFlowBenchmark(__a    )
                 benchmark.run()
                 self.assertTrue(Path(os.path.join(__a				,			"inf_time.csv"    )    ).exists()    )
                 self.assertTrue(Path(os.path.join(__a				,			"inf_mem.csv"    )    ).exists()    )
                 self.assertTrue(Path(os.path.join(__a				,			"env.csv"    )    ).exists()    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"sshleifer/tiny-gpt2"

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

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




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_						:     int	     =		{'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Dict	     =		[
         'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'VanForImageClassification',
         'VanModel',
         'VanPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     a_						:     Union[str, Any]	     =		_LazyModule(__name__, globals()['__file__'], _import_structure)