"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_UpperCamelCase     :		Any											=					logging.get_logger(__name__)


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					List[Any]										=						"upernet"


      def __init__(     self	,    a=None	,    a=5_1_2	,    a=0.02	,    a=[1, 2, 3, 6]	,    a=True	,    a=0.4	,    a=3_8_4	,    a=2_5_6	,    a=1	,    a=False	,    a=2_5_5	,    **a	,    ) ->      List[Any]:
             super().__init__(**a							)

             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'							)
                    lowercase__    :	Tuple     					=			CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']							)
             elif isinstance(a	,    a							):
                    lowercase__    :	Any     					=			backbone_config.get('model_type'							)
                    lowercase__    :	Any     					=			CONFIG_MAPPING[backbone_model_type]
                    lowercase__    :	str     					=			config_class.from_dict(a							)

             lowercase__    :	Optional[Any]     					=			backbone_config
             lowercase__    :	Any     					=			hidden_size
             lowercase__    :	Dict     					=			initializer_range
             lowercase__    :	Tuple     					=			pool_scales
             lowercase__    :	List[Any]     					=			use_auxiliary_head
             lowercase__    :	Any     					=			auxiliary_loss_weight
             lowercase__    :	List[Any]     					=			auxiliary_in_channels
             lowercase__    :	Any     					=			auxiliary_channels
             lowercase__    :	str     					=			auxiliary_num_convs
             lowercase__    :	Tuple     					=			auxiliary_concat_input
             lowercase__    :	List[Any]     					=			loss_ignore_index


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Any     					=			copy.deepcopy(self.__dict__							)
             lowercase__    :	Union[str, Any]     					=			self.backbone_config.to_dict()
             lowercase__    :	Optional[Any]     					=			self.__class__.model_type
             return output


"""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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             for model_result in results.values():
                    for batch_size, sequence_length in zip(model_result['bs']	,    model_result['ss']							):
                           lowercase__    :	str     					=			model_result['result'][batch_size][sequence_length]
                           self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a							)
             lowercase__    :	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							) ->      int:
             lowercase__    :	List[str]     					=			'sgugger/tiny-distilbert-classification'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    only_pretrain_model=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	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							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	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							) ->      Optional[Any]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[Any]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	Tuple     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Dict     					=			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:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[str]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	List[str]     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Any     					=			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:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Tuple     					=			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							) ->      str:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[int]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	str     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	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 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[str]     					=			'patrickvonplaten/t5-tiny-random'
             lowercase__    :	Any     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	List[str]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	int     					=			TensorFlowBenchmark(a	,    configs=[config]							)
             lowercase__    :	Union[str, Any]     					=			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							) ->      Any:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    use_xla=a	,    multi_process=a	,    )
             lowercase__    :	Any     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Dict     					=			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							) ->      List[str]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[Any]     					=			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	,    )
                    lowercase__    :	Union[str, Any]     					=			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 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Tuple     					=			'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:
                    lowercase__    :	Optional[Any]     					=			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	,    )
                    lowercase__    :	Optional[int]     					=			TensorFlowBenchmark(a							)
                    lowercase__    :	Optional[Any]     					=			benchmark.run()
                    _check_summary_is_not_empty(result.inference_summary							)
                    self.assertTrue(Path(os.path.join(a	,    'log.txt'							)							).exists()							)

