



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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         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(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     eager_mode=_lowerCAmelCase       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase			)
         _lowerCAmelCase 			=  benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result			)
         self.check_results_dict_not_empty(results.memory_inference_result			)





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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase			)
         _lowerCAmelCase 			=  benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result			)
         self.check_results_dict_not_empty(results.memory_inference_result			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''
         _lowerCAmelCase 			=  AutoConfig.from_pretrained(_lowerCAmelCase			)
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     eager_mode=_lowerCAmelCase       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase       ,     [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 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''
         _lowerCAmelCase 			=  AutoConfig.from_pretrained(_lowerCAmelCase			)
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase       ,     [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 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase			)
         _lowerCAmelCase 			=  benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result			)
         self.check_results_dict_not_empty(results.memory_train_result			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''
         _lowerCAmelCase 			=  AutoConfig.from_pretrained(_lowerCAmelCase			)
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase       ,     [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 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''patrickvonplaten/t5-tiny-random'''
         _lowerCAmelCase 			=  AutoConfig.from_pretrained(_lowerCAmelCase			)
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase       ,     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 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''
         _lowerCAmelCase 			=  TensorFlowBenchmarkArguments(
             models=[MODEL_ID]       ,     training=_lowerCAmelCase       ,     inference=_lowerCAmelCase       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     use_xla=_lowerCAmelCase       ,     multi_process=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  TensorFlowBenchmark(_lowerCAmelCase			)
         _lowerCAmelCase 			=  benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result			)
         self.check_results_dict_not_empty(results.memory_inference_result			)





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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''sshleifer/tiny-gpt2'''

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

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)