"""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   ):


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple					):
      for model_result in results.values():
        for batch_size, sequence_length in zip(model_result["""bs"""]   ,				model_result["""ss"""]					):
          _UpperCAmelCase						 =      model_result["""result"""][batch_size][sequence_length]
          self.assertIsNotNone(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				eager_mode=__lowerCAmelCase   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      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	:  List[str]					):
      _UpperCAmelCase						 =      """sgugger/tiny-distilbert-classification"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				only_pretrain_model=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      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	:  int					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      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	:  Any					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				eager_mode=__lowerCAmelCase   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				[config]					)
      _UpperCAmelCase						 =      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	:  Dict					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				[config]					)
      _UpperCAmelCase						 =      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	:  Optional[int]					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      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	:  Dict					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				[config]					)
      _UpperCAmelCase						 =      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	:  Optional[Any]					):
      _UpperCAmelCase						 =      """patrickvonplaten/t5-tiny-random"""
      _UpperCAmelCase						 =      AutoConfig.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase   ,				configs=[config]					)
      _UpperCAmelCase						 =      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	:  int					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      _UpperCAmelCase						 =      TensorFlowBenchmarkArguments(
          models=[MODEL_ID]   ,				training=__lowerCAmelCase   ,				inference=__lowerCAmelCase   ,				sequence_lengths=[8]   ,				batch_sizes=[1]   ,				use_xla=__lowerCAmelCase   ,				multi_process=__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
      _UpperCAmelCase						 =      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	:  Tuple					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""
      with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase						 =      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   ,				)
        _UpperCAmelCase						 =      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	:  str					):
      _UpperCAmelCase						 =      """sshleifer/tiny-gpt2"""

      def _check_summary_is_not_empty(__lowerCAmelCase	:  List[str]					):
        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:
        _UpperCAmelCase						 =      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   ,				)
        _UpperCAmelCase						 =      TensorFlowBenchmark(__lowerCAmelCase					)
        _UpperCAmelCase						 =      benchmark.run()
        _check_summary_is_not_empty(result.inference_summary					)
        self.assertTrue(Path(os.path.join(__lowerCAmelCase   ,				"""log.txt"""					)					).exists()					)

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

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