

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 ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					self				,				A_						) ->				List[Any]:






           """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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """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 		UpperCamelCase_						(					self						) ->				Tuple:






           """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 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """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 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """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 		UpperCamelCase_						(					self						) ->				Tuple:






           """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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """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 		UpperCamelCase_						(					self						) ->				int:






           """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 		UpperCamelCase_						(					self						) ->				Tuple:






           """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 		UpperCamelCase_						(					self						) ->				List[str]:






           """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


snake_case__				= {
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TrajectoryTransformerModel',
         'TrajectoryTransformerPreTrainedModel',
         'load_tf_weights_in_trajectory_transformer',
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)