



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    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    "sshleifer/tiny-gpt2"
														_lowerCAmelCase     :							Tuple    =    TensorFlowBenchmarkArguments(
														    models=[MODEL_ID],			training=__a,			inference=__a,			sequence_lengths=[8],			batch_sizes=[1],			eager_mode=__a,			multi_process=__a,			)
														_lowerCAmelCase     :							str    =    TensorFlowBenchmark(__a)
														_lowerCAmelCase     :							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    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    "sshleifer/tiny-gpt2"
														_lowerCAmelCase     :							Optional[Any]    =    TensorFlowBenchmarkArguments(
														    models=[MODEL_ID],			training=__a,			inference=__a,			sequence_lengths=[8],			batch_sizes=[1],			multi_process=__a,			)
														_lowerCAmelCase     :							List[Any]    =    TensorFlowBenchmark(__a)
														_lowerCAmelCase     :							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    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    "sshleifer/tiny-gpt2"
														_lowerCAmelCase     :							int    =    AutoConfig.from_pretrained(__a)
														_lowerCAmelCase     :							List[Any]    =    TensorFlowBenchmarkArguments(
														    models=[MODEL_ID],			training=__a,			inference=__a,			sequence_lengths=[8],			batch_sizes=[1],			multi_process=__a,			)
														_lowerCAmelCase     :							Any    =    TensorFlowBenchmark(__a,			[config])
														_lowerCAmelCase     :							Optional[Any]    =    benchmark.run()
														self.check_results_dict_not_empty(results.time_inference_result)
														self.check_results_dict_not_empty(results.memory_inference_result)







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    "patrickvonplaten/t5-tiny-random"
														_lowerCAmelCase     :							List[str]    =    AutoConfig.from_pretrained(__a)
														_lowerCAmelCase     :							Tuple    =    TensorFlowBenchmarkArguments(
														    models=[MODEL_ID],			training=__a,			inference=__a,			sequence_lengths=[8],			batch_sizes=[1],			multi_process=__a,			)
														_lowerCAmelCase     :							Optional[Any]    =    TensorFlowBenchmark(__a,			configs=[config])
														_lowerCAmelCase     :							str    =    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    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    "sshleifer/tiny-gpt2"
														with tempfile.TemporaryDirectory() as tmp_dir:
																					_lowerCAmelCase     :							Optional[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,			)
																					_lowerCAmelCase     :							List[str]    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    "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     :							str    =    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     :							List[Any]    =    TensorFlowBenchmark(__a)
																					_lowerCAmelCase     :							Tuple    =    benchmark.run()
																					_check_summary_is_not_empty(result.inference_summary)
																					self.assertTrue(Path(os.path.join(__a,			"log.txt")).exists())



from typing import Dict

from .base import GenericTensor, Pipeline


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'		)
            A__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    Any:





        '''simple docstring'''







        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()


    def __call__(      self :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        return super().__call__(*lowercase_,**lowercase_		)

