





"""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 					__a     (     self   ,      lowerCAmelCase__   )			->				Dict:
								for model_result in results.values():
									for batch_size, sequence_length in zip(model_result["bs"]   ,      model_result["ss"]   ):
										a   :	str			=       model_result["result"][batch_size][sequence_length]
										self.assertIsNotNone(lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Dict			=       "sshleifer/tiny-gpt2"
								a   :	List[str]			=       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,      training=lowerCAmelCase__   ,      inference=lowerCAmelCase__   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=lowerCAmelCase__   ,      multi_process=lowerCAmelCase__   ,      )
								a   :	Any			=       TensorFlowBenchmark(lowerCAmelCase__   )
								a   :	List[Any]			=       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )






							def 					__a     (     self   )			->				str:
								a   :	Any			=       "sgugger/tiny-distilbert-classification"
								a   :	List[str]			=       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,      training=lowerCAmelCase__   ,      inference=lowerCAmelCase__   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=lowerCAmelCase__   ,      only_pretrain_model=lowerCAmelCase__   ,      )
								a   :	Optional[Any]			=       TensorFlowBenchmark(lowerCAmelCase__   )
								a   :	int			=       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )






							def 					__a     (     self   )			->				str:
								a   :	List[Any]			=       "sshleifer/tiny-gpt2"
								a   :	Optional[Any]			=       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,      training=lowerCAmelCase__   ,      inference=lowerCAmelCase__   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=lowerCAmelCase__   ,      )
								a   :	List[Any]			=       TensorFlowBenchmark(lowerCAmelCase__   )
								a   :	str			=       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )






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






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






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






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






							def 					__a     (     self   )			->				int:
								a   :	Any			=       "patrickvonplaten/t5-tiny-random"
								a   :	List[str]			=       AutoConfig.from_pretrained(lowerCAmelCase__   )
								a   :	List[str]			=       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,      training=lowerCAmelCase__   ,      inference=lowerCAmelCase__   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=lowerCAmelCase__   ,      )
								a   :	Union[str, Any]			=       TensorFlowBenchmark(lowerCAmelCase__   ,      configs=[config]   )
								a   :	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 					__a     (     self   )			->				str:
								a   :	int			=       "sshleifer/tiny-gpt2"
								a   :	Any			=       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]   ,      training=lowerCAmelCase__   ,      inference=lowerCAmelCase__   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      use_xla=lowerCAmelCase__   ,      multi_process=lowerCAmelCase__   ,      )
								a   :	Optional[int]			=       TensorFlowBenchmark(lowerCAmelCase__   )
								a   :	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 					__a     (     self   )			->				Optional[int]:
								a   :	Any			=       "sshleifer/tiny-gpt2"
								with tempfile.TemporaryDirectory() as tmp_dir:
									a   :	Any			=       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__   ,      )
									a   :	Any			=       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 					__a     (     self   )			->				int:
								a   :	int			=       "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:
									a   :	Tuple			=       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__   ,      )
									a   :	str			=       TensorFlowBenchmark(lowerCAmelCase__   )
									a   :	Optional[Any]			=       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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a		:				List[Any]  			=    TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a		:				Union[str, Any]  			=    TaTokenizerFast

a		:				Union[str, Any]  			=    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				str  			=    [
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				List[str]  			=    ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				Optional[Any]  			=    ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a		:				List[Any]  			=    _LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)


