
"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__    =		{"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__    =		["""FNetTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__    =		["""FNetTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__    =		[
							    """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FNetForMaskedLM""",
							    """FNetForMultipleChoice""",
							    """FNetForNextSentencePrediction""",
							    """FNetForPreTraining""",
							    """FNetForQuestionAnswering""",
							    """FNetForSequenceClassification""",
							    """FNetForTokenClassification""",
							    """FNetLayer""",
							    """FNetModel""",
							    """FNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

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

"""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	,		_SCREAMING_SNAKE_CASE			):
			for model_result in results.values():
				for batch_size, sequence_length in zip(model_result['bs']	,		model_result['ss']			):
					__lowerCAmelCase      :						Optional[Any]						      =				model_result['result'][batch_size][sequence_length]
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Tuple						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				'sgugger/tiny-distilbert-classification'
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		only_pretrain_model=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[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			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Tuple						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						Dict						      =				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      :						Any						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Any						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						List[Any]						      =				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      :						Optional[int]						      =				'patrickvonplaten/t5-tiny-random'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		configs=[config]			)
			__lowerCAmelCase      :						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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		use_xla=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				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      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]	,		inference=_SCREAMING_SNAKE_CASE	,		save_to_csv=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		inference_time_csv_file=os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_time.csv'			)	,		inference_memory_csv_file=os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_mem.csv'			)	,		env_info_csv_file=os.path.join(_SCREAMING_SNAKE_CASE	,		'env.csv'			)	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
				__lowerCAmelCase      :						Tuple						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
				benchmark.run()
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_time.csv'			)			).exists()			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'inf_mem.csv'			)			).exists()			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'env.csv'			)			).exists()			)







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

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

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