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 				__UpperCamelCase   (    self      :       int,_A      :       List[Any]    ):







							"""simple docstring"""






							for model_result in results.values():
									for batch_size, sequence_length in zip(model_result["bs"],model_result["ss"]    ):
											SCREAMING_SNAKE_CASE_  :		Any				      =    model_result["result"][batch_size][sequence_length]
											self.assertIsNotNone(_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "sgugger/tiny-distilbert-classification"
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,only_pretrain_model=_A,)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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"""






							SCREAMING_SNAKE_CASE_  :		str				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    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      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],eager_mode=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		int				      =    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      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Dict				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "patrickvonplaten/t5-tiny-random"
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmark(_A,configs=[config]    )
							SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],use_xla=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "sshleifer/tiny-gpt2"
							with tempfile.TemporaryDirectory() as tmp_dir:
									SCREAMING_SNAKE_CASE_  :		Union[str, 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,)
									SCREAMING_SNAKE_CASE_  :		Dict				      =    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      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "sshleifer/tiny-gpt2"

							def _check_summary_is_not_empty(_A      :       Optional[Any]    ):
									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:
									SCREAMING_SNAKE_CASE_  :		int				      =    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,)
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    TensorFlowBenchmark(_A    )
									SCREAMING_SNAKE_CASE_  :		int				      =    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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowercase  							=					{"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTNeoXForCausalLM",
	    "GPTNeoXForQuestionAnswering",
	    "GPTNeoXForSequenceClassification",
	    "GPTNeoXForTokenClassification",
	    "GPTNeoXLayer",
	    "GPTNeoXModel",
	    "GPTNeoXPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

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





