
import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for model_result in results.values():
			for batch_size, sequence_length in zip(model_result['bs']		,    model_result['ss']       ):
				lowercase       =    model_result['result'][batch_size][sequence_length]
				self.assertIsNotNone(_UpperCamelCase       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result       )
		self.check_results_dict_not_empty(results.memory_inference_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sgugger/tiny-distilbert-classification'
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    only_pretrain_model=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result       )
		self.check_results_dict_not_empty(results.memory_inference_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    torchscript=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result       )
		self.check_results_dict_not_empty(results.memory_inference_result       )





	@unittest.skipIf(torch_device == 'cpu'		,    'Cant do half precision'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    fpaa=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result       )
		self.check_results_dict_not_empty(results.memory_inference_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    AutoConfig.from_pretrained(_UpperCamelCase       )
		# set architectures equal to `None`
		lowercase       =    None
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase		,    configs=[config]       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result       )
		self.check_results_dict_not_empty(results.memory_inference_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_train_result       )
		self.check_results_dict_not_empty(results.memory_train_result       )





	@unittest.skipIf(torch_device == 'cpu'		,    'Can\'t do half precision'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    fpaa=_UpperCamelCase		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_train_result       )
		self.check_results_dict_not_empty(results.memory_train_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    AutoConfig.from_pretrained(_UpperCamelCase       )
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase		,    configs=[config]       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result       )
		self.check_results_dict_not_empty(results.memory_inference_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tinier_bart'
		lowercase       =    AutoConfig.from_pretrained(_UpperCamelCase       )
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase		,    configs=[config]       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_inference_result       )
		self.check_results_dict_not_empty(results.memory_inference_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		lowercase       =    AutoConfig.from_pretrained(_UpperCamelCase       )
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase		,    configs=[config]       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_train_result       )
		self.check_results_dict_not_empty(results.memory_train_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tinier_bart'
		lowercase       =    AutoConfig.from_pretrained(_UpperCamelCase       )
		lowercase       =    PyTorchBenchmarkArguments(
		    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    multi_process=_UpperCamelCase		,    )
		lowercase       =    PyTorchBenchmark(_UpperCamelCase		,    configs=[config]       )
		lowercase       =    benchmark.run()
		self.check_results_dict_not_empty(results.time_train_result       )
		self.check_results_dict_not_empty(results.memory_train_result       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'
		with tempfile.TemporaryDirectory() as tmp_dir:
			lowercase       =    PyTorchBenchmarkArguments(
			    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    save_to_csv=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    inference_time_csv_file=os.path.join(_UpperCamelCase		,    'inf_time.csv'       )		,    train_memory_csv_file=os.path.join(_UpperCamelCase		,    'train_mem.csv'       )		,    inference_memory_csv_file=os.path.join(_UpperCamelCase		,    'inf_mem.csv'       )		,    train_time_csv_file=os.path.join(_UpperCamelCase		,    'train_time.csv'       )		,    env_info_csv_file=os.path.join(_UpperCamelCase		,    'env.csv'       )		,    multi_process=_UpperCamelCase		,    )
			lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
			benchmark.run()
			self.assertTrue(Path(os.path.join(_UpperCamelCase		,    'inf_time.csv'       )       ).exists()       )
			self.assertTrue(Path(os.path.join(_UpperCamelCase		,    'train_time.csv'       )       ).exists()       )
			self.assertTrue(Path(os.path.join(_UpperCamelCase		,    'inf_mem.csv'       )       ).exists()       )
			self.assertTrue(Path(os.path.join(_UpperCamelCase		,    'train_mem.csv'       )       ).exists()       )
			self.assertTrue(Path(os.path.join(_UpperCamelCase		,    'env.csv'       )       ).exists()       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'sshleifer/tiny-gpt2'

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

		with tempfile.TemporaryDirectory() as tmp_dir:
			lowercase       =    PyTorchBenchmarkArguments(
			    models=[MODEL_ID]		,    training=_UpperCamelCase		,    inference=_UpperCamelCase		,    sequence_lengths=[8]		,    batch_sizes=[1]		,    log_filename=os.path.join(_UpperCamelCase		,    'log.txt'       )		,    log_print=_UpperCamelCase		,    trace_memory_line_by_line=_UpperCamelCase		,    multi_process=_UpperCamelCase		,    )
			lowercase       =    PyTorchBenchmark(_UpperCamelCase       )
			lowercase       =    benchmark.run()
			_check_summary_is_not_empty(result.inference_summary       )
			_check_summary_is_not_empty(result.train_summary       )
			self.assertTrue(Path(os.path.join(_UpperCamelCase		,    'log.txt'       )       ).exists()       )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


_A   =     {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextTokenizer"]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextFeatureExtractor"]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFSpeech2TextForConditionalGeneration",
			    "TFSpeech2TextModel",
			    "TFSpeech2TextPreTrainedModel",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Speech2TextForConditionalGeneration",
			    "Speech2TextModel",
			    "Speech2TextPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

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



