






'''simple docstring'''







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 _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Dict  ):
														for model_result in results.values():
																					for batch_size, sequence_length in zip(model_result["""bs"""]    ,							model_result["""ss"""]  ):
																												lowerCamelCase__      :			str 						= model_result["""result"""][batch_size][sequence_length]
																												self.assertIsNotNone(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			Union[str, Any] 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			List[Any] 						= PyTorchBenchmark(UpperCamelCase__  )
														lowerCamelCase__      :			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 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Union[str, Any] 						= """sgugger/tiny-distilbert-classification"""
														lowerCamelCase__      :			List[Any] 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							only_pretrain_model=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Dict 						= PyTorchBenchmark(UpperCamelCase__  )
														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:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			Optional[int] 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							torchscript=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			str 						= PyTorchBenchmark(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= 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 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			Dict 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			Dict 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							fpaa=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			List[Any] 						= PyTorchBenchmark(UpperCamelCase__  )
														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:       List[str]  ):
														lowerCamelCase__      :			Optional[int] 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			Optional[Any] 						= AutoConfig.from_pretrained(UpperCamelCase__  )
														# set architectures equal to `None`
														lowerCamelCase__      :			List[Any] 						= None
														lowerCamelCase__      :			Any 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Optional[Any] 						= PyTorchBenchmark(UpperCamelCase__    ,							configs=[config]  )
														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:       Union[str, Any]  ):
														lowerCamelCase__      :			List[Any] 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			Dict 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Tuple 						= PyTorchBenchmark(UpperCamelCase__  )
														lowerCamelCase__      :			int 						= 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 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Optional[Any] 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			Any 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							fpaa=UpperCamelCase__    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			str 						= PyTorchBenchmark(UpperCamelCase__  )
														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:       Dict  ):
														lowerCamelCase__      :			int 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			List[Any] 						= AutoConfig.from_pretrained(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			List[Any] 						= PyTorchBenchmark(UpperCamelCase__    ,							configs=[config]  )
														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 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Tuple 						= """sshleifer/tinier_bart"""
														lowerCamelCase__      :			Optional[Any] 						= AutoConfig.from_pretrained(UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			List[Any] 						= PyTorchBenchmark(UpperCamelCase__    ,							configs=[config]  )
														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 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Any 						= """sshleifer/tiny-gpt2"""
														lowerCamelCase__      :			Union[str, Any] 						= AutoConfig.from_pretrained(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Tuple 						= PyTorchBenchmark(UpperCamelCase__    ,							configs=[config]  )
														lowerCamelCase__      :			Dict 						= 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:       Any  ):
														lowerCamelCase__      :			str 						= """sshleifer/tinier_bart"""
														lowerCamelCase__      :			Optional[int] 						= AutoConfig.from_pretrained(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= PyTorchBenchmarkArguments(
														    models=[MODEL_ID]    ,							training=UpperCamelCase__    ,							inference=UpperCamelCase__    ,							sequence_lengths=[8]    ,							batch_sizes=[1]    ,							multi_process=UpperCamelCase__    ,							)
														lowerCamelCase__      :			List[Any] 						= PyTorchBenchmark(UpperCamelCase__    ,							configs=[config]  )
														lowerCamelCase__      :			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 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			str 						= """sshleifer/tiny-gpt2"""
														with tempfile.TemporaryDirectory() as tmp_dir:
																					lowerCamelCase__      :			str 						= 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__    ,							)
																					lowerCamelCase__      :			int 						= 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 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Dict 						= """sshleifer/tiny-gpt2"""

														def _check_summary_is_not_empty(UpperCamelCase__:       str  ):
																					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:
																					lowerCamelCase__      :			Dict 						= 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__    ,							)
																					lowerCamelCase__      :			Union[str, Any] 						= PyTorchBenchmark(UpperCamelCase__  )
																					lowerCamelCase__      :			Optional[Any] 						= 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()  )









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A			:    Dict					  ={
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    Union[str, Any]					  =[
			    '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GitForCausalLM''',
			    '''GitModel''',
			    '''GitPreTrainedModel''',
			    '''GitVisionModel''',
			]

if TYPE_CHECKING:
			from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
			from .processing_git import GitProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_git import (
						    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GitForCausalLM,
						    GitModel,
						    GitPreTrainedModel,
						    GitVisionModel,
						)

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

