

"""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        snake_case      (							unittest.TestCase    ):

				def 				lowercase_	(      self				:							List[str]	,    UpperCamelCase__				:							Union[str, Any])->  List[str]:



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							List[str])->  int:



						'''simple docstring'''


						__lowerCAmelCase:			Union[str, Any]        =     """sshleifer/tiny-gpt2"""
						__lowerCAmelCase:			Tuple        =     TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]	,    training=__UpperCamelCase	,    inference=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=__UpperCamelCase	,    multi_process=__UpperCamelCase	,    )
						__lowerCAmelCase:			str        =     TensorFlowBenchmark(__UpperCamelCase)
						__lowerCAmelCase:			Any        =     benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result)
						self.check_results_dict_not_empty(results.memory_inference_result)

				def 				lowercase_	(      self				:							Optional[Any])->  Dict:



						'''simple docstring'''


						__lowerCAmelCase:			str        =     """sgugger/tiny-distilbert-classification"""
						__lowerCAmelCase:			Dict        =     TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]	,    training=__UpperCamelCase	,    inference=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=__UpperCamelCase	,    only_pretrain_model=__UpperCamelCase	,    )
						__lowerCAmelCase:			Dict        =     TensorFlowBenchmark(__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 				lowercase_	(      self				:							List[str])->  Dict:



						'''simple docstring'''


						__lowerCAmelCase:			Dict        =     """sshleifer/tiny-gpt2"""
						__lowerCAmelCase:			Any        =     TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]	,    training=__UpperCamelCase	,    inference=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=__UpperCamelCase	,    )
						__lowerCAmelCase:			Any        =     TensorFlowBenchmark(__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 				lowercase_	(      self				:							Union[str, Any])->  Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:			Union[str, Any]        =     """sshleifer/tiny-gpt2"""
						__lowerCAmelCase:			Dict        =     AutoConfig.from_pretrained(__UpperCamelCase)
						__lowerCAmelCase:			Tuple        =     TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]	,    training=__UpperCamelCase	,    inference=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=__UpperCamelCase	,    multi_process=__UpperCamelCase	,    )
						__lowerCAmelCase:			Optional[int]        =     TensorFlowBenchmark(__UpperCamelCase	,    [config])
						__lowerCAmelCase:			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 				lowercase_	(      self				:							Optional[int])->  str:



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							Optional[Any])->  Tuple:



						'''simple docstring'''


						__lowerCAmelCase:			str        =     """sshleifer/tiny-gpt2"""
						__lowerCAmelCase:			Optional[int]        =     TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]	,    training=__UpperCamelCase	,    inference=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=__UpperCamelCase	,    )
						__lowerCAmelCase:			Dict        =     TensorFlowBenchmark(__UpperCamelCase)
						__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 				lowercase_	(      self				:							Optional[int])->  List[str]:



						'''simple docstring'''


						__lowerCAmelCase:			int        =     """sshleifer/tiny-gpt2"""
						__lowerCAmelCase:			int        =     AutoConfig.from_pretrained(__UpperCamelCase)
						__lowerCAmelCase:			str        =     TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]	,    training=__UpperCamelCase	,    inference=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=__UpperCamelCase	,    )
						__lowerCAmelCase:			List[str]        =     TensorFlowBenchmark(__UpperCamelCase	,    [config])
						__lowerCAmelCase:			int        =     benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result)
						self.check_results_dict_not_empty(results.memory_train_result)

				def 				lowercase_	(      self				:							str)->  Optional[int]:



						'''simple docstring'''


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

				@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU")) == 0	,    "Cannot do xla on CPU.")
				def 				lowercase_	(      self				:							str)->  Any:



						'''simple docstring'''


						__lowerCAmelCase:			List[Any]        =     """sshleifer/tiny-gpt2"""
						__lowerCAmelCase:			str        =     TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]	,    training=__UpperCamelCase	,    inference=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    use_xla=__UpperCamelCase	,    multi_process=__UpperCamelCase	,    )
						__lowerCAmelCase:			str        =     TensorFlowBenchmark(__UpperCamelCase)
						__lowerCAmelCase:			Any        =     benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result)
						self.check_results_dict_not_empty(results.memory_inference_result)

				def 				lowercase_	(      self				:							List[str])->  Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:			int        =     """sshleifer/tiny-gpt2"""
						with tempfile.TemporaryDirectory() as tmp_dir:
								__lowerCAmelCase:			Optional[Any]        =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]	,    inference=__UpperCamelCase	,    save_to_csv=__UpperCamelCase	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    inference_time_csv_file=os.path.join(__UpperCamelCase	,    "inf_time.csv")	,    inference_memory_csv_file=os.path.join(__UpperCamelCase	,    "inf_mem.csv")	,    env_info_csv_file=os.path.join(__UpperCamelCase	,    "env.csv")	,    multi_process=__UpperCamelCase	,    )
								__lowerCAmelCase:			List[str]        =     TensorFlowBenchmark(__UpperCamelCase)
								benchmark.run()
								self.assertTrue(Path(os.path.join(__UpperCamelCase	,    "inf_time.csv")).exists())
								self.assertTrue(Path(os.path.join(__UpperCamelCase	,    "inf_mem.csv")).exists())
								self.assertTrue(Path(os.path.join(__UpperCamelCase	,    "env.csv")).exists())




				def 				lowercase_	(      self				:							str)->  Dict:



						'''simple docstring'''


						__lowerCAmelCase:			str        =     """sshleifer/tiny-gpt2"""

						def _check_summary_is_not_empty(UpperCamelCase__				:							Union[str, Any]):
								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:			List[Any]        =     TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]	,    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	,    eager_mode=__UpperCamelCase	,    multi_process=__UpperCamelCase	,    )
								__lowerCAmelCase:			Optional[Any]        =     TensorFlowBenchmark(__UpperCamelCase)
								__lowerCAmelCase:			List[str]        =     benchmark.run()
								_check_summary_is_not_empty(result.inference_summary)
								self.assertTrue(Path(os.path.join(__UpperCamelCase	,    "log.txt")).exists())



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






