






'''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      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       for model_result in results.values():
             for batch_size, sequence_length in zip(model_result["""bs"""]      ,  model_result["""ss"""]			):
                   UpperCamelCase_					: Union[str, Any]            =				model_result["""result"""][batch_size][sequence_length]
                   self.assertIsNotNone(__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Any            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: Dict            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				PyTorchBenchmark(__lowerCAmelCase			)
       UpperCamelCase_					: 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			):
       UpperCamelCase_					: Dict            =				"""sgugger/tiny-distilbert-classification"""
       UpperCamelCase_					: List[Any]            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  only_pretrain_model=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				PyTorchBenchmark(__lowerCAmelCase			)
       UpperCamelCase_					: 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			):
       UpperCamelCase_					: Tuple            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: List[str]            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  torchscript=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: List[str]            =				PyTorchBenchmark(__lowerCAmelCase			)
       UpperCamelCase_					: Optional[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(torch_device == """cpu"""      ,  """Cant do half precision"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: str            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  fpaa=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Dict            =				PyTorchBenchmark(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				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			):
       UpperCamelCase_					: Optional[Any]            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: int            =				AutoConfig.from_pretrained(__lowerCAmelCase			)
       # set architectures equal to `None`
       UpperCamelCase_					: List[str]            =				None
       UpperCamelCase_					: List[str]            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Dict            =				PyTorchBenchmark(__lowerCAmelCase      ,  configs=[config]			)
       UpperCamelCase_					: 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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: Dict            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				PyTorchBenchmark(__lowerCAmelCase			)
       UpperCamelCase_					: List[Any]            =				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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: Optional[int]            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  fpaa=__lowerCAmelCase      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Any            =				PyTorchBenchmark(__lowerCAmelCase			)
       UpperCamelCase_					: 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			):
       UpperCamelCase_					: List[Any]            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: Dict            =				AutoConfig.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: Any            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: int            =				PyTorchBenchmark(__lowerCAmelCase      ,  configs=[config]			)
       UpperCamelCase_					: 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			):
       UpperCamelCase_					: Union[str, Any]            =				"""sshleifer/tinier_bart"""
       UpperCamelCase_					: Any            =				AutoConfig.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Tuple            =				PyTorchBenchmark(__lowerCAmelCase      ,  configs=[config]			)
       UpperCamelCase_					: 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			):
       UpperCamelCase_					: Dict            =				"""sshleifer/tiny-gpt2"""
       UpperCamelCase_					: List[Any]            =				AutoConfig.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: str            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Optional[Any]            =				PyTorchBenchmark(__lowerCAmelCase      ,  configs=[config]			)
       UpperCamelCase_					: Tuple            =				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			):
       UpperCamelCase_					: List[str]            =				"""sshleifer/tinier_bart"""
       UpperCamelCase_					: Tuple            =				AutoConfig.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: Union[str, Any]            =				PyTorchBenchmarkArguments(
           models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  multi_process=__lowerCAmelCase      ,  )
       UpperCamelCase_					: List[str]            =				PyTorchBenchmark(__lowerCAmelCase      ,  configs=[config]			)
       UpperCamelCase_					: Dict            =				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			):
       UpperCamelCase_					: Union[str, Any]            =				"""sshleifer/tiny-gpt2"""
       with tempfile.TemporaryDirectory() as tmp_dir:
             UpperCamelCase_					: Dict            =				PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,  training=__lowerCAmelCase      ,  inference=__lowerCAmelCase      ,  save_to_csv=__lowerCAmelCase      ,  sequence_lengths=[8]      ,  batch_sizes=[1]      ,  inference_time_csv_file=os.path.join(__lowerCAmelCase      ,  """inf_time.csv"""			)      ,  train_memory_csv_file=os.path.join(__lowerCAmelCase      ,  """train_mem.csv"""			)      ,  inference_memory_csv_file=os.path.join(__lowerCAmelCase      ,  """inf_mem.csv"""			)      ,  train_time_csv_file=os.path.join(__lowerCAmelCase      ,  """train_time.csv"""			)      ,  env_info_csv_file=os.path.join(__lowerCAmelCase      ,  """env.csv"""			)      ,  multi_process=__lowerCAmelCase      ,  )
             UpperCamelCase_					: int            =				PyTorchBenchmark(__lowerCAmelCase			)
             benchmark.run()
             self.assertTrue(Path(os.path.join(__lowerCAmelCase      ,  """inf_time.csv"""			)			).exists()			)
             self.assertTrue(Path(os.path.join(__lowerCAmelCase      ,  """train_time.csv"""			)			).exists()			)
             self.assertTrue(Path(os.path.join(__lowerCAmelCase      ,  """inf_mem.csv"""			)			).exists()			)
             self.assertTrue(Path(os.path.join(__lowerCAmelCase      ,  """train_mem.csv"""			)			).exists()			)
             self.assertTrue(Path(os.path.join(__lowerCAmelCase      ,  """env.csv"""			)			).exists()			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				"""sshleifer/tiny-gpt2"""

       def _check_summary_is_not_empty(__lowerCAmelCase			):
             self.assertTrue(hasattr(__lowerCAmelCase      ,  """sequential"""			)			)
             self.assertTrue(hasattr(__lowerCAmelCase      ,  """cumulative"""			)			)
             self.assertTrue(hasattr(__lowerCAmelCase      ,  """current"""			)			)
             self.assertTrue(hasattr(__lowerCAmelCase      ,  """total"""			)			)

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














'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






