






'''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 		UpperCamelCase__     (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"""]							):
																				lowerCamelCase__								=	model_result["""result"""][batch_size][sequence_length]
																				self.assertIsNotNone(_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sgugger/tiny-distilbert-classification"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,only_pretrain_model=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,torchscript=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,fpaa=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								# set architectures equal to `None`
								lowerCamelCase__								=	None
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,fpaa=_lowerCAmelCase ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tinier_bart"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tinier_bart"""
								lowerCamelCase__								=	AutoConfig.from_pretrained(_lowerCAmelCase							)
								lowerCamelCase__								=	PyTorchBenchmarkArguments(
								    models=[MODEL_ID] ,training=_lowerCAmelCase ,inference=_lowerCAmelCase ,sequence_lengths=[8] ,batch_sizes=[1] ,multi_process=_lowerCAmelCase ,)
								lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase ,configs=[config]							)
								lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
														lowerCamelCase__								=	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 ,)
														lowerCamelCase__								=	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							):
								lowerCamelCase__								=	"""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:
														lowerCamelCase__								=	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 ,)
														lowerCamelCase__								=	PyTorchBenchmark(_lowerCAmelCase							)
														lowerCamelCase__								=	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"""


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


A	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.ModuleList(__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = False       ,  __a = True       ,  ):
										for i, (image, scale, controlnet) in enumerate(zip(__a       ,  __a       ,  self.nets      )      ):
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     controlnet(
													    __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  )

													# merge samples
													if i == 0:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     down_samples, mid_sample
													else:
																__lowerCAmelCase							       =     [
																    samples_prev + samples_curr
																    for samples_prev, samples_curr in zip(__a       ,  __a      )
																]
																mid_block_res_sample += mid_sample

										return down_block_res_samples, mid_block_res_sample

							def      snake_case							(	self       ,  __a       ,  __a = True       ,  __a = None       ,  __a = False       ,  __a = None       ,  ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     save_directory
										for controlnet in self.nets:
													controlnet.save_pretrained(
													    __a       ,  is_main_process=__a       ,  save_function=__a       ,  safe_serialization=__a       ,  variant=__a       ,  )

													idx += 1
													__lowerCAmelCase							       =     model_path_to_save + f"_{idx}"



							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     []

										# load controlnet and append to list until no controlnet directory exists anymore
										# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
										# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
										__lowerCAmelCase							       =     pretrained_model_path
										while os.path.isdir(__a      ):
													__lowerCAmelCase							       =     ControlNetModel.from_pretrained(__a       ,  **__a      )
													controlnets.append(__a      )

													idx += 1
													__lowerCAmelCase							       =     pretrained_model_path + f"_{idx}"

										logger.info(f"{len(__a      )} controlnets loaded from {pretrained_model_path}."      )

										if len(__a      ) == 0:
													raise ValueError(
													    f"No ControlNets found under {os.path.dirname(__a      )}. Expected at least {pretrained_model_path + '_0'}."      )

										return cls(__a      )




