



"""simple docstring"""


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						Dict=1				):






				'''simple docstring'''



				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('''.'''				)[n_shave_prefix_segments:]				)
				else:
								return ".".join(path.split('''.'''				)[:n_shave_prefix_segments]				)







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Tuple=0				):






				'''simple docstring'''



				_UpperCAmelCase					=    []
				for old_item in old_list:
								_UpperCAmelCase					=    old_item.replace('''in_layers.0'''					,     '''norm1'''				)
								_UpperCAmelCase					=    new_item.replace('''in_layers.2'''					,     '''conv1'''				)

								_UpperCAmelCase					=    new_item.replace('''out_layers.0'''					,     '''norm2'''				)
								_UpperCAmelCase					=    new_item.replace('''out_layers.3'''					,     '''conv2'''				)

								_UpperCAmelCase					=    new_item.replace('''emb_layers.1'''					,     '''time_emb_proj'''				)
								_UpperCAmelCase					=    new_item.replace('''skip_connection'''					,     '''conv_shortcut'''				)

								_UpperCAmelCase					=    shave_segments(_SCREAMING_SNAKE_CASE					,     n_shave_prefix_segments=_SCREAMING_SNAKE_CASE				)

								mapping.append({'''old''': old_item, '''new''': new_item}				)

				return mapping







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						List[str]=0				):






				'''simple docstring'''



				_UpperCAmelCase					=    []
				for old_item in old_list:
								_UpperCAmelCase					=    old_item

								_UpperCAmelCase					=    new_item.replace('''norm.weight'''					,     '''group_norm.weight'''				)
								_UpperCAmelCase					=    new_item.replace('''norm.bias'''					,     '''group_norm.bias'''				)

								_UpperCAmelCase					=    new_item.replace('''proj_out.weight'''					,     '''proj_attn.weight'''				)
								_UpperCAmelCase					=    new_item.replace('''proj_out.bias'''					,     '''proj_attn.bias'''				)

								_UpperCAmelCase					=    shave_segments(_SCREAMING_SNAKE_CASE					,     n_shave_prefix_segments=_SCREAMING_SNAKE_CASE				)

								mapping.append({'''old''': old_item, '''new''': new_item}				)

				return mapping







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Dict=None					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=None					,     _SCREAMING_SNAKE_CASE   :						Dict=None				):






				'''simple docstring'''



				assert isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												_UpperCAmelCase					=    old_checkpoint[path]
												_UpperCAmelCase					=    old_tensor.shape[0] // 3

												_UpperCAmelCase					=    (-1, channels) if len(old_tensor.shape				) == 3 else (-1)

												_UpperCAmelCase					=    old_tensor.shape[0] // config['''num_head_channels'''] // 3

												_UpperCAmelCase					=    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
												_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    old_tensor.split(channels // num_heads					,     dim=1				)

												_UpperCAmelCase					=    query.reshape(_SCREAMING_SNAKE_CASE				)
												_UpperCAmelCase					=    key.reshape(_SCREAMING_SNAKE_CASE				)
												_UpperCAmelCase					=    value.reshape(_SCREAMING_SNAKE_CASE				)

				for path in paths:
								_UpperCAmelCase					=    path['''new''']

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								_UpperCAmelCase					=    new_path.replace('''middle_block.0'''					,     '''mid_block.resnets.0'''				)
								_UpperCAmelCase					=    new_path.replace('''middle_block.1'''					,     '''mid_block.attentions.0'''				)
								_UpperCAmelCase					=    new_path.replace('''middle_block.2'''					,     '''mid_block.resnets.1'''				)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																_UpperCAmelCase					=    new_path.replace(replacement['''old''']					,     replacement['''new''']				)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												_UpperCAmelCase					=    old_checkpoint[path['''old''']][:, :, 0]
								else:
												_UpperCAmelCase					=    old_checkpoint[path['''old''']]







def  lowercase						(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    {}

				_UpperCAmelCase					=    checkpoint['''time_embed.0.weight''']
				_UpperCAmelCase					=    checkpoint['''time_embed.0.bias''']
				_UpperCAmelCase					=    checkpoint['''time_embed.2.weight''']
				_UpperCAmelCase					=    checkpoint['''time_embed.2.bias''']

				_UpperCAmelCase					=    checkpoint['''input_blocks.0.0.weight''']
				_UpperCAmelCase					=    checkpoint['''input_blocks.0.0.bias''']

				_UpperCAmelCase					=    checkpoint['''out.0.weight''']
				_UpperCAmelCase					=    checkpoint['''out.0.bias''']
				_UpperCAmelCase					=    checkpoint['''out.2.weight''']
				_UpperCAmelCase					=    checkpoint['''out.2.bias''']

				# Retrieves the keys for the input blocks only
				_UpperCAmelCase					=    len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in checkpoint if '''input_blocks''' in layer}				)
				_UpperCAmelCase					=    {
				    layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
				    for layer_id in range(_SCREAMING_SNAKE_CASE				)
				}

				# Retrieves the keys for the middle blocks only
				_UpperCAmelCase					=    len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in checkpoint if '''middle_block''' in layer}				)
				_UpperCAmelCase					=    {
				    layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
				    for layer_id in range(_SCREAMING_SNAKE_CASE				)
				}

				# Retrieves the keys for the output blocks only
				_UpperCAmelCase					=    len({'''.'''.join(layer.split('''.'''				)[:2]				) for layer in checkpoint if '''output_blocks''' in layer}				)
				_UpperCAmelCase					=    {
				    layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
				    for layer_id in range(_SCREAMING_SNAKE_CASE				)
				}

				for i in range(1					,     _SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    (i - 1) // (config['''num_res_blocks'''] + 1)
								_UpperCAmelCase					=    (i - 1) % (config['''num_res_blocks'''] + 1)

								_UpperCAmelCase					=    [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
								_UpperCAmelCase					=    [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

								if f'input_blocks.{i}.0.op.weight' in checkpoint:
												_UpperCAmelCase					=    checkpoint[
												    f'input_blocks.{i}.0.op.weight'
												]
												_UpperCAmelCase					=    checkpoint[
												    f'input_blocks.{i}.0.op.bias'
												]
												continue

								_UpperCAmelCase					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    {'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
								_UpperCAmelCase					=    {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
								assign_to_checkpoint(
								    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     additional_replacements=[meta_path, resnet_op]					,     config=_SCREAMING_SNAKE_CASE				)

								if len(_SCREAMING_SNAKE_CASE				):
												_UpperCAmelCase					=    renew_attention_paths(_SCREAMING_SNAKE_CASE				)
												_UpperCAmelCase					=    {
												    '''old''': f'input_blocks.{i}.1',
												    '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
												}
												_UpperCAmelCase					=    {
												    f'input_blocks.{i}.1.qkv.bias': {
												        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
												        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
												        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
												    },
												    f'input_blocks.{i}.1.qkv.weight': {
												        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
												        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
												        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
												    },
												}
												assign_to_checkpoint(
												    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     additional_replacements=[meta_path]					,     attention_paths_to_split=_SCREAMING_SNAKE_CASE					,     config=_SCREAMING_SNAKE_CASE					,     )

				_UpperCAmelCase					=    middle_blocks[0]
				_UpperCAmelCase					=    middle_blocks[1]
				_UpperCAmelCase					=    middle_blocks[2]

				_UpperCAmelCase					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
				assign_to_checkpoint(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     config=_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
				assign_to_checkpoint(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     config=_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    renew_attention_paths(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    {
				    '''middle_block.1.qkv.bias''': {
				        '''key''': '''mid_block.attentions.0.key.bias''',
				        '''query''': '''mid_block.attentions.0.query.bias''',
				        '''value''': '''mid_block.attentions.0.value.bias''',
				    },
				    '''middle_block.1.qkv.weight''': {
				        '''key''': '''mid_block.attentions.0.key.weight''',
				        '''query''': '''mid_block.attentions.0.query.weight''',
				        '''value''': '''mid_block.attentions.0.value.weight''',
				    },
				}
				assign_to_checkpoint(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     attention_paths_to_split=_SCREAMING_SNAKE_CASE					,     config=_SCREAMING_SNAKE_CASE				)

				for i in range(_SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    i // (config['''num_res_blocks'''] + 1)
								_UpperCAmelCase					=    i % (config['''num_res_blocks'''] + 1)
								_UpperCAmelCase					=    [shave_segments(_SCREAMING_SNAKE_CASE					,     2				) for name in output_blocks[i]]
								_UpperCAmelCase					=    {}

								for layer in output_block_layers:
												_UpperCAmelCase		,						_UpperCAmelCase					=    layer.split('''.'''				)[0], shave_segments(_SCREAMING_SNAKE_CASE					,     1				)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE				)
												else:
																_UpperCAmelCase					=    [layer_name]

								if len(_SCREAMING_SNAKE_CASE				) > 1:
												_UpperCAmelCase					=    [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
												_UpperCAmelCase					=    [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

												_UpperCAmelCase					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE				)
												_UpperCAmelCase					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE				)

												_UpperCAmelCase					=    {'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
												assign_to_checkpoint(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     additional_replacements=[meta_path]					,     config=_SCREAMING_SNAKE_CASE				)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																_UpperCAmelCase					=    list(output_block_list.values()				).index(['''conv.weight''', '''conv.bias''']				)
																_UpperCAmelCase					=    checkpoint[
																    f'output_blocks.{i}.{index}.conv.weight'
																]
																_UpperCAmelCase					=    checkpoint[
																    f'output_blocks.{i}.{index}.conv.bias'
																]

																# Clear attentions as they have been attributed above.
																if len(_SCREAMING_SNAKE_CASE				) == 2:
																				_UpperCAmelCase					=    []

												if len(_SCREAMING_SNAKE_CASE				):
																_UpperCAmelCase					=    renew_attention_paths(_SCREAMING_SNAKE_CASE				)
																_UpperCAmelCase					=    {
																    '''old''': f'output_blocks.{i}.1',
																    '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																}
																_UpperCAmelCase					=    {
																    f'output_blocks.{i}.1.qkv.bias': {
																        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
																        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
																        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
																    },
																    f'output_blocks.{i}.1.qkv.weight': {
																        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
																        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
																        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
																    },
																}
																assign_to_checkpoint(
																    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     additional_replacements=[meta_path]					,     attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions				) else None					,     config=_SCREAMING_SNAKE_CASE					,     )
								else:
												_UpperCAmelCase					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE					,     n_shave_prefix_segments=1				)
												for path in resnet_0_paths:
																_UpperCAmelCase					=    '''.'''.join(['''output_blocks''', str(_SCREAMING_SNAKE_CASE				), path['''old''']]				)
																_UpperCAmelCase					=    '''.'''.join(['''up_blocks''', str(_SCREAMING_SNAKE_CASE				), '''resnets''', str(_SCREAMING_SNAKE_CASE				), path['''new''']]				)

																_UpperCAmelCase					=    checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
					__A   :							Any										=   argparse.ArgumentParser()

					parser.add_argument(
					    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
					)

					parser.add_argument(
					    "--config_file",
					    default=None,
					    type=str,
					    required=True,
					    help="The config json file corresponding to the architecture.",
					)

					parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

					__A   :							Optional[int]										=   parser.parse_args()

					__A   :							List[Any]										=   torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__A   :							str										=   json.loads(f.read())

					__A   :							Tuple										=   convert_ldm_checkpoint(checkpoint, config)

					if "ldm" in config:
										del config["ldm"]

					__A   :							List[Any]										=   UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

					try:
										__A   :							Dict										=   DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
										__A   :							str										=   VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

										__A   :							Optional[Any]										=   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
										pipe.save_pretrained(args.dump_path)
					except:  # noqa: E722
										model.save_pretrained(args.dump_path)








"""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  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->Tuple:
							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs''']				,   model_result['''ss''']   ):
															_UpperCAmelCase					=    model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__UpperCamelCase   )



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



