




"""simple docstring"""

import argparse

import torch

from diffusers import UNetaDConditionModel




def       snake_case__	(       lowerCAmelCase   :		List[str]				,					lowerCAmelCase   :		str				,					lowerCAmelCase   :		List[str]				,					lowerCAmelCase   :		Tuple=None				,					lowerCAmelCase   :		List[str]=None				,					lowerCAmelCase   :		Optional[Any]=None		):
					assert isinstance(lowerCAmelCase				,					lowerCAmelCase		), "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():
															__snake_case          =	old_checkpoint[path]
															__snake_case          =	old_tensor.shape[0] // 3

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

															__snake_case          =	old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

															__snake_case          =	query.reshape(lowerCAmelCase		)
															__snake_case          =	key.reshape(lowerCAmelCase		)
															__snake_case          =	value.reshape(lowerCAmelCase		)

					for path in paths:
										__snake_case          =	path["""new"""]

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
										__snake_case          =	old_checkpoint[path["""old"""]]
										__snake_case          =	["""proj_attn.weight"""]
										__snake_case          =	["""proj_out.weight""", """proj_in.weight"""]
										if any(k in new_path for k in names		):
															__snake_case          =	weight[:, :, 0]
										elif any(k in new_path for k in names_a		) and len(weight.shape		) > 2 and ".attentions." not in new_path:
															__snake_case          =	weight[:, :, 0]
										else:
															__snake_case          =	weight




def       snake_case__	(       lowerCAmelCase   :		Tuple				,					lowerCAmelCase   :		List[str]=0		):
					__snake_case          =	[]
					for old_item in old_list:
										__snake_case          =	old_item

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

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

										#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

					return mapping




def       snake_case__	(       lowerCAmelCase   :		Optional[Any]				,					lowerCAmelCase   :		List[Any]=1		):
					if n_shave_prefix_segments >= 0:
										return ".".join(path.split("""."""		)[n_shave_prefix_segments:]		)
					else:
										return ".".join(path.split("""."""		)[:n_shave_prefix_segments]		)




def       snake_case__	(       lowerCAmelCase   :		List[str]				,					lowerCAmelCase   :		Optional[int]=0		):
					__snake_case          =	[]
					for old_item in old_list:
										mapping.append({"""old""": old_item, """new""": old_item}		)

					return mapping




def       snake_case__	(       lowerCAmelCase   :		str				,					lowerCAmelCase   :		Any=0		):
					__snake_case          =	[]
					for old_item in old_list:
										__snake_case          =	old_item.replace("""in_layers.0"""				,					"""norm1"""		)
										__snake_case          =	new_item.replace("""in_layers.2"""				,					"""conv1"""		)

										__snake_case          =	new_item.replace("""out_layers.0"""				,					"""norm2"""		)
										__snake_case          =	new_item.replace("""out_layers.3"""				,					"""conv2"""		)

										__snake_case          =	new_item.replace("""emb_layers.1"""				,					"""time_emb_proj"""		)
										__snake_case          =	new_item.replace("""skip_connection"""				,					"""conv_shortcut"""		)

										__snake_case          =	shave_segments(lowerCAmelCase				,					n_shave_prefix_segments=lowerCAmelCase		)

										if "temopral_conv" not in old_item:
															mapping.append({"""old""": old_item, """new""": new_item}		)

					return mapping




def       snake_case__	(       lowerCAmelCase   :		int				,					lowerCAmelCase   :		List[Any]				,					lowerCAmelCase   :		List[Any]=None				,					lowerCAmelCase   :		Dict=False		):
					__snake_case          =	{}
					__snake_case          =	list(checkpoint.keys()		)

					__snake_case          =	"""model.diffusion_model."""

					# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
					if sum(k.startswith("""model_ema"""		) for k in keys		) > 100 and extract_ema:
										print(F'''Checkpoint {path} has both EMA and non-EMA weights.'''		)
										print(
										    """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
										    """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""		)
										for key in keys:
															if key.startswith("""model.diffusion_model"""		):
																				__snake_case          =	"""model_ema.""" + """""".join(key.split("""."""		)[1:]		)
																				__snake_case          =	checkpoint.pop(lowerCAmelCase		)
					else:
										if sum(k.startswith("""model_ema"""		) for k in keys		) > 100:
															print(
															    """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
															    """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""		)

										for key in keys:
															__snake_case          =	checkpoint.pop(lowerCAmelCase		)

					__snake_case          =	{}

					__snake_case          =	unet_state_dict["""time_embed.0.weight"""]
					__snake_case          =	unet_state_dict["""time_embed.0.bias"""]
					__snake_case          =	unet_state_dict["""time_embed.2.weight"""]
					__snake_case          =	unet_state_dict["""time_embed.2.bias"""]

					if config["class_embed_type"] is None:
										# No parameters to port
										...
					elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
										__snake_case          =	unet_state_dict["""label_emb.0.0.weight"""]
										__snake_case          =	unet_state_dict["""label_emb.0.0.bias"""]
										__snake_case          =	unet_state_dict["""label_emb.0.2.weight"""]
										__snake_case          =	unet_state_dict["""label_emb.0.2.bias"""]
					else:
										raise NotImplementedError(F'''Not implemented `class_embed_type`: {config["class_embed_type"]}'''		)

					__snake_case          =	unet_state_dict["""input_blocks.0.0.weight"""]
					__snake_case          =	unet_state_dict["""input_blocks.0.0.bias"""]

					__snake_case          =	[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""		)]
					__snake_case          =	renew_attention_paths(lowerCAmelCase		)
					__snake_case          =	{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
					assign_to_checkpoint(lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

					__snake_case          =	unet_state_dict["""out.0.weight"""]
					__snake_case          =	unet_state_dict["""out.0.bias"""]
					__snake_case          =	unet_state_dict["""out.2.weight"""]
					__snake_case          =	unet_state_dict["""out.2.bias"""]

					# Retrieves the keys for the input blocks only
					__snake_case          =	len({""".""".join(layer.split("""."""		)[:2]		) for layer in unet_state_dict if """input_blocks""" in layer}		)
					__snake_case          =	{
					    layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
					    for layer_id in range(lowerCAmelCase		)
					}

					# Retrieves the keys for the middle blocks only
					__snake_case          =	len({""".""".join(layer.split("""."""		)[:2]		) for layer in unet_state_dict if """middle_block""" in layer}		)
					__snake_case          =	{
					    layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
					    for layer_id in range(lowerCAmelCase		)
					}

					# Retrieves the keys for the output blocks only
					__snake_case          =	len({""".""".join(layer.split("""."""		)[:2]		) for layer in unet_state_dict if """output_blocks""" in layer}		)
					__snake_case          =	{
					    layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
					    for layer_id in range(lowerCAmelCase		)
					}

					for i in range(1				,					lowerCAmelCase		):
										__snake_case          =	(i - 1) // (config["""layers_per_block"""] + 1)
										__snake_case          =	(i - 1) % (config["""layers_per_block"""] + 1)

										__snake_case          =	[
										    key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key and F'''input_blocks.{i}.0.op''' not in key
										]
										__snake_case          =	[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
										__snake_case          =	[key for key in input_blocks[i] if F'''input_blocks.{i}.2''' in key]

										if F'''input_blocks.{i}.op.weight''' in unet_state_dict:
															__snake_case          =	unet_state_dict.pop(
															    F'''input_blocks.{i}.op.weight'''		)
															__snake_case          =	unet_state_dict.pop(
															    F'''input_blocks.{i}.op.bias'''		)

										__snake_case          =	renew_resnet_paths(lowerCAmelCase		)
										__snake_case          =	{"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
										assign_to_checkpoint(
										    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

										__snake_case          =	[key for key in resnets if """temopral_conv""" in key]
										__snake_case          =	renew_temp_conv_paths(lowerCAmelCase		)
										__snake_case          =	{
										    """old""": F'''input_blocks.{i}.0.temopral_conv''',
										    """new""": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
										}
										assign_to_checkpoint(
										    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

										if len(lowerCAmelCase		):
															__snake_case          =	renew_attention_paths(lowerCAmelCase		)
															__snake_case          =	{"""old""": F'''input_blocks.{i}.1''', """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
															assign_to_checkpoint(
															    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

										if len(lowerCAmelCase		):
															__snake_case          =	renew_attention_paths(lowerCAmelCase		)
															__snake_case          =	{
															    """old""": F'''input_blocks.{i}.2''',
															    """new""": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
															}
															assign_to_checkpoint(
															    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

					__snake_case          =	middle_blocks[0]
					__snake_case          =	[key for key in resnet_a if """temopral_conv""" in key]
					__snake_case          =	middle_blocks[1]
					__snake_case          =	middle_blocks[2]
					__snake_case          =	middle_blocks[3]
					__snake_case          =	[key for key in resnet_a if """temopral_conv""" in key]

					__snake_case          =	renew_resnet_paths(lowerCAmelCase		)
					__snake_case          =	{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
					assign_to_checkpoint(
					    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					config=lowerCAmelCase				,					additional_replacements=[meta_path]		)

					__snake_case          =	renew_temp_conv_paths(lowerCAmelCase		)
					__snake_case          =	{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
					assign_to_checkpoint(
					    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					config=lowerCAmelCase				,					additional_replacements=[meta_path]		)

					__snake_case          =	renew_resnet_paths(lowerCAmelCase		)
					__snake_case          =	{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
					assign_to_checkpoint(
					    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					config=lowerCAmelCase				,					additional_replacements=[meta_path]		)

					__snake_case          =	renew_temp_conv_paths(lowerCAmelCase		)
					__snake_case          =	{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
					assign_to_checkpoint(
					    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					config=lowerCAmelCase				,					additional_replacements=[meta_path]		)

					__snake_case          =	renew_attention_paths(lowerCAmelCase		)
					__snake_case          =	{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
					assign_to_checkpoint(
					    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

					__snake_case          =	renew_attention_paths(lowerCAmelCase		)
					__snake_case          =	{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
					assign_to_checkpoint(
					    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

					for i in range(lowerCAmelCase		):
										__snake_case          =	i // (config["""layers_per_block"""] + 1)
										__snake_case          =	i % (config["""layers_per_block"""] + 1)
										__snake_case          =	[shave_segments(lowerCAmelCase				,					2		) for name in output_blocks[i]]
										__snake_case          =	{}

										for layer in output_block_layers:
															__snake_case      ,      __snake_case          =	layer.split("""."""		)[0], shave_segments(lowerCAmelCase				,					1		)
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(lowerCAmelCase		)
															else:
																				__snake_case          =	[layer_name]

										if len(lowerCAmelCase		) > 1:
															__snake_case          =	[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
															__snake_case          =	[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
															__snake_case          =	[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

															__snake_case          =	renew_resnet_paths(lowerCAmelCase		)
															__snake_case          =	renew_resnet_paths(lowerCAmelCase		)

															__snake_case          =	{"""old""": F'''output_blocks.{i}.0''', """new""": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
															assign_to_checkpoint(
															    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

															__snake_case          =	[key for key in resnets if """temopral_conv""" in key]
															__snake_case          =	renew_temp_conv_paths(lowerCAmelCase		)
															__snake_case          =	{
															    """old""": F'''output_blocks.{i}.0.temopral_conv''',
															    """new""": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
															}
															assign_to_checkpoint(
															    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

															__snake_case          =	{k: sorted(lowerCAmelCase		) for k, v in output_block_list.items()}
															if ["conv.bias", "conv.weight"] in output_block_list.values():
																				__snake_case          =	list(output_block_list.values()		).index(["""conv.bias""", """conv.weight"""]		)
																				__snake_case          =	unet_state_dict[
																				    F'''output_blocks.{i}.{index}.conv.weight'''
																				]
																				__snake_case          =	unet_state_dict[
																				    F'''output_blocks.{i}.{index}.conv.bias'''
																				]

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

															if len(lowerCAmelCase		):
																				__snake_case          =	renew_attention_paths(lowerCAmelCase		)
																				__snake_case          =	{
																				    """old""": F'''output_blocks.{i}.1''',
																				    """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																				}
																				assign_to_checkpoint(
																				    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)

															if len(lowerCAmelCase		):
																				__snake_case          =	renew_attention_paths(lowerCAmelCase		)
																				__snake_case          =	{
																				    """old""": F'''output_blocks.{i}.2''',
																				    """new""": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
																				}
																				assign_to_checkpoint(
																				    lowerCAmelCase				,					lowerCAmelCase				,					lowerCAmelCase				,					additional_replacements=[meta_path]				,					config=lowerCAmelCase		)
										else:
															__snake_case          =	renew_resnet_paths(lowerCAmelCase				,					n_shave_prefix_segments=1		)
															for path in resnet_0_paths:
																				__snake_case          =	""".""".join(["""output_blocks""", str(lowerCAmelCase		), path["""old"""]]		)
																				__snake_case          =	""".""".join(["""up_blocks""", str(lowerCAmelCase		), """resnets""", str(lowerCAmelCase		), path["""new"""]]		)
																				__snake_case          =	unet_state_dict[old_path]

															__snake_case          =	[l for l in output_block_layers if """temopral_conv""" in l]
															for path in temopral_conv_paths:
																				__snake_case          =	path.split("""temopral_conv."""		)[-1]
																				__snake_case          =	""".""".join(["""output_blocks""", str(lowerCAmelCase		), str(lowerCAmelCase		), """temopral_conv""", pruned_path]		)
																				__snake_case          =	""".""".join(["""up_blocks""", str(lowerCAmelCase		), """temp_convs""", str(lowerCAmelCase		), pruned_path]		)
																				__snake_case          =	unet_state_dict[old_path]

					return new_checkpoint


if __name__ == "__main__":
		UpperCamelCase__ :    Optional[int] 					=							argparse.ArgumentParser()

		parser.add_argument(
		    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
		)
		parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
		UpperCamelCase__ :    Union[str, Any] 					=							parser.parse_args()

		UpperCamelCase__ :    Optional[Any] 					=							torch.load(args.checkpoint_path, map_location="cpu")
		UpperCamelCase__ :    List[str] 					=							UNetaDConditionModel()

		UpperCamelCase__ :    int 					=							convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

		UpperCamelCase__ :    Optional[int] 					=							set(unet.state_dict().keys()) - set(converted_ckpt.keys())
		UpperCamelCase__ :    Optional[int] 					=							set(converted_ckpt.keys()) - set(unet.state_dict().keys())

		assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

		# load state_dict
		unet.load_state_dict(converted_ckpt)

		unet.save_pretrained(args.dump_path)

		# -- finish converting the unet --








"""simple docstring"""

import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
		import torch

		from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
		import pyanvml.pyanvml as nvml


UpperCamelCase__ :    List[str] 					=							logging.get_logger(__name__)
class     A__      (      lowercase_     ):
	A_							:				PyTorchBenchmarkArguments
	A_							:				PretrainedConfig
	A_							:				str						     =      "PyTorch"




	@property
	def 							a_		(   self  :	Optional[Any]	)					-> Tuple:
						return torch.__version__




	def 							a_		(   self  :	int      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_inference	)




	def 							a_		(   self  :	Dict      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_inference_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_inference	)




	def 							a_		(   self  :	str      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> float:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_speed(_train	)




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> [Memory, Optional[MemorySummary]]:
						__snake_case          =	self._prepare_train_func(lowercase      ,    lowercase      ,    lowercase	)
						return self._measure_memory(_train	)




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

						if self.args.torchscript:
											__snake_case          =	True

						__snake_case          =	(
						    hasattr(lowercase      ,    """architectures"""	)
						    and isinstance(config.architectures      ,    lowercase	)
						    and len(config.architectures	) > 0
						)
						if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
																__snake_case          =	config.architectures[0]
																__snake_case          =	__import__("""transformers"""      ,    fromlist=[model_class]	)
																__snake_case          =	getattr(lowercase      ,    lowercase	)
																__snake_case          =	model_cls(lowercase	)
											except ImportError:
																raise ImportError(
																    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""	)
						else:
											__snake_case          =	MODEL_MAPPING[config.__class__](lowercase	)

						model.eval()
						model.to(self.args.device	)

						# encoder-decoder has vocab size saved differently
						__snake_case          =	config.vocab_size if hasattr(lowercase      ,    """vocab_size"""	) else config.encoder.vocab_size
						__snake_case          =	torch.randint(lowercase      ,    (batch_size, sequence_length)      ,    dtype=torch.long      ,    device=self.args.device	)

						if self.args.fpaa:
											logger.info("""Running training in Mixed Precision..."""	)
											if not self.args.is_gpu:
																raise ValueError("""Mixed precision is possible only for GPU."""	)
											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

						if self.args.torchscript:
											with torch.no_grad():
																__snake_case          =	torch.jit.trace(lowercase      ,    lowercase	)
						else:
											__snake_case          =	model

						def encoder_decoder_forward():
											with torch.no_grad():
																__snake_case          =	inference_model(lowercase      ,    decoder_input_ids=lowercase	)
											return outputs

						def encoder_forward():
											with torch.no_grad():
																__snake_case          =	inference_model(lowercase	)
											return outputs

						__snake_case          =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
						return _forward




	def 							a_		(   self  :	Optional[int]      ,    lowercase  :	str      ,    lowercase  :	int      ,    lowercase  :	int	)					-> Callable[[], None]:
						__snake_case          =	self.config_dict[model_name]

						__snake_case          =	(
						    hasattr(lowercase      ,    """architectures"""	)
						    and isinstance(config.architectures      ,    lowercase	)
						    and len(config.architectures	) > 0
						)
						if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
																__snake_case          =	config.architectures[0]
																__snake_case          =	__import__("""transformers"""      ,    fromlist=[model_class]	)
																__snake_case          =	getattr(lowercase      ,    lowercase	)
																__snake_case          =	model_cls(lowercase	)
											except ImportError:
																raise ImportError(
																    f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""	)
						else:
											__snake_case          =	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase	)

						if self.args.torchscript:
											raise NotImplementedError("""Training for torchscript is currently not implemented"""	)
						else:
											__snake_case          =	model

						model.train()
						model.to(self.args.device	)

						# encoder-decoder has vocab size saved differently
						__snake_case          =	config.vocab_size if hasattr(lowercase      ,    """vocab_size"""	) else config.encoder.vocab_size
						__snake_case          =	torch.randint(lowercase      ,    (batch_size, sequence_length)      ,    dtype=torch.long      ,    device=self.args.device	)

						if self.args.fpaa:
											logger.info("""Running training in Mixed Precision..."""	)
											if not self.args.is_gpu:
																raise ValueError("""Mixed precision is possible only for GPU."""	)

											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

						def compute_loss_and_backprob_encoder():
											__snake_case          =	train_model(lowercase      ,    labels=lowercase	)[0]
											loss.backward()
											return loss

						def compute_loss_and_backprob_encoder_decoder():
											__snake_case          =	train_model(lowercase      ,    decoder_input_ids=lowercase      ,    labels=lowercase	)[0]
											loss.backward()
											return loss

						__snake_case          =	(
						    compute_loss_and_backprob_encoder_decoder
						    if config.is_encoder_decoder
						    else compute_loss_and_backprob_encoder
						)
						return _train




	def 							a_		(   self  :	Tuple      ,    lowercase  :	List[Any]	)					-> float:
						try:
											if self.args.is_tpu or self.args.torchscript:
																# run additional 10 times to stabilize compilation for tpu and torchscript
																logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""	)
																timeit.repeat(
																    lowercase      ,    repeat=1      ,    number=5      ,    )

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											__snake_case          =	timeit.repeat(
											    lowercase      ,    repeat=self.args.repeat      ,    number=10      ,    )

											if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																import torch_xla.debug.metrics as met

																self.print_fn(met.metrics_report()	)

											return min(lowercase	) / 10.0
						except RuntimeError as e:
											self.print_fn(f'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A"




	def 							a_		(   self  :	Union[str, Any]      ,    lowercase  :	Callable[[], None]	)					-> [Memory, MemorySummary]:
						try:
											if self.args.trace_memory_line_by_line:
																__snake_case          =	start_memory_tracing("""transformers"""	)

											if self.args.is_tpu:
																# tpu
																raise NotImplementedError(
																    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
																    """ `--no-memory` or `args.memory=False`"""	)
											elif self.args.is_gpu:
																if not is_pyanvml_available():
																					logger.warning(
																					    """py3nvml not installed, we won't log GPU memory usage. """
																					    """Install py3nvml (pip install py3nvml) to log information about GPU."""	)
																					__snake_case          =	"""N/A"""
																else:
																					logger.info(
																					    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
																					    """ on the same GPU."""	)
																					# init nvml
																					nvml.nvmlInit()
																					func()
																					__snake_case          =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																					__snake_case          =	nvml.nvmlDeviceGetMemoryInfo(lowercase	)
																					__snake_case          =	meminfo.used
																					__snake_case          =	Memory(lowercase	)
																					# shutdown nvml
																					nvml.nvmlShutdown()
											else:
																# cpu
																__snake_case          =	measure_peak_memory_cpu(lowercase	)
																__snake_case          =	Memory(lowercase	) if isinstance(lowercase      ,    lowercase	) else memory_bytes

											if self.args.trace_memory_line_by_line:
																__snake_case          =	stop_memory_tracing(lowercase	)
											else:
																__snake_case          =	None

											return memory, summary
						except RuntimeError as e:
											self.print_fn(f'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A", None


