



"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A   :							str										=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A   :							Any										=   []
for i in range(6):
					# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
					rename_keys.append(
					    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
					)
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
					rename_keys.append(
					    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
					)
					rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
					rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
					# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
					)
					rename_keys.append(
					    (
					        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
					        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
					    )
					)
					rename_keys.append(
					    (
					        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
					        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
					    )
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

					# q, k, v projections in self/cross-attention in decoder for conditional DETR
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
					)

					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
					)
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
					)
					# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
					)
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
					rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
					rename_keys.append(
					    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
					)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				_UpperCAmelCase					=    state_dict.pop(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    val







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				_UpperCAmelCase					=    OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_UpperCAmelCase					=    key.replace('''backbone.0.body'''					,     '''backbone.conv_encoder.model'''				)
												_UpperCAmelCase					=    value
								else:
												_UpperCAmelCase					=    value

				return new_state_dict







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






				'''simple docstring'''



				_UpperCAmelCase					=    ''''''
				if is_panoptic:
								_UpperCAmelCase					=    '''conditional_detr.'''

				# first: transformer encoder
				for i in range(6				):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								_UpperCAmelCase					=    state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'				)
								_UpperCAmelCase					=    state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'				)
								# next, add query, keys and values (in that order) to the state dict
								_UpperCAmelCase					=    in_proj_weight[:256, :]
								_UpperCAmelCase					=    in_proj_bias[:256]
								_UpperCAmelCase					=    in_proj_weight[256:512, :]
								_UpperCAmelCase					=    in_proj_bias[256:512]
								_UpperCAmelCase					=    in_proj_weight[-256:, :]
								_UpperCAmelCase					=    in_proj_bias[-256:]







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_UpperCAmelCase					=    Image.open(requests.get(_SCREAMING_SNAKE_CASE					,     stream=_SCREAMING_SNAKE_CASE				).raw				)

				return im







@torch.no_grad()
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								_UpperCAmelCase					=    '''resnet101'''
				if "dc5" in model_name:
								_UpperCAmelCase					=    True
				_UpperCAmelCase					=    '''panoptic''' in model_name
				if is_panoptic:
								_UpperCAmelCase					=    250
				else:
								_UpperCAmelCase					=    91
								_UpperCAmelCase					=    '''huggingface/label-files'''
								_UpperCAmelCase					=    '''coco-detection-id2label.json'''
								_UpperCAmelCase					=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     repo_type='''dataset'''				)					,     '''r'''				)				)
								_UpperCAmelCase					=    {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
								_UpperCAmelCase					=    idalabel
								_UpperCAmelCase					=    {v: k for k, v in idalabel.items()}

				# load image processor
				_UpperCAmelCase					=    '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				_UpperCAmelCase					=    ConditionalDetrImageProcessor(format=_SCREAMING_SNAKE_CASE				)

				# prepare image
				_UpperCAmelCase					=    prepare_img()
				_UpperCAmelCase					=    image_processor(images=_SCREAMING_SNAKE_CASE					,     return_tensors='''pt'''				)
				_UpperCAmelCase					=    encoding['''pixel_values''']

				logger.info(f'Converting model {model_name}...'				)

				# load original model from torch hub
				_UpperCAmelCase					=    torch.hub.load('''DeppMeng/ConditionalDETR'''					,     _SCREAMING_SNAKE_CASE					,     pretrained=_SCREAMING_SNAKE_CASE				).eval()
				_UpperCAmelCase					=    conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												_UpperCAmelCase					=    '''conditional_detr.''' + src
								rename_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    rename_backbone_keys(_SCREAMING_SNAKE_CASE				)
				# query, key and value matrices need special treatment
				read_in_q_k_v(_SCREAMING_SNAKE_CASE					,     is_panoptic=_SCREAMING_SNAKE_CASE				)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_UpperCAmelCase					=    '''conditional_detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''conditional_detr'''				)
												    and not key.startswith('''class_labels_classifier'''				)
												    and not key.startswith('''bbox_predictor'''				)
												):
																_UpperCAmelCase					=    state_dict.pop(_SCREAMING_SNAKE_CASE				)
																_UpperCAmelCase					=    val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_UpperCAmelCase					=    state_dict.pop(_SCREAMING_SNAKE_CASE				)
																_UpperCAmelCase					=    val
												elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
																continue
												else:
																_UpperCAmelCase					=    state_dict.pop(_SCREAMING_SNAKE_CASE				)
																_UpperCAmelCase					=    val
								else:
												if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
																_UpperCAmelCase					=    state_dict.pop(_SCREAMING_SNAKE_CASE				)
																_UpperCAmelCase					=    val
    # finally, create HuggingFace model and load state dict
				_UpperCAmelCase					=    ConditionalDetrForSegmentation(_SCREAMING_SNAKE_CASE				) if is_panoptic else ConditionalDetrForObjectDetection(_SCREAMING_SNAKE_CASE				)
				model.load_state_dict(_SCREAMING_SNAKE_CASE				)
				model.eval()
				model.push_to_hub(repo_id=_SCREAMING_SNAKE_CASE					,     organization='''DepuMeng'''					,     commit_message='''Add model'''				)
				# verify our conversion
				_UpperCAmelCase					=    conditional_detr(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE				)
				assert torch.allclose(outputs.logits					,     original_outputs['''pred_logits''']					,     atol=1E-4				)
				assert torch.allclose(outputs.pred_boxes					,     original_outputs['''pred_boxes''']					,     atol=1E-4				)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks					,     original_outputs['''pred_masks''']					,     atol=1E-4				)

				# Save model and image processor
				logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'				)
				Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
				model.save_pretrained(_SCREAMING_SNAKE_CASE				)
				image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)


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

					parser.add_argument(
					    "--model_name",
					    default="conditional_detr_resnet50",
					    type=str,
					    help="Name of the CONDITIONAL_DETR model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					__A   :							str										=   parser.parse_args()
					convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad					,     model.parameters()				)
				_UpperCAmelCase					=    sum([np.prod(p.size()				) for p in model_parameters]				)
				return params


__A   :							Dict										=   logging.getLogger(__name__)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				if metric == "rouge2":
								_UpperCAmelCase					=    '''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								_UpperCAmelCase					=    '''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "em":
								_UpperCAmelCase					=    '''{val_avg_em:.4f}-{step_count}'''
				else:
								raise NotImplementedError(
								    f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
								    ''' function.'''				)

				_UpperCAmelCase					=    ModelCheckpoint(
				    dirpath=_SCREAMING_SNAKE_CASE					,     filename=_SCREAMING_SNAKE_CASE					,     monitor=f'val_{metric}'					,     mode='''max'''					,     save_top_k=3					,     every_n_epochs=1					,     )
				return checkpoint_callback







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]				):






				'''simple docstring'''



				return EarlyStopping(
				    monitor=f'val_{metric}'					,     mode='''min''' if '''loss''' in metric else '''max'''					,     patience=_SCREAMING_SNAKE_CASE					,     verbose=_SCREAMING_SNAKE_CASE					,     )
class 			_a (					pl.Callback):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str   )->Tuple:
							_UpperCAmelCase					=    {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
							pl_module.logger.log_metrics(__UpperCamelCase   )



			@rank_zero_only
			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Tuple=True   )->None:
							logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'   )
							_UpperCAmelCase					=    trainer.callback_metrics
							trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}   )
							# Log results
							_UpperCAmelCase					=    Path(pl_module.hparams.output_dir   )
							if type_path == "test":
											_UpperCAmelCase					=    od / '''test_results.txt'''
											_UpperCAmelCase					=    od / '''test_generations.txt'''
							else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											_UpperCAmelCase					=    od / F'{type_path}_results/{trainer.global_step:05d}.txt'
											_UpperCAmelCase					=    od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
											results_file.parent.mkdir(exist_ok=__UpperCamelCase   )
											generations_file.parent.mkdir(exist_ok=__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''a+'''   ) as writer:
											for key in sorted(__UpperCamelCase   ):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															_UpperCAmelCase					=    metrics[key]
															if isinstance(__UpperCamelCase				,   torch.Tensor   ):
																			_UpperCAmelCase					=    val.item()
															_UpperCAmelCase					=    F'{key}: {val:.6f}\n'
															writer.write(__UpperCamelCase   )

							if not save_generations:
											return

							if "preds" in metrics:
											_UpperCAmelCase					=    '''\n'''.join(metrics['''preds''']   )
											generations_file.open('''w+'''   ).write(__UpperCamelCase   )



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Dict   )->Union[str, Any]:
							try:
											_UpperCAmelCase					=    pl_module.model.model.num_parameters()
							except AttributeError:
											_UpperCAmelCase					=    pl_module.model.num_parameters()

							_UpperCAmelCase					=    count_trainable_parameters(__UpperCamelCase   )
							# mp stands for million parameters
							trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6}   )



			@rank_zero_only
			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    pl.LightningModule   )->List[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							return self._write_logs(__UpperCamelCase				,   __UpperCamelCase				,   '''test'''   )



			@rank_zero_only
			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    pl.Trainer				,   __UpperCamelCase     :    List[str]   )->Optional[Any]:
							save_json(pl_module.metrics				,   pl_module.metrics_save_path   )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")



