




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str     ):
					__lowerCAmelCase				   =       SwinConfig(
					    embed_dim=1_92      ,      depths=(2, 2, 18, 2)      ,      num_heads=(6, 12, 24, 48)      ,      window_size=12      ,      out_features=["stage2", "stage3", "stage4"]      ,      )

					__lowerCAmelCase				   =       DetaConfig(
					    backbone_config=SCREAMING_SNAKE_CASE_      ,      num_queries=9_00      ,      encoder_ffn_dim=20_48      ,      decoder_ffn_dim=20_48      ,      num_feature_levels=5      ,      assign_first_stage=SCREAMING_SNAKE_CASE_      ,      with_box_refine=SCREAMING_SNAKE_CASE_      ,      two_stage=SCREAMING_SNAKE_CASE_      ,      )

					# set labels
					__lowerCAmelCase				   =       "huggingface/label-files"
					if "o365" in model_name:
										__lowerCAmelCase				   =       3_66
										__lowerCAmelCase				   =       "object365-id2label.json"
					else:
										__lowerCAmelCase				   =       91
										__lowerCAmelCase				   =       "coco-detection-id2label.json"

					__lowerCAmelCase				   =       num_labels
					__lowerCAmelCase				   =       json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      repo_type="dataset"     )     )      ,      "r"     )     )
					__lowerCAmelCase				   =       {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
					__lowerCAmelCase				   =       idalabel
					__lowerCAmelCase				   =       {v: k for k, v in idalabel.items()}

					return config







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict     ):
					__lowerCAmelCase				   =       []

					# stem
					# fmt: off
					rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")     )
					rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")     )
					rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")     )
					rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")     )
					# stages
					for i in range(len(config.backbone_config.depths     )     ):
										for j in range(config.backbone_config.depths[i]     ):
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

										if i < 3:
															rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.reduction.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")     )
															rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.bias""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")     )

					rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")     )
					rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")     )
					rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")     )
					rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")     )
					rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")     )
					rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")     )

					# transformer encoder
					for i in range(config.encoder_layers     ):
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")     )
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")     )

					# transformer decoder
					for i in range(config.decoder_layers     ):
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")     )
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")     )

					# fmt: on

					return rename_keys







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					__lowerCAmelCase				   =       dct.pop(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       val







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__lowerCAmelCase				   =       [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
					for i in range(len(backbone_config.depths     )     ):
										__lowerCAmelCase				   =       num_features[i]
										for j in range(backbone_config.depths[i]     ):
															# fmt: off
															# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
															__lowerCAmelCase				   =       state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
															__lowerCAmelCase				   =       state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
															# next, add query, keys and values (in that order) to the state dict
															__lowerCAmelCase				   =       in_proj_weight[:dim, :]
															__lowerCAmelCase				   =       in_proj_bias[: dim]
															__lowerCAmelCase				   =       in_proj_weight[
															    dim : dim * 2, :
															]
															__lowerCAmelCase				   =       in_proj_bias[
															    dim : dim * 2
															]
															__lowerCAmelCase				   =       in_proj_weight[
															    -dim :, :
															]
															__lowerCAmelCase				   =       in_proj_bias[-dim :]
															# fmt: on







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				str     ):
					# transformer decoder self-attention layers
					__lowerCAmelCase				   =       config.d_model
					for i in range(config.decoder_layers     ):
										# read in weights + bias of input projection layer of self-attention
										__lowerCAmelCase				   =       state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
										__lowerCAmelCase				   =       state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""     )
										# next, add query, keys and values (in that order) to the state dict
										__lowerCAmelCase				   =       in_proj_weight[:hidden_size, :]
										__lowerCAmelCase				   =       in_proj_bias[:hidden_size]
										__lowerCAmelCase				   =       in_proj_weight[
										    hidden_size : hidden_size * 2, :
										]
										__lowerCAmelCase				   =       in_proj_bias[hidden_size : hidden_size * 2]
										__lowerCAmelCase				   =       in_proj_weight[-hidden_size:, :]
										__lowerCAmelCase				   =       in_proj_bias[-hidden_size:]







def 	_a      (						):
					__lowerCAmelCase				   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
					__lowerCAmelCase				   =       Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,      stream=SCREAMING_SNAKE_CASE_     ).raw     )

					return im







@torch.no_grad()
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					__lowerCAmelCase				   =       get_deta_config(SCREAMING_SNAKE_CASE_     )

					# load original state dict
					if model_name == "deta-swin-large":
										__lowerCAmelCase				   =       hf_hub_download(repo_id="nielsr/deta-checkpoints"      ,      filename="adet_swin_ft.pth"     )
					elif model_name == "deta-swin-large-o365":
										__lowerCAmelCase				   =       hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"      ,      filename="deta_swin_pt_o365.pth"     )
					else:
										raise ValueError(F"""Model name {model_name} not supported"""     )

					__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_      ,      map_location="cpu"     )["model"]

					# original state dict
					for name, param in state_dict.items():
										print(SCREAMING_SNAKE_CASE_      ,      param.shape     )

					# rename keys
					__lowerCAmelCase				   =       create_rename_keys(SCREAMING_SNAKE_CASE_     )
					for src, dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					read_in_swin_q_k_v(SCREAMING_SNAKE_CASE_      ,      config.backbone_config     )
					read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

					# fix some prefixes
					for key in state_dict.copy().keys():
										if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
															__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
															__lowerCAmelCase				   =       val
										if "input_proj" in key:
															__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
															__lowerCAmelCase				   =       val
										if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
															__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
															__lowerCAmelCase				   =       val

    # finally, create HuggingFace model and load state dict
					__lowerCAmelCase				   =       DetaForObjectDetection(SCREAMING_SNAKE_CASE_     )
					model.load_state_dict(SCREAMING_SNAKE_CASE_     )
					model.eval()

					__lowerCAmelCase				   =       "cuda" if torch.cuda.is_available() else "cpu"
					model.to(SCREAMING_SNAKE_CASE_     )

					# load image processor
					__lowerCAmelCase				   =       DetaImageProcessor(format="coco_detection"     )

					# verify our conversion on image
					__lowerCAmelCase				   =       prepare_img()
					__lowerCAmelCase				   =       processor(images=SCREAMING_SNAKE_CASE_      ,      return_tensors="pt"     )
					__lowerCAmelCase				   =       encoding["pixel_values"]
					__lowerCAmelCase				   =       model(pixel_values.to(SCREAMING_SNAKE_CASE_     )     )

					# verify logits
					print("Logits:"      ,      outputs.logits[0, :3, :3]     )
					print("Boxes:"      ,      outputs.pred_boxes[0, :3, :3]     )
					if model_name == "deta-swin-large":
										__lowerCAmelCase				   =       torch.tensor(
										    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]     )
										__lowerCAmelCase				   =       torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]     )
					elif model_name == "deta-swin-large-o365":
										__lowerCAmelCase				   =       torch.tensor(
										    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]     )
										__lowerCAmelCase				   =       torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]     )
					assert torch.allclose(outputs.logits[0, :3, :3]      ,      expected_logits.to(SCREAMING_SNAKE_CASE_     )      ,      atol=1E-4     )
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,      expected_boxes.to(SCREAMING_SNAKE_CASE_     )      ,      atol=1E-4     )
					print("Everything ok!"     )

					if pytorch_dump_folder_path:
										# Save model and processor
										logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""     )
										Path(SCREAMING_SNAKE_CASE_     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )
										model.save_pretrained(SCREAMING_SNAKE_CASE_     )
										processor.save_pretrained(SCREAMING_SNAKE_CASE_     )

					# Push to hub
					if push_to_hub:
										print("Pushing model and processor to hub..."     )
										model.push_to_hub(F"""jozhang97/{model_name}"""     )
										processor.push_to_hub(F"""jozhang97/{model_name}"""     )


if __name__ == "__main__":
					UpperCamelCase__    			=			argparse.ArgumentParser()

					parser.add_argument(
					    """--model_name""",
					    type=str,
					    default="""deta-swin-large""",
					    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
					    help="""Name of the 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.""",
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)
					UpperCamelCase__    			=			parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










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 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       filter(lambda SCREAMING_SNAKE_CASE_     : p.requires_grad      ,      model.parameters()     )
					__lowerCAmelCase				   =       sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					if metric == "rouge2":
										__lowerCAmelCase				   =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										__lowerCAmelCase				   =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										__lowerCAmelCase				   =       "{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."     )

					__lowerCAmelCase				   =       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 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					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      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
											pl_module.logger.log_metrics(_A			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A=True			):




											"""simple docstring"""



											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       Path(pl_module.hparams.output_dir			)
											if type_path == "test":
																__lowerCAmelCase				   =       od / "test_results.txt"
																__lowerCAmelCase				   =       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.
																__lowerCAmelCase				   =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																__lowerCAmelCase				   =       od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																results_file.parent.mkdir(exist_ok=_A			)
																generations_file.parent.mkdir(exist_ok=_A			)
											with open(_A		,			"a+"			) as writer:
																for key in sorted(_A			):
																					if key in ["log", "progress_bar", "preds"]:
																										continue
																					__lowerCAmelCase				   =       metrics[key]
																					if isinstance(_A		,			torch.Tensor			):
																										__lowerCAmelCase				   =       val.item()
																					__lowerCAmelCase				   =       f"""{key}: {val:.6f}\n"""
																					writer.write(_A			)

											if not save_generations:
																return

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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											try:
																__lowerCAmelCase				   =       pl_module.model.model.num_parameters()
											except AttributeError:
																__lowerCAmelCase				   =       pl_module.model.num_parameters()

											__lowerCAmelCase				   =       count_trainable_parameters(_A			)
											# 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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											return self._write_logs(_A		,			_A		,			"test"			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")




