


'''simple docstring'''






import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)


def A_					(				__SCREAMING_SNAKE_CASE :					Dict	,   __SCREAMING_SNAKE_CASE :					Union[str, Any]=False	,   __SCREAMING_SNAKE_CASE :					Tuple=False	,   __SCREAMING_SNAKE_CASE :					Optional[Any]="data2vec."       )     ->  List[str]:
				__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = '''backbone.''' if is_semantic else ''''''

				__SCREAMING_SNAKE_CASE					:							Tuple			   = []
				for i in range(config.num_hidden_layers       ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")       )
								rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")       )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")       )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")       )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")       )
								rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")       )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")       )
								rename_keys.append(
								    (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")       )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")       )
								rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")       )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
				        (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
				        (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
				    ]       )

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ('''mask_token''', f"""{hf_prefix}embeddings.mask_token"""),
								        (
								            '''rel_pos_bias.relative_position_bias_table''',
								            f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
								        ),
								        (
								            '''rel_pos_bias.relative_position_index''',
								            f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
								        ),
								        ('''norm.weight''', '''layernorm.weight'''),
								        ('''norm.bias''', '''layernorm.bias'''),
								    ]       )
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
								        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
								        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
								        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
								    ]       )
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('''fc_norm.weight''', f"""{hf_prefix}pooler.layernorm.weight"""),
								        ('''fc_norm.bias''', f"""{hf_prefix}pooler.layernorm.bias"""),
								        ('''head.weight''', '''classifier.weight'''),
								        ('''head.bias''', '''classifier.bias'''),
								    ]       )

				return rename_keys


def A_					(				__SCREAMING_SNAKE_CASE :					int	,   __SCREAMING_SNAKE_CASE :					Optional[int]	,   __SCREAMING_SNAKE_CASE :					int=False	,   __SCREAMING_SNAKE_CASE :					List[Any]=False	,   __SCREAMING_SNAKE_CASE :					Optional[Any]="data2vec_vision."       )     ->  Union[str, Any]:
				for i in range(config.num_hidden_layers       ):
								__SCREAMING_SNAKE_CASE					:							Tuple			   = '''backbone.''' if is_semantic else ''''''
								# queries, keys and values
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""       )
								__SCREAMING_SNAKE_CASE					:							str			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""       )

								__SCREAMING_SNAKE_CASE					:							List[Any]			   = in_proj_weight[
								    : config.hidden_size, :
								]
								__SCREAMING_SNAKE_CASE					:							List[str]			   = q_bias
								__SCREAMING_SNAKE_CASE					:							Tuple			   = in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = in_proj_weight[
								    -config.hidden_size :, :
								]
								__SCREAMING_SNAKE_CASE					:							List[str]			   = v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								__SCREAMING_SNAKE_CASE					:							Dict			   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""       )

								__SCREAMING_SNAKE_CASE					:							Dict			   = gamma_a
								__SCREAMING_SNAKE_CASE					:							Dict			   = gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												__SCREAMING_SNAKE_CASE					:							int			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""       )
												__SCREAMING_SNAKE_CASE					:							List[Any]			   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""       )

												__SCREAMING_SNAKE_CASE					:							Dict			   = table
												__SCREAMING_SNAKE_CASE					:							Tuple			   = index


def A_					(				)     ->  Optional[Any]:
				__SCREAMING_SNAKE_CASE					:							Optional[int]			   = argparse.ArgumentParser(
				    '''Convert Data2VecVision to HF for image classification and pretraining'''	,   add_help=__SCREAMING_SNAKE_CASE       )
				parser.add_argument('''--hf_checkpoint_name'''	,   type=__SCREAMING_SNAKE_CASE       )
				parser.add_argument('''--input_size'''	,   default=2_24	,   type=__SCREAMING_SNAKE_CASE	,   help='''images input size'''       )
				parser.add_argument('''--beit_checkpoint'''	,   default=''''''	,   help='''beit checkpoint'''       )

				return parser.parse_args()


def A_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					int	,   __SCREAMING_SNAKE_CASE :					Any       )     ->  int:
				def load_state_dict(__SCREAMING_SNAKE_CASE :					Dict	,   __SCREAMING_SNAKE_CASE :					List[Any]	,   __SCREAMING_SNAKE_CASE :					List[Any]=""	,   __SCREAMING_SNAKE_CASE :					Dict="relative_position_index"       ):
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = []
								__SCREAMING_SNAKE_CASE					:							Dict			   = []
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = []
								# copy state_dict so _load_from_state_dict can modify it
								__SCREAMING_SNAKE_CASE					:							int			   = getattr(__SCREAMING_SNAKE_CASE	,   '''_metadata'''	,   __SCREAMING_SNAKE_CASE       )
								__SCREAMING_SNAKE_CASE					:							List[str]			   = state_dict.copy()
								if metadata is not None:
												__SCREAMING_SNAKE_CASE					:							Optional[int]			   = metadata

								def load(__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					Any=""       ):
												__SCREAMING_SNAKE_CASE					:							List[str]			   = {} if metadata is None else metadata.get(prefix[:-1]	,   {}       )
												module._load_from_state_dict(
												    __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )
												for name, child in module._modules.items():
																if child is not None:
																				load(__SCREAMING_SNAKE_CASE	,   prefix + name + '''.'''       )

								load(__SCREAMING_SNAKE_CASE	,   prefix=__SCREAMING_SNAKE_CASE       )

								__SCREAMING_SNAKE_CASE					:							Any			   = []
								__SCREAMING_SNAKE_CASE					:							int			   = []
								for key in missing_keys:
												__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = True
												for ignore_key in ignore_missing.split('''|'''       ):
																if ignore_key in key:
																				__SCREAMING_SNAKE_CASE					:							Optional[int]			   = False
																				break
												if keep_flag:
																warn_missing_keys.append(__SCREAMING_SNAKE_CASE       )
												else:
																ignore_missing_keys.append(__SCREAMING_SNAKE_CASE       )

								__SCREAMING_SNAKE_CASE					:							Tuple			   = warn_missing_keys

								if len(__SCREAMING_SNAKE_CASE       ) > 0:
												print(
												    '''Weights of {} not initialized from pretrained model: {}'''.format(
												        model.__class__.__name__	,   __SCREAMING_SNAKE_CASE       )       )
								if len(__SCREAMING_SNAKE_CASE       ) > 0:
												print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__	,   __SCREAMING_SNAKE_CASE       )       )
								if len(__SCREAMING_SNAKE_CASE       ) > 0:
												print(
												    '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
												        model.__class__.__name__	,   __SCREAMING_SNAKE_CASE       )       )
								if len(__SCREAMING_SNAKE_CASE       ) > 0:
												print('''\n'''.join(__SCREAMING_SNAKE_CASE       )       )

				__SCREAMING_SNAKE_CASE					:							List[str]			   = {
				    '''pretrained''': False,
				    '''use_shared_rel_pos_bias''': True,
				    '''use_abs_pos_emb''': False,
				    '''init_values''': 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        '''num_classes''': 10_00,
								        '''use_mean_pooling''': True,
								        '''init_scale''': 0.001,
								        '''use_rel_pos_bias''': True,
								    }       )

				__SCREAMING_SNAKE_CASE					:							Tuple			   = create_model(
				    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''	,   **__SCREAMING_SNAKE_CASE	,   )
				__SCREAMING_SNAKE_CASE					:							Dict			   = model.patch_embed.patch_size
				__SCREAMING_SNAKE_CASE					:							str			   = (args.input_size // patch_size[0], args.input_size // patch_size[1])
				__SCREAMING_SNAKE_CASE					:							Tuple			   = torch.load(args.beit_checkpoint	,   map_location='''cpu'''       )

				print(f"""Load ckpt from {args.beit_checkpoint}"""       )
				__SCREAMING_SNAKE_CASE					:							str			   = None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												__SCREAMING_SNAKE_CASE					:							List[Any]			   = checkpoint[model_key]
												print(f"""Load state_dict by model_key = {model_key}"""       )
												break

				__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(checkpoint_model.keys()       )
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(__SCREAMING_SNAKE_CASE       )

								if "relative_position_bias_table" in key:
												__SCREAMING_SNAKE_CASE					:							int			   = checkpoint_model[key]
												__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = rel_pos_bias.size()
												__SCREAMING_SNAKE_CASE					:							Tuple			   = model.state_dict()[key].size()
												__SCREAMING_SNAKE_CASE					:							Optional[int]			   = model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   prefix=''''''       )

				return model


def A_					(				)     ->  Union[str, Any]:
				__SCREAMING_SNAKE_CASE					:							Optional[int]			   = get_args()

				__SCREAMING_SNAKE_CASE					:							int			   = '''ft1k''' in args.hf_checkpoint_name
				__SCREAMING_SNAKE_CASE					:							Any			   = '''large''' in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				__SCREAMING_SNAKE_CASE					:							List[Any]			   = DataaVecVisionConfig()
				if is_finetuned:
								__SCREAMING_SNAKE_CASE					:							Dict			   = True
								__SCREAMING_SNAKE_CASE					:							str			   = False
								__SCREAMING_SNAKE_CASE					:							Any			   = True
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = 10_00

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = '''huggingface/label-files'''
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = '''imagenet-1k-id2label.json'''
								__SCREAMING_SNAKE_CASE					:							int			   = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   repo_type='''dataset'''       )	,   '''r'''       )       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = {int(__SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = idalabel
								__SCREAMING_SNAKE_CASE					:							Dict			   = {v: k for k, v in idalabel.items()}
				else:
								__SCREAMING_SNAKE_CASE					:							int			   = False
								__SCREAMING_SNAKE_CASE					:							Dict			   = True
								__SCREAMING_SNAKE_CASE					:							str			   = False

				if is_large:
								__SCREAMING_SNAKE_CASE					:							int			   = 10_24
								__SCREAMING_SNAKE_CASE					:							List[str]			   = 40_96
								__SCREAMING_SNAKE_CASE					:							Any			   = 24
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = 16

				# 2. Load Beit model
				__SCREAMING_SNAKE_CASE					:							Tuple			   = load_beit_model(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE       )
				orig_model.eval()

				# 3. Forward Beit model
				__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = BeitImageProcessor(size=config.image_size	,   do_center_crop=__SCREAMING_SNAKE_CASE       )
				__SCREAMING_SNAKE_CASE					:							Dict			   = Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = image_processor(images=__SCREAMING_SNAKE_CASE	,   return_tensors='''pt'''       )
				__SCREAMING_SNAKE_CASE					:							Dict			   = encoding['''pixel_values''']

				__SCREAMING_SNAKE_CASE					:							Dict			   = (pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								__SCREAMING_SNAKE_CASE					:							List[str]			   = orig_model(*__SCREAMING_SNAKE_CASE       )

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								__SCREAMING_SNAKE_CASE					:							Any			   = DataaVecVisionForImageClassification(__SCREAMING_SNAKE_CASE       )
								hf_model.eval()
								__SCREAMING_SNAKE_CASE					:							str			   = False
								__SCREAMING_SNAKE_CASE					:							str			   = '''data2vec_vision.'''
				else:
								__SCREAMING_SNAKE_CASE					:							List[str]			   = DataaVecVisionModel(__SCREAMING_SNAKE_CASE       )
								hf_model.eval()
								__SCREAMING_SNAKE_CASE					:							str			   = True
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = ''''''

				__SCREAMING_SNAKE_CASE					:							List[str]			   = create_rename_keys(__SCREAMING_SNAKE_CASE	,   hf_prefix=__SCREAMING_SNAKE_CASE	,   has_lm_head=__SCREAMING_SNAKE_CASE       )
				__SCREAMING_SNAKE_CASE					:							int			   = orig_model.state_dict()
				for src, dest in rename_keys:
								__SCREAMING_SNAKE_CASE					:							List[str]			   = state_dict.pop(__SCREAMING_SNAKE_CASE       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = val

				read_in_q_k_v(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   hf_prefix=__SCREAMING_SNAKE_CASE	,   has_lm_head=__SCREAMING_SNAKE_CASE       )
				__SCREAMING_SNAKE_CASE					:							Optional[int]			   = hf_model.load_state_dict(__SCREAMING_SNAKE_CASE	,   strict=__SCREAMING_SNAKE_CASE       )
				print('''HF missing'''	,   __SCREAMING_SNAKE_CASE       )
				print('''HF unexpected_keys'''	,   __SCREAMING_SNAKE_CASE       )

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								__SCREAMING_SNAKE_CASE					:							Any			   = hf_model(__SCREAMING_SNAKE_CASE       )

				__SCREAMING_SNAKE_CASE					:							Dict			   = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				__SCREAMING_SNAKE_CASE					:							str			   = torch.max(torch.abs(hf_output - orig_model_output       )       ).item()

				print(f"""max_absolute_diff = {max_absolute_diff}"""       )
				__SCREAMING_SNAKE_CASE					:							List[Any]			   = torch.allclose(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   atol=1E-3       )
				print('''Do both models output the same tensors?'''	,   '''🔥''' if success else '''💩'''       )
				if not success:
								raise Exception('''Something went wRoNg'''       )

				# 7. Save
				print(f"""Saving to {args.hf_checkpoint_name}"""       )
				hf_model.save_pretrained(args.hf_checkpoint_name       )
				image_processor.save_pretrained(args.hf_checkpoint_name       )


if __name__ == "__main__":
						main()
						# Run the following to convert checkpoints
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"






'''simple docstring'''






import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_A      =     {
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  Dict:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = search.split()
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = dict((g.split('''='''       ) for g in groups)       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(entries.keys()       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = [[f"""--{k} {v}""" for v in vs.split(''':'''       )] for k, vs in entries.items()]
					__SCREAMING_SNAKE_CASE					:							int			   = [list(__SCREAMING_SNAKE_CASE       ) for x in itertools.product(*__SCREAMING_SNAKE_CASE       )]
					return matrix, entry_names


def A_					(				)     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sys.argv[0]

					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = argparse.ArgumentParser(
					    usage=(
					        '''\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'''
					        ''' refer to `run_eval.py -h` for the complete list.'''
					    )       )
					parser.add_argument(
					    '''--search'''	,   type=__SCREAMING_SNAKE_CASE	,   required=__SCREAMING_SNAKE_CASE	,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''	,   )
					parser.add_argument(
					    '''--bs'''	,   type=__SCREAMING_SNAKE_CASE	,   default=8	,   required=__SCREAMING_SNAKE_CASE	,   help='''initial batch size (may get reduced if it\'s too big)'''       )
					parser.add_argument('''--task'''	,   type=__SCREAMING_SNAKE_CASE	,   help='''used for task_specific_params + metrics'''       )
					parser.add_argument(
					    '''--info'''	,   nargs='''?'''	,   type=__SCREAMING_SNAKE_CASE	,   const=datetime_now()	,   help=(
					        '''add custom notes to be printed before the results table. If no value is passed, the current datetime'''
					        ''' string will be used.'''
					    )	,   )
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = parser.parse_known_args()
					# we share some of the args
					args_main.extend(['''--task''', args.task]       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = [prog] + args_main

					# to support variations like translation_en_to_de"
					__SCREAMING_SNAKE_CASE					:							List[str]			   = '''translation''' if '''translation''' in args.task else '''summarization'''

					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Tuple			   = parse_search_arg(args.search       )
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = task_score_names[task]  # score cols first
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {col: len(str(__SCREAMING_SNAKE_CASE       )       ) for col in col_names}
					__SCREAMING_SNAKE_CASE					:							Dict			   = []
					for r in matrix:
										__SCREAMING_SNAKE_CASE					:							List[str]			   = dict((x.replace('''--'''	,   ''''''       ).split() for x in r)       )
										__SCREAMING_SNAKE_CASE					:							Tuple			   = ''' '''.join(__SCREAMING_SNAKE_CASE       ).split()
										args_exp.extend(['''--bs''', str(args.bs       )]       )  # in case we need to reduce its size due to CUDA OOM
										__SCREAMING_SNAKE_CASE					:							Dict			   = args_normal + args_exp

										# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = run_generate(verbose=__SCREAMING_SNAKE_CASE       )
										# make sure scores are first in the table
										__SCREAMING_SNAKE_CASE					:							Any			   = OrderedDict()
										for score in task_score_names[task]:
															__SCREAMING_SNAKE_CASE					:							int			   = scores[score]
										result.update(__SCREAMING_SNAKE_CASE       )
										results.append(__SCREAMING_SNAKE_CASE       )

										# find widest entries
										for k, v in result.items():
															__SCREAMING_SNAKE_CASE					:							List[Any]			   = len(str(__SCREAMING_SNAKE_CASE       )       )
															if l > col_widths[k]:
																				__SCREAMING_SNAKE_CASE					:							Any			   = l

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = sorted(__SCREAMING_SNAKE_CASE	,   key=operator.itemgetter(*task_score_names[task]       )	,   reverse=__SCREAMING_SNAKE_CASE       )
					print(''' | '''.join([f"""{col:{col_widths[col]}}""" for col in col_names]       )       )
					print(''' | '''.join([f"""{"-"*col_widths[col]}""" for col in col_names]       )       )
					for row in results_sorted:
										print(''' | '''.join([f"""{row[col]:{col_widths[col]}}""" for col in col_names]       )       )

					__SCREAMING_SNAKE_CASE					:							Any			   = results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = [f"""--{k} {v}""" for k, v in best.items()]
					__SCREAMING_SNAKE_CASE					:							Tuple			   = ['''--bs''', str(args.bs       )]
					if args.info:
										print(f"""\nInfo: {args.info}"""       )
					print('''\nBest score args:'''       )
					print(''' '''.join(args_main + best_args + dyn_args       )       )

					return results_sorted


if __name__ == "__main__":
						# Usage:
						# [normal-run_eval_search.py cmd plus] \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						#
						# Example:
						# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
						# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
						# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						run_search()


