


'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


lowerCAmelCase   : Any             =				Mapping[str, np.ndarray]
lowerCAmelCase   : Tuple             =				Mapping[str, Any]  # Is a nested dict.
lowerCAmelCase   : Any             =				0.01




@dataclasses.dataclass(frozen=SCREAMING_SNAKE_CASE)
class        _UpperCamelCase	:


				'''simple docstring'''


				_snake_case						       =				42  # [num_res, num_atom_type, 3]

				# Amino-acid type for each residue represented as an integer between 0 and
				# 20, where 20 is 'X'.
				_snake_case						       =				42  # [num_res]

				# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
				# is present and 0.0 if not. This should be used for loss masking.
				_snake_case						       =				42  # [num_res, num_atom_type]

				# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
				_snake_case						       =				42  # [num_res]

				# B-factors, or temperature factors, of each residue (in sq. angstroms units),
				# representing the displacement of the residue from its ground truth mean
				# value.
				_snake_case						       =				42  # [num_res, num_atom_type]

				# Chain indices for multi-chain predictions
				_snake_case						       =				None

				# Optional remark about the protein. Included as a comment in output PDB
				# files
				_snake_case						       =				None

				# Templates used to generate this protein (prediction-only)
				_snake_case						       =				None

				# Chain corresponding to each parent
				_snake_case						       =				None




def 	_A  (       A							)  ->				Protein:
	lowercase						:		int   					=		r"(\[[A-Z]+\]\n)"
	lowercase						:		List[str]   					=		[tag.strip() for tag in re.split(A							,A							) if len(A							) > 0]
	lowercase						:		Iterator[Tuple[str, List[str]]]   					=		zip(tags[0::2]							,[l.split("\n"							) for l in tags[1::2]]							)

	lowercase						:		List[str]   					=		["N", "CA", "C"]
	lowercase						:		int   					=		None
	lowercase						:		Dict   					=		None
	lowercase						:		Union[str, Any]   					=		None
	for g in groups:
		if "[PRIMARY]" == g[0]:
			lowercase						:		Optional[Any]   					=		g[1][0].strip()
			for i in range(len(A							)							):
				if seq[i] not in residue_constants.restypes:
					lowercase						:		Optional[int]   					=		"X"  # FIXME: strings are immutable
			lowercase						:		Tuple   					=		np.array(
			    [residue_constants.restype_order.get(A							,residue_constants.restype_num							) for res_symbol in seq]							)
		elif "[TERTIARY]" == g[0]:
			lowercase						:		List[List[float]]   					=		[]
			for axis in range(3							):
				tertiary.append(list(map(A							,g[1][axis].split()							)							)							)
			lowercase						:		Optional[Any]   					=		np.array(A							)
			lowercase						:		Optional[Any]   					=		np.zeros((len(tertiary[0]							) // 3, residue_constants.atom_type_num, 3)							).astype(np.floataa							)
			for i, atom in enumerate(A							):
				lowercase						:		List[str]   					=		np.transpose(tertiary_np[:, i::3]							)
			atom_positions *= PICO_TO_ANGSTROM
		elif "[MASK]" == g[0]:
			lowercase						:		Tuple   					=		np.array(list(map({"-": 0, "+": 1}.get							,g[1][0].strip()							)							)							)
			lowercase						:		Any   					=		np.zeros(
			    (
			        len(A							),
			        residue_constants.atom_type_num,
			    )							).astype(np.floataa							)
			for i, atom in enumerate(A							):
				lowercase						:		Dict   					=		1
			atom_mask *= mask[..., None]

	assert aatype is not None

	return Protein(
	    atom_positions=A							,atom_mask=A							,aatype=A							,residue_index=np.arange(len(A							)							)							,b_factors=A							,)




def 	_A  (       A							,A = 0							)  ->				List[str]:
	lowercase						:		List[str]   					=		[]

	lowercase						:		Tuple   					=		prot.remark
	if remark is not None:
		pdb_headers.append(F'''REMARK {remark}'''							)

	lowercase						:		int   					=		prot.parents
	lowercase						:		List[Any]   					=		prot.parents_chain_index
	if parents is not None and parents_chain_index is not None:
		lowercase						:		List[str]   					=		[p for i, p in zip(A							,A							) if i == chain_id]

	if parents is None or len(A							) == 0:
		lowercase						:		str   					=		["N/A"]

	pdb_headers.append(F'''PARENT {' '.join(A							)}'''							)

	return pdb_headers




def 	_A  (       A							,A							)  ->				str:
	lowercase						:		List[str]   					=		[]
	lowercase						:		Optional[Any]   					=		pdb_str.split("\n"							)

	lowercase						:		Optional[Any]   					=		prot.remark
	if remark is not None:
		out_pdb_lines.append(F'''REMARK {remark}'''							)

	lowercase						:		List[List[str]]
	if prot.parents is not None and len(prot.parents							) > 0:
		lowercase						:		Any   					=		[]
		if prot.parents_chain_index is not None:
			lowercase						:		Dict[str, List[str]]   					=		{}
			for p, i in zip(prot.parents							,prot.parents_chain_index							):
				parent_dict.setdefault(str(A							)							,[]							)
				parent_dict[str(A							)].append(A							)

			lowercase						:		str   					=		max([int(A							) for chain_idx in parent_dict]							)
			for i in range(max_idx + 1							):
				lowercase						:		Dict   					=		parent_dict.get(str(A							)							,["N/A"]							)
				parents_per_chain.append(A							)
		else:
			parents_per_chain.append(list(prot.parents							)							)
	else:
		lowercase						:		List[Any]   					=		[["N/A"]]

	def make_parent_line(A							) -> str:
		return F'''PARENT {' '.join(A							)}'''

	out_pdb_lines.append(make_parent_line(parents_per_chain[0]							)							)

	lowercase						:		Dict   					=		0
	for i, l in enumerate(A							):
		if "PARENT" not in l and "REMARK" not in l:
			out_pdb_lines.append(A							)
		if "TER" in l and "END" not in lines[i + 1]:
			chain_counter += 1
			if not chain_counter >= len(A							):
				lowercase						:		Any   					=		parents_per_chain[chain_counter]
			else:
				lowercase						:		List[Any]   					=		["N/A"]

			out_pdb_lines.append(make_parent_line(A							)							)

	return "\n".join(A							)




def 	_A  (       A							)  ->				str:
	lowercase						:		Optional[Any]   					=		residue_constants.restypes + ["X"]

	def res_atoa(A							) -> str:
		return residue_constants.restype_atoa.get(restypes[r]							,"UNK"							)

	lowercase						:		Dict   					=		residue_constants.atom_types

	lowercase						:		List[str]   					=		[]

	lowercase						:		List[Any]   					=		prot.atom_mask
	lowercase						:		Any   					=		prot.aatype
	lowercase						:		Union[str, Any]   					=		prot.atom_positions
	lowercase						:		Optional[Any]   					=		prot.residue_index.astype(np.intaa							)
	lowercase						:		List[str]   					=		prot.b_factors
	lowercase						:		Optional[int]   					=		prot.chain_index

	if np.any(aatype > residue_constants.restype_num							):
		raise ValueError("Invalid aatypes."							)

	lowercase						:		Optional[Any]   					=		get_pdb_headers(A							)
	if len(A							) > 0:
		pdb_lines.extend(A							)

	lowercase						:		Union[str, Any]   					=		aatype.shape[0]
	lowercase						:		List[Any]   					=		1
	lowercase						:		List[str]   					=		0
	lowercase						:		Tuple   					=		string.ascii_uppercase
	lowercase						:		Union[str, Any]   					=		None
	# Add all atom sites.
	for i in range(A							):
		lowercase						:		str   					=		res_atoa(aatype[i]							)
		for atom_name, pos, mask, b_factor in zip(A							,atom_positions[i]							,atom_mask[i]							,b_factors[i]							):
			if mask < 0.5:
				continue

			lowercase						:		Union[str, Any]   					=		"ATOM"
			lowercase						:		Dict   					=		atom_name if len(A							) == 4 else F''' {atom_name}'''
			lowercase						:		Any   					=		""
			lowercase						:		Dict   					=		""
			lowercase						:		Optional[Any]   					=		1.00
			lowercase						:		Union[str, Any]   					=		atom_name[0]  # Protein supports only C, N, O, S, this works.
			lowercase						:		str   					=		""

			lowercase						:		int   					=		"A"
			if chain_index is not None:
				lowercase						:		Optional[int]   					=		chain_tags[chain_index[i]]

			# PDB is a columnar format, every space matters here!
			lowercase						:		Optional[int]   					=		(
			    F'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
			    F'''{res_name_a:>3} {chain_tag:>1}'''
			    F'''{residue_index[i]:>4}{insertion_code:>1}   '''
			    F'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
			    F'''{occupancy:>6.2f}{b_factor:>6.2f}          '''
			    F'''{element:>2}{charge:>2}'''
			)
			pdb_lines.append(A							)
			atom_index += 1

		lowercase						:		int   					=		i == n - 1
		if chain_index is not None:
			if i != n - 1 and chain_index[i + 1] != prev_chain_index:
				lowercase						:		str   					=		True
				lowercase						:		str   					=		chain_index[i + 1]

		if should_terminate:
			# Close the chain.
			lowercase						:		List[Any]   					=		"TER"
			lowercase						:		int   					=		(
			    F'''{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]							):>3} {chain_tag:>1}{residue_index[i]:>4}'''
			)
			pdb_lines.append(A							)
			atom_index += 1

			if i != n - 1:
				# "prev" is a misnomer here. This happens at the beginning of
				# each new chain.
				pdb_lines.extend(get_pdb_headers(A							,A							)							)

	pdb_lines.append("END"							)
	pdb_lines.append(""							)
	return "\n".join(A							)




def 	_A  (       A							)  ->				np.ndarray:
	return residue_constants.STANDARD_ATOM_MASK[prot.aatype]






def 	_A  (       A							,A							,A = None							,A = None							,A = None							,A = None							,A = None							,)  ->				Protein:
	return Protein(
	    aatype=features["aatype"]							,atom_positions=result["final_atom_positions"]							,atom_mask=result["final_atom_mask"]							,residue_index=features["residue_index"] + 1							,b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"]							)							,chain_index=A							,remark=A							,parents=A							,parents_chain_index=A							,)






'''simple docstring'''
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling




def 	_A  (       A							,A							)  ->				str:
	lowercase						:		Optional[int]   					=		old_name

	if "patch_embed" in old_name:
		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		old_name.split("."							)

		if layer == "0":
			lowercase						:		int   					=		old_name.replace("0"							,"convolution1"							)
		elif layer == "1":
			lowercase						:		List[str]   					=		old_name.replace("1"							,"batchnorm_before"							)
		elif layer == "3":
			lowercase						:		Dict   					=		old_name.replace("3"							,"convolution2"							)
		else:
			lowercase						:		Union[str, Any]   					=		old_name.replace("4"							,"batchnorm_after"							)

	if "network" in old_name and re.search(r"\d\.\d"							,A							):
		lowercase						:		List[str]   					=		r"\b\d{2}\b"
		if bool(re.search(A							,A							)							):
			lowercase						:		str   					=		re.search(r"\d\.\d\d."							,A							).group()
		else:
			lowercase						:		int   					=		re.search(r"\d\.\d."							,A							).group()
		if int(match[0]							) < 6:
			lowercase						:		str   					=		old_name.replace(A							,""							)
			lowercase						:		List[str]   					=		trimmed_name.replace("network"							,match[0] + ".meta4D_layers.blocks." + match[2:-1]							)
			lowercase						:		Optional[Any]   					=		"intermediate_stages." + trimmed_name
		else:
			lowercase						:		str   					=		old_name.replace(A							,""							)
			if int(match[2]							) < num_meta4D_last_stage:
				lowercase						:		Optional[int]   					=		trimmed_name.replace("network"							,"meta4D_layers.blocks." + match[2]							)
			else:
				lowercase						:		List[Any]   					=		str(int(match[2]							) - num_meta4D_last_stage							)
				lowercase						:		List[Any]   					=		trimmed_name.replace("network"							,"meta3D_layers.blocks." + layer_index							)
				if "norm1" in old_name:
					lowercase						:		str   					=		trimmed_name.replace("norm1"							,"layernorm1"							)
				elif "norm2" in old_name:
					lowercase						:		Optional[Any]   					=		trimmed_name.replace("norm2"							,"layernorm2"							)
				elif "fc1" in old_name:
					lowercase						:		Optional[int]   					=		trimmed_name.replace("fc1"							,"linear_in"							)
				elif "fc2" in old_name:
					lowercase						:		str   					=		trimmed_name.replace("fc2"							,"linear_out"							)

			lowercase						:		Dict   					=		"last_stage." + trimmed_name

	elif "network" in old_name and re.search(r".\d."							,A							):
		lowercase						:		Union[str, Any]   					=		old_name.replace("network"							,"intermediate_stages"							)

	if "fc" in new_name:
		lowercase						:		Any   					=		new_name.replace("fc"							,"convolution"							)
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		lowercase						:		Optional[Any]   					=		new_name.replace("norm1"							,"batchnorm_before"							)
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		lowercase						:		List[str]   					=		new_name.replace("norm2"							,"batchnorm_after"							)
	if "proj" in new_name:
		lowercase						:		Optional[int]   					=		new_name.replace("proj"							,"projection"							)
	if "dist_head" in new_name:
		lowercase						:		Tuple   					=		new_name.replace("dist_head"							,"distillation_classifier"							)
	elif "head" in new_name:
		lowercase						:		Tuple   					=		new_name.replace("head"							,"classifier"							)
	elif "patch_embed" in new_name:
		lowercase						:		Optional[int]   					=		"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		lowercase						:		str   					=		new_name.replace("norm"							,"layernorm"							)
		lowercase						:		List[Any]   					=		"efficientformer." + new_name
	else:
		lowercase						:		Optional[Any]   					=		"efficientformer.encoder." + new_name

	return new_name




def 	_A  (       A							,A							)  ->				Optional[Any]:
	for key in checkpoint.copy().keys():
		lowercase						:		List[str]   					=		checkpoint.pop(A							)
		lowercase						:		int   					=		val

	return checkpoint




def 	_A  (       )  ->				Optional[int]:
	lowercase						:		str   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowercase						:		Optional[Any]   					=		Image.open(requests.get(A							,stream=A							).raw							)

	return image




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		torch.load(A							,map_location="cpu"							)["model"]
	lowercase						:		int   					=		EfficientFormerConfig.from_json_file(A							)
	lowercase						:		Tuple   					=		EfficientFormerForImageClassificationWithTeacher(A							)
	lowercase						:		int   					=		"_".join(checkpoint_path.split("/"							)[-1].split("."							)[0].split("_"							)[:-1]							)

	lowercase						:		Optional[int]   					=		config.depths[-1] - config.num_metaad_blocks + 1
	lowercase						:		int   					=		convert_torch_checkpoint(A							,A							)

	model.load_state_dict(A							)
	model.eval()

	lowercase						:		List[Any]   					=		{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	# prepare image
	lowercase						:		Tuple   					=		prepare_img()
	lowercase						:		Optional[int]   					=		2_5_6
	lowercase						:		str   					=		2_2_4
	lowercase						:		List[str]   					=		EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}							,crop_size={"height": crop_size, "width": crop_size}							,resample=pillow_resamplings["bicubic"]							,)
	lowercase						:		Union[str, Any]   					=		processor(images=A							,return_tensors="pt"							).pixel_values

	# original processing pipeline
	lowercase						:		Tuple   					=		Compose(
	    [
	        Resize(A							,interpolation=pillow_resamplings["bicubic"]							),
	        CenterCrop(A							),
	        ToTensor(),
	        Normalize(A							,A							),
	    ]							)
	lowercase						:		List[Any]   					=		image_transforms(A							).unsqueeze(0							)

	assert torch.allclose(A							,A							)

	lowercase						:		Union[str, Any]   					=		model(A							)
	lowercase						:		Any   					=		outputs.logits

	lowercase						:		List[str]   					=		(1, 1_0_0_0)

	if "l1" in model_name:
		lowercase						:		Any   					=		torch.Tensor(
		    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]							)
		assert torch.allclose(logits[0, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		lowercase						:		List[Any]   					=		torch.Tensor(
		    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]							)
		assert torch.allclose(logits[0, :1_0]							,A							,atol=1e-3							)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		lowercase						:		Optional[int]   					=		torch.Tensor(
		    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]							)
		assert logits.shape == expected_shape
	else:
		raise ValueError(
		    F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''							)

	# Save Checkpoints
	Path(A							).mkdir(exist_ok=A							)
	model.save_pretrained(A							)
	print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)
	processor.save_pretrained(A							)
	print(F'''Processor successfuly saved at {pytorch_dump_path}'''							)

	if push_to_hub:
		print("Pushing model to the hub..."							)

		model.push_to_hub(
		    repo_id=F'''Bearnardd/{pytorch_dump_path}'''							,commit_message="Add model"							,use_temp_dir=A							,)
		processor.push_to_hub(
		    repo_id=F'''Bearnardd/{pytorch_dump_path}'''							,commit_message="Add image processor"							,use_temp_dir=A							,)


if __name__ == "__main__":
					lowerCAmelCase   : Tuple             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--pytorch_model_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to EfficientFormer pytorch checkpoint.""",
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The json file for EfficientFormer model config.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)

					parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
					parser.add_argument(
					    """--no-push_to_hub""",
					    dest="""push_to_hub""",
					    action="""store_false""",
					    help="""Do not push model and image processor to the hub""",
					)
					parser.set_defaults(push_to_hub=True)

					lowerCAmelCase   : Optional[int]             =				parser.parse_args()
					convert_efficientformer_checkpoint(
					    checkpoint_path=args.pytorch_model_path,
					    efficientformer_config_file=args.config_file,
					    pytorch_dump_path=args.pytorch_dump_path,
					    push_to_hub=args.push_to_hub,
					)


