import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def    A			(					_lowercase		):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE							:					List[Any]           =							[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
						SCREAMING_SNAKE_CASE							:					Any           =							True if '''large''' in model_name or '''huge''' in model_name else False
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							True if '''large''' in model_name or '''huge''' in model_name else False
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							True if '''large''' in model_name or '''huge''' in model_name else False

						if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
												if "fl3" in model_name:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[3, 3, 3, 3]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[5, 5, 5, 5]
												elif "fl4" in model_name:
																		SCREAMING_SNAKE_CASE							:					Any           =							[4, 4, 4, 4]
																		SCREAMING_SNAKE_CASE							:					str           =							[3, 3, 3, 3]

						if "tiny" in model_name or "small" in model_name or "base" in model_name:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[3, 3, 3, 3]
												if "lrf" in model_name:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							[3, 3, 3, 3]
												else:
																		SCREAMING_SNAKE_CASE							:					Any           =							[2, 2, 2, 2]

						if "tiny" in model_name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							96
						elif "small" in model_name:
												SCREAMING_SNAKE_CASE							:					List[Any]           =							96
						elif "base" in model_name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							128
						elif "large" in model_name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							192
						elif "xlarge" in model_name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							256
						elif "huge" in model_name:
												SCREAMING_SNAKE_CASE							:					List[str]           =							352

						# set label information
						SCREAMING_SNAKE_CASE							:					Dict           =							'''huggingface/label-files'''
						if "large" in model_name or "huge" in model_name:
												SCREAMING_SNAKE_CASE							:					Any           =							'''imagenet-22k-id2label.json'''
						else:
												SCREAMING_SNAKE_CASE							:					Dict           =							'''imagenet-1k-id2label.json'''

						SCREAMING_SNAKE_CASE							:					Dict           =							json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Dict           =							{int(_lowercase		): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE							:					Dict           =							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					List[Any]           =							FocalNetConfig(
						    embed_dim=_lowercase	,						depths=_lowercase	,						focal_levels=_lowercase	,						focal_windows=_lowercase	,						use_conv_embed=_lowercase	,						idalabel=_lowercase	,						labelaid=_lowercase	,						use_post_layernorm=_lowercase	,						use_layerscale=_lowercase	,						)

						return config
def    A			(					_lowercase		):

						'''simple docstring'''





						if "patch_embed.proj" in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							name.replace('''patch_embed.proj'''	,						'''embeddings.patch_embeddings.projection'''		)
						if "patch_embed.norm" in name:
												SCREAMING_SNAKE_CASE							:					List[str]           =							name.replace('''patch_embed.norm'''	,						'''embeddings.norm'''		)
						if "layers" in name:
												SCREAMING_SNAKE_CASE							:					int           =							'''encoder.''' + name
						if "encoder.layers" in name:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							name.replace('''encoder.layers'''	,						'''encoder.stages'''		)
						if "downsample.proj" in name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							name.replace('''downsample.proj'''	,						'''downsample.projection'''		)
						if "blocks" in name:
												SCREAMING_SNAKE_CASE							:					int           =							name.replace('''blocks'''	,						'''layers'''		)
						if "modulation.f.weight" in name or "modulation.f.bias" in name:
												SCREAMING_SNAKE_CASE							:					List[str]           =							name.replace('''modulation.f'''	,						'''modulation.projection_in'''		)
						if "modulation.h.weight" in name or "modulation.h.bias" in name:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							name.replace('''modulation.h'''	,						'''modulation.projection_context'''		)
						if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
												SCREAMING_SNAKE_CASE							:					str           =							name.replace('''modulation.proj'''	,						'''modulation.projection_out'''		)

						if name == "norm.weight":
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''layernorm.weight'''
						if name == "norm.bias":
												SCREAMING_SNAKE_CASE							:					Tuple           =							'''layernorm.bias'''

						if "head" in name:
												SCREAMING_SNAKE_CASE							:					str           =							name.replace('''head'''	,						'''classifier'''		)
						else:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''focalnet.''' + name

						return name
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE							:					Tuple           =							{
						    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
						    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
						    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
						    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
						    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
						    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
						    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
						    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
						    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
						    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
						}
						# fmt: on

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							model_name_to_url[model_name]
						print('''Checkpoint URL: '''	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					Dict           =							torch.hub.load_state_dict_from_url(_lowercase	,						map_location='''cpu'''		)['''model''']

						# rename keys
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					List[str]           =							state_dict.pop(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Any           =							val

						SCREAMING_SNAKE_CASE							:					Any           =							get_focalnet_config(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Dict           =							FocalNetForImageClassification(_lowercase		)
						model.eval()

						# load state dict
						model.load_state_dict(_lowercase		)

						# verify conversion
						SCREAMING_SNAKE_CASE							:					str           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''

						SCREAMING_SNAKE_CASE							:					str           =							BitImageProcessor(
						    do_resize=_lowercase	,						size={'''shortest_edge''': 256}	,						resample=PILImageResampling.BILINEAR	,						do_center_crop=_lowercase	,						crop_size=224	,						do_normalize=_lowercase	,						image_mean=_lowercase	,						image_std=_lowercase	,						)
						SCREAMING_SNAKE_CASE							:					str           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)
						SCREAMING_SNAKE_CASE							:					str           =							processor(images=_lowercase	,						return_tensors='''pt'''		)

						SCREAMING_SNAKE_CASE							:					List[Any]           =							transforms.Compose(
						    [
						        transforms.Resize(256		),
						        transforms.CenterCrop(224		),
						        transforms.ToTensor(),
						        transforms.Normalize(mean=[0.485, 0.456, 0.406]	,						std=[0.229, 0.224, 0.225]		),
						    ]		)

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							image_transforms(_lowercase		).unsqueeze(0		)

						# verify pixel_values
						assert torch.allclose(inputs.pixel_values	,						_lowercase	,						atol=1e-4		)

						SCREAMING_SNAKE_CASE							:					Dict           =							model(**_lowercase		)

						SCREAMING_SNAKE_CASE							:					Tuple           =							outputs.logits.argmax(-1		).item()
						print('''Predicted class:'''	,						model.config.idalabel[predicted_class_idx]		)

						print('''First values of logits:'''	,						outputs.logits[0, :3]		)

						if model_name == "focalnet-tiny":
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.tensor([0.2166, -0.4368, 0.2191]		)
						elif model_name == "focalnet-tiny-lrf":
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.tensor([1.1669, 0.0125, -0.1695]		)
						elif model_name == "focalnet-small":
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.tensor([0.4917, -0.0430, 0.1341]		)
						elif model_name == "focalnet-small-lrf":
												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.tensor([-0.2588, -0.5342, -0.2331]		)
						elif model_name == "focalnet-base":
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor([-0.1655, -0.4090, -0.1730]		)
						elif model_name == "focalnet-base-lrf":
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.tensor([0.5306, -0.0483, -0.3928]		)
						assert torch.allclose(outputs.logits[0, :3]	,						_lowercase	,						atol=1e-4		)
						print('''Looks ok!'''		)

						if pytorch_dump_folder_path is not None:
												print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""		)
												model.save_pretrained(_lowercase		)
												processor.save_pretrained(_lowercase		)

						if push_to_hub:
												print(f"""Pushing model and processor of {model_name} to the hub..."""		)
												model.push_to_hub(f"""{model_name}"""		)
												processor.push_to_hub(f"""{model_name}"""		)


if __name__ == "__main__":
					__UpperCamelCase	: List[Any]    		=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='focalnet-tiny',
					    type=str,
					    help='Name of the FocalNet model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub',
					    action='store_true',
					    help='Whether to push the model and processor to the hub.',
					)

					__UpperCamelCase	: Dict    		=  parser.parse_args()
					convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

# TODO Update this
__UpperCamelCase	: List[str]    		=  {
    'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""esm"""


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Any=768					,	UpperCamelCase__			:       Optional[Any]=12					,	UpperCamelCase__			:       str=12					,	UpperCamelCase__			:       Optional[int]=3072					,	UpperCamelCase__			:       Optional[Any]=0.1					,	UpperCamelCase__			:       Any=0.1					,	UpperCamelCase__			:       Union[str, Any]=1026					,	UpperCamelCase__			:       Dict=0.02					,	UpperCamelCase__			:       Any=1E-12					,	UpperCamelCase__			:       Dict="absolute"					,	UpperCamelCase__			:       Optional[Any]=True					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       Optional[int]=False					,	UpperCamelCase__			:       Tuple=False					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Optional[Any]=None					,	**UpperCamelCase__			:       Any					,	):





												'''simple docstring'''





												super().__init__(pad_token_id=UpperCamelCase__					,	mask_token_id=UpperCamelCase__					,	**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_size
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Dict           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					Any           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					str           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					List[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Tuple           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							layer_norm_eps
												SCREAMING_SNAKE_CASE							:					Dict           =							position_embedding_type
												SCREAMING_SNAKE_CASE							:					Any           =							use_cache
												SCREAMING_SNAKE_CASE							:					Dict           =							emb_layer_norm_before
												SCREAMING_SNAKE_CASE							:					List[str]           =							token_dropout
												SCREAMING_SNAKE_CASE							:					List[Any]           =							is_folding_model
												if is_folding_model:
																		if esmfold_config is None:
																								logger.info('''No esmfold_config supplied for folding model, using default values.'''		)
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							EsmFoldConfig()
																		elif isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							EsmFoldConfig(**UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							esmfold_config
																		if vocab_list is None:
																								logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''		)
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							get_default_vocab_list()
																		else:
																								SCREAMING_SNAKE_CASE							:					Optional[Any]           =							vocab_list
												else:
																		SCREAMING_SNAKE_CASE							:					str           =							None
																		SCREAMING_SNAKE_CASE							:					int           =							None
												if self.esmfold_config is not None and getattr(self.esmfold_config					,	'''use_esm_attn_map'''					,	UpperCamelCase__		):
																		raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''		)



						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().to_dict()
												if isinstance(self.esmfold_config					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.esmfold_config.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	None
						UpperCamelCase_  =	True
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	0

						UpperCamelCase_  =	True
						UpperCamelCase_  =	False

						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												if self.trunk is None:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrunkConfig()
												elif isinstance(self.trunk					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							TrunkConfig(**self.trunk		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.trunk.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	48
						UpperCamelCase_  =	1_024
						UpperCamelCase_  =	128
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	32
						UpperCamelCase_  =	0
						UpperCamelCase_  =	0
						UpperCamelCase_  =	False
						UpperCamelCase_  =	4
						UpperCamelCase_  =	128
						UpperCamelCase_  =	None


						def  __A ( self			:       Any		):





												'''simple docstring'''





												if self.structure_module is None:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							StructureModuleConfig()
												elif isinstance(self.structure_module					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							StructureModuleConfig(**self.structure_module		)

												if self.max_recycles <= 0:
																		raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}."""		)
												if self.sequence_state_dim % self.sequence_state_dim != 0:
																		raise ValueError(
																		    '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
																		    f""" {self.sequence_state_dim} and {self.sequence_state_dim}."""		)
												if self.pairwise_state_dim % self.pairwise_state_dim != 0:
																		raise ValueError(
																		    '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
																		    f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""		)

												SCREAMING_SNAKE_CASE							:					Dict           =							self.sequence_state_dim // self.sequence_head_width
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.pairwise_state_dim // self.pairwise_head_width

												if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
																		raise ValueError(
																		    '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
																		    f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""		)
												if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
																		raise ValueError(
																		    '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
																		    f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""		)
												if self.pairwise_state_dim % 2 != 0:
																		raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""		)

												if self.dropout >= 0.4:
																		raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}."""		)



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							asdict(self		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.structure_module.to_dict()
												return output






@dataclass
class       lowercase__		:
						UpperCamelCase_  =	384
						UpperCamelCase_  =	128
						UpperCamelCase_  =	16
						UpperCamelCase_  =	128
						UpperCamelCase_  =	12
						UpperCamelCase_  =	4
						UpperCamelCase_  =	8
						UpperCamelCase_  =	0.1
						UpperCamelCase_  =	8
						UpperCamelCase_  =	1
						UpperCamelCase_  =	2
						UpperCamelCase_  =	7
						UpperCamelCase_  =	10
						UpperCamelCase_  =	1E-8
						UpperCamelCase_  =	1E5


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return asdict(self		)






def    A			(					):
						return (
						    "<cls>",
						    "<pad>",
						    "<eos>",
						    "<unk>",
						    "L",
						    "A",
						    "G",
						    "V",
						    "S",
						    "E",
						    "R",
						    "T",
						    "I",
						    "D",
						    "P",
						    "K",
						    "Q",
						    "N",
						    "F",
						    "Y",
						    "M",
						    "H",
						    "W",
						    "C",
						    "X",
						    "B",
						    "U",
						    "Z",
						    "O",
						    ".",
						    "-",
						    "<null_1>",
						    "<mask>",
						)





