
from dataclasses import asdict, dataclass
from typing import Optional

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


lowercase__		:str   =    logging.get_logger(__name__)

# TODO Update this
lowercase__		: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		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Union[str, Any]       					='''esm'''




							def __init__(				self   ,A__=None   ,A__=None   ,A__=None   ,A__=7_6_8   ,A__=1_2   ,A__=1_2   ,A__=3_0_7_2   ,A__=0.1   ,A__=0.1   ,A__=1_0_2_6   ,A__=0.02   ,A__=1E-12   ,A__="absolute"   ,A__=True   ,A__=None   ,A__=False   ,A__=False   ,A__=None   ,A__=None   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,mask_token_id=A__   ,**A__)

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





							def  A__      (				self):
												lowercase							=						super().to_dict()
												if isinstance(self.esmfold_config   ,A__):
																	lowercase							=						self.esmfold_config.to_dict()
												return output







@dataclass
class 				lowercase		:
							lowercase_       :       str       					=None
							lowercase_       :       bool       					=True
							lowercase_       :       bool       					=False
							lowercase_       :       bool       					=False
							lowercase_       :       bool       					=False
							lowercase_       :       float       					=0

							lowercase_       :       bool       					=True
							lowercase_       :       bool       					=False

							lowercase_       :       int       					=128
							lowercase_       :       "TrunkConfig"       					=None




							def  A__      (				self):
												if self.trunk is None:
																	lowercase							=						TrunkConfig()
												elif isinstance(self.trunk   ,A__):
																	lowercase							=						TrunkConfig(**self.trunk)





							def  A__      (				self):
												lowercase							=						asdict(self)
												lowercase							=						self.trunk.to_dict()
												return output







@dataclass
class 				lowercase		:
							lowercase_       :       int       					=48
							lowercase_       :       int       					=1024
							lowercase_       :       int       					=128
							lowercase_       :       int       					=32
							lowercase_       :       int       					=32
							lowercase_       :       int       					=32
							lowercase_       :       float       					=0
							lowercase_       :       float       					=0
							lowercase_       :       bool       					=False
							lowercase_       :       int       					=4
							lowercase_       :       Optional[int]       					=128
							lowercase_       :       "StructureModuleConfig"       					=None




							def  A__      (				self):
												if self.structure_module is None:
																	lowercase							=						StructureModuleConfig()
												elif isinstance(self.structure_module   ,A__):
																	lowercase							=						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}.')

												lowercase							=						self.sequence_state_dim // self.sequence_head_width
												lowercase							=						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):
												lowercase							=						asdict(self)
												lowercase							=						self.structure_module.to_dict()
												return output







@dataclass
class 				lowercase		:
							lowercase_       :       int       					=384
							lowercase_       :       int       					=128
							lowercase_       :       int       					=16
							lowercase_       :       int       					=128
							lowercase_       :       int       					=12
							lowercase_       :       int       					=4
							lowercase_       :       int       					=8
							lowercase_       :       float       					=0.1
							lowercase_       :       int       					=8
							lowercase_       :       int       					=1
							lowercase_       :       int       					=2
							lowercase_       :       int       					=7
							lowercase_       :       int       					=10
							lowercase_       :       float       					=1e-8
							lowercase_       :       float       					=1e5




							def  A__      (				self):
												return asdict(self)



def        UpperCamelCase    (							):
					'''simple docstring'''
					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>",
					)









"""simple docstring"""







import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Any:
		lowercase__   :    Optional[int]				=			[]
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
		        f"""stage{idx}.patch_embed.proj.weight""",
		    )	)
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
		        f"""stage{idx}.patch_embed.proj.bias""",
		    )	)
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
		        f"""stage{idx}.patch_embed.norm.weight""",
		    )	)
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
		        f"""stage{idx}.patch_embed.norm.bias""",
		    )	)
		return embed



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Dict:
		lowercase__   :    str				=			[]
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
		    )	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")	)
		return attention_weights



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Tuple:
		lowercase__   :    List[str]				=			[]
		token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')	)
		return token



def      __UpperCAmelCase      (    )							->      Optional[int]:
		lowercase__   :    List[str]				=			[]
		head.append(('''layernorm.weight''', '''norm.weight''')	)
		head.append(('''layernorm.bias''', '''norm.bias''')	)
		head.append(('''classifier.weight''', '''head.weight''')	)
		head.append(('''classifier.bias''', '''head.bias''')	)
		return head



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      int:
		lowercase__   :    List[Any]				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Optional[Any]				=			10_00

		lowercase__   :    Optional[Any]				=			'''huggingface/label-files'''
		lowercase__   :    Dict				=			num_labels
		lowercase__   :    Union[str, Any]				=			json.load(open(cached_download(hf_hub_url(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)	)						,     '''r'''	)	)
		lowercase__   :    int				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}

		lowercase__   :    Optional[Any]				=			idalabel
		lowercase__   :    str				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    Any				=			CvtConfig(num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

		# For depth size 13 (13 = 1+2+10)
		if cvt_model.rsplit('''/'''						,     1	)[-1][4:6] == "13":
				lowercase__   :    int				=			[1, 2, 10]

		# For depth size 21 (21 = 1+4+16)
		elif cvt_model.rsplit('''/'''						,     1	)[-1][4:6] == "21":
				lowercase__   :    int				=			[1, 4, 16]

		# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
		else:
				lowercase__   :    List[Any]				=			[2, 2, 20]
				lowercase__   :    Any				=			[3, 12, 16]
				lowercase__   :    Tuple				=			[1_92, 7_68, 10_24]

		lowercase__   :    List[Any]				=			CvtForImageClassification(__lowerCamelCase	)
		lowercase__   :    str				=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
		lowercase__   :    List[str]				=			image_size
		lowercase__   :    Union[str, Any]				=			torch.load(__lowerCamelCase						,     map_location=torch.device('''cpu'''	)	)

		lowercase__   :    int				=			OrderedDict()
		lowercase__   :    List[Any]				=			[]

		for idx in range(len(config.depth	)	):
				if config.cls_token[idx]:
						lowercase__   :    Any				=			list_of_state_dict + cls_token(__lowerCamelCase	)
				lowercase__   :    Any				=			list_of_state_dict + embeddings(__lowerCamelCase	)
				for cnt in range(config.depth[idx]	):
						lowercase__   :    Tuple				=			list_of_state_dict + attention(__lowerCamelCase						,     __lowerCamelCase	)

		lowercase__   :    List[Any]				=			list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(__lowerCamelCase	)
		for i in range(len(__lowerCamelCase	)	):
				lowercase__   :    Optional[Any]				=			original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(__lowerCamelCase	)
		model.save_pretrained(__lowerCamelCase	)
		image_processor.save_pretrained(__lowerCamelCase	)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							parser.add_argument(
							    '--cvt_model',
							    default='cvt-w24',
							    type=str,
							    help='Name of the cvt model you\'d like to convert.',
							)
							parser.add_argument(
							    '--image_size',
							    default=384,
							    type=int,
							    help='Input Image Size',
							)
							parser.add_argument(
							    '--cvt_file_name',
							    default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
							    type=str,
							    help='Input Image Size',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)

							lowerCAmelCase_   						=   parser.parse_args()
							convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
