






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

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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





def  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						Dict=False							):




						'''simple docstring'''






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

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ('''cls_token''', '''vit.embeddings.cls_token'''),
						        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
						        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
						        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
						    ]							)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ('''norm.weight''', '''layernorm.weight'''),
												        ('''norm.bias''', '''layernorm.bias'''),
												        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
												        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
												    ]							)

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												SCREAMING_SNAKE_CASE__     :				Tuple									=[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''							) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('''norm.weight''', '''vit.layernorm.weight'''),
												        ('''norm.bias''', '''vit.layernorm.bias'''),
												        ('''head.weight''', '''classifier.weight'''),
												        ('''head.bias''', '''classifier.bias'''),
												    ]							)

						return rename_keys





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Optional[int]=False							):




						'''simple docstring'''






						for i in range(config.num_hidden_layers							):
												if base_model:
																		SCREAMING_SNAKE_CASE__     :				List[Any]									=''''''
												else:
																		SCREAMING_SNAKE_CASE__     :				Optional[int]									='''vit.'''
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												SCREAMING_SNAKE_CASE__     :				List[Any]									=state_dict.pop(f"blocks.{i}.attn.qkv.weight"							)
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=state_dict.pop(f"blocks.{i}.attn.qkv.bias"							)
												# next, add query, keys and values (in that order) to the state dict
												SCREAMING_SNAKE_CASE__     :				int									=in_proj_weight[
												    : config.hidden_size, :
												]
												SCREAMING_SNAKE_CASE__     :				str									=in_proj_bias[: config.hidden_size]
												SCREAMING_SNAKE_CASE__     :				str									=in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE__     :				str									=in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												SCREAMING_SNAKE_CASE__     :				List[str]									=in_proj_weight[
												    -config.hidden_size :, :
												]
												SCREAMING_SNAKE_CASE__     :				Dict									=in_proj_bias[-config.hidden_size :]





def  _a(  UpperCamelCase__				:						int							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				int									=['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__,	UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						str,	UpperCamelCase__				:						Optional[Any]							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Optional[int]									=dct.pop(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__     :				Optional[int]									=val





def  _a(  ):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE__     :				List[Any]									=Image.open(requests.get(UpperCamelCase__,	stream=UpperCamelCase__							).raw							)
						return im





@torch.no_grad()
def  _a(  UpperCamelCase__				:						int,	UpperCamelCase__				:						List[str]							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				List[str]									=ViTConfig()
						SCREAMING_SNAKE_CASE__     :				int									=False
						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						if vit_name[-5:] == "in21k":
												SCREAMING_SNAKE_CASE__     :				Tuple									=True
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=int(vit_name[-1_2:-1_0]							)
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=int(vit_name[-9:-6]							)
						else:
												SCREAMING_SNAKE_CASE__     :				Any									=1_0_0_0
												SCREAMING_SNAKE_CASE__     :				List[Any]									='''huggingface/label-files'''
												SCREAMING_SNAKE_CASE__     :				int									='''imagenet-1k-id2label.json'''
												SCREAMING_SNAKE_CASE__     :				List[Any]									=json.load(open(hf_hub_download(UpperCamelCase__,	UpperCamelCase__,	repo_type='''dataset'''							),	'''r'''							)							)
												SCREAMING_SNAKE_CASE__     :				List[str]									={int(UpperCamelCase__							): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE__     :				int									=idalabel
												SCREAMING_SNAKE_CASE__     :				str									={v: k for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE__     :				List[Any]									=int(vit_name[-6:-4]							)
												SCREAMING_SNAKE_CASE__     :				int									=int(vit_name[-3:]							)
						# size of the architecture
						if "deit" in vit_name:
												if vit_name[9:].startswith('''tiny'''							):
																		SCREAMING_SNAKE_CASE__     :				Optional[int]									=1_9_2
																		SCREAMING_SNAKE_CASE__     :				Dict									=7_6_8
																		SCREAMING_SNAKE_CASE__     :				Optional[int]									=1_2
																		SCREAMING_SNAKE_CASE__     :				Tuple									=3
												elif vit_name[9:].startswith('''small'''							):
																		SCREAMING_SNAKE_CASE__     :				Any									=3_8_4
																		SCREAMING_SNAKE_CASE__     :				Any									=1_5_3_6
																		SCREAMING_SNAKE_CASE__     :				Any									=1_2
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=6
												else:
																		pass
						else:
												if vit_name[4:].startswith('''small'''							):
																		SCREAMING_SNAKE_CASE__     :				str									=7_6_8
																		SCREAMING_SNAKE_CASE__     :				Optional[int]									=2_3_0_4
																		SCREAMING_SNAKE_CASE__     :				List[str]									=8
																		SCREAMING_SNAKE_CASE__     :				List[Any]									=8
												elif vit_name[4:].startswith('''base'''							):
																		pass
												elif vit_name[4:].startswith('''large'''							):
																		SCREAMING_SNAKE_CASE__     :				str									=1_0_2_4
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]									=4_0_9_6
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]									=2_4
																		SCREAMING_SNAKE_CASE__     :				Dict									=1_6
												elif vit_name[4:].startswith('''huge'''							):
																		SCREAMING_SNAKE_CASE__     :				List[Any]									=1_2_8_0
																		SCREAMING_SNAKE_CASE__     :				int									=5_1_2_0
																		SCREAMING_SNAKE_CASE__     :				Tuple									=3_2
																		SCREAMING_SNAKE_CASE__     :				int									=1_6

    # load original model from timm
						SCREAMING_SNAKE_CASE__     :				str									=timm.create_model(UpperCamelCase__,	pretrained=UpperCamelCase__							)
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						SCREAMING_SNAKE_CASE__     :				Optional[int]									=timm_model.state_dict()
						if base_model:
												remove_classification_head_(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__     :				List[str]									=create_rename_keys(UpperCamelCase__,	UpperCamelCase__							)
						for src, dest in rename_keys:
												rename_key(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
						read_in_q_k_v(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												SCREAMING_SNAKE_CASE__     :				List[str]									=ViTModel(UpperCamelCase__							).eval()
						else:
												SCREAMING_SNAKE_CASE__     :				Dict									=ViTForImageClassification(UpperCamelCase__							).eval()
						model.load_state_dict(UpperCamelCase__							)

						# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
						if "deit" in vit_name:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DeiTImageProcessor(size=config.image_size							)
						else:
												SCREAMING_SNAKE_CASE__     :				Any									=ViTImageProcessor(size=config.image_size							)
						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=image_processor(images=prepare_img(),	return_tensors='''pt'''							)
						SCREAMING_SNAKE_CASE__     :				Dict									=encoding['''pixel_values''']
						SCREAMING_SNAKE_CASE__     :				Any									=model(UpperCamelCase__							)

						if base_model:
												SCREAMING_SNAKE_CASE__     :				str									=timm_model.forward_features(UpperCamelCase__							)
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(UpperCamelCase__,	outputs.pooler_output,	atol=1e-3							)
						else:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=timm_model(UpperCamelCase__							)
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(UpperCamelCase__,	outputs.logits,	atol=1e-3							)

						Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
						print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"							)
						model.save_pretrained(UpperCamelCase__							)
						print(f"Saving image processor to {pytorch_dump_folder_path}"							)
						image_processor.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
		a_					= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--vit_name',
		    default='vit_base_patch16_224',
		    type=str,
		    help='Name of the ViT timm 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.'
		)

		a_					= parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					= logging.get_logger(__name__)

a_					= {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""gpt_neox"""




 def __init__(      self	:							List[Any]					,      __lowercase	:							Union[str, Any]=5_04_32					,      __lowercase	:							int=61_44					,      __lowercase	:							Tuple=44					,      __lowercase	:							List[str]=64					,      __lowercase	:							str=2_45_76					,      __lowercase	:							Dict="gelu"					,      __lowercase	:							Tuple=0.25					,      __lowercase	:							Tuple=1_00_00					,      __lowercase	:							Tuple=0.0					,      __lowercase	:							str=0.0					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Dict=20_48					,      __lowercase	:							Any=0.02					,      __lowercase	:							Dict=1e-5					,      __lowercase	:							List[Any]=True					,      __lowercase	:							str=0					,      __lowercase	:							Optional[Any]=2					,      __lowercase	:							Tuple=False					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Optional[Any]=None					,      **__lowercase	:							Any					,      )	->						Dict:
       super().__init__(bos_token_id=__lowercase					,      eos_token_id=__lowercase					,      **__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Any									=hidden_size
       SCREAMING_SNAKE_CASE__     :				str									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Any									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Dict									=hidden_act
       SCREAMING_SNAKE_CASE__     :				str									=rotary_pct
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=rotary_emb_base
       SCREAMING_SNAKE_CASE__     :				List[Any]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_dropout
       SCREAMING_SNAKE_CASE__     :				str									=classifier_dropout
       SCREAMING_SNAKE_CASE__     :				Any									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=layer_norm_eps
       SCREAMING_SNAKE_CASE__     :				Any									=use_cache
       SCREAMING_SNAKE_CASE__     :				Tuple									=tie_word_embeddings
       SCREAMING_SNAKE_CASE__     :				Tuple									=use_parallel_residual
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=rope_scaling
       self._rope_scaling_validation()

       if self.hidden_size % self.num_attention_heads != 0:
             raise ValueError(
                 '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       if self.rope_scaling is None:
             return

       if not isinstance(self.rope_scaling					,      __lowercase      ) or len(self.rope_scaling      ) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 F"got {self.rope_scaling}"      )
       SCREAMING_SNAKE_CASE__     :				int									=self.rope_scaling.get('''type'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.rope_scaling.get('''factor'''					,      __lowercase      )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"      )
       if rope_scaling_factor is None or not isinstance(__lowercase					,      __lowercase      ) or rope_scaling_factor <= 1.0:
             raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"      )