



import argparse
import json
from pathlib import Path

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

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


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

def 			A_      (	__a     :				Optional[int] ,		__a     :				str=False		):

				"""simple docstring"""
				a__				      =		[]
				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"""),
								    ]		)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								a__				      =		[(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_      (	__a     :				Dict ,		__a     :				List[Any] ,		__a     :				Tuple=False		):

				"""simple docstring"""


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

def 			A_      (	__a     :				Optional[int]		):

				"""simple docstring"""
				a__				      =		["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase__ ,		lowerCamelCase__		)

def 			A_      (	__a     :				Tuple ,		__a     :				List[str] ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		dct.pop(lowerCamelCase__		)
				a__				      =		val

def 			A_      (	):

				"""simple docstring"""
				a__				      =		"http://images.cocodataset.org/val2017/000000039769.jpg"
				a__				      =		Image.open(requests.get(lowerCamelCase__ ,		stream=lowerCamelCase__		).raw		)
				return im

@torch.no_grad()
def 			A_      (	__a     :				Optional[int] ,		__a     :				List[Any] ,		__a     :				Dict=True		):

				"""simple docstring"""
				a__				      =		ViTConfig()
				# patch_size
				if model_name[-1] == "8":
								a__				      =		8
				# set labels if required
				if not base_model:
								a__				      =		1_000
								a__				      =		"huggingface/label-files"
								a__				      =		"imagenet-1k-id2label.json"
								a__				      =		json.load(open(hf_hub_download(lowerCamelCase__ ,		lowerCamelCase__ ,		repo_type="""dataset"""		) ,		"""r"""		)		)
								a__				      =		{int(lowerCamelCase__		): v for k, v in idalabel.items()}
								a__				      =		idalabel
								a__				      =		{v: k for k, v in idalabel.items()}
				# size of the architecture
				if model_name in ["dino_vits8", "dino_vits16"]:
								a__				      =		384
								a__				      =		1_536
								a__				      =		12
								a__				      =		6

				# load original model from torch hub
				a__				      =		torch.hub.load("""facebookresearch/dino:main""" ,		lowerCamelCase__		)
				original_model.eval()

				# load state_dict of original model, remove and rename some keys
				a__				      =		original_model.state_dict()
				if base_model:
								remove_classification_head_(lowerCamelCase__		)
				a__				      =		create_rename_keys(lowerCamelCase__ ,		base_model=lowerCamelCase__		)
				for src, dest in rename_keys:
								rename_key(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__		)
				read_in_q_k_v(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__		)

				# load HuggingFace model
				if base_model:
								a__				      =		ViTModel(lowerCamelCase__ ,		add_pooling_layer=lowerCamelCase__		).eval()
				else:
								a__				      =		ViTForImageClassification(lowerCamelCase__		).eval()
				model.load_state_dict(lowerCamelCase__		)

				# Check outputs on an image, prepared by ViTImageProcessor
				a__				      =		ViTImageProcessor()
				a__				      =		image_processor(images=prepare_img() ,		return_tensors="""pt"""		)
				a__				      =		encoding["pixel_values"]
				a__				      =		model(lowerCamelCase__		)

				if base_model:
								a__				      =		original_model(lowerCamelCase__		)
								assert torch.allclose(lowerCamelCase__ ,		outputs.last_hidden_state[:, 0, :] ,		atol=1e-1		)
				else:
								a__				      =		original_model(lowerCamelCase__		)
								assert logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCamelCase__ ,		outputs.logits ,		atol=1e-3		)

				Path(lowerCamelCase__		).mkdir(exist_ok=lowerCamelCase__		)
				print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(lowerCamelCase__		)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(lowerCamelCase__		)


if __name__ == "__main__":
	UpperCAmelCase							 =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""dino_vitb16""",
	    type=str,
	    help="""Name of the model trained with DINO 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(
	    """--base_model""",
	    action="""store_true""",
	    help="""Whether to only convert the base model (no projection head weights).""",
	)

	parser.set_defaults(base_model=True)
	UpperCAmelCase							 =					parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@register_to_config
							def __init__(							self  ,  a_ = 128  ,  a_ = 256  ,  a_ = 2_000.0  ,  a_ = 768  ,  a_ = 12  ,  a_ = 12  ,  a_ = 64  ,  a_ = 2_048  ,  a_ = 0.1  ,  ):
											super().__init__()

											a__				      =		nn.Sequential(
											    nn.Linear(a_  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  nn.Linear(d_model * 4  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  )

											a__				      =		nn.Embedding(a_  ,  a_		)
											a__				      =		False

											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)

											a__				      =		nn.Dropout(p=a_		)

											a__				      =		nn.ModuleList()
											for lyr_num in range(a_		):
															# FiLM conditional T5 decoder
															a__				      =		DecoderLayer(d_model=a_  ,  d_kv=a_  ,  num_heads=a_  ,  d_ff=a_  ,  dropout_rate=a_		)
															self.decoders.append(a_		)

											a__				      =		TaLayerNorm(a_		)

											a__				      =		nn.Dropout(p=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		torch.mul(query_input.unsqueeze(-1		)  ,  key_input.unsqueeze(-2		)		)
											return mask.unsqueeze(-3		)







							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__      ,			a__      ,			a__				      =		decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

											# decoder_noise_time is in [0, 1), so rescale to expected timing range.
											a__				      =		get_timestep_embedding(
											    decoder_noise_time * self.config.max_decoder_noise_time  ,  embedding_dim=self.config.d_model  ,  max_period=self.config.max_decoder_noise_time  ,  ).to(dtype=self.dtype		)

											a__				      =		self.conditioning_emb(a_		).unsqueeze(1		)

											assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

											a__				      =		decoder_input_tokens.shape[1]

											# If we want to use relative positions for audio context, we can just offset
											# this sequence by the length of encodings_and_masks.
											a__				      =		torch.broadcast_to(
											    torch.arange(a_  ,  device=decoder_input_tokens.device		)  ,  (batch, seq_length)  ,  )

											a__				      =		self.position_encoding(a_		)

											a__				      =		self.continuous_inputs_projection(a_		)
											inputs += position_encodings
											a__				      =		self.dropout(a_		)

											# decoder: No padding present.
											a__				      =		torch.ones(
											    decoder_input_tokens.shape[:2]  ,  device=decoder_input_tokens.device  ,  dtype=inputs.dtype		)

											# Translate encoding masks to encoder-decoder masks.
											a__				      =		[(x, self.encoder_decoder_mask(a_  ,  a_		)) for x, y in encodings_and_masks]

											# cross attend style: concat encodings
											a__				      =		torch.cat([x[0] for x in encodings_and_encdec_masks]  ,  dim=1		)
											a__				      =		torch.cat([x[1] for x in encodings_and_encdec_masks]  ,  dim=-1		)

											for lyr in self.decoders:
															a__				      =		lyr(
															    a_  ,  conditioning_emb=a_  ,  encoder_hidden_states=a_  ,  encoder_attention_mask=a_  ,  )[0]

											a__				      =		self.decoder_norm(a_		)
											a__				      =		self.post_dropout(a_		)

											a__				      =		self.spec_out(a_		)
											return spec_out




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.ModuleList()

											# cond self attention: layer 0
											self.layer.append(
											    TaLayerSelfAttentionCond(d_model=a_  ,  d_kv=a_  ,  num_heads=a_  ,  dropout_rate=a_		)		)

											# cross attention: layer 1
											self.layer.append(
											    TaLayerCrossAttention(
											        d_model=a_  ,  d_kv=a_  ,  num_heads=a_  ,  dropout_rate=a_  ,  layer_norm_epsilon=a_  ,  )		)

											# Film Cond MLP + dropout: last layer
											self.layer.append(
											    TaLayerFFCond(d_model=a_  ,  d_ff=a_  ,  dropout_rate=a_  ,  layer_norm_epsilon=a_		)		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer[0](
											    a_  ,  conditioning_emb=a_  ,  attention_mask=a_  ,  )

											if encoder_hidden_states is not None:
															a__				      =		torch.where(encoder_attention_mask > 0  ,  0  ,  -1E1_0		).to(
															    encoder_hidden_states.dtype		)

															a__				      =		self.layer[1](
															    a_  ,  key_value_states=a_  ,  attention_mask=a_  ,  )

											# Apply Film Conditional Feed Forward layer
											a__				      =		self.layer[-1](a_  ,  a_		)

											return (hidden_states,)




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaLayerNorm(a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											# pre_self_attention_layer_norm
											a__				      =		self.layer_norm(a_		)

											if conditioning_emb is not None:
															a__				      =		self.FiLMLayer(a_  ,  a_		)

											# Self-attention block
											a__				      =		self.attention(a_		)

											a__				      =		hidden_states + self.dropout(a_		)

											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer_norm(a_		)
											a__				      =		self.attention(
											    a_  ,  encoder_hidden_states=a_  ,  attention_mask=attention_mask.squeeze(1		)  ,  )
											a__				      =		hidden_states + self.dropout(a_		)
											return layer_output




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaDenseGatedActDense(d_model=a_  ,  d_ff=a_  ,  dropout_rate=a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None		):
											a__				      =		self.layer_norm(a_		)
											if conditioning_emb is not None:
															a__				      =		self.film(a_  ,  a_		)

											a__				      =		self.DenseReluDense(a_		)
											a__				      =		hidden_states + self.dropout(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Dropout(a_		)
											a__				      =		NewGELUActivation()







							def _a      (							self  ,  a_		):
											a__				      =		self.act(self.wi_a(a_		)		)
											a__				      =		self.wi_a(a_		)
											a__				      =		hidden_gelu * hidden_linear
											a__				      =		self.dropout(a_		)

											a__				      =		self.wo(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.Parameter(torch.ones(a_		)		)
											a__				      =		eps







							def _a      (							self  ,  a_		):
											# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
											# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
											# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
											# half-precision inputs is done in fp32

											a__				      =		hidden_states.to(torch.floataa		).pow(2		).mean(-1  ,  keepdim=a_		)
											a__				      =		hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

											# convert into half-precision if necessary
											if self.weight.dtype in [torch.floataa, torch.bfloataa]:
															a__				      =		hidden_states.to(self.weight.dtype		)

											return self.weight * hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def _a      (							self  ,  a_		):
											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044_715 * torch.pow(a_  ,  3.0		))		))




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  out_features * 2  ,  bias=a_		)







							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		self.scale_bias(a_		)
											a__      ,			a__				      =		torch.chunk(a_  ,  2  ,  -1		)
											a__				      =		x * (1 + scale) + shift
											return x

