



import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)
def   UpperCAmelCase							(		A__				,  A__=False				,  A__=False				,  A__="data2vec."     )							->					List[Any]:
	_snake_case					:							Union[str, Any]					     =   """backbone.""" if is_semantic else """"""

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

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
	        (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
	        (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
	    ]     )

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ("""mask_token""", f'''{hf_prefix}embeddings.mask_token'''),
		        (
		            """rel_pos_bias.relative_position_bias_table""",
		            f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
		        ),
		        (
		            """rel_pos_bias.relative_position_index""",
		            f'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
		        ),
		        ("""norm.weight""", """layernorm.weight"""),
		        ("""norm.bias""", """layernorm.bias"""),
		    ]     )
	elif is_semantic:
		# semantic segmentation classification heads
		rename_keys.extend(
		    [
		        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
		        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
		        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
		        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
		    ]     )
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ("""fc_norm.weight""", f'''{hf_prefix}pooler.layernorm.weight'''),
		        ("""fc_norm.bias""", f'''{hf_prefix}pooler.layernorm.bias'''),
		        ("""head.weight""", """classifier.weight"""),
		        ("""head.bias""", """classifier.bias"""),
		    ]     )

	return rename_keys
def   UpperCAmelCase							(		A__				,  A__				,  A__=False				,  A__=False				,  A__="data2vec_vision."     )							->					Any:
	for i in range(config.num_hidden_layers     ):
		_snake_case					:							List[str]					     =   """backbone.""" if is_semantic else """"""
		# queries, keys and values
		_snake_case					:							Union[str, Any]					     =   state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''     )
		_snake_case					:							List[Any]					     =   state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''     )
		_snake_case					:							Dict					     =   state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''     )

		_snake_case					:							Any					     =   in_proj_weight[
		    : config.hidden_size, :
		]
		_snake_case					:							Union[str, Any]					     =   q_bias
		_snake_case					:							Any					     =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		_snake_case					:							Optional[int]					     =   in_proj_weight[
		    -config.hidden_size :, :
		]
		_snake_case					:							Optional[int]					     =   v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		_snake_case					:							List[Any]					     =   state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''     )
		_snake_case					:							int					     =   state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''     )

		_snake_case					:							Any					     =   gamma_a
		_snake_case					:							Dict					     =   gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			_snake_case					:							int					     =   state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''     )
			_snake_case					:							str					     =   state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''     )

			_snake_case					:							List[Any]					     =   table
			_snake_case					:							str					     =   index
def   UpperCAmelCase							(		)							->					Optional[Any]:
	_snake_case					:							Tuple					     =   argparse.ArgumentParser(
	    """Convert Data2VecVision to HF for image classification and pretraining"""				,  add_help=A__     )
	parser.add_argument("""--hf_checkpoint_name"""				,  type=A__     )
	parser.add_argument("""--input_size"""				,  default=2_24				,  type=A__				,  help="""images input size"""     )
	parser.add_argument("""--beit_checkpoint"""				,  default=""""""				,  help="""beit checkpoint"""     )

	return parser.parse_args()
def   UpperCAmelCase							(		A__				,  A__				,  A__     )							->					List[str]:
	def load_state_dict(A__				,  A__				,  A__=""				,  A__="relative_position_index"     ):
		_snake_case					:							Dict					     =   []
		_snake_case					:							Optional[Any]					     =   []
		_snake_case					:							Dict					     =   []
		# copy state_dict so _load_from_state_dict can modify it
		_snake_case					:							str					     =   getattr(A__				,  """_metadata"""				,  A__     )
		_snake_case					:							Union[str, Any]					     =   state_dict.copy()
		if metadata is not None:
			_snake_case					:							Union[str, Any]					     =   metadata

		def load(A__				,  A__=""     ):
			_snake_case					:							List[str]					     =   {} if metadata is None else metadata.get(prefix[:-1]				,  {}     )
			module._load_from_state_dict(
			    A__				,  A__				,  A__				,  A__				,  A__				,  A__				,  A__     )
			for name, child in module._modules.items():
				if child is not None:
					load(A__				,  prefix + name + """."""     )

		load(A__				,  prefix=A__     )

		_snake_case					:							Optional[int]					     =   []
		_snake_case					:							int					     =   []
		for key in missing_keys:
			_snake_case					:							List[str]					     =   True
			for ignore_key in ignore_missing.split("""|"""     ):
				if ignore_key in key:
					_snake_case					:							str					     =   False
					break
			if keep_flag:
				warn_missing_keys.append(A__     )
			else:
				ignore_missing_keys.append(A__     )

		_snake_case					:							Any					     =   warn_missing_keys

		if len(A__     ) > 0:
			print(
			    """Weights of {} not initialized from pretrained model: {}""".format(
			        model.__class__.__name__				,  A__     )     )
		if len(A__     ) > 0:
			print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__				,  A__     )     )
		if len(A__     ) > 0:
			print(
			    """Ignored weights of {} not initialized from pretrained model: {}""".format(
			        model.__class__.__name__				,  A__     )     )
		if len(A__     ) > 0:
			print("""\n""".join(A__     )     )

	_snake_case					:							Optional[Any]					     =   {
	    """pretrained""": False,
	    """use_shared_rel_pos_bias""": True,
	    """use_abs_pos_emb""": False,
	    """init_values""": 0.1,
	}

	if is_finetuned:
		model_kwargs.update(
		    {
		        """num_classes""": 10_00,
		        """use_mean_pooling""": True,
		        """init_scale""": 0.001,
		        """use_rel_pos_bias""": True,
		    }     )

	_snake_case					:							Union[str, Any]					     =   create_model(
	    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""				,  **A__				,  )
	_snake_case					:							List[str]					     =   model.patch_embed.patch_size
	_snake_case					:							Optional[Any]					     =   (args.input_size // patch_size[0], args.input_size // patch_size[1])
	_snake_case					:							List[str]					     =   torch.load(args.beit_checkpoint				,  map_location="""cpu"""     )

	print(f'''Load ckpt from {args.beit_checkpoint}'''     )
	_snake_case					:							List[Any]					     =   None
	for model_key in ("model", "module"):
		if model_key in checkpoint:
			_snake_case					:							Dict					     =   checkpoint[model_key]
			print(f'''Load state_dict by model_key = {model_key}'''     )
			break

	_snake_case					:							str					     =   list(checkpoint_model.keys()     )
	for key in all_keys:
		if "relative_position_index" in key:
			checkpoint_model.pop(A__     )

		if "relative_position_bias_table" in key:
			_snake_case					:							Optional[int]					     =   checkpoint_model[key]
			_snake_case     , _snake_case					:							Union[str, Any]					     =   rel_pos_bias.size()
			_snake_case     , _snake_case					:							str					     =   model.state_dict()[key].size()
			_snake_case					:							Any					     =   model.patch_embed.patch_shape
			if dst_patch_shape[0] != dst_patch_shape[1]:
				raise NotImplementedError()

	load_state_dict(A__				,  A__				,  prefix=""""""     )

	return model
def   UpperCAmelCase							(		)							->					str:
	_snake_case					:							List[Any]					     =   get_args()

	_snake_case					:							str					     =   """ft1k""" in args.hf_checkpoint_name
	_snake_case					:							str					     =   """large""" in args.hf_checkpoint_name

	if is_finetuned:
		# To convert Beit's data2vec_vision to HF you need to copy
		# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
		# into this folder.
		import modeling_finetune  # noqa: F401
	else:
		# To convert Beit's data2vec_vision to HF you need to copy
		# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
		# into this folder
		# IMPORTANT: Note that for now we've only converted the down-stream
		# model and not the full pretrained model. This means for the integration
		# test you need to add a `return x` after the following line:
		# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
		# to make the integration test pass.
		import modeling_cyclical  # noqa: F401

	# 1. Create model config
	_snake_case					:							Optional[Any]					     =   DataaVecVisionConfig()
	if is_finetuned:
		_snake_case					:							str					     =   True
		_snake_case					:							str					     =   False
		_snake_case					:							int					     =   True
		_snake_case					:							Optional[Any]					     =   10_00

		_snake_case					:							str					     =   """huggingface/label-files"""
		_snake_case					:							str					     =   """imagenet-1k-id2label.json"""
		_snake_case					:							str					     =   json.load(open(hf_hub_download(A__				,  A__				,  repo_type="""dataset"""     )				,  """r"""     )     )
		_snake_case					:							List[str]					     =   {int(A__     ): v for k, v in idalabel.items()}
		_snake_case					:							Tuple					     =   idalabel
		_snake_case					:							int					     =   {v: k for k, v in idalabel.items()}
	else:
		_snake_case					:							Tuple					     =   False
		_snake_case					:							Tuple					     =   True
		_snake_case					:							Dict					     =   False

	if is_large:
		_snake_case					:							Tuple					     =   10_24
		_snake_case					:							Tuple					     =   40_96
		_snake_case					:							Any					     =   24
		_snake_case					:							str					     =   16

	# 2. Load Beit model
	_snake_case					:							Dict					     =   load_beit_model(A__				,  A__				,  A__     )
	orig_model.eval()

	# 3. Forward Beit model
	_snake_case					:							int					     =   BeitImageProcessor(size=config.image_size				,  do_center_crop=A__     )
	_snake_case					:							int					     =   Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""     )
	_snake_case					:							Optional[int]					     =   image_processor(images=A__				,  return_tensors="""pt"""     )
	_snake_case					:							str					     =   encoding["""pixel_values"""]

	_snake_case					:							Any					     =   (pixel_values,) if is_finetuned else (pixel_values, None)
	with torch.no_grad():
		_snake_case					:							List[str]					     =   orig_model(*A__     )

	# 4. Load HF Data2VecVision model
	if is_finetuned:
		_snake_case					:							str					     =   DataaVecVisionForImageClassification(A__     )
		hf_model.eval()
		_snake_case					:							Any					     =   False
		_snake_case					:							Tuple					     =   """data2vec_vision."""
	else:
		_snake_case					:							int					     =   DataaVecVisionModel(A__     )
		hf_model.eval()
		_snake_case					:							Optional[Any]					     =   True
		_snake_case					:							Any					     =   """"""

	_snake_case					:							Union[str, Any]					     =   create_rename_keys(A__				,  hf_prefix=A__				,  has_lm_head=A__     )
	_snake_case					:							List[Any]					     =   orig_model.state_dict()
	for src, dest in rename_keys:
		_snake_case					:							str					     =   state_dict.pop(A__     )
		_snake_case					:							str					     =   val

	read_in_q_k_v(A__				,  A__				,  hf_prefix=A__				,  has_lm_head=A__     )
	_snake_case     , _snake_case					:							int					     =   hf_model.load_state_dict(A__				,  strict=A__     )
	print("""HF missing"""				,  A__     )
	print("""HF unexpected_keys"""				,  A__     )

	# 5. Forward HF Data2VecVision model
	with torch.no_grad():
		_snake_case					:							List[str]					     =   hf_model(A__     )

	_snake_case					:							Tuple					     =   hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

	# 6. Compare
	_snake_case					:							Tuple					     =   torch.max(torch.abs(hf_output - orig_model_output     )     ).item()

	print(f'''max_absolute_diff = {max_absolute_diff}'''     )
	_snake_case					:							Optional[Any]					     =   torch.allclose(A__				,  A__				,  atol=1E-3     )
	print("""Do both models output the same tensors?"""				,  """🔥""" if success else """💩"""     )
	if not success:
		raise Exception("""Something went wRoNg"""     )

	# 7. Save
	print(f'''Saving to {args.hf_checkpoint_name}'''     )
	hf_model.save_pretrained(args.hf_checkpoint_name     )
	image_processor.save_pretrained(args.hf_checkpoint_name     )


if __name__ == "__main__":
						main()
						# Run the following to convert checkpoints
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"










import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def   UpperCAmelCase							(		A__				,  A__				,  A__ = False				,  A__ = 1				,  A__ = 1				,  A__ = 1_00_00				,  )							->					Optional[int]:
	assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"

	_snake_case					:							Tuple					     =   embedding_dim // 2
	_snake_case					:							Optional[Any]					     =   -math.log(A__     ) * torch.arange(
	    start=0				,  end=A__				,  dtype=torch.floataa				,  device=timesteps.device     )
	_snake_case					:							Dict					     =   exponent / (half_dim - downscale_freq_shift)

	_snake_case					:							Optional[int]					     =   torch.exp(A__     )
	_snake_case					:							List[str]					     =   timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	_snake_case					:							Optional[int]					     =   scale * emb

	# concat sine and cosine embeddings
	_snake_case					:							Union[str, Any]					     =   torch.cat([torch.sin(A__     ), torch.cos(A__     )]				,  dim=-1     )

	# flip sine and cosine embeddings
	if flip_sin_to_cos:
		_snake_case					:							Optional[Any]					     =   torch.cat([emb[:, half_dim:], emb[:, :half_dim]]				,  dim=-1     )

	# zero pad
	if embedding_dim % 2 == 1:
		_snake_case					:							Optional[Any]					     =   torch.nn.functional.pad(A__				,  (0, 1, 0, 0)     )
	return emb
def   UpperCAmelCase							(		A__				,  A__				,  A__=False				,  A__=0     )							->					Optional[Any]:
	_snake_case					:							str					     =   np.arange(A__				,  dtype=np.floataa     )
	_snake_case					:							int					     =   np.arange(A__				,  dtype=np.floataa     )
	_snake_case					:							Union[str, Any]					     =   np.meshgrid(A__				,  A__     )  # here w goes first
	_snake_case					:							Optional[int]					     =   np.stack(A__				,  axis=0     )

	_snake_case					:							List[Any]					     =   grid.reshape([2, 1, grid_size, grid_size]     )
	_snake_case					:							Optional[int]					     =   get_ad_sincos_pos_embed_from_grid(A__				,  A__     )
	if cls_token and extra_tokens > 0:
		_snake_case					:							Any					     =   np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed]				,  axis=0     )
	return pos_embed
def   UpperCAmelCase							(		A__				,  A__     )							->					List[str]:
	if embed_dim % 2 != 0:
		raise ValueError("""embed_dim must be divisible by 2"""     )

	# use half of dimensions to encode grid_h
	_snake_case					:							int					     =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,  grid[0]     )  # (H*W, D/2)
	_snake_case					:							List[str]					     =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2				,  grid[1]     )  # (H*W, D/2)

	_snake_case					:							List[Any]					     =   np.concatenate([emb_h, emb_w]				,  axis=1     )  # (H*W, D)
	return emb
def   UpperCAmelCase							(		A__				,  A__     )							->					str:
	if embed_dim % 2 != 0:
		raise ValueError("""embed_dim must be divisible by 2"""     )

	_snake_case					:							Union[str, Any]					     =   np.arange(embed_dim // 2				,  dtype=np.floataa     )
	omega /= embed_dim / 2.0
	_snake_case					:							Optional[Any]					     =   1.0 / 1_00_00**omega  # (D/2,)

	_snake_case					:							Union[str, Any]					     =   pos.reshape(-1     )  # (M,)
	_snake_case					:							Dict					     =   np.einsum("""m,d->md"""				,  A__				,  A__     )  # (M, D/2), outer product

	_snake_case					:							Union[str, Any]					     =   np.sin(A__     )  # (M, D/2)
	_snake_case					:							Tuple					     =   np.cos(A__     )  # (M, D/2)

	_snake_case					:							Union[str, Any]					     =   np.concatenate([emb_sin, emb_cos]				,  axis=1     )  # (M, D)
	return emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__=2_24						,	SCREAMING_SNAKE_CASE__=2_24						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=7_68						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Tuple					     =   (height // patch_size) * (width // patch_size)
					_snake_case					:							Union[str, Any]					     =   flatten
					_snake_case					:							str					     =   layer_norm

					_snake_case					:							List[Any]					     =   nn.Convad(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	kernel_size=(patch_size, patch_size)						,	stride=SCREAMING_SNAKE_CASE__						,	bias=SCREAMING_SNAKE_CASE__      )
					if layer_norm:
						_snake_case					:							Dict					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__						,	elementwise_affine=SCREAMING_SNAKE_CASE__						,	eps=1e-6      )
					else:
						_snake_case					:							Union[str, Any]					     =   None

					_snake_case					:							Tuple					     =   get_ad_sincos_pos_embed(SCREAMING_SNAKE_CASE__						,	int(num_patches**0.5      )      )
					self.register_buffer("""pos_embed"""						,	torch.from_numpy(SCREAMING_SNAKE_CASE__      ).float().unsqueeze(0      )						,	persistent=SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   self.proj(SCREAMING_SNAKE_CASE__      )
					if self.flatten:
						_snake_case					:							Any					     =   latent.flatten(2      ).transpose(1						,	2      )  # BCHW -> BNC
					if self.layer_norm:
						_snake_case					:							Any					     =   self.norm(SCREAMING_SNAKE_CASE__      )
					return latent + self.pos_embed





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = "silu"						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__=None						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							List[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					if cond_proj_dim is not None:
						_snake_case					:							Dict					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	bias=SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							Tuple					     =   None

					_snake_case					:							int					     =   get_activation(SCREAMING_SNAKE_CASE__      )

					if out_dim is not None:
						_snake_case					:							Any					     =   out_dim
					else:
						_snake_case					:							int					     =   time_embed_dim
					_snake_case					:							str					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					if post_act_fn is None:
						_snake_case					:							Tuple					     =   None
					else:
						_snake_case					:							Optional[Any]					     =   get_activation(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					if condition is not None:
						_snake_case					:							int					     =   sample + self.cond_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Dict					     =   self.linear_a(SCREAMING_SNAKE_CASE__      )

					if self.act is not None:
						_snake_case					:							Optional[int]					     =   self.act(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Any					     =   self.linear_a(SCREAMING_SNAKE_CASE__      )

					if self.post_act is not None:
						_snake_case					:							str					     =   self.post_act(SCREAMING_SNAKE_CASE__      )
					return sample





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							List[str]					     =   num_channels
					_snake_case					:							List[Any]					     =   flip_sin_to_cos
					_snake_case					:							List[str]					     =   downscale_freq_shift







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   get_timestep_embedding(
					    SCREAMING_SNAKE_CASE__						,	self.num_channels						,	flip_sin_to_cos=self.flip_sin_to_cos						,	downscale_freq_shift=self.downscale_freq_shift						,	)
					return t_emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 2_56						,	SCREAMING_SNAKE_CASE__ = 1.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=False      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Any					     =   nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE__      ) * scale						,	requires_grad=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   log
					_snake_case					:							Any					     =   flip_sin_to_cos

					if set_W_to_weight:
						# to delete later
						_snake_case					:							int					     =   nn.Parameter(torch.randn(SCREAMING_SNAKE_CASE__      ) * scale						,	requires_grad=SCREAMING_SNAKE_CASE__      )

						_snake_case					:							List[Any]					     =   self.W







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					if self.log:
						_snake_case					:							Any					     =   torch.log(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Any					     =   x[:, None] * self.weight[None, :] * 2 * np.pi

					if self.flip_sin_to_cos:
						_snake_case					:							Dict					     =   torch.cat([torch.cos(SCREAMING_SNAKE_CASE__      ), torch.sin(SCREAMING_SNAKE_CASE__      )]						,	dim=-1      )
					else:
						_snake_case					:							int					     =   torch.cat([torch.sin(SCREAMING_SNAKE_CASE__      ), torch.cos(SCREAMING_SNAKE_CASE__      )]						,	dim=-1      )
					return out





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							List[Any]					     =   height
					_snake_case					:							Optional[int]					     =   width
					_snake_case					:							Any					     =   num_embed
					_snake_case					:							int					     =   embed_dim

					_snake_case					:							Optional[Any]					     =   nn.Embedding(self.num_embed						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   nn.Embedding(self.height						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   nn.Embedding(self.width						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   self.emb(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							List[str]					     =   self.height_emb(torch.arange(self.height						,	device=index.device      ).view(1						,	self.height      )      )

					# 1 x H x D -> 1 x H x 1 x D
					_snake_case					:							List[str]					     =   height_emb.unsqueeze(2      )

					_snake_case					:							List[Any]					     =   self.width_emb(torch.arange(self.width						,	device=index.device      ).view(1						,	self.width      )      )

					# 1 x W x D -> 1 x 1 x W x D
					_snake_case					:							List[str]					     =   width_emb.unsqueeze(1      )

					_snake_case					:							int					     =   height_emb + width_emb

					# 1 x H x W x D -> 1 x L xD
					_snake_case					:							int					     =   pos_emb.view(1						,	self.height * self.width						,	-1      )

					_snake_case					:							str					     =   emb + pos_emb[:, : emb.shape[1], :]

					return emb





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Any					     =   dropout_prob > 0
					_snake_case					:							Optional[int]					     =   nn.Embedding(num_classes + use_cfg_embedding						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   num_classes
					_snake_case					:							Tuple					     =   dropout_prob







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					if force_drop_ids is None:
						_snake_case					:							Dict					     =   torch.rand(labels.shape[0]						,	device=labels.device      ) < self.dropout_prob
					else:
						_snake_case					:							List[str]					     =   torch.tensor(force_drop_ids == 1      )
					_snake_case					:							List[Any]					     =   torch.where(SCREAMING_SNAKE_CASE__						,	self.num_classes						,	SCREAMING_SNAKE_CASE__      )
					return labels







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					_snake_case					:							Union[str, Any]					     =   self.dropout_prob > 0
					if (self.training and use_dropout) or (force_drop_ids is not None):
						_snake_case					:							Dict					     =   self.token_drop(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.embedding_table(SCREAMING_SNAKE_CASE__      )
					return embeddings





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 10_24						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 10						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							str					     =   num_image_text_embeds
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds * cross_attention_dim      )
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   text_embeds.shape[0]

					# image
					_snake_case					:							Optional[Any]					     =   self.image_embeds(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   image_text_embeds.reshape(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds						,	-1      )

					# text
					_snake_case					:							Dict					     =   self.text_proj(SCREAMING_SNAKE_CASE__      )

					return torch.cat([image_text_embeds, text_embeds]						,	dim=1      )





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 32						,	):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Dict					     =   num_image_text_embeds
					_snake_case					:							int					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds * cross_attention_dim      )
					_snake_case					:							Optional[int]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   image_embeds.shape[0]

					# image
					_snake_case					:							Tuple					     =   self.image_embeds(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   image_embeds.reshape(SCREAMING_SNAKE_CASE__						,	self.num_image_text_embeds						,	-1      )
					_snake_case					:							Union[str, Any]					     =   self.norm(SCREAMING_SNAKE_CASE__      )
					return image_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=0.1      ):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Optional[Any]					     =   Timesteps(num_channels=2_56						,	flip_sin_to_cos=SCREAMING_SNAKE_CASE__						,	downscale_freq_shift=1      )
					_snake_case					:							Optional[Any]					     =   TimestepEmbedding(in_channels=2_56						,	time_embed_dim=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Dict					     =   LabelEmbedding(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   self.time_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   self.timestep_embedder(timesteps_proj.to(dtype=SCREAMING_SNAKE_CASE__      )      )  # (N, D)

					_snake_case					:							Tuple					     =   self.class_embedder(SCREAMING_SNAKE_CASE__      )  # (N, D)

					_snake_case					:							Tuple					     =   timesteps_emb + class_labels  # (N, D)

					return conditioning





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = 64      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							List[Any]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   AttentionPooling(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							str					     =   self.norma(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   self.pool(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   self.norma(SCREAMING_SNAKE_CASE__      )
					return hidden_states





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							str					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							int					     =   self.text_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   self.text_norm(SCREAMING_SNAKE_CASE__      )

					# image
					_snake_case					:							Any					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )

					return time_image_embeds + time_text_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Union[str, Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   self.image_norm(SCREAMING_SNAKE_CASE__      )
					return time_image_embeds





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = 7_68						,	SCREAMING_SNAKE_CASE__ = 15_36      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Optional[Any]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   nn.LayerNorm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   nn.Sequential(
					    nn.Convad(3						,	16						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(16						,	16						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(16						,	32						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(32						,	32						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(32						,	96						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(96						,	96						,	3						,	padding=1      )						,	nn.SiLU()						,	nn.Convad(96						,	2_56						,	3						,	padding=1						,	stride=2      )						,	nn.SiLU()						,	nn.Convad(2_56						,	4						,	3						,	padding=1      )						,	)







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Dict					     =   self.image_proj(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   self.image_norm(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   self.input_hint_block(SCREAMING_SNAKE_CASE__      )
					return time_image_embeds, hint





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None      ):






					"""simple docstring"""

					super().__init__()
					_snake_case					:							Union[str, Any]					     =   dtype
					_snake_case					:							Optional[int]					     =   nn.Parameter(torch.randn(1						,	SCREAMING_SNAKE_CASE__      ) / embed_dim**0.5      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Dict					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Optional[int]					     =   nn.Linear(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=self.dtype      )
					_snake_case					:							Dict					     =   num_heads
					_snake_case					:							Tuple					     =   embed_dim // self.num_heads







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case     , _snake_case     , _snake_case					:							Dict					     =   x.size()

					def shape(SCREAMING_SNAKE_CASE__      ):
						# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
						_snake_case					:							Tuple					     =   x.view(SCREAMING_SNAKE_CASE__						,	-1						,	self.num_heads						,	self.dim_per_head      )
						# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
						_snake_case					:							Optional[int]					     =   x.transpose(1						,	2      )
						# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
						_snake_case					:							Optional[int]					     =   x.reshape(bs * self.num_heads						,	-1						,	self.dim_per_head      )
						# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
						_snake_case					:							Optional[int]					     =   x.transpose(1						,	2      )
						return x

					_snake_case					:							int					     =   x.mean(dim=1						,	keepdim=SCREAMING_SNAKE_CASE__      ) + self.positional_embedding.to(x.dtype      )
					_snake_case					:							Any					     =   torch.cat([class_token, x]						,	dim=1      )  # (bs, length+1, width)

					# (bs*n_heads, class_token_length, dim_per_head)
					_snake_case					:							Optional[int]					     =   shape(self.q_proj(SCREAMING_SNAKE_CASE__      )      )
					# (bs*n_heads, length+class_token_length, dim_per_head)
					_snake_case					:							str					     =   shape(self.k_proj(SCREAMING_SNAKE_CASE__      )      )
					_snake_case					:							List[str]					     =   shape(self.v_proj(SCREAMING_SNAKE_CASE__      )      )

					# (bs*n_heads, class_token_length, length+class_token_length):
					_snake_case					:							Dict					     =   1 / math.sqrt(math.sqrt(self.dim_per_head      )      )
					_snake_case					:							Any					     =   torch.einsum("""bct,bcs->bts"""						,	q * scale						,	k * scale      )  # More stable with f16 than dividing afterwards
					_snake_case					:							Tuple					     =   torch.softmax(weight.float()						,	dim=-1      ).type(weight.dtype      )

					# (bs*n_heads, dim_per_head, class_token_length)
					_snake_case					:							Optional[int]					     =   torch.einsum("""bts,bcs->bct"""						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					# (bs, length+1, width)
					_snake_case					:							Tuple					     =   a.reshape(SCREAMING_SNAKE_CASE__						,	-1						,	1      ).transpose(1						,	2      )

					return a[:, 0, :]  # cls_token





