
"""simple docstring"""




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_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_="data2vec."      ):
					SCREAMING_SNAKE_CASE           =     "backbone." if is_semantic else ""

					SCREAMING_SNAKE_CASE           =     []
					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_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_=False,					SCREAMING_SNAKE_CASE_="data2vec_vision."      ):
					for i in range(config.num_hidden_layers      ):
										SCREAMING_SNAKE_CASE           =     "backbone." if is_semantic else ""
										# queries, keys and values
										SCREAMING_SNAKE_CASE           =     state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''      )
										SCREAMING_SNAKE_CASE           =     state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''      )
										SCREAMING_SNAKE_CASE           =     state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''      )

										SCREAMING_SNAKE_CASE           =     in_proj_weight[
										    : config.hidden_size, :
										]
										SCREAMING_SNAKE_CASE           =     q_bias
										SCREAMING_SNAKE_CASE           =     in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE           =     in_proj_weight[
										    -config.hidden_size :, :
										]
										SCREAMING_SNAKE_CASE           =     v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										SCREAMING_SNAKE_CASE           =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''      )
										SCREAMING_SNAKE_CASE           =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''      )

										SCREAMING_SNAKE_CASE           =     gamma_a
										SCREAMING_SNAKE_CASE           =     gamma_a

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

															SCREAMING_SNAKE_CASE           =     table
															SCREAMING_SNAKE_CASE           =     index

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser(
					    'Convert Data2VecVision to HF for image classification and pretraining',					add_help=lowercase__      )
					parser.add_argument('--hf_checkpoint_name',					type=lowercase__      )
					parser.add_argument('--input_size',					default=2_2_4,					type=lowercase__,					help='images input size'      )
					parser.add_argument('--beit_checkpoint',					default='',					help='beit checkpoint'      )

					return parser.parse_args()

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					def load_state_dict(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="",					SCREAMING_SNAKE_CASE_="relative_position_index"      ):
										SCREAMING_SNAKE_CASE           =     []
										SCREAMING_SNAKE_CASE           =     []
										SCREAMING_SNAKE_CASE           =     []
										# copy state_dict so _load_from_state_dict can modify it
										SCREAMING_SNAKE_CASE           =     getattr(lowercase__,					'_metadata',					lowercase__      )
										SCREAMING_SNAKE_CASE           =     state_dict.copy()
										if metadata is not None:
															SCREAMING_SNAKE_CASE           =     metadata

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

										load(lowercase__,					prefix=lowercase__      )

										SCREAMING_SNAKE_CASE           =     []
										SCREAMING_SNAKE_CASE           =     []
										for key in missing_keys:
															SCREAMING_SNAKE_CASE           =     True
															for ignore_key in ignore_missing.split('|'      ):
																				if ignore_key in key:
																									SCREAMING_SNAKE_CASE           =     False
																									break
															if keep_flag:
																				warn_missing_keys.append(lowercase__      )
															else:
																				ignore_missing_keys.append(lowercase__      )

										SCREAMING_SNAKE_CASE           =     warn_missing_keys

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

					SCREAMING_SNAKE_CASE           =     {
					    "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': 1_0_0_0,
										        'use_mean_pooling': True,
										        'init_scale': 0.001,
										        'use_rel_pos_bias': True,
										    }      )

					SCREAMING_SNAKE_CASE           =     create_model(
					    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224',					**lowercase__,					)
					SCREAMING_SNAKE_CASE           =     model.patch_embed.patch_size
					SCREAMING_SNAKE_CASE           =     (args.input_size // patch_size[0], args.input_size // patch_size[1])
					SCREAMING_SNAKE_CASE           =     torch.load(args.beit_checkpoint,					map_location='cpu'      )

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

					SCREAMING_SNAKE_CASE           =     list(checkpoint_model.keys()      )
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(lowercase__      )

										if "relative_position_bias_table" in key:
															SCREAMING_SNAKE_CASE           =     checkpoint_model[key]
															SCREAMING_SNAKE_CASE           =     rel_pos_bias.size()
															SCREAMING_SNAKE_CASE           =     model.state_dict()[key].size()
															SCREAMING_SNAKE_CASE           =     model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(lowercase__,					lowercase__,					prefix=''      )

					return model

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     get_args()

					SCREAMING_SNAKE_CASE           =     "ft1k" in args.hf_checkpoint_name
					SCREAMING_SNAKE_CASE           =     "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
					SCREAMING_SNAKE_CASE           =     DataaVecVisionConfig()
					if is_finetuned:
										SCREAMING_SNAKE_CASE           =     True
										SCREAMING_SNAKE_CASE           =     False
										SCREAMING_SNAKE_CASE           =     True
										SCREAMING_SNAKE_CASE           =     1_0_0_0

										SCREAMING_SNAKE_CASE           =     "huggingface/label-files"
										SCREAMING_SNAKE_CASE           =     "imagenet-1k-id2label.json"
										SCREAMING_SNAKE_CASE           =     json.load(open(hf_hub_download(lowercase__,					lowercase__,					repo_type='dataset'      ),					'r'      )      )
										SCREAMING_SNAKE_CASE           =     {int(lowercase__      ): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE           =     idalabel
										SCREAMING_SNAKE_CASE           =     {v: k for k, v in idalabel.items()}
					else:
										SCREAMING_SNAKE_CASE           =     False
										SCREAMING_SNAKE_CASE           =     True
										SCREAMING_SNAKE_CASE           =     False

					if is_large:
										SCREAMING_SNAKE_CASE           =     1_0_2_4
										SCREAMING_SNAKE_CASE           =     4_0_9_6
										SCREAMING_SNAKE_CASE           =     2_4
										SCREAMING_SNAKE_CASE           =     1_6

					# 2. Load Beit model
					SCREAMING_SNAKE_CASE           =     load_beit_model(lowercase__,					lowercase__,					lowercase__      )
					orig_model.eval()

					# 3. Forward Beit model
					SCREAMING_SNAKE_CASE           =     BeitImageProcessor(size=config.image_size,					do_center_crop=lowercase__      )
					SCREAMING_SNAKE_CASE           =     Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'      )
					SCREAMING_SNAKE_CASE           =     image_processor(images=lowercase__,					return_tensors='pt'      )
					SCREAMING_SNAKE_CASE           =     encoding["pixel_values"]

					SCREAMING_SNAKE_CASE           =     (pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =     orig_model(*lowercase__      )

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										SCREAMING_SNAKE_CASE           =     DataaVecVisionForImageClassification(lowercase__      )
										hf_model.eval()
										SCREAMING_SNAKE_CASE           =     False
										SCREAMING_SNAKE_CASE           =     "data2vec_vision."
					else:
										SCREAMING_SNAKE_CASE           =     DataaVecVisionModel(lowercase__      )
										hf_model.eval()
										SCREAMING_SNAKE_CASE           =     True
										SCREAMING_SNAKE_CASE           =     ""

					SCREAMING_SNAKE_CASE           =     create_rename_keys(lowercase__,					hf_prefix=lowercase__,					has_lm_head=lowercase__      )
					SCREAMING_SNAKE_CASE           =     orig_model.state_dict()
					for src, dest in rename_keys:
										SCREAMING_SNAKE_CASE           =     state_dict.pop(lowercase__      )
										SCREAMING_SNAKE_CASE           =     val

					read_in_q_k_v(lowercase__,					lowercase__,					hf_prefix=lowercase__,					has_lm_head=lowercase__      )
					SCREAMING_SNAKE_CASE           =     hf_model.load_state_dict(lowercase__,					strict=lowercase__      )
					print('HF missing',					lowercase__      )
					print('HF unexpected_keys',					lowercase__      )

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =     hf_model(lowercase__      )

					SCREAMING_SNAKE_CASE           =     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					SCREAMING_SNAKE_CASE           =     torch.max(torch.abs(hf_output - orig_model_output      )      ).item()

					print(f'''max_absolute_diff = {max_absolute_diff}'''      )
					SCREAMING_SNAKE_CASE           =     torch.allclose(lowercase__,					lowercase__,					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"


"""simple docstring"""




from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin




@dataclass
class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				torch.FloatTensor






class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				@register_to_config
				def __init__(    self		,     lowercase__ = 16		,     lowercase__ = 88		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = 32		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = "geglu"		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = "layer_norm"		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     use_linear_projection
									SCREAMING_SNAKE_CASE           =     num_attention_heads
									SCREAMING_SNAKE_CASE           =     attention_head_dim
									SCREAMING_SNAKE_CASE           =     num_attention_heads * attention_head_dim

									# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
									# Define whether input is continuous or discrete depending on configuration
									SCREAMING_SNAKE_CASE           =     (in_channels is not None) and (patch_size is None)
									SCREAMING_SNAKE_CASE           =     num_vector_embeds is not None
									SCREAMING_SNAKE_CASE           =     in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
														SCREAMING_SNAKE_CASE           =     (
														    f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
														    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
														    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
														    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
														    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
														)
														deprecate('norm_type!=num_embeds_ada_norm'		,     '1.0.0'		,     lowercase__		,     standard_warn=lowercase__      )
														SCREAMING_SNAKE_CASE           =     'ada_norm'

									if self.is_input_continuous and self.is_input_vectorized:
														raise ValueError(
														    f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
														    ' sure that either `in_channels` or `num_vector_embeds` is None.'      )
									elif self.is_input_vectorized and self.is_input_patches:
														raise ValueError(
														    f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
														    ' sure that either `num_vector_embeds` or `num_patches` is None.'      )
									elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
														raise ValueError(
														    f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
														    f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''      )

									# 2. Define input layers
									if self.is_input_continuous:
														SCREAMING_SNAKE_CASE           =     in_channels

														SCREAMING_SNAKE_CASE           =     torch.nn.GroupNorm(num_groups=lowercase__		,     num_channels=lowercase__		,     eps=1E-6		,     affine=lowercase__      )
														if use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     kernel_size=1		,     stride=1		,     padding=0      )
									elif self.is_input_vectorized:
														assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
														assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     num_vector_embeds
														SCREAMING_SNAKE_CASE           =     self.height * self.width

														SCREAMING_SNAKE_CASE           =     ImagePositionalEmbeddings(
														    num_embed=lowercase__		,     embed_dim=lowercase__		,     height=self.height		,     width=self.width      )
									elif self.is_input_patches:
														assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size

														SCREAMING_SNAKE_CASE           =     patch_size
														SCREAMING_SNAKE_CASE           =     PatchEmbed(
														    height=lowercase__		,     width=lowercase__		,     patch_size=lowercase__		,     in_channels=lowercase__		,     embed_dim=lowercase__		,     )

									# 3. Define transformers blocks
									SCREAMING_SNAKE_CASE           =     nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            lowercase__		,     lowercase__		,     lowercase__		,     dropout=lowercase__		,     cross_attention_dim=lowercase__		,     activation_fn=lowercase__		,     num_embeds_ada_norm=lowercase__		,     attention_bias=lowercase__		,     only_cross_attention=lowercase__		,     upcast_attention=lowercase__		,     norm_type=lowercase__		,     norm_elementwise_affine=lowercase__		,     )
									        for d in range(lowercase__      )
									    ]      )

									# 4. Define output layers
									SCREAMING_SNAKE_CASE           =     in_channels if out_channels is None else out_channels
									if self.is_input_continuous:
														# TODO: should use out_channels for continuous projections
														if use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     kernel_size=1		,     stride=1		,     padding=0      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     self.num_vector_embeds - 1      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__		,     elementwise_affine=lowercase__		,     eps=1E-6      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     2 * inner_dim      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     patch_size * patch_size * self.out_channels      )




				def 							A (    self		,     lowercase__		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									if attention_mask is not None and attention_mask.ndim == 2:
														# assume that mask is expressed as:
														#   (1 = keep,      0 = discard)
														# convert mask into a bias that can be added to attention scores:
														#       (keep = +0,     discard = -10000.0)
														SCREAMING_SNAKE_CASE           =     (1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     attention_mask.unsqueeze(1      )

									# convert encoder_attention_mask to a bias the same way we do for attention_mask
									if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
														SCREAMING_SNAKE_CASE           =     (1 - encoder_attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     encoder_attention_mask.unsqueeze(1      )

									# 1. Input
									if self.is_input_continuous:
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     hidden_states.shape
														SCREAMING_SNAKE_CASE           =     hidden_states

														SCREAMING_SNAKE_CASE           =     self.norm(lowercase__      )
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.latent_image_embedding(lowercase__      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     self.pos_embed(lowercase__      )

									# 2. Blocks
									for block in self.transformer_blocks:
														SCREAMING_SNAKE_CASE           =     block(
														    lowercase__		,     attention_mask=lowercase__		,     encoder_hidden_states=lowercase__		,     encoder_attention_mask=lowercase__		,     timestep=lowercase__		,     cross_attention_kwargs=lowercase__		,     class_labels=lowercase__		,     )

									# 3. Output
									if self.is_input_continuous:
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()

														SCREAMING_SNAKE_CASE           =     hidden_states + residual
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      )
														SCREAMING_SNAKE_CASE           =     self.out(lowercase__      )
														# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
														SCREAMING_SNAKE_CASE           =     logits.permute(0		,     2		,     1      )

														# log(p(x_0))
														SCREAMING_SNAKE_CASE           =     F.log_softmax(logits.double()		,     dim=1      ).float()
									elif self.is_input_patches:
														# TODO: cleanup!
														SCREAMING_SNAKE_CASE           =     self.transformer_blocks[0].norma.emb(
														    lowercase__		,     lowercase__		,     hidden_dtype=hidden_states.dtype      )
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.proj_out_a(F.silu(lowercase__      )      ).chunk(2		,     dim=1      )
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      ) * (1 + scale[:, None]) + shift[:, None]
														SCREAMING_SNAKE_CASE           =     self.proj_out_a(lowercase__      )

														# unpatchify
														SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     int(hidden_states.shape[1] ** 0.5      )
														SCREAMING_SNAKE_CASE           =     hidden_states.reshape(
														    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
														SCREAMING_SNAKE_CASE           =     torch.einsum('nhwpqc->nchpwq'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     hidden_states.reshape(
														    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)      )

									if not return_dict:
														return (output,)

									return TransformeraDModelOutput(sample=lowercase__      )
