





"""simple docstring"""




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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def 	a_    (							_lowercase	,						_lowercase=False	,						_lowercase=False       ):
						_UpperCamelCase      : Tuple         =   '''backbone.''' if is_semantic else ''''''

						_UpperCamelCase      : List[str]         =   []
						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"""beit.encoder.layer.{i}.layernorm_before.weight""")       )
												rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")       )
												rename_keys.append(
												    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")       )
												rename_keys.append(
												    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")       )
												rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")       )
												rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")       )
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")       )
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")       )
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")       )
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")       )

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (F"""{prefix}cls_token""", '''beit.embeddings.cls_token'''),
						        (F"""{prefix}patch_embed.proj.weight""", '''beit.embeddings.patch_embeddings.projection.weight'''),
						        (F"""{prefix}patch_embed.proj.bias""", '''beit.embeddings.patch_embeddings.projection.bias'''),
						        (F"""{prefix}pos_embed""", '''beit.embeddings.position_embeddings'''),
						    ]       )

						if has_lm_head:
												# mask token + layernorm
												rename_keys.extend(
												    [
												        ('''mask_token''', '''beit.embeddings.mask_token'''),
												        ('''norm.weight''', '''layernorm.weight'''),
												        ('''norm.bias''', '''layernorm.bias'''),
												    ]       )
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
												        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
												        ('''head.weight''', '''classifier.weight'''),
												        ('''head.bias''', '''classifier.bias'''),
												    ]       )

						return rename_keys





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase=False	,						_lowercase=False       ):







						for i in range(config.num_hidden_layers       ):
												_UpperCamelCase      : List[str]         =   '''backbone.''' if is_semantic else ''''''
												# queries, keys and values
												_UpperCamelCase      : List[str]         =   state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""       )
												_UpperCamelCase      : Dict         =   state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""       )
												_UpperCamelCase      : Union[str, Any]         =   state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""       )

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

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												_UpperCamelCase      : Dict         =   state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""       )
												_UpperCamelCase      : List[str]         =   state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""       )

												_UpperCamelCase      : Optional[int]         =   gamma_a
												_UpperCamelCase      : int         =   gamma_a





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : str         =   dct.pop(_UpperCamelCase       )
						_UpperCamelCase      : Union[str, Any]         =   val





def 	a_    (							):
						_UpperCamelCase      : Any         =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						_UpperCamelCase      : Optional[Any]         =   Image.open(requests.get(_UpperCamelCase	,						stream=_UpperCamelCase       ).raw       )
						return im





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase=False       ):
						_UpperCamelCase      : Union[str, Any]         =   False if '''rvlcdip''' in checkpoint_url else True
						_UpperCamelCase      : str         =   BeitConfig(use_absolute_position_embeddings=_UpperCamelCase	,						use_mask_token=_UpperCamelCase       )

						# size of the architecture
						if "large" in checkpoint_url or "dit-l" in checkpoint_url:
												_UpperCamelCase      : List[str]         =   1024
												_UpperCamelCase      : Optional[Any]         =   4096
												_UpperCamelCase      : Optional[Any]         =   24
												_UpperCamelCase      : Tuple         =   16

						# labels
						if "rvlcdip" in checkpoint_url:
												_UpperCamelCase      : Any         =   16
												_UpperCamelCase      : int         =   '''huggingface/label-files'''
												_UpperCamelCase      : Optional[Any]         =   '''rvlcdip-id2label.json'''
												_UpperCamelCase      : Optional[int]         =   json.load(open(hf_hub_download(_UpperCamelCase	,						_UpperCamelCase	,						repo_type='''dataset'''       )	,						'''r'''       )       )
												_UpperCamelCase      : Any         =   {int(_UpperCamelCase       ): v for k, v in idalabel.items()}
												_UpperCamelCase      : Union[str, Any]         =   idalabel
												_UpperCamelCase      : List[Any]         =   {v: k for k, v in idalabel.items()}

						# load state_dict of original model, remove and rename some keys
						_UpperCamelCase      : int         =   torch.hub.load_state_dict_from_url(_UpperCamelCase	,						map_location='''cpu'''       )['''model''']

						_UpperCamelCase      : Optional[int]         =   create_rename_keys(_UpperCamelCase	,						has_lm_head=_UpperCamelCase       )
						for src, dest in rename_keys:
												rename_key(_UpperCamelCase	,						_UpperCamelCase	,						_UpperCamelCase       )
						read_in_q_k_v(_UpperCamelCase	,						_UpperCamelCase	,						has_lm_head=_UpperCamelCase       )

						# load HuggingFace model
						_UpperCamelCase      : Dict         =   BeitForMaskedImageModeling(_UpperCamelCase       ) if has_lm_head else BeitForImageClassification(_UpperCamelCase       )
						model.eval()
						model.load_state_dict(_UpperCamelCase       )

						# Check outputs on an image
						_UpperCamelCase      : List[str]         =   BeitImageProcessor(
						    size=config.image_size	,						resample=PILImageResampling.BILINEAR	,						do_center_crop=_UpperCamelCase       )
						_UpperCamelCase      : Optional[int]         =   prepare_img()

						_UpperCamelCase      : Tuple         =   image_processor(images=_UpperCamelCase	,						return_tensors='''pt'''       )
						_UpperCamelCase      : int         =   encoding['''pixel_values''']

						_UpperCamelCase      : Tuple         =   model(_UpperCamelCase       )
						_UpperCamelCase      : Tuple         =   outputs.logits

						# verify logits
						_UpperCamelCase      : Dict         =   [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
						assert logits.shape == torch.Size(_UpperCamelCase       ), "Shape of logits not as expected"

						Path(_UpperCamelCase       ).mkdir(exist_ok=_UpperCamelCase       )
						print(F"""Saving model to {pytorch_dump_folder_path}"""       )
						model.save_pretrained(_UpperCamelCase       )
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
						image_processor.save_pretrained(_UpperCamelCase       )

						if push_to_hub:
												if has_lm_head:
																		_UpperCamelCase      : List[Any]         =   '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
												else:
																		_UpperCamelCase      : Optional[int]         =   '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
												image_processor.push_to_hub(
												    repo_path_or_name=Path(_UpperCamelCase	,						_UpperCamelCase       )	,						organization='''nielsr'''	,						commit_message='''Add image processor'''	,						use_temp_dir=_UpperCamelCase	,						)
												model.push_to_hub(
												    repo_path_or_name=Path(_UpperCamelCase	,						_UpperCamelCase       )	,						organization='''nielsr'''	,						commit_message='''Add model'''	,						use_temp_dir=_UpperCamelCase	,						)


if __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser()

						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
						    type=str,
						    help="""URL to the original PyTorch checkpoint (.pth file).""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
						)
						parser.add_argument(
						    """--push_to_hub""",
						    action="""store_true""",
						)
						UpperCamelCase_									=parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



