



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    ='pix2struct_text_model'
							__lowerCamelCase	:	str    =['past_key_values']
							__lowerCamelCase	:	Any    ={
							    'hidden_size': 'hidden_size',
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}





							def __init__(   self		:		int      ,				__lowercase		:		Dict=50244      ,				__lowercase		:		int=768      ,				__lowercase		:		Optional[Any]=64      ,				__lowercase		:		Any=2048      ,				__lowercase		:		Tuple=12      ,				__lowercase		:		List[str]=12      ,				__lowercase		:		int=32      ,				__lowercase		:		Tuple=128      ,				__lowercase		:		Union[str, Any]=0.1      ,				__lowercase		:		List[str]=1E-6      ,				__lowercase		:		Tuple=1.0      ,				__lowercase		:		str="gelu_new"      ,				__lowercase		:		Optional[int]=0      ,				__lowercase		:		int=False      ,				__lowercase		:		Optional[int]=0      ,				__lowercase		:		List[Any]=1      ,				__lowercase		:		Optional[int]=False      ,				__lowercase		:		Optional[Any]=True      ,				**__lowercase		:		Optional[int]      ,				):

											'''simple docstring'''
											__a       =		vocab_size
											__a       =		hidden_size
											__a       =		d_kv
											__a       =		d_ff
											__a       =		num_layers
											__a       =		num_heads
											__a       =		relative_attention_num_buckets
											__a       =		relative_attention_max_distance
											__a       =		dropout_rate
											__a       =		layer_norm_epsilon
											__a       =		initializer_factor
											__a       =		use_cache

											__a       =		eos_token_id
											__a       =		decoder_start_token_id

											# for backwards compatibility
											__a       =		dense_act_fn

											super().__init__(
											    pad_token_id=__UpperCamelCase      ,				eos_token_id=__UpperCamelCase      ,				decoder_start_token_id=__UpperCamelCase      ,				tie_word_embeddings=__UpperCamelCase      ,				is_decoder=__UpperCamelCase      ,				**__UpperCamelCase      ,				)







							@classmethod
							def 						UpperCamelCase_					(   cls		:		str      ,				__lowercase		:		Union[str, os.PathLike]      ,				**__lowercase		:		Dict	):

											'''simple docstring'''
											cls._set_token_in_kwargs(__UpperCamelCase	)

											__a     ,					__a       =		cls.get_config_dict(__UpperCamelCase      ,				**__UpperCamelCase	)

											# get the text config dict if we are loading from Pix2StructConfig
											if config_dict.get("""model_type"""	) == "pix2struct":
															__a       =		config_dict["""text_config"""]

											if "model_type" in config_dict and hasattr(cls      ,				"""model_type"""	) and config_dict["model_type"] != cls.model_type:
															logger.warning(
															    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
															    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."	)

											return cls.from_dict(__UpperCamelCase      ,				**__UpperCamelCase	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    ='pix2struct_vision_model'





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Optional[Any]=768      ,				__lowercase		:		Any=768      ,				__lowercase		:		Tuple=2048      ,				__lowercase		:		List[Any]=64      ,				__lowercase		:		Dict=12      ,				__lowercase		:		Dict=12      ,				__lowercase		:		Union[str, Any]="gelu_new"      ,				__lowercase		:		Optional[int]=1E-6      ,				__lowercase		:		str=0.0      ,				__lowercase		:		int=0.0      ,				__lowercase		:		Optional[int]=1E-10      ,				__lowercase		:		Any=1.0      ,				__lowercase		:		List[str]=4096      ,				__lowercase		:		Any=32      ,				__lowercase		:		List[Any]=128      ,				**__lowercase		:		Union[str, Any]      ,				):

											'''simple docstring'''
											super().__init__(**__UpperCamelCase	)

											__a       =		hidden_size
											__a       =		patch_embed_hidden_size
											__a       =		d_ff
											__a       =		dropout_rate
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		initializer_range
											__a       =		initializer_factor
											__a       =		attention_dropout
											__a       =		layer_norm_eps
											__a       =		dense_act_fn
											__a       =		seq_len
											__a       =		relative_attention_num_buckets
											__a       =		relative_attention_max_distance
											__a       =		d_kv







							@classmethod
							def 						UpperCamelCase_					(   cls		:		Optional[Any]      ,				__lowercase		:		Union[str, os.PathLike]      ,				**__lowercase		:		List[Any]	):

											'''simple docstring'''
											cls._set_token_in_kwargs(__UpperCamelCase	)

											__a     ,					__a       =		cls.get_config_dict(__UpperCamelCase      ,				**__UpperCamelCase	)

											# get the vision config dict if we are loading from Pix2StructConfig
											if config_dict.get("""model_type"""	) == "pix2struct":
															__a       =		config_dict["""vision_config"""]

											if "model_type" in config_dict and hasattr(cls      ,				"""model_type"""	) and config_dict["model_type"] != cls.model_type:
															logger.warning(
															    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
															    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."	)

											return cls.from_dict(__UpperCamelCase      ,				**__UpperCamelCase	)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Union[str, Any]    ='pix2struct'
							__lowerCamelCase	:	Dict    =True





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		int=None      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		Dict=1.0      ,				__lowercase		:		List[Any]=0.02      ,				__lowercase		:		Dict=False      ,				__lowercase		:		Tuple=False      ,				__lowercase		:		int=True      ,				**__lowercase		:		int      ,				):

											'''simple docstring'''
											super().__init__(tie_word_embeddings=__UpperCamelCase      ,				is_encoder_decoder=__UpperCamelCase      ,				**__UpperCamelCase	)

											if text_config is None:
															__a       =		{}
															logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""	)

											if vision_config is None:
															__a       =		{}
															logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""	)

											__a       =		PixaStructTextConfig(**__UpperCamelCase	)
											__a       =		PixaStructVisionConfig(**__UpperCamelCase	)

											__a       =		self.text_config.decoder_start_token_id
											__a       =		self.text_config.pad_token_id
											__a       =		self.text_config.eos_token_id

											__a       =		initializer_factor
											__a       =		initializer_range

											__a       =		self.initializer_range
											__a       =		self.initializer_range

											__a       =		is_vqa





							@classmethod
							def 						UpperCamelCase_					(   cls		:		List[str]      ,				__lowercase		:		PixaStructTextConfig      ,				__lowercase		:		PixaStructVisionConfig      ,				**__lowercase		:		int	):

											'''simple docstring'''
											return cls(text_config=text_config.to_dict()      ,				vision_config=vision_config.to_dict()      ,				**__UpperCamelCase	)







							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		copy.deepcopy(self.__dict__	)
											__a       =		self.text_config.to_dict()
											__a       =		self.vision_config.to_dict()
											__a       =		self.__class__.model_type
											return output









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()
lowerCamelCase__			= logging.get_logger(__name__)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			str=False				,     _SCREAMING_SNAKE_CASE       :			Tuple=False    ):
				"""simple docstring"""







				__a       =		"""backbone.""" if is_semantic else """"""

				__a       =		[]
				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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]=False				,     _SCREAMING_SNAKE_CASE       :			Any=False    ):
				"""simple docstring"""







				for i in range(config.num_hidden_layers    ):
								__a       =		"""backbone.""" if is_semantic else """"""
								# queries, keys and values
								__a       =		state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"    )
								__a       =		state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"    )
								__a       =		state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"    )

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

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

								__a       =		gamma_a
								__a       =		gamma_a

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""simple docstring"""







				__a       =		dct.pop(_SCREAMING_SNAKE_CASE    )
				__a       =		val

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__a       =		Image.open(requests.get(_SCREAMING_SNAKE_CASE				,     stream=_SCREAMING_SNAKE_CASE    ).raw    )
				return im

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			str=False    ):
				"""simple docstring"""







				__a       =		False if """rvlcdip""" in checkpoint_url else True
				__a       =		BeitConfig(use_absolute_position_embeddings=_SCREAMING_SNAKE_CASE				,     use_mask_token=_SCREAMING_SNAKE_CASE    )

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								__a       =		1024
								__a       =		4096
								__a       =		24
								__a       =		16

				# labels
				if "rvlcdip" in checkpoint_url:
								__a       =		16
								__a       =		"""huggingface/label-files"""
								__a       =		"""rvlcdip-id2label.json"""
								__a       =		json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     repo_type="""dataset"""    )				,     """r"""    )    )
								__a       =		{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
								__a       =		idalabel
								__a       =		{v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				__a       =		torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE				,     map_location="""cpu"""    )["""model"""]

				__a       =		create_rename_keys(_SCREAMING_SNAKE_CASE				,     has_lm_head=_SCREAMING_SNAKE_CASE    )
				for src, dest in rename_keys:
								rename_key(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				read_in_q_k_v(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     has_lm_head=_SCREAMING_SNAKE_CASE    )

				# load HuggingFace model
				__a       =		BeitForMaskedImageModeling(_SCREAMING_SNAKE_CASE    ) if has_lm_head else BeitForImageClassification(_SCREAMING_SNAKE_CASE    )
				model.eval()
				model.load_state_dict(_SCREAMING_SNAKE_CASE    )

				# Check outputs on an image
				__a       =		BeitImageProcessor(
				    size=config.image_size				,     resample=PILImageResampling.BILINEAR				,     do_center_crop=_SCREAMING_SNAKE_CASE    )
				__a       =		prepare_img()

				__a       =		image_processor(images=_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""    )
				__a       =		encoding["""pixel_values"""]

				__a       =		model(_SCREAMING_SNAKE_CASE    )
				__a       =		outputs.logits

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

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

				if push_to_hub:
								if has_lm_head:
												__a       =		"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
								else:
												__a       =		"""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(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )				,     organization="""nielsr"""				,     commit_message="""Add image processor"""				,     use_temp_dir=_SCREAMING_SNAKE_CASE				,     )
								model.push_to_hub(
								    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )				,     organization="""nielsr"""				,     commit_message="""Add model"""				,     use_temp_dir=_SCREAMING_SNAKE_CASE				,     )


if __name__ == "__main__":
						lowerCamelCase__			= 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""",
						)
						lowerCamelCase__			= parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
