



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			= {
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrajectoryTransformerModel""",
						    """TrajectoryTransformerPreTrainedModel""",
						    """load_tf_weights_in_trajectory_transformer""",
						]


if TYPE_CHECKING:
						from .configuration_trajectory_transformer import (
						    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TrajectoryTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trajectory_transformer import (
												    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TrajectoryTransformerModel,
												    TrajectoryTransformerPreTrainedModel,
												    load_tf_weights_in_trajectory_transformer,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





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)
