"""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()
__lowerCAmelCase    :							Tuple													=						logging.get_logger(__name__)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=False			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		lowerCAmelCase__        =   """backbone.""" if is_semantic else """"""

		lowerCAmelCase__        =   []
		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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=False			,  lowerCamelCase__=False     ):





		"""simple docstring"""




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

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

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

				lowerCAmelCase__        =   gamma_a
				lowerCAmelCase__        =   gamma_a







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   dct.pop(lowerCamelCase__     )
		lowerCAmelCase__        =   val







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




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







@torch.no_grad()
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		lowerCAmelCase__        =   False if """rvlcdip""" in checkpoint_url else True
		lowerCAmelCase__        =   BeitConfig(use_absolute_position_embeddings=lowerCamelCase__			,  use_mask_token=lowerCamelCase__     )

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

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

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

		lowerCAmelCase__        =   create_rename_keys(lowerCamelCase__			,  has_lm_head=lowerCamelCase__     )
		for src, dest in rename_keys:
				rename_key(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		read_in_q_k_v(lowerCamelCase__			,  lowerCamelCase__			,  has_lm_head=lowerCamelCase__     )

		# load HuggingFace model
		lowerCAmelCase__        =   BeitForMaskedImageModeling(lowerCamelCase__     ) if has_lm_head else BeitForImageClassification(lowerCamelCase__     )
		model.eval()
		model.load_state_dict(lowerCamelCase__     )

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

		lowerCAmelCase__        =   image_processor(images=lowerCamelCase__			,  return_tensors="""pt"""     )
		lowerCAmelCase__        =   encoding["""pixel_values"""]

		lowerCAmelCase__        =   model(lowerCamelCase__     )
		lowerCAmelCase__        =   outputs.logits

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

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

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


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, Any]													=						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    :							int													=						parser.parse_args()
			convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""




import pprint

import requests

__lowerCAmelCase    :							Union[str, Any]													=						"https://zenquotes.io/api"







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		return requests.get(API_ENDPOINT_URL + """/today"""     ).json()







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		return requests.get(API_ENDPOINT_URL + """/random"""     ).json()


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, Any]													=						random_quotes()
			pprint.pprint(response)




