


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def      __lowerCamelCase		(				lowerCamelCase__						:		Optional[Any]	):





			'''simple docstring'''




			if "cls_token" in name:
						lowerCamelCase  						=   name.replace("""cls_token"""   ,				"""vit.embeddings.cls_token"""	)
			if "mask_token" in name:
						lowerCamelCase  						=   name.replace("""mask_token"""   ,				"""decoder.mask_token"""	)
			if "decoder_pos_embed" in name:
						lowerCamelCase  						=   name.replace("""decoder_pos_embed"""   ,				"""decoder.decoder_pos_embed"""	)
			if "pos_embed" in name and "decoder" not in name:
						lowerCamelCase  						=   name.replace("""pos_embed"""   ,				"""vit.embeddings.position_embeddings"""	)
			if "patch_embed.proj" in name:
						lowerCamelCase  						=   name.replace("""patch_embed.proj"""   ,				"""vit.embeddings.patch_embeddings.projection"""	)
			if "patch_embed.norm" in name:
						lowerCamelCase  						=   name.replace("""patch_embed.norm"""   ,				"""vit.embeddings.norm"""	)
			if "decoder_blocks" in name:
						lowerCamelCase  						=   name.replace("""decoder_blocks"""   ,				"""decoder.decoder_layers"""	)
			if "blocks" in name:
						lowerCamelCase  						=   name.replace("""blocks"""   ,				"""vit.encoder.layer"""	)
			if "attn.proj" in name:
						lowerCamelCase  						=   name.replace("""attn.proj"""   ,				"""attention.output.dense"""	)
			if "attn" in name:
						lowerCamelCase  						=   name.replace("""attn"""   ,				"""attention.self"""	)
			if "norm1" in name:
						lowerCamelCase  						=   name.replace("""norm1"""   ,				"""layernorm_before"""	)
			if "norm2" in name:
						lowerCamelCase  						=   name.replace("""norm2"""   ,				"""layernorm_after"""	)
			if "mlp.fc1" in name:
						lowerCamelCase  						=   name.replace("""mlp.fc1"""   ,				"""intermediate.dense"""	)
			if "mlp.fc2" in name:
						lowerCamelCase  						=   name.replace("""mlp.fc2"""   ,				"""output.dense"""	)
			if "decoder_embed" in name:
						lowerCamelCase  						=   name.replace("""decoder_embed"""   ,				"""decoder.decoder_embed"""	)
			if "decoder_norm" in name:
						lowerCamelCase  						=   name.replace("""decoder_norm"""   ,				"""decoder.decoder_norm"""	)
			if "decoder_pred" in name:
						lowerCamelCase  						=   name.replace("""decoder_pred"""   ,				"""decoder.decoder_pred"""	)
			if "norm.weight" in name and "decoder" not in name:
						lowerCamelCase  						=   name.replace("""norm.weight"""   ,				"""vit.layernorm.weight"""	)
			if "norm.bias" in name and "decoder" not in name:
						lowerCamelCase  						=   name.replace("""norm.bias"""   ,				"""vit.layernorm.bias"""	)

			return name






def      __lowerCamelCase		(				lowerCamelCase__						:		List[str]   ,				lowerCamelCase__						:		Optional[int]	):





			'''simple docstring'''




			for key in orig_state_dict.copy().keys():
						lowerCamelCase  						=   orig_state_dict.pop(lowerCamelCase__	)

						if "qkv" in key:
									lowerCamelCase  						=   key.split("""."""	)
									lowerCamelCase  						=   int(key_split[1]	)
									if "decoder_blocks" in key:
												lowerCamelCase  						=   config.decoder_hidden_size
												lowerCamelCase  						=   """decoder.decoder_layers."""
												if "weight" in key:
															lowerCamelCase  						=   val[:dim, :]
															lowerCamelCase  						=   val[dim : dim * 2, :]
															lowerCamelCase  						=   val[-dim:, :]
												elif "bias" in key:
															lowerCamelCase  						=   val[:dim]
															lowerCamelCase  						=   val[dim : dim * 2]
															lowerCamelCase  						=   val[-dim:]
									else:
												lowerCamelCase  						=   config.hidden_size
												lowerCamelCase  						=   """vit.encoder.layer."""
												if "weight" in key:
															lowerCamelCase  						=   val[:dim, :]
															lowerCamelCase  						=   val[dim : dim * 2, :]
															lowerCamelCase  						=   val[-dim:, :]
												elif "bias" in key:
															lowerCamelCase  						=   val[:dim]
															lowerCamelCase  						=   val[dim : dim * 2]
															lowerCamelCase  						=   val[-dim:]

						else:
									lowerCamelCase  						=   val

			return orig_state_dict






def      __lowerCamelCase		(				lowerCamelCase__						:		List[str]   ,				lowerCamelCase__						:		Tuple	):





			'''simple docstring'''




			lowerCamelCase  						=   ViTMAEConfig()
			if "large" in checkpoint_url:
						lowerCamelCase  						=   1024
						lowerCamelCase  						=   4096
						lowerCamelCase  						=   24
						lowerCamelCase  						=   16
			elif "huge" in checkpoint_url:
						lowerCamelCase  						=   14
						lowerCamelCase  						=   1280
						lowerCamelCase  						=   5120
						lowerCamelCase  						=   32
						lowerCamelCase  						=   16

			lowerCamelCase  						=   ViTMAEForPreTraining(lowerCamelCase__	)

			lowerCamelCase  						=   torch.hub.load_state_dict_from_url(lowerCamelCase__   ,				map_location="""cpu"""	)["""model"""]

			lowerCamelCase  						=   ViTMAEImageProcessor(size=config.image_size	)

			lowerCamelCase  						=   convert_state_dict(lowerCamelCase__   ,				lowerCamelCase__	)

			model.load_state_dict(lowerCamelCase__	)
			model.eval()

			lowerCamelCase  						=   """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

			lowerCamelCase  						=   Image.open(requests.get(lowerCamelCase__   ,				stream=lowerCamelCase__	).raw	)
			lowerCamelCase  						=   ViTMAEImageProcessor(size=config.image_size	)
			lowerCamelCase  						=   image_processor(images=lowerCamelCase__   ,				return_tensors="""pt"""	)

			# forward pass
			torch.manual_seed(2	)
			lowerCamelCase  						=   model(**lowerCamelCase__	)
			lowerCamelCase  						=   outputs.logits

			if "large" in checkpoint_url:
						lowerCamelCase  						=   torch.tensor(
						    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]	)
			elif "huge" in checkpoint_url:
						lowerCamelCase  						=   torch.tensor(
						    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]	)
			else:
						lowerCamelCase  						=   torch.tensor(
						    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]	)

			# verify logits
			assert torch.allclose(logits[0, :3, :3]   ,				lowerCamelCase__   ,				atol=1E-4	)

			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 __name__ == "__main__":
		UpperCAmelCase					:  List[str]			=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint_url",
		    default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
		    type=str,
		    help="URL of the checkpoint you\'d like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)

		UpperCAmelCase					:  Optional[int]			=						parser.parse_args()
		convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






'''simple docstring'''





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					analyze_text(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					list(""" """ + ascii_lowercase			)
    # what is our total sum of probabilities.
    lowerCAmelCase__ :  List[Any]							      =					sum(single_char_strings.values()			)

    # one length string
    lowerCAmelCase__ :  Optional[int]							      =					0
    # for each alpha we go in our dict and if it is in it we calculate entropy
    for ch in my_alphas:
        if ch in single_char_strings:
            lowerCAmelCase__ :  List[Any]							      =					single_char_strings[ch]
            lowerCAmelCase__ :  List[Any]							      =					my_str / all_sum
            my_fir_sum += prob * math.loga(UpperCamelCase			)  # entropy formula.

    # print entropy
    print(f"""{round(-1 * my_fir_sum			):.1f}"""			)

    # two len string
    lowerCAmelCase__ :  Dict							      =					sum(two_char_strings.values()			)
    lowerCAmelCase__ :  int							      =					0
    # for each alpha (two in size) calculate entropy.
    for cha in my_alphas:
        for cha in my_alphas:
            lowerCAmelCase__ :  Union[str, Any]							      =					cha + cha
            if sequence in two_char_strings:
                lowerCAmelCase__ :  Dict							      =					two_char_strings[sequence]
                lowerCAmelCase__ :  Tuple							      =					int(UpperCamelCase			) / all_sum
                my_sec_sum += prob * math.loga(UpperCamelCase			)

    # print second entropy
    print(f"""{round(-1 * my_sec_sum			):.1f}"""			)

    # print the difference between them
    print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)			):.1f}"""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					Counter()  # type: ignore
    lowerCAmelCase__ :  Tuple							      =					Counter()  # type: ignore
    single_char_strings[text[-1]] += 1

    # first case when we have space at start.
    two_char_strings[" " + text[0]] += 1
    for i in range(0							,    len(UpperCamelCase			) - 1			):
        single_char_strings[text[i]] += 1
        two_char_strings[text[i : i + 2]] += 1
    return single_char_strings, two_char_strings
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    import doctest

    doctest.testmod()
    # text = (
    #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
    #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
    #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
    #     "beloved evident through no service elderly is. Blind there if every no so "
    #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
    #     "of message cottage windows do besides against uncivil.  Delightful "
    #     "unreserved impossible few estimating men favourable see entreaties. She "
    #     "propriety immediate was improving. He or entrance humoured likewise "
    #     "moderate. Much nor game son say feel. Fat make met can must form into "
    #     "gate. Me we offending prevailed discovery. "
    # )

    # calculate_prob(text)


if __name__ == "__main__":
 main()


