


'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




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

			return name






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			for key in orig_state_dict.copy().keys():
						__A=	orig_state_dict.pop(__lowerCAmelCase )

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

						else:
									__A=	val

			return orig_state_dict






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




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

			__A=	ViTMAEForPreTraining(__lowerCAmelCase )

			__A=	torch.hub.load_state_dict_from_url(__lowerCAmelCase,map_location='cpu' )['model']

			__A=	ViTMAEImageProcessor(size=config.image_size )

			__A=	convert_state_dict(__lowerCAmelCase,__lowerCAmelCase )

			model.load_state_dict(__lowerCAmelCase )
			model.eval()

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

			__A=	Image.open(requests.get(__lowerCAmelCase,stream=__lowerCAmelCase ).raw )
			__A=	ViTMAEImageProcessor(size=config.image_size )
			__A=	image_processor(images=__lowerCAmelCase,return_tensors='pt' )

			# forward pass
			torch.manual_seed(2 )
			__A=	model(**__lowerCAmelCase )
			__A=	outputs.logits

			if "large" in checkpoint_url:
						__A=	torch.tensor(
						    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]] )
			elif "huge" in checkpoint_url:
						__A=	torch.tensor(
						    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]] )
			else:
						__A=	torch.tensor(
						    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]] )

			# 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__  			=				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__  			=				parser.parse_args()
					convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, TensorType

_A  =	logging.get_logger(__name__)

_A  =	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''imagegpt'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(   self							,							_UpperCamelCase=512 + 1							,							_UpperCamelCase=32 * 32							,							_UpperCamelCase=512							,							_UpperCamelCase=24							,							_UpperCamelCase=8							,							_UpperCamelCase=None							,							_UpperCamelCase="quick_gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> Tuple:
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      n_positions
							lowerCAmelCase_         =      n_embd
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      n_inner
							lowerCAmelCase_         =      activation_function
							lowerCAmelCase_         =      resid_pdrop
							lowerCAmelCase_         =      embd_pdrop
							lowerCAmelCase_         =      attn_pdrop
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scale_attn_weights
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      scale_attn_by_inverse_layer_idx
							lowerCAmelCase_         =      reorder_and_upcast_attn
							lowerCAmelCase_         =      tie_word_embeddings

							super().__init__(tie_word_embeddings=_UpperCamelCase							,							**_UpperCamelCase	)





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "sequence"}),
							    ]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = 1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = 3							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      self._generate_dummy_images(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      dict(preprocessor(images=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)	)

							return inputs



