



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



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

			return name



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			for key in orig_state_dict.copy().keys():
						lowercase__   					=			orig_state_dict.pop(_UpperCAmelCase					)

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

						else:
									lowercase__   					=			val

			return orig_state_dict



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			ViTMAEConfig()
			if "large" in checkpoint_url:
						lowercase__   					=			1024
						lowercase__   					=			4096
						lowercase__   					=			24
						lowercase__   					=			16
			elif "huge" in checkpoint_url:
						lowercase__   					=			14
						lowercase__   					=			1280
						lowercase__   					=			5120
						lowercase__   					=			32
						lowercase__   					=			16

			lowercase__   					=			ViTMAEForPreTraining(_UpperCAmelCase					)

			lowercase__   					=			torch.hub.load_state_dict_from_url(_UpperCAmelCase  ,					map_location="cpu"					)['model']

			lowercase__   					=			ViTMAEImageProcessor(size=config.image_size					)

			lowercase__   					=			convert_state_dict(_UpperCAmelCase  ,					_UpperCAmelCase					)

			model.load_state_dict(_UpperCAmelCase					)
			model.eval()

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

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

			# forward pass
			torch.manual_seed(2					)
			lowercase__   					=			model(**_UpperCAmelCase					)
			lowercase__   					=			outputs.logits

			if "large" in checkpoint_url:
						lowercase__   					=			torch.tensor(
						    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]					)
			elif "huge" in checkpoint_url:
						lowercase__   					=			torch.tensor(
						    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]					)
			else:
						lowercase__   					=			torch.tensor(
						    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]					)

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

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			model.save_pretrained(_UpperCAmelCase					)

			print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
			image_processor.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							lowercase_						=       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."""
							)

							lowercase_						=       parser.parse_args()
							convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Any       						="""perceiver"""




				def __init__(  self   :  Any,   __lowercase   :  Optional[Any]=256,   __lowercase   :  List[str]=1280,   __lowercase   :  Dict=768,   __lowercase   :  int=1,   __lowercase   :  Dict=26,   __lowercase   :  Any=8,   __lowercase   :  List[Any]=8,   __lowercase   :  Dict=None,   __lowercase   :  List[Any]=None,   __lowercase   :  str="kv",   __lowercase   :  str=1,   __lowercase   :  Optional[Any]=1,   __lowercase   :  str="gelu",   __lowercase   :  List[str]=0.1,   __lowercase   :  int=0.02,   __lowercase   :  Union[str, Any]=1e-1_2,   __lowercase   :  Optional[Any]=True,   __lowercase   :  Optional[Any]=262,   __lowercase   :  str=2048,   __lowercase   :  Optional[Any]=56,   __lowercase   :  str=[368, 496],   __lowercase   :  str=16,   __lowercase   :  int=1920,   __lowercase   :  Dict=16,   __lowercase   :  List[Any]=[1, 16, 224, 224],   **__lowercase   :  str,   ):
							super().__init__(**__lowercase  )

							lowercase__   					=			num_latents
							lowercase__   					=			d_latents
							lowercase__   					=			d_model
							lowercase__   					=			num_blocks
							lowercase__   					=			num_self_attends_per_block
							lowercase__   					=			num_self_attention_heads
							lowercase__   					=			num_cross_attention_heads
							lowercase__   					=			qk_channels
							lowercase__   					=			v_channels
							lowercase__   					=			cross_attention_shape_for_attention
							lowercase__   					=			self_attention_widening_factor
							lowercase__   					=			cross_attention_widening_factor
							lowercase__   					=			hidden_act
							lowercase__   					=			attention_probs_dropout_prob
							lowercase__   					=			initializer_range
							lowercase__   					=			layer_norm_eps
							lowercase__   					=			use_query_residual
							# masked language modeling attributes
							lowercase__   					=			vocab_size
							lowercase__   					=			max_position_embeddings
							# image classification attributes
							lowercase__   					=			image_size
							# flow attributes
							lowercase__   					=			train_size
							# multimodal autoencoding attributes
							lowercase__   					=			num_frames
							lowercase__   					=			audio_samples_per_frame
							lowercase__   					=			samples_per_patch
							lowercase__   					=			output_shape
class 			_snake_case     (   lowercase__):




				@property
				def 							A__ (  self   :  Optional[int]  ):
							if self.task == "multiple-choice":
										lowercase__   					=			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowercase__   					=			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("inputs", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]  )




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							return 1e-4




				def 							A__ (  self   :  Tuple,   __lowercase   :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional[TensorType] = None,   __lowercase   :  int = 3,   __lowercase   :  int = 40,   __lowercase   :  int = 40,   ):
							# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

							if isinstance(__lowercase,   __lowercase  ):
										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										lowercase__   					=			compute_effective_axis_dimension(
										    __lowercase,   fixed_dimension=OnnxConfig.default_fixed_batch,   num_token_to_add=0  )
										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										lowercase__   					=			preprocessor.num_special_tokens_to_add(__lowercase  )
										lowercase__   					=			compute_effective_axis_dimension(
										    __lowercase,   fixed_dimension=OnnxConfig.default_fixed_sequence,   num_token_to_add=__lowercase  )
										# Generate dummy inputs according to compute batch and sequence
										lowercase__   					=			[" ".join(["a"]  ) * seq_length] * batch_size
										lowercase__   					=			dict(preprocessor(__lowercase,   return_tensors=__lowercase  )  )
										lowercase__   					=			inputs.pop("input_ids"  )
										return inputs
							elif isinstance(__lowercase,   __lowercase  ) and preprocessor.model_input_names[0] == "pixel_values":
										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										lowercase__   					=			compute_effective_axis_dimension(__lowercase,   fixed_dimension=OnnxConfig.default_fixed_batch  )
										lowercase__   					=			self._generate_dummy_images(__lowercase,   __lowercase,   __lowercase,   __lowercase  )
										lowercase__   					=			dict(preprocessor(images=__lowercase,   return_tensors=__lowercase  )  )
										lowercase__   					=			inputs.pop("pixel_values"  )
										return inputs
							else:
										raise ValueError(
										    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."  )




