




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




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

						return name




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Any:
						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase      =			orig_state_dict.pop(snake_case       )

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

												else:
																		_UpperCAmelCase      =			val

						return orig_state_dict




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				List[str]:
						_UpperCAmelCase      =			ViTMAEConfig()
						if "large" in checkpoint_url:
												_UpperCAmelCase      =			1_0_2_4
												_UpperCAmelCase      =			4_0_9_6
												_UpperCAmelCase      =			2_4
												_UpperCAmelCase      =			1_6
						elif "huge" in checkpoint_url:
												_UpperCAmelCase      =			1_4
												_UpperCAmelCase      =			1_2_8_0
												_UpperCAmelCase      =			5_1_2_0
												_UpperCAmelCase      =			3_2
												_UpperCAmelCase      =			1_6

						_UpperCAmelCase      =			ViTMAEForPreTraining(snake_case       )

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

						_UpperCAmelCase      =			ViTMAEImageProcessor(size=config.image_size       )

						_UpperCAmelCase      =			convert_state_dict(snake_case			,     snake_case       )

						model.load_state_dict(snake_case       )
						model.eval()

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

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

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

						if "large" in checkpoint_url:
												_UpperCAmelCase      =			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:
												_UpperCAmelCase      =			torch.tensor(
												    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]       )
						else:
												_UpperCAmelCase      =			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]			,     snake_case			,     atol=1E-4       )

						print(f"Saving model to {pytorch_dump_folder_path}"       )
						model.save_pretrained(snake_case       )

						print(f"Saving image processor to {pytorch_dump_folder_path}"       )
						image_processor.save_pretrained(snake_case       )


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

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





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


a													=			logging.get_logger(__name__)

a													=			"T5Config"




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				jnp.ndarray:
						_UpperCAmelCase      =			jnp.zeros_like(snake_case       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 0].set(snake_case       )

						_UpperCAmelCase      =			jnp.where(shifted_input_ids == -1_0_0			,     snake_case			,     snake_case       )
						return shifted_input_ids




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig