





'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  return name




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  for key in orig_state_dict.copy().keys():
    _snake_case     		=				orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

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

    else:
      _snake_case     		=				val

  return orig_state_dict




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				ViTMAEConfig()
  if "large" in checkpoint_url:
    _snake_case     		=				10_24
    _snake_case     		=				40_96
    _snake_case     		=				24
    _snake_case     		=				16
  elif "huge" in checkpoint_url:
    _snake_case     		=				14
    _snake_case     		=				12_80
    _snake_case     		=				51_20
    _snake_case     		=				32
    _snake_case     		=				16

  _snake_case     		=				ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__					,	map_location="cpu"       )["model"]

  _snake_case     		=				ViTMAEImageProcessor(size=config.image_size       )

  _snake_case     		=				convert_state_dict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  model.load_state_dict(SCREAMING_SNAKE_CASE__       )
  model.eval()

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

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

  # forward pass
  torch.manual_seed(2       )
  _snake_case     		=				model(**SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				outputs.logits

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

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

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


if __name__ == "__main__":
 __magic_name__   :						int    =			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."""
 )

 __magic_name__   :						List[str]    =			parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''



import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
 __magic_name__   :						Union[str, Any]    =			pd.read_csv("""sample_data.csv""", header=None)
 __magic_name__   :						Optional[Any]    =			df.shape[:1][0]
 # If you're using some other dataset input the target column
 __magic_name__   :						str    =			df.iloc[:, 1:2]
 __magic_name__   :						Dict    =			actual_data.values.reshape(len_data, 1)
 __magic_name__   :						Tuple    =			MinMaxScaler().fit_transform(actual_data)
 __magic_name__   :						Union[str, Any]    =			10
 __magic_name__   :						Optional[int]    =			5
 __magic_name__   :						Any    =			20
 __magic_name__   :						int    =			len_data - periods * look_back
 __magic_name__   :						Union[str, Any]    =			actual_data[:division]
 __magic_name__   :						int    =			actual_data[division - look_back :]
 __magic_name__   ,    __magic_name__   :						List[str]    =			[], []
 __magic_name__   ,    __magic_name__   :						Tuple    =			[], []

 for i in range(0, len(train_data) - forward_days - look_back + 1):
  train_x.append(train_data[i : i + look_back])
  train_y.append(train_data[i + look_back : i + look_back + forward_days])
 for i in range(0, len(test_data) - forward_days - look_back + 1):
  test_x.append(test_data[i : i + look_back])
  test_y.append(test_data[i + look_back : i + look_back + forward_days])
 __magic_name__   :						List[str]    =			np.array(train_x)
 __magic_name__   :						List[str]    =			np.array(test_x)
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in train_y])
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in test_y])

 __magic_name__   :						Tuple    =			Sequential()
 model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
 model.add(LSTM(64, input_shape=(128, 1)))
 model.add(Dense(forward_days))
 model.compile(loss="""mean_squared_error""", optimizer="""adam""")
 __magic_name__   :						int    =			model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __magic_name__   :						int    =			model.predict(x_test)


