


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :Tuple             =							logging.get_logger(__name__)

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		Optional[Any]=False	)  ->		Union[str, Any]:
							_UpperCAmelCase					=      []
							for i in range(config.num_hidden_layers	):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')	)
														rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')	)
														rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')	)
														rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')	)
														rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')	)
														rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')	)
														rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')	)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('cls_token', 'deit.embeddings.cls_token'),
							        ('dist_token', 'deit.embeddings.distillation_token'),
							        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
							        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
							        ('pos_embed', 'deit.embeddings.position_embeddings'),
							    ]	)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('norm.weight', 'layernorm.weight'),
														        ('norm.bias', 'layernorm.bias'),
														        ('pre_logits.fc.weight', 'pooler.dense.weight'),
														        ('pre_logits.fc.bias', 'pooler.dense.bias'),
														    ]	)

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														_UpperCAmelCase					=      [(pair[0], pair[1][4:]) if pair[1].startswith('deit'	) else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ('norm.weight', 'deit.layernorm.weight'),
														        ('norm.bias', 'deit.layernorm.bias'),
														        ('head.weight', 'cls_classifier.weight'),
														        ('head.bias', 'cls_classifier.bias'),
														        ('head_dist.weight', 'distillation_classifier.weight'),
														        ('head_dist.bias', 'distillation_classifier.bias'),
														    ]	)

							return rename_keys

def 	lowerCAmelCase__			(      a__:		List[str]      ,  a__:		str      ,  a__:		Optional[int]=False	)  ->		Any:
							for i in range(config.num_hidden_layers	):
														if base_model:
																					_UpperCAmelCase					=      """"""
														else:
																					_UpperCAmelCase					=      """deit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														_UpperCAmelCase					=      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''	)
														_UpperCAmelCase					=      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''	)
														# next, add query, keys and values (in that order) to the state dict
														_UpperCAmelCase					=      in_proj_weight[
														    : config.hidden_size, :
														]
														_UpperCAmelCase					=      in_proj_bias[: config.hidden_size]
														_UpperCAmelCase					=      in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														_UpperCAmelCase					=      in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														_UpperCAmelCase					=      in_proj_weight[
														    -config.hidden_size :, :
														]
														_UpperCAmelCase					=      in_proj_bias[-config.hidden_size :]

def 	lowerCAmelCase__			(      a__:		List[Any]      ,  a__:		Tuple      ,  a__:		str	)  ->		Dict:
							_UpperCAmelCase					=      dct.pop(__SCREAMING_SNAKE_CASE	)
							_UpperCAmelCase					=      val

def 	lowerCAmelCase__			(      )  ->		List[Any]:
							_UpperCAmelCase					=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
							_UpperCAmelCase					=      Image.open(requests.get(__SCREAMING_SNAKE_CASE      ,  stream=__SCREAMING_SNAKE_CASE	).raw	)
							return im

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		str	)  ->		Any:
							_UpperCAmelCase					=      DeiTConfig()
							# all deit models have fine-tuned heads
							_UpperCAmelCase					=      False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							_UpperCAmelCase					=      1_0_0_0
							_UpperCAmelCase					=      """huggingface/label-files"""
							_UpperCAmelCase					=      """imagenet-1k-id2label.json"""
							_UpperCAmelCase					=      json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  repo_type='dataset'	)      ,  'r'	)	)
							_UpperCAmelCase					=      {int(__SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
							_UpperCAmelCase					=      idalabel
							_UpperCAmelCase					=      {v: k for k, v in idalabel.items()}
							_UpperCAmelCase					=      int(deit_name[-6:-4]	)
							_UpperCAmelCase					=      int(deit_name[-3:]	)
							# size of the architecture
							if deit_name[9:].startswith('tiny'	):
														_UpperCAmelCase					=      1_9_2
														_UpperCAmelCase					=      7_6_8
														_UpperCAmelCase					=      1_2
														_UpperCAmelCase					=      3
							elif deit_name[9:].startswith('small'	):
														_UpperCAmelCase					=      3_8_4
														_UpperCAmelCase					=      1_5_3_6
														_UpperCAmelCase					=      1_2
														_UpperCAmelCase					=      6
							if deit_name[9:].startswith('base'	):
														pass
							elif deit_name[4:].startswith('large'	):
														_UpperCAmelCase					=      1_0_2_4
														_UpperCAmelCase					=      4_0_9_6
														_UpperCAmelCase					=      2_4
														_UpperCAmelCase					=      1_6

							# load original model from timm
							_UpperCAmelCase					=      timm.create_model(__SCREAMING_SNAKE_CASE      ,  pretrained=__SCREAMING_SNAKE_CASE	)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							_UpperCAmelCase					=      timm_model.state_dict()
							_UpperCAmelCase					=      create_rename_keys(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE	)
							for src, dest in rename_keys:
														rename_key(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE	)
							read_in_q_k_v(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE	)

							# load HuggingFace model
							_UpperCAmelCase					=      DeiTForImageClassificationWithTeacher(__SCREAMING_SNAKE_CASE	).eval()
							model.load_state_dict(__SCREAMING_SNAKE_CASE	)

							# Check outputs on an image, prepared by DeiTImageProcessor
							_UpperCAmelCase					=      int(
							    (2_5_6 / 2_2_4) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							_UpperCAmelCase					=      DeiTImageProcessor(size=__SCREAMING_SNAKE_CASE      ,  crop_size=config.image_size	)
							_UpperCAmelCase					=      image_processor(images=prepare_img()      ,  return_tensors='pt'	)
							_UpperCAmelCase					=      encoding["""pixel_values"""]
							_UpperCAmelCase					=      model(__SCREAMING_SNAKE_CASE	)

							_UpperCAmelCase					=      timm_model(__SCREAMING_SNAKE_CASE	)
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(__SCREAMING_SNAKE_CASE      ,  outputs.logits      ,  atol=1e-3	)

							Path(__SCREAMING_SNAKE_CASE	).mkdir(exist_ok=__SCREAMING_SNAKE_CASE	)
							print(F'''Saving model {deit_name} 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__":
		lowerCAmelCase__  :List[str]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--deit_name''',
		    default='''vit_deit_base_distilled_patch16_224''',
		    type=str,
		    help='''Name of the DeiT timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		lowerCAmelCase__  :int             =							parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)




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__":
		lowerCAmelCase__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :str             =							[], []

		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])
		lowerCAmelCase__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
