


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a_  :				List[str]						= logging.get_logger(__name__)





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  SwinConfig(
     embed_dim=192					,				depths=(2, 2, 18, 2)					,				num_heads=(6, 12, 24, 48)					,				window_size=12					,				out_features=["stage2", "stage3", "stage4"]					,				)

 lowerCamelCase_      =  DetaConfig(
     backbone_config=__snake_case					,				num_queries=900					,				encoder_ffn_dim=2048					,				decoder_ffn_dim=2048					,				num_feature_levels=5					,				assign_first_stage=__snake_case					,				with_box_refine=__snake_case					,				two_stage=__snake_case					,				)

 # set labels
 lowerCamelCase_      =  'huggingface/label-files'
 if "o365" in model_name:
  lowerCamelCase_      =  366
  lowerCamelCase_      =  'object365-id2label.json'
 else:
  lowerCamelCase_      =  91
  lowerCamelCase_      =  'coco-detection-id2label.json'

 lowerCamelCase_      =  num_labels
 lowerCamelCase_      =  json.load(open(cached_download(hf_hub_url(__snake_case					,				__snake_case					,				repo_type="dataset"		)		)					,				"r"		)		)
 lowerCamelCase_      =  {int(__snake_case		): v for k, v in idalabel.items()}
 lowerCamelCase_      =  idalabel
 lowerCamelCase_      =  {v: k for k, v in idalabel.items()}

 return config





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 lowerCamelCase_      =  []

 # stem
 # fmt: off
 rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")		)
 rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")		)
 rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")		)
 rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")		)
 # stages
 for i in range(len(config.backbone_config.depths		)		):
  for j in range(config.backbone_config.depths[i]		):
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')		)

  if i < 3:
   rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')		)
   rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')		)

 rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")		)
 rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")		)
 rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")		)
 rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")		)
 rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")		)
 rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")		)

 # transformer encoder
 for i in range(config.encoder_layers		):
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')		)
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')		)

 # transformer decoder
 for i in range(config.decoder_layers		):
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')		)
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')		)

 # fmt: on

 return rename_keys





def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  dct.pop(__snake_case		)
 lowerCamelCase_      =  val





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_      =  [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
 for i in range(len(backbone_config.depths		)		):
  lowerCamelCase_      =  num_features[i]
  for j in range(backbone_config.depths[i]		):
   # fmt: off
   # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
   lowerCamelCase_      =  state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
   lowerCamelCase_      =  state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''		)
   # next, add query, keys and values (in that order) to the state dict
   lowerCamelCase_      =  in_proj_weight[:dim, :]
   lowerCamelCase_      =  in_proj_bias[: dim]
   lowerCamelCase_      =  in_proj_weight[
       dim : dim * 2, :
   ]
   lowerCamelCase_      =  in_proj_bias[
       dim : dim * 2
   ]
   lowerCamelCase_      =  in_proj_weight[
       -dim :, :
   ]
   lowerCamelCase_      =  in_proj_bias[-dim :]
   # fmt: on





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						List[str]		):
 # transformer decoder self-attention layers
 lowerCamelCase_      =  config.d_model
 for i in range(config.decoder_layers		):
  # read in weights + bias of input projection layer of self-attention
  lowerCamelCase_      =  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
  lowerCamelCase_      =  state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''		)
  # next, add query, keys and values (in that order) to the state dict
  lowerCamelCase_      =  in_proj_weight[:hidden_size, :]
  lowerCamelCase_      =  in_proj_bias[:hidden_size]
  lowerCamelCase_      =  in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  lowerCamelCase_      =  in_proj_bias[hidden_size : hidden_size * 2]
  lowerCamelCase_      =  in_proj_weight[-hidden_size:, :]
  lowerCamelCase_      =  in_proj_bias[-hidden_size:]





def __snake_case					(      ):
 lowerCamelCase_      =  'http://images.cocodataset.org/val2017/000000039769.jpg'
 lowerCamelCase_      =  Image.open(requests.get(__snake_case					,				stream=__snake_case		).raw		)

 return im





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  get_deta_config(__snake_case		)

 # load original state dict
 if model_name == "deta-swin-large":
  lowerCamelCase_      =  hf_hub_download(repo_id="nielsr/deta-checkpoints"					,				filename="adet_swin_ft.pth"		)
 elif model_name == "deta-swin-large-o365":
  lowerCamelCase_      =  hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"					,				filename="deta_swin_pt_o365.pth"		)
 else:
  raise ValueError(F'''Model name {model_name} not supported'''		)

 lowerCamelCase_      =  torch.load(__snake_case					,				map_location="cpu"		)['model']

 # original state dict
 for name, param in state_dict.items():
  print(__snake_case					,				param.shape		)

 # rename keys
 lowerCamelCase_      =  create_rename_keys(__snake_case		)
 for src, dest in rename_keys:
  rename_key(__snake_case					,				__snake_case					,				__snake_case		)
 read_in_swin_q_k_v(__snake_case					,				config.backbone_config		)
 read_in_decoder_q_k_v(__snake_case					,				__snake_case		)

 # fix some prefixes
 for key in state_dict.copy().keys():
  if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
   lowerCamelCase_      =  state_dict.pop(__snake_case		)
   lowerCamelCase_      =  val
  if "input_proj" in key:
   lowerCamelCase_      =  state_dict.pop(__snake_case		)
   lowerCamelCase_      =  val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   lowerCamelCase_      =  state_dict.pop(__snake_case		)
   lowerCamelCase_      =  val

    # finally, create HuggingFace model and load state dict
 lowerCamelCase_      =  DetaForObjectDetection(__snake_case		)
 model.load_state_dict(__snake_case		)
 model.eval()

 lowerCamelCase_      =  'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(__snake_case		)

 # load image processor
 lowerCamelCase_      =  DetaImageProcessor(format="coco_detection"		)

 # verify our conversion on image
 lowerCamelCase_      =  prepare_img()
 lowerCamelCase_      =  processor(images=__snake_case					,				return_tensors="pt"		)
 lowerCamelCase_      =  encoding['pixel_values']
 lowerCamelCase_      =  model(pixel_values.to(__snake_case		)		)

 # verify logits
 print("Logits:"					,				outputs.logits[0, :3, :3]		)
 print("Boxes:"					,				outputs.pred_boxes[0, :3, :3]		)
 if model_name == "deta-swin-large":
  lowerCamelCase_      =  torch.tensor(
      [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]		)
  lowerCamelCase_      =  torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]		)
 elif model_name == "deta-swin-large-o365":
  lowerCamelCase_      =  torch.tensor(
      [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]		)
  lowerCamelCase_      =  torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]		)
 assert torch.allclose(outputs.logits[0, :3, :3]					,				expected_logits.to(__snake_case		)					,				atol=1E-4		)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,				expected_boxes.to(__snake_case		)					,				atol=1E-4		)
 print("Everything ok!"		)

 if pytorch_dump_folder_path:
  # Save model and processor
  logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''		)
  Path(__snake_case		).mkdir(exist_ok=__snake_case		)
  model.save_pretrained(__snake_case		)
  processor.save_pretrained(__snake_case		)

 # Push to hub
 if push_to_hub:
  print("Pushing model and processor to hub..."		)
  model.push_to_hub(F'''jozhang97/{model_name}'''		)
  processor.push_to_hub(F'''jozhang97/{model_name}'''		)


if __name__ == "__main__":
     a_  :				Any						= argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""",
         type=str,
         default="""deta-swin-large""",
         choices=["""deta-swin-large""", """deta-swin-large-o365"""],
         help="""Name of the model you\'d like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         help="""Path to the folder to output PyTorch model.""",
     )
     parser.add_argument(
         """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
     )
     a_  :				Any						= parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''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__":
  lowercase__    :      List[Any]						=    pd.read_csv('''sample_data.csv''', header=None)
  lowercase__    :      Union[str, Any]						=    df.shape[:1][0]
  # If you're using some other dataset input the target column
  lowercase__    :      Any						=    df.iloc[:, 1:2]
  lowercase__    :      int						=    actual_data.values.reshape(len_data, 1)
  lowercase__    :      int						=    MinMaxScaler().fit_transform(actual_data)
  lowercase__    :      Dict						=    10
  lowercase__    :      List[str]						=    5
  lowercase__    :      Dict						=    20
  lowercase__    :      Dict						=    len_data - periods * look_back
  lowercase__    :      Any						=    actual_data[:division]
  lowercase__    :      Optional[int]						=    actual_data[division - look_back :]
  lowercase__	,							lowercase__    :      Optional[Any]						=    [], []
  lowercase__	,							lowercase__    :      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])
  lowercase__    :      List[Any]						=    np.array(train_x)
  lowercase__    :      List[Any]						=    np.array(test_x)
  lowercase__    :      str						=    np.array([list(i.ravel()) for i in train_y])
  lowercase__    :      Any						=    np.array([list(i.ravel()) for i in test_y])

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)