

'''simple docstring'''



import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def       snake_case   (	snake_case      : Union[str, Any]			) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							384
    lowerCAmelCase 						=							7
    if "tiny" in model_name:
        lowerCAmelCase 						=							96
        lowerCAmelCase 						=							(2, 2, 6, 2)
        lowerCAmelCase 						=							(3, 6, 12, 24)
    elif "small" in model_name:
        lowerCAmelCase 						=							96
        lowerCAmelCase 						=							(2, 2, 18, 2)
        lowerCAmelCase 						=							(3, 6, 12, 24)
    elif "base" in model_name:
        lowerCAmelCase 						=							128
        lowerCAmelCase 						=							(2, 2, 18, 2)
        lowerCAmelCase 						=							(4, 8, 16, 32)
        lowerCAmelCase 						=							12
        lowerCAmelCase 						=							512
    elif "large" in model_name:
        lowerCAmelCase 						=							192
        lowerCAmelCase 						=							(2, 2, 18, 2)
        lowerCAmelCase 						=							(6, 12, 24, 48)
        lowerCAmelCase 						=							12
        lowerCAmelCase 						=							768

    # set label information
    lowerCAmelCase 						=							150
    lowerCAmelCase 						=							'''huggingface/label-files'''
    lowerCAmelCase 						=							'''ade20k-id2label.json'''
    lowerCAmelCase 						=							json.load(open(hf_hub_download(snake_case					,		snake_case					,		repo_type='dataset'			)					,		'r'			)			)
    lowerCAmelCase 						=							{int(snake_case			): v for k, v in idalabel.items()}
    lowerCAmelCase 						=							{v: k for k, v in idalabel.items()}

    lowerCAmelCase 						=							SwinConfig(
        embed_dim=snake_case					,		depths=snake_case					,		num_heads=snake_case					,		window_size=snake_case					,		out_features=['stage1', 'stage2', 'stage3', 'stage4']					,		)
    lowerCAmelCase 						=							UperNetConfig(
        backbone_config=snake_case					,		auxiliary_in_channels=snake_case					,		num_labels=snake_case					,		idalabel=snake_case					,		labelaid=snake_case					,		)

    return config


def       snake_case   (	snake_case      : Optional[Any]			) ->  Tuple:


    """simple docstring"""


    lowerCAmelCase 						=							[]

    # fmt: off
    # stem
    rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')			)
    rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')			)
    rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')			)
    rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.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.stages.{i}.blocks.{j}.norm1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight')			)
            rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias')			)

        if i < 3:
            rename_keys.append((F'backbone.stages.{i}.downsample.reduction.weight', F'backbone.encoder.layers.{i}.downsample.reduction.weight')			)
            rename_keys.append((F'backbone.stages.{i}.downsample.norm.weight', F'backbone.encoder.layers.{i}.downsample.norm.weight')			)
            rename_keys.append((F'backbone.stages.{i}.downsample.norm.bias', F'backbone.encoder.layers.{i}.downsample.norm.bias')			)
        rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')			)
        rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')			)

    # decode head
    rename_keys.extend(
        [
            ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
            ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
            ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
            ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
        ]			)
    # fmt: on

    return rename_keys


def       snake_case   (	snake_case      : List[str]					,		snake_case      : Union[str, Any]					,		snake_case      : Dict			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							dct.pop(snake_case			)
    lowerCAmelCase 						=							val


def       snake_case   (	snake_case      : int					,		snake_case      : Dict			) ->  int:


    """simple docstring"""


    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.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'			)
            lowerCAmelCase 						=							state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.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   (	snake_case      : Optional[int]			) ->  Optional[int]:


    """simple docstring"""


    lowerCAmelCase 						=							x.shape
    lowerCAmelCase 						=							x.reshape(snake_case					,		4					,		in_channel // 4			)
    lowerCAmelCase 						=							x[:, [0, 2, 1, 3], :].transpose(1					,		2			).reshape(snake_case					,		snake_case			)
    return x


def       snake_case   (	snake_case      : List[Any]			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							x.shape
    lowerCAmelCase 						=							x.reshape(snake_case					,		in_channel // 4					,		4			)
    lowerCAmelCase 						=							x[:, :, [0, 2, 1, 3]].transpose(1					,		2			).reshape(snake_case					,		snake_case			)

    return x


def       snake_case   (	snake_case      : Any			) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							x.shape[0]
    lowerCAmelCase 						=							x.reshape(4					,		in_channel // 4			)
    lowerCAmelCase 						=							x[[0, 2, 1, 3], :].transpose(0					,		1			).reshape(snake_case			)
    return x


def       snake_case   (	snake_case      : Union[str, Any]			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							x.shape[0]
    lowerCAmelCase 						=							x.reshape(in_channel // 4					,		4			)
    lowerCAmelCase 						=							x[:, [0, 2, 1, 3]].transpose(0					,		1			).reshape(snake_case			)
    return x


def       snake_case   (	snake_case      : Union[str, Any]					,		snake_case      : List[Any]					,		snake_case      : Optional[int]			) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							{
        '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
        '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
        '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
        '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
    }
    lowerCAmelCase 						=							model_name_to_url[model_name]
    lowerCAmelCase 						=							torch.hub.load_state_dict_from_url(snake_case					,		map_location='cpu'					,		file_name=snake_case			)[
        '''state_dict'''
    ]

    for name, param in state_dict.items():
        print(snake_case					,		param.shape			)

    lowerCAmelCase 						=							get_upernet_config(snake_case			)
    lowerCAmelCase 						=							UperNetForSemanticSegmentation(snake_case			)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        lowerCAmelCase 						=							state_dict.pop(snake_case			)
        if "bn" in key:
            lowerCAmelCase 						=							key.replace('bn'					,		'batch_norm'			)
        lowerCAmelCase 						=							val

    # rename keys
    lowerCAmelCase 						=							create_rename_keys(snake_case			)
    for src, dest in rename_keys:
        rename_key(snake_case					,		snake_case					,		snake_case			)
    read_in_q_k_v(snake_case					,		config.backbone_config			)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                lowerCAmelCase 						=							reverse_correct_unfold_reduction_order(snake_case			)
            if "norm" in key:
                lowerCAmelCase 						=							reverse_correct_unfold_norm_order(snake_case			)

    model.load_state_dict(snake_case			)

    # verify on image
    lowerCAmelCase 						=							'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    lowerCAmelCase 						=							Image.open(requests.get(snake_case					,		stream=snake_case			).raw			).convert('RGB'			)

    lowerCAmelCase 						=							SegformerImageProcessor()
    lowerCAmelCase 						=							processor(snake_case					,		return_tensors='pt'			).pixel_values

    with torch.no_grad():
        lowerCAmelCase 						=							model(snake_case			)
        lowerCAmelCase 						=							outputs.logits

    print(logits.shape			)
    print('First values of logits:'					,		logits[0, 0, :3, :3]			)
    # assert values
    if model_name == "upernet-swin-tiny":
        lowerCAmelCase 						=							torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]			)
    elif model_name == "upernet-swin-small":
        lowerCAmelCase 						=							torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]			)
    elif model_name == "upernet-swin-base":
        lowerCAmelCase 						=							torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]			)
    elif model_name == "upernet-swin-large":
        lowerCAmelCase 						=							torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]			)
    print('Logits:'					,		outputs.logits[0, 0, :3, :3]			)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]					,		snake_case					,		atol=1e-4			)
    print('Looks ok!'			)

    if pytorch_dump_folder_path is not None:
        print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
        model.save_pretrained(snake_case			)
        print(F'Saving processor to {pytorch_dump_folder_path}'			)
        processor.save_pretrained(snake_case			)

    if push_to_hub:
        print(F'Pushing model and processor for {model_name} to hub'			)
        model.push_to_hub(F'openmmlab/{model_name}'			)
        processor.push_to_hub(F'openmmlab/{model_name}'			)


if __name__ == "__main__":
     _UpperCamelCase						:  Any							       =				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="upernet-swin-tiny",
         type=str,
         choices=[F"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
         help="Name of the Swin + UperNet 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."
     )
     parser.add_argument(
         "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
     )

     _UpperCamelCase						:  Union[str, Any]							       =				parser.parse_args()
     convert_upernet_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__":
							_UpperCamelCase					      =   pd.read_csv('sample_data.csv', header=None)
							_UpperCamelCase					      =   df.shape[:1][0]
							# If you're using some other dataset input the target column
							_UpperCamelCase					      =   df.iloc[:, 1:2]
							_UpperCamelCase					      =   actual_data.values.reshape(len_data, 1)
							_UpperCamelCase					      =   MinMaxScaler().fit_transform(actual_data)
							_UpperCamelCase					      =   10
							_UpperCamelCase					      =   5
							_UpperCamelCase					      =   20
							_UpperCamelCase					      =   len_data - periods * look_back
							_UpperCamelCase					      =   actual_data[:division]
							_UpperCamelCase					      =   actual_data[division - look_back :]
							_UpperCamelCase      ,     _UpperCamelCase					      =   [], []
							_UpperCamelCase      ,     _UpperCamelCase					      =   [], []

							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])
							_UpperCamelCase					      =   np.array(train_x)
							_UpperCamelCase					      =   np.array(test_x)
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in train_y])
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in test_y])

							_UpperCamelCase					      =   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')
							_UpperCamelCase					      =   model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							_UpperCamelCase					      =   model.predict(x_test)




