'''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  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  3_8_4
    __UpperCamelCase	:	Optional[int]		    =  7
    if "tiny" in model_name:
        __UpperCamelCase	:	int		    =  9_6
        __UpperCamelCase	:	Any		    =  (2, 2, 6, 2)
        __UpperCamelCase	:	Any		    =  (3, 6, 1_2, 2_4)
    elif "small" in model_name:
        __UpperCamelCase	:	Optional[int]		    =  9_6
        __UpperCamelCase	:	List[str]		    =  (2, 2, 1_8, 2)
        __UpperCamelCase	:	Optional[int]		    =  (3, 6, 1_2, 2_4)
    elif "base" in model_name:
        __UpperCamelCase	:	Optional[Any]		    =  1_2_8
        __UpperCamelCase	:	List[Any]		    =  (2, 2, 1_8, 2)
        __UpperCamelCase	:	int		    =  (4, 8, 1_6, 3_2)
        __UpperCamelCase	:	Union[str, Any]		    =  1_2
        __UpperCamelCase	:	Union[str, Any]		    =  5_1_2
    elif "large" in model_name:
        __UpperCamelCase	:	str		    =  1_9_2
        __UpperCamelCase	:	Optional[Any]		    =  (2, 2, 1_8, 2)
        __UpperCamelCase	:	str		    =  (6, 1_2, 2_4, 4_8)
        __UpperCamelCase	:	List[Any]		    =  1_2
        __UpperCamelCase	:	Dict		    =  7_6_8

    # set label information
    __UpperCamelCase	:	Any		    =  1_5_0
    __UpperCamelCase	:	List[str]		    =  'huggingface/label-files'
    __UpperCamelCase	:	Tuple		    =  'ade20k-id2label.json'
    __UpperCamelCase	:	int		    =  json.load(open(hf_hub_download(_a				,     _a				,     repo_type='dataset'			)				,     'r'			)			)
    __UpperCamelCase	:	Any		    =  {int(_a			): v for k, v in idalabel.items()}
    __UpperCamelCase	:	int		    =  {v: k for k, v in idalabel.items()}

    __UpperCamelCase	:	Dict		    =  SwinConfig(
        embed_dim=_a				,     depths=_a				,     num_heads=_a				,     window_size=_a				,     out_features=['stage1', 'stage2', 'stage3', 'stage4']				,     )
    __UpperCamelCase	:	int		    =  UperNetConfig(
        backbone_config=_a				,     auxiliary_in_channels=_a				,     num_labels=_a				,     idalabel=_a				,     labelaid=_a				,     )

    return config







def  _UpperCamelCase (     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  []

    # 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  _UpperCamelCase (     _a	:	int				,     _a	:	List[Any]				,     _a	:	Any			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  dct.pop(_a			)
    __UpperCamelCase	:	List[str]		    =  val







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Dict		    =  [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
    for i in range(len(backbone_config.depths			)			):
        __UpperCamelCase	:	Dict		    =  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)
            __UpperCamelCase	:	List[str]		    =  state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""			)
            __UpperCamelCase	:	Union[str, Any]		    =  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
            __UpperCamelCase	:	List[Any]		    =  in_proj_weight[:dim, :]
            __UpperCamelCase	:	List[Any]		    =  in_proj_bias[: dim]
            __UpperCamelCase	:	Union[str, Any]		    =  in_proj_weight[
                dim : dim * 2, :
            ]
            __UpperCamelCase	:	Union[str, Any]		    =  in_proj_bias[
                dim : dim * 2
            ]
            __UpperCamelCase	:	List[Any]		    =  in_proj_weight[
                -dim :, :
            ]
            __UpperCamelCase	:	Any		    =  in_proj_bias[-dim :]
            # fmt: on







def  _UpperCamelCase (     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  x.shape
    __UpperCamelCase	:	Optional[Any]		    =  x.reshape(_a				,     4				,     in_channel // 4			)
    __UpperCamelCase	:	Optional[int]		    =  x[:, [0, 2, 1, 3], :].transpose(1				,     2			).reshape(_a				,     _a			)
    return x







def  _UpperCamelCase (     _a	:	Optional[int]			):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	Optional[Any]		    =  x.shape
    __UpperCamelCase	:	Union[str, Any]		    =  x.reshape(_a				,     in_channel // 4				,     4			)
    __UpperCamelCase	:	List[str]		    =  x[:, :, [0, 2, 1, 3]].transpose(1				,     2			).reshape(_a				,     _a			)

    return x







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  x.shape[0]
    __UpperCamelCase	:	Union[str, Any]		    =  x.reshape(4				,     in_channel // 4			)
    __UpperCamelCase	:	Union[str, Any]		    =  x[[0, 2, 1, 3], :].transpose(0				,     1			).reshape(_a			)
    return x







def  _UpperCamelCase (     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Dict		    =  x.shape[0]
    __UpperCamelCase	:	Any		    =  x.reshape(in_channel // 4				,     4			)
    __UpperCamelCase	:	Union[str, Any]		    =  x[:, [0, 2, 1, 3]].transpose(0				,     1			).reshape(_a			)
    return x







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	Dict				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  {
        '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',
    }
    __UpperCamelCase	:	Optional[Any]		    =  model_name_to_url[model_name]
    __UpperCamelCase	:	Optional[int]		    =  torch.hub.load_state_dict_from_url(_a				,     map_location='cpu'				,     file_name=_a			)[
        'state_dict'
    ]

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

    __UpperCamelCase	:	Tuple		    =  get_upernet_config(_a			)
    __UpperCamelCase	:	Dict		    =  UperNetForSemanticSegmentation(_a			)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        __UpperCamelCase	:	str		    =  state_dict.pop(_a			)
        if "bn" in key:
            __UpperCamelCase	:	str		    =  key.replace('bn'				,     'batch_norm'			)
        __UpperCamelCase	:	Any		    =  val

    # rename keys
    __UpperCamelCase	:	str		    =  create_rename_keys(_a			)
    for src, dest in rename_keys:
        rename_key(_a				,     _a				,     _a			)
    read_in_q_k_v(_a				,     config.backbone_config			)

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

    model.load_state_dict(_a			)

    # verify on image
    __UpperCamelCase	:	Any		    =  'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
    __UpperCamelCase	:	List[str]		    =  Image.open(requests.get(_a				,     stream=_a			).raw			).convert('RGB'			)

    __UpperCamelCase	:	List[str]		    =  SegformerImageProcessor()
    __UpperCamelCase	:	Dict		    =  processor(_a				,     return_tensors='pt'			).pixel_values

    with torch.no_grad():
        __UpperCamelCase	:	Dict		    =  model(_a			)
        __UpperCamelCase	:	Optional[Any]		    =  outputs.logits

    print(logits.shape			)
    print('First values of logits:'				,     logits[0, 0, :3, :3]			)
    # assert values
    if model_name == "upernet-swin-tiny":
        __UpperCamelCase	:	Tuple		    =  torch.tensor(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]			)
    elif model_name == "upernet-swin-small":
        __UpperCamelCase	:	Tuple		    =  torch.tensor(
            [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]			)
    elif model_name == "upernet-swin-base":
        __UpperCamelCase	:	Any		    =  torch.tensor(
            [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]			)
    elif model_name == "upernet-swin-large":
        __UpperCamelCase	:	List[Any]		    =  torch.tensor(
            [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]			)
    print('Logits:'				,     outputs.logits[0, 0, :3, :3]			)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]				,     _a				,     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(_a			)
        print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
        processor.save_pretrained(_a			)

    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__":
  a=  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.'''
  )

  a=  parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''
import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizer
     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase	:	List[Any]		    =  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             '<unk>',
         ]
         __UpperCamelCase	:	List[str]		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	Union[str, Any]		    =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

         __UpperCamelCase	:	Optional[int]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	Tuple		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file     ,			'w'    ) as fp:
             fp.write(json.dumps(_lowerCamelCase    )    )
         with open(self.merges_file     ,			'w'    ) as fp:
             fp.write('\n'.join(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  OpenAIGPTTokenizer(self.vocab_file     ,			self.merges_file    )

         __UpperCamelCase	:	List[Any]		    =  'lower'
         __UpperCamelCase	:	Union[str, Any]		    =  ['low', 'er</w>']
         __UpperCamelCase	:	Tuple		    =  tokenizer.tokenize(_lowerCamelCase    )
         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  tokens + ['<unk>']
         __UpperCamelCase	:	Optional[Any]		    =  [1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=1_5    ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Dict		    =  self.rust_tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )

                 # Simple input
                 __UpperCamelCase	:	int		    =  'This is a simple input'
                 __UpperCamelCase	:	Any		    =  ['This is a simple input 1', 'This is a simple input 2']
                 __UpperCamelCase	:	Union[str, Any]		    =  ('This is a simple input', 'This is a pair')
                 __UpperCamelCase	:	Any		    =  [
                     ('This is a simple input 1', 'This is a simple input 2'),
                     ('This is a simple pair 1', 'This is a simple pair 2'),
                 ]

                 # Simple input tests
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)







     def lowerCAmelCase     (		self    ):
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass



