



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

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

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

    return config





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				[]

    # 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	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				dct.pop(_UpperCAmelCase				)
    __a						       =				val





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
    for i in range(len(backbone_config.depths				)				):
        __a						       =				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)
            __a						       =				state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'				)
            __a						       =				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
            __a						       =				in_proj_weight[:dim, :]
            __a						       =				in_proj_bias[: dim]
            __a						       =				in_proj_weight[
                dim : dim * 2, :
            ]
            __a						       =				in_proj_bias[
                dim : dim * 2
            ]
            __a						       =				in_proj_weight[
                -dim :, :
            ]
            __a						       =				in_proj_bias[-dim :]
            # fmt: on





def      __snake_case	(			_UpperCAmelCase				):
    __a       ,   __a						       =				x.shape
    __a						       =				x.reshape(_UpperCAmelCase   , 4   , in_channel // 4				)
    __a						       =				x[:, [0, 2, 1, 3], :].transpose(1   , 2				).reshape(_UpperCAmelCase   , _UpperCAmelCase				)
    return x





def      __snake_case	(			_UpperCAmelCase				):
    __a       ,   __a						       =				x.shape
    __a						       =				x.reshape(_UpperCAmelCase   , in_channel // 4   , 4				)
    __a						       =				x[:, :, [0, 2, 1, 3]].transpose(1   , 2				).reshape(_UpperCAmelCase   , _UpperCAmelCase				)

    return x





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				x.shape[0]
    __a						       =				x.reshape(4   , in_channel // 4				)
    __a						       =				x[[0, 2, 1, 3], :].transpose(0   , 1				).reshape(_UpperCAmelCase				)
    return x





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				x.shape[0]
    __a						       =				x.reshape(in_channel // 4   , 4				)
    __a						       =				x[:, [0, 2, 1, 3]].transpose(0   , 1				).reshape(_UpperCAmelCase				)
    return x





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				{
        '''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''',
    }
    __a						       =				model_name_to_url[model_name]
    __a						       =				torch.hub.load_state_dict_from_url(_UpperCAmelCase   , map_location='''cpu'''   , file_name=_UpperCAmelCase				)[
        '''state_dict'''
    ]

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

    __a						       =				get_upernet_config(_UpperCAmelCase				)
    __a						       =				UperNetForSemanticSegmentation(_UpperCAmelCase				)
    model.eval()

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

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

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

    model.load_state_dict(_UpperCAmelCase				)

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

    __a						       =				SegformerImageProcessor()
    __a						       =				processor(_UpperCAmelCase   , return_tensors='''pt'''				).pixel_values

    with torch.no_grad():
        __a						       =				model(_UpperCAmelCase				)
        __a						       =				outputs.logits

    print(logits.shape				)
    print('''First values of logits:'''   , logits[0, 0, :3, :3]				)
    # assert values
    if model_name == "upernet-swin-tiny":
        __a						       =				torch.tensor(
            [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]				)
    elif model_name == "upernet-swin-small":
        __a						       =				torch.tensor(
            [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]				)
    elif model_name == "upernet-swin-base":
        __a						       =				torch.tensor(
            [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]				)
    elif model_name == "upernet-swin-large":
        __a						       =				torch.tensor(
            [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]				)
    print('''Logits:'''   , outputs.logits[0, 0, :3, :3]				)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]   , _UpperCAmelCase   , 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(_UpperCAmelCase				)
        print(f'Saving processor to {pytorch_dump_folder_path}'				)
        processor.save_pretrained(_UpperCAmelCase				)

    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__":
 __snake_case						:Union[str, 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.'''
 )

 __snake_case						:int      		=     parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                        "tokenizer."       )

             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

             if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
                    copyfile(self.vocab_file						,					A       )

             return (out_vocab_file,)

