



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)










import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case						:List[Any]      		=     logging.getLogger(__name__)

class     _A       :



       def __init__(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				False



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           if not self.initialized:
               __a						       =				RagRetriever(
                   __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				True



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           self.retriever.index.init_index()





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return doc_ids, retrieved_doc_embeds


class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None):


           '''simple docstring'''

           if index is not None and index.is_initialized() and len(__SCREAMING_SNAKE_CASE) > 0:
               raise ValueError(
                   '''When using Ray for distributed fine-tuning, '''
                   '''you\'ll need to provide the paths instead, '''
                   '''as the dataset and the index are loaded '''
                   '''separately. More info in examples/rag/use_own_knowledge_dataset.py ''')
           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				retrieval_workers
           if len(self.retrieval_workers) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
                       for worker in self.retrieval_workers
                   ])



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           logger.info('''initializing retrieval''')

           if len(self.retrieval_workers) > 0:
               ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
           else:
               # Non-distributed training. Load index into this same process.
               self.index.init_index()



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           if len(self.retrieval_workers) > 0:
               # Select a random retrieval actor.
               __a						       =				self.retrieval_workers[random.randint(0     ,		len(self.retrieval_workers) - 1)]
               __a       ,   __a						       =				ray.get(random_worker.retrieve.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))
           else:
               __a       ,   __a						       =				self._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase			(		cls							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return super(__SCREAMING_SNAKE_CASE     ,		cls).get_tokenizers(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)





       @classmethod
       def 		_lowerCamelCase			(		cls							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				kwargs.pop('''config'''     ,		__SCREAMING_SNAKE_CASE) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     ,		config=__SCREAMING_SNAKE_CASE)
           __a						       =				rag_tokenizer.question_encoder
           __a						       =				rag_tokenizer.generator
           if indexed_dataset is not None:
               __a						       =				'''custom'''
               __a						       =				CustomHFIndex(config.retrieval_vector_size     ,		__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				cls._build_index(__SCREAMING_SNAKE_CASE)
           return cls(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		retrieval_workers=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		)





