

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(    SCREAMING_SNAKE_CASE__			:	Any      )							->      int:





    '''simple docstring'''






    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(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			repo_type='dataset'      )    ,			'r'      )      )
    A__          =   {int(SCREAMING_SNAKE_CASE__      ): v for k, v in idalabel.items()}
    A__          =   {v: k for k, v in idalabel.items()}

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

    return config
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Dict:





    '''simple docstring'''






    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(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   dct.pop(SCREAMING_SNAKE_CASE__      )
    A__          =   val
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      Any:





    '''simple docstring'''






    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(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Optional[Any]:





    '''simple docstring'''






    A__      ,						A__          =   x.shape
    A__          =   x.reshape(SCREAMING_SNAKE_CASE__    ,			4    ,			in_channel // 4      )
    A__          =   x[:, [0, 2, 1, 3], :].transpose(1    ,			2      ).reshape(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      List[str]:





    '''simple docstring'''






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

    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(4    ,			in_channel // 4      )
    A__          =   x[[0, 2, 1, 3], :].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any      )							->      List[Any]:





    '''simple docstring'''






    A__          =   x.shape[0]
    A__          =   x.reshape(in_channel // 4    ,			4      )
    A__          =   x[:, [0, 2, 1, 3]].transpose(0    ,			1      ).reshape(SCREAMING_SNAKE_CASE__      )
    return x
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Union[str, Any]:





    '''simple docstring'''






    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(SCREAMING_SNAKE_CASE__    ,			map_location='cpu'    ,			file_name=SCREAMING_SNAKE_CASE__      )[
        'state_dict'
    ]

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

    A__          =   get_upernet_config(SCREAMING_SNAKE_CASE__      )
    A__          =   UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE__      )
    model.eval()

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

    # rename keys
    A__          =   create_rename_keys(SCREAMING_SNAKE_CASE__      )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    read_in_q_k_v(SCREAMING_SNAKE_CASE__    ,			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(SCREAMING_SNAKE_CASE__      )
            if "norm" in key:
                A__          =   reverse_correct_unfold_norm_order(SCREAMING_SNAKE_CASE__      )

    model.load_state_dict(SCREAMING_SNAKE_CASE__      )

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

    A__          =   SegformerImageProcessor()
    A__          =   processor(SCREAMING_SNAKE_CASE__    ,			return_tensors='pt'      ).pixel_values

    with torch.no_grad():
        A__          =   model(SCREAMING_SNAKE_CASE__      )
        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(
            [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]      )
    elif model_name == "upernet-swin-small":
        A__          =   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":
        A__          =   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":
        A__          =   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]    ,			SCREAMING_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(SCREAMING_SNAKE_CASE__      )
        print(f'Saving processor to {pytorch_dump_folder_path}'      )
        processor.save_pretrained(SCREAMING_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__":
     lowercase_       		=  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."
     )

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









import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_a      	=  logging.getLogger()

_a      	=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							os.makedirs(UpperCAmelCase       ,    exist_ok=UpperCAmelCase    )
							_UpperCAmelCase									=			{'source': 'What is love ?', 'target': 'life'}
							_UpperCAmelCase									=			{'train': 12, 'val': 2, 'test': 2}
							for split in ["train", "test", "val"]:
													for field in ["source", "target"]:
																			_UpperCAmelCase									=			'\n'.join([contents[field]] * n_lines[split]    )
																			with open(os.path.join(UpperCAmelCase       ,    F"""{split}.{field}"""    )       ,    'w'    ) as f:
																									f.write(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = "pytorch"    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'output'    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'data'    )
							self._create_dummy_data(data_dir=UpperCAmelCase    )

							_UpperCAmelCase									=			F"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

							if gpus > 0:
													testargs.append(F"""--gpus={gpus}"""    )
													if is_apex_available():
																			testargs.append('--fp16'    )
							else:
													testargs.append('--gpus=0'    )
													testargs.append('--distributed_backend=ddp_cpu'    )
													testargs.append('--num_processes=2'    )

							_UpperCAmelCase									=			[sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
							execute_subprocess_async(UpperCAmelCase       ,    env=self.get_env()    )

							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'metrics.json'    )
							with open(UpperCAmelCase    ) as f:
													_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							return result

	@require_torch_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self._run_finetune(gpus=1    )
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,    0.2    )

	@require_torch_multi_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self._run_finetune(gpus=2    )
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,    0.2    )

	@require_torch_gpu
	@require_ray
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self._run_finetune(gpus=1       ,    distributed_retriever='ray'    )
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,    0.2    )

	@require_torch_multi_gpu
	@require_ray
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self._run_finetune(gpus=1       ,    distributed_retriever='ray'    )
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,    0.2    )
