
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					(    __lowerCamelCase     : List[str]    ):
       snake_case       :   Union[str, Any]							=      384
       snake_case       :   List[str]							=      7
       if "tiny" in model_name:
              snake_case       :   Optional[int]							=      96
              snake_case       :   int							=      (2, 2, 6, 2)
              snake_case       :   Tuple							=      (3, 6, 12, 24)
       elif "small" in model_name:
              snake_case       :   List[str]							=      96
              snake_case       :   Optional[int]							=      (2, 2, 18, 2)
              snake_case       :   List[Any]							=      (3, 6, 12, 24)
       elif "base" in model_name:
              snake_case       :   str							=      128
              snake_case       :   Dict							=      (2, 2, 18, 2)
              snake_case       :   int							=      (4, 8, 16, 32)
              snake_case       :   Dict							=      12
              snake_case       :   Tuple							=      512
       elif "large" in model_name:
              snake_case       :   List[Any]							=      192
              snake_case       :   Dict							=      (2, 2, 18, 2)
              snake_case       :   Tuple							=      (6, 12, 24, 48)
              snake_case       :   List[str]							=      12
              snake_case       :   str							=      768

       # set label information
       snake_case       :   int							=      150
       snake_case       :   List[Any]							=      "huggingface/label-files"
       snake_case       :   Optional[int]							=      "ade20k-id2label.json"
       snake_case       :   List[Any]							=      json.load(open(hf_hub_download(__lowerCamelCase				,					__lowerCamelCase				,					repo_type="dataset"    )				,					"r"    )    )
       snake_case       :   List[str]							=      {int(__lowerCamelCase    ): v for k, v in idalabel.items()}
       snake_case       :   Optional[Any]							=      {v: k for k, v in idalabel.items()}

       snake_case       :   Tuple							=      SwinConfig(
           embed_dim=__lowerCamelCase				,					depths=__lowerCamelCase				,					num_heads=__lowerCamelCase				,					window_size=__lowerCamelCase				,					out_features=["stage1", "stage2", "stage3", "stage4"]				,					)
       snake_case       :   Any							=      UperNetConfig(
           backbone_config=__lowerCamelCase				,					auxiliary_in_channels=__lowerCamelCase				,					num_labels=__lowerCamelCase				,					idalabel=__lowerCamelCase				,					labelaid=__lowerCamelCase				,					)

       return config


def 			UpperCamelCase					(    __lowerCamelCase     : Any    ):
       snake_case       :   Union[str, 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					(    __lowerCamelCase     : Tuple				,					__lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : List[str]    ):
       snake_case       :   int							=      dct.pop(__lowerCamelCase    )
       snake_case       :   List[str]							=      val


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


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


def 			UpperCamelCase					(    __lowerCamelCase     : Dict    ):
       snake_case				,					snake_case       :   Optional[int]							=      x.shape
       snake_case       :   Dict							=      x.reshape(__lowerCamelCase				,					in_channel // 4				,					4    )
       snake_case       :   Optional[int]							=      x[:, :, [0, 2, 1, 3]].transpose(1				,					2    ).reshape(__lowerCamelCase				,					__lowerCamelCase    )

       return x


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   Optional[int]							=      x.shape[0]
       snake_case       :   Tuple							=      x.reshape(4				,					in_channel // 4    )
       snake_case       :   Tuple							=      x[[0, 2, 1, 3], :].transpose(0				,					1    ).reshape(__lowerCamelCase    )
       return x


def 			UpperCamelCase					(    __lowerCamelCase     : Tuple    ):
       snake_case       :   Optional[int]							=      x.shape[0]
       snake_case       :   Optional[int]							=      x.reshape(in_channel // 4				,					4    )
       snake_case       :   Optional[Any]							=      x[:, [0, 2, 1, 3]].transpose(0				,					1    ).reshape(__lowerCamelCase    )
       return x


def 			UpperCamelCase					(    __lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : List[Any]    ):
       snake_case       :   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",
       }
       snake_case       :   Union[str, Any]							=      model_name_to_url[model_name]
       snake_case       :   int							=      torch.hub.load_state_dict_from_url(__lowerCamelCase				,					map_location="cpu"				,					file_name=__lowerCamelCase    )[
           "state_dict"
       ]

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

       snake_case       :   List[Any]							=      get_upernet_config(__lowerCamelCase    )
       snake_case       :   List[str]							=      UperNetForSemanticSegmentation(__lowerCamelCase    )
       model.eval()

       # replace "bn" => "batch_norm"
       for key in state_dict.copy().keys():
              snake_case       :   List[Any]							=      state_dict.pop(__lowerCamelCase    )
              if "bn" in key:
                     snake_case       :   Dict							=      key.replace("bn"				,					"batch_norm"    )
              snake_case       :   Optional[Any]							=      val

       # rename keys
       snake_case       :   Union[str, Any]							=      create_rename_keys(__lowerCamelCase    )
       for src, dest in rename_keys:
              rename_key(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
       read_in_q_k_v(__lowerCamelCase				,					config.backbone_config    )

       # fix downsample parameters
       for key, value in state_dict.items():
              if "downsample" in key:
                     if "reduction" in key:
                            snake_case       :   Union[str, Any]							=      reverse_correct_unfold_reduction_order(__lowerCamelCase    )
                     if "norm" in key:
                            snake_case       :   Union[str, Any]							=      reverse_correct_unfold_norm_order(__lowerCamelCase    )

       model.load_state_dict(__lowerCamelCase    )

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

       snake_case       :   Optional[int]							=      SegformerImageProcessor()
       snake_case       :   List[Any]							=      processor(__lowerCamelCase				,					return_tensors="pt"    ).pixel_values

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

       print(logits.shape    )
       print("First values of logits:"				,					logits[0, 0, :3, :3]    )
       # assert values
       if model_name == "upernet-swin-tiny":
              snake_case       :   int							=      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":
              snake_case       :   List[Any]							=      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":
              snake_case       :   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":
              snake_case       :   List[str]							=      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]				,					__lowerCamelCase				,					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(__lowerCamelCase    )
              print(f"""Saving processor to {pytorch_dump_folder_path}"""    )
              processor.save_pretrained(__lowerCamelCase    )

       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__":
   __lowerCamelCase							=       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."""
   )

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

__lowerCamelCase							=       TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__lowerCamelCase							=       TaTokenizerFast

__lowerCamelCase							=       {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """MT5EncoderModel""",
       """MT5ForConditionalGeneration""",
       """MT5ForQuestionAnswering""",
       """MT5Model""",
       """MT5PreTrainedModel""",
       """MT5Stack""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   __lowerCamelCase							=       _LazyModule(
       __name__,
       globals()["""__file__"""],
       _import_structure,
       extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
       module_spec=__spec__,
   )





