






'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase							=logging.get_logger()




@dataclass
class     A    :






 """simple docstring"""





 __a			:						nn.Module
 __a			:						List[nn.Module]      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 __a			:						list      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							len(list(m.modules() ) ) == 1 or isinstance(__lowerCAmelCase			,     nn.Convad ) or isinstance(__lowerCAmelCase			,     nn.BatchNormad )
       if has_not_submodules:
             self.traced.append(__lowerCAmelCase )
 def __call__(							self			,     __lowerCAmelCase ):
       for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook ) )
       self.module(__lowerCAmelCase )
       [x.remove() for x in self.handles]
       return self





 @property
 def   _UpperCAmelCase       (							self ):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda __lowerCAmelCase : len(list(x.state_dict().keys() ) ) > 0			,     self.traced ) )






@dataclass
class     A    :






 """simple docstring"""





 __a			:						nn.Module
 __a			:						nn.Module
 __a			:						int      =						1
 __a			:						List      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 __a			:						List      =						field(default_factory=SCREAMING_SNAKE_CASE__						)
 __a			:						bool      =						True
 def __call__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							Tracker(self.dest )(__lowerCAmelCase ).parametrized
       UpperCamelCase_				:   Optional[Any]					    =							Tracker(self.src )(__lowerCAmelCase ).parametrized

       UpperCamelCase_				:   str					    =							list(filter(lambda __lowerCAmelCase : type(__lowerCAmelCase ) not in self.src_skip			,     __lowerCAmelCase ) )
       UpperCamelCase_				:   Union[str, Any]					    =							list(filter(lambda __lowerCAmelCase : type(__lowerCAmelCase ) not in self.dest_skip			,     __lowerCAmelCase ) )

       if len(__lowerCAmelCase ) != len(__lowerCAmelCase ) and self.raise_if_mismatch:
             raise Exception(
                 F"Numbers of operations are different. Source module has {len(__lowerCAmelCase )} operations while"
                 F" destination module has {len(__lowerCAmelCase )}." )

       for dest_m, src_m in zip(__lowerCAmelCase			,     __lowerCAmelCase ):
             dest_m.load_state_dict(src_m.state_dict() )
             if self.verbose == 1:
                   print(F"Transfered from={src_m} to={dest_m}" )






class     A    (      nn.Module						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase ):
       super().__init__()

       UpperCamelCase_				:   List[Tuple[str, nn.Module]]					    =							[]
       # - get the stem
       feature_blocks.append(("""conv1""", model.stem) )
       # - get all the feature blocks
       for k, v in model.trunk_output.named_children():
             assert k.startswith("""block""" ), F"Unexpected layer name {k}"
             UpperCamelCase_				:   Dict					    =							len(__lowerCAmelCase ) + 1
             feature_blocks.append((F"res{block_index}", v) )

       UpperCamelCase_				:   str					    =							nn.ModuleDict(__lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return get_trunk_forward_outputs(
           __lowerCAmelCase			,     out_feat_keys=__lowerCAmelCase			,     feature_blocks=self._feature_blocks			,     )






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[int]					    =							x.split("""-""" )
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )





 def __getitem__(							self			,     __lowerCAmelCase ):
       # default to timm!
       if x not in self:
             UpperCamelCase_				:   Dict					    =							self.convert_name_to_timm(__lowerCAmelCase )
             UpperCamelCase_				:   List[str]					    =							partial(lambda: (timm.create_model(__lowerCAmelCase			,     pretrained=__lowerCAmelCase ).eval(), None) )

       else:
             UpperCamelCase_				:   Tuple					    =							super().__getitem__(__lowerCAmelCase )

       return val






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __getitem__(							self			,     __lowerCAmelCase ):
       if "seer" in x and "in1k" not in x:
             UpperCamelCase_				:   Optional[Any]					    =							RegNetModel
       else:
             UpperCamelCase_				:   Dict					    =							RegNetForImageClassification
       return val






def 							snake_case					(     a_							: Tuple    ,   a_							: Optional[Any]    ,   a_							: List[Tuple[str, str]]	)       ->				Any:





      """simple docstring"""

      for from_key, to_key in keys:
            UpperCamelCase_				:   List[str]					    =							from_state_dict[from_key].clone()
            print(f"Copied key={from_key} to={to_key}"	)
      return to_state_dict



def 							snake_case					(     a_							: str    ,   a_							: Callable[[], nn.Module]    ,   a_							: Callable[[], nn.Module]    ,   a_							: RegNetConfig    ,   a_							: Path    ,   a_							: bool = True    ,   )       ->				List[Any]:





      """simple docstring"""

      print(f"Converting {name}..."	)
      with torch.no_grad():
            UpperCamelCase_    ,			UpperCamelCase_				:   List[Any]					    =							from_model_func()
            UpperCamelCase_				:   Tuple					    =							our_model_func(a_	).eval()
            UpperCamelCase_				:   Dict					    =							ModuleTransfer(src=a_    ,   dest=a_    ,   raise_if_mismatch=a_	)
            UpperCamelCase_				:   str					    =							torch.randn((1, 3, 224, 224)	)
            module_transfer(a_	)

      if from_state_dict is not None:
            UpperCamelCase_				:   List[str]					    =							[]
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  UpperCamelCase_				:   List[Any]					    =							[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
            UpperCamelCase_				:   int					    =							manually_copy_vissl_head(a_    ,   our_model.state_dict()    ,   a_	)
            our_model.load_state_dict(a_	)

      UpperCamelCase_				:   Optional[int]					    =							our_model(a_    ,   output_hidden_states=a_	)
      UpperCamelCase_				:   List[Any]					    =							(
          our_outputs.logits if isinstance(a_    ,   a_	) else our_outputs.last_hidden_state
      )

      UpperCamelCase_				:   Optional[int]					    =							from_model(a_	)
      UpperCamelCase_				:   List[str]					    =							from_output[-1] if type(a_	) is list else from_output

      # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
      if "seer" in name and "in1k" in name:
            UpperCamelCase_				:   Optional[Any]					    =							our_outputs.hidden_states[-1]

      assert torch.allclose(a_    ,   a_	), "The model logits don't match the original one."

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / name    ,   commit_message="""Add model"""    ,   use_temp_dir=a_    ,   )

            UpperCamelCase_				:   Any					    =							224 if """seer""" not in name else 384
            # we can use the convnext one
            UpperCamelCase_				:   int					    =							AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""    ,   size=a_	)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name    ,   commit_message="""Add image processor"""    ,   use_temp_dir=a_    ,   )

            print(f"Pushed {name}"	)



def 							snake_case					(     a_							: Path    ,   a_							: str = None    ,   a_							: bool = True	)       ->				Optional[int]:





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =							"""imagenet-1k-id2label.json"""
      UpperCamelCase_				:   List[Any]					    =							1_000
      UpperCamelCase_				:   Dict					    =							(1, num_labels)

      UpperCamelCase_				:   int					    =							"""huggingface/label-files"""
      UpperCamelCase_				:   Tuple					    =							num_labels
      UpperCamelCase_				:   List[str]					    =							json.load(open(cached_download(hf_hub_url(a_    ,   a_    ,   repo_type="""dataset"""	)	)    ,   """r"""	)	)
      UpperCamelCase_				:   int					    =							{int(a_	): v for k, v in idalabel.items()}

      UpperCamelCase_				:   Tuple					    =							idalabel
      UpperCamelCase_				:   List[str]					    =							{v: k for k, v in idalabel.items()}

      UpperCamelCase_				:   Union[str, Any]					    =							partial(a_    ,   num_labels=a_    ,   idalabel=a_    ,   labelaid=a_	)

      UpperCamelCase_				:   Optional[Any]					    =							{
          """regnet-x-002""": ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]    ,   hidden_sizes=[24, 56, 152, 368]    ,   groups_width=8    ,   layer_type="""x"""	),
          """regnet-x-004""": ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]    ,   hidden_sizes=[32, 64, 160, 384]    ,   groups_width=16    ,   layer_type="""x"""	),
          """regnet-x-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]    ,   hidden_sizes=[48, 96, 240, 528]    ,   groups_width=24    ,   layer_type="""x"""	),
          """regnet-x-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]    ,   hidden_sizes=[64, 128, 288, 672]    ,   groups_width=16    ,   layer_type="""x"""	),
          """regnet-x-016""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]    ,   hidden_sizes=[72, 168, 408, 912]    ,   groups_width=24    ,   layer_type="""x"""	),
          """regnet-x-032""": ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]    ,   hidden_sizes=[96, 192, 432, 1_008]    ,   groups_width=48    ,   layer_type="""x"""	),
          """regnet-x-040""": ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]    ,   hidden_sizes=[80, 240, 560, 1_360]    ,   groups_width=40    ,   layer_type="""x"""	),
          """regnet-x-064""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]    ,   hidden_sizes=[168, 392, 784, 1_624]    ,   groups_width=56    ,   layer_type="""x"""	),
          """regnet-x-080""": ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]    ,   hidden_sizes=[80, 240, 720, 1_920]    ,   groups_width=120    ,   layer_type="""x"""	),
          """regnet-x-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]    ,   hidden_sizes=[224, 448, 896, 2_240]    ,   groups_width=112    ,   layer_type="""x"""	),
          """regnet-x-160""": ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]    ,   hidden_sizes=[256, 512, 896, 2_048]    ,   groups_width=128    ,   layer_type="""x"""	),
          """regnet-x-320""": ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]    ,   hidden_sizes=[336, 672, 1_344, 2_520]    ,   groups_width=168    ,   layer_type="""x"""	),
          # y variant
          """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,   hidden_sizes=[24, 56, 152, 368]    ,   groups_width=8	),
          """regnet-y-004""": ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]    ,   hidden_sizes=[48, 104, 208, 440]    ,   groups_width=8	),
          """regnet-y-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]    ,   hidden_sizes=[48, 112, 256, 608]    ,   groups_width=16	),
          """regnet-y-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]    ,   hidden_sizes=[64, 128, 320, 768]    ,   groups_width=16	),
          """regnet-y-016""": ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]    ,   hidden_sizes=[48, 120, 336, 888]    ,   groups_width=24	),
          """regnet-y-032""": ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]    ,   hidden_sizes=[72, 216, 576, 1_512]    ,   groups_width=24	),
          """regnet-y-040""": ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]    ,   hidden_sizes=[128, 192, 512, 1_088]    ,   groups_width=64	),
          """regnet-y-064""": ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]    ,   hidden_sizes=[144, 288, 576, 1_296]    ,   groups_width=72	),
          """regnet-y-080""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]    ,   hidden_sizes=[168, 448, 896, 2_016]    ,   groups_width=56	),
          """regnet-y-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]    ,   hidden_sizes=[224, 448, 896, 2_240]    ,   groups_width=112	),
          """regnet-y-160""": ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]    ,   hidden_sizes=[224, 448, 1_232, 3_024]    ,   groups_width=112	),
          """regnet-y-320""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232	),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232	),
          """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]    ,   hidden_sizes=[328, 984, 1_968, 4_920]    ,   groups_width=328	),
          """regnet-y-1280-seer""": RegNetConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[528, 1_056, 2_904, 7_392]    ,   groups_width=264	),
          """regnet-y-2560-seer""": RegNetConfig(
              depths=[3, 7, 16, 1]    ,   hidden_sizes=[640, 1_696, 2_544, 5_088]    ,   groups_width=640	),
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,   groups_width=1_010	),
          # finetuned on imagenet
          """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]    ,   hidden_sizes=[232, 696, 1_392, 3_712]    ,   groups_width=232	),
          """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]    ,   hidden_sizes=[328, 984, 1_968, 4_920]    ,   groups_width=328	),
          """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[528, 1_056, 2_904, 7_392]    ,   groups_width=264	),
          """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]    ,   hidden_sizes=[640, 1_696, 2_544, 5_088]    ,   groups_width=640	),
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]    ,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]    ,   groups_width=1_010	),
      }

      UpperCamelCase_				:   str					    =							NameToOurModelFuncMap()
      UpperCamelCase_				:   List[Any]					    =							NameToFromModelFuncMap()
      # add seer weights logic

      def load_using_classy_vision(a_							: str    ,   a_							: Callable[[], nn.Module]	) -> Tuple[nn.Module, Dict]:
            UpperCamelCase_				:   List[str]					    =							torch.hub.load_state_dict_from_url(a_    ,   model_dir=str(a_	)    ,   map_location="""cpu"""	)
            UpperCamelCase_				:   Dict					    =							model_func()
            # check if we have a head, if yes add it
            UpperCamelCase_				:   str					    =							files["""classy_state_dict"""]["""base_model"""]["""model"""]
            UpperCamelCase_				:   Any					    =							model_state_dict["""trunk"""]
            model.load_state_dict(a_	)
            return model.eval(), model_state_dict["heads"]

      # pretrained
      UpperCamelCase_				:   Tuple					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   List[Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   List[str]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)    ,   )

      UpperCamelCase_				:   Dict					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""    ,   lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27    ,   group_width=1_010    ,   w_a=1_744    ,   w_a=620.83    ,   w_m=2.52	)	)	)    ,   )

      # IN1K finetuned
      UpperCamelCase_				:   Union[str, Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   Tuple					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,   )

      UpperCamelCase_				:   Optional[Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)    ,   )

      UpperCamelCase_				:   List[Any]					    =							partial(
          a_    ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""    ,   lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27    ,   group_width=1_010    ,   w_a=1_744    ,   w_a=620.83    ,   w_m=2.52	)	)	)    ,   )

      if model_name:
            convert_weight_and_push(
                a_    ,   names_to_from_model_map[model_name]    ,   names_to_ours_model_map[model_name]    ,   names_to_config[model_name]    ,   a_    ,   a_    ,   )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      a_    ,   names_to_from_model_map[model_name]    ,   names_to_ours_model_map[model_name]    ,   a_    ,   a_    ,   a_    ,   )
      return config, expected_shape


if __name__ == "__main__":
      UpperCamelCase							=argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default=None,
          type=str,
          help=(
              "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
              " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=Path,
          required=True,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub",
          default=True,
          type=bool,
          required=False,
          help="If True, push model and image processor to the hub.",
      )

      UpperCamelCase							=parser.parse_args()

      UpperCamelCase							=args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)














'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

      if is_vision_available():
            from transformers import MaskFormerImageProcessor

if is_vision_available():
      from PIL import Image




class     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=2			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase=10			,     __lowerCAmelCase=3			,     __lowerCAmelCase=32 * 4			,     __lowerCAmelCase=32 * 6			,     __lowerCAmelCase=4			,     __lowerCAmelCase=32			,     ):
       UpperCamelCase_				:   List[str]					    =							parent
       UpperCamelCase_				:   List[Any]					    =							batch_size
       UpperCamelCase_				:   int					    =							is_training
       UpperCamelCase_				:   Union[str, Any]					    =							use_auxiliary_loss
       UpperCamelCase_				:   Optional[Any]					    =							num_queries
       UpperCamelCase_				:   List[str]					    =							num_channels
       UpperCamelCase_				:   Optional[int]					    =							min_size
       UpperCamelCase_				:   Union[str, Any]					    =							max_size
       UpperCamelCase_				:   Optional[int]					    =							num_labels
       UpperCamelCase_				:   List[Any]					    =							mask_feature_size
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
           __lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							torch.ones([self.batch_size, self.min_size, self.max_size]			,     device=__lowerCAmelCase )

       UpperCamelCase_				:   Dict					    =							(
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]			,     device=__lowerCAmelCase ) > 0.5
       ).float()
       UpperCamelCase_				:   List[Any]					    =							(torch.rand((self.batch_size, self.num_labels)			,     device=__lowerCAmelCase ) > 0.5).long()

       UpperCamelCase_				:   Dict					    =							self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels
 def   _UpperCAmelCase       (							self ):
       return MaskFormerConfig.from_backbone_and_decoder_configs(
           backbone_config=SwinConfig(
               depths=[1, 1, 1, 1]			,     )			,     decoder_config=DetrConfig(
               decoder_ffn_dim=1_28			,     num_queries=self.num_queries			,     decoder_attention_heads=2			,     d_model=self.mask_feature_size			,     )			,     mask_feature_size=self.mask_feature_size			,     fpn_feature_size=self.mask_feature_size			,     num_channels=self.num_channels			,     num_labels=self.num_labels			,     )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							self.prepare_config_and_inputs()
       UpperCamelCase_				:   int					    =							{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
       return config, inputs_dict
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							output.encoder_hidden_states
       UpperCamelCase_				:   Any					    =							output.pixel_decoder_hidden_states
       UpperCamelCase_				:   Union[str, Any]					    =							output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(__lowerCAmelCase )			,     len(config.backbone_config.depths ) )
       self.parent.assertTrue(len(__lowerCAmelCase )			,     len(config.backbone_config.depths ) )
       self.parent.assertTrue(len(__lowerCAmelCase )			,     config.decoder_config.decoder_layers )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=False ):
       with torch.no_grad():
             UpperCamelCase_				:   Tuple					    =							MaskFormerModel(config=__lowerCAmelCase )
             model.to(__lowerCAmelCase )
             model.eval()

             UpperCamelCase_				:   str					    =							model(pixel_values=__lowerCAmelCase			,     pixel_mask=__lowerCAmelCase )
             UpperCamelCase_				:   Union[str, Any]					    =							model(__lowerCAmelCase			,     output_hidden_states=__lowerCAmelCase )
       # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
       # encoder and pixel decoder
       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape			,     (self.batch_size, self.num_queries, self.mask_feature_size)			,     )
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
       self.parent.assertTrue(output.encoder_last_hidden_state is not None )

       if output_hidden_states:
             self.check_output_hidden_state(__lowerCAmelCase			,     __lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[int]					    =							MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()

       def comm_check_on_output(__lowerCAmelCase ):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
             self.parent.assertTrue(result.encoder_last_hidden_state is not None )
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape			,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)			,     )
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape			,     (self.batch_size, self.num_queries, self.num_labels + 1) )

       with torch.no_grad():
             UpperCamelCase_				:   List[Any]					    =							model(pixel_values=__lowerCAmelCase			,     pixel_mask=__lowerCAmelCase )
             UpperCamelCase_				:   List[str]					    =							model(__lowerCAmelCase )

             comm_check_on_output(__lowerCAmelCase )

             UpperCamelCase_				:   Dict					    =							model(
                 pixel_values=__lowerCAmelCase			,     pixel_mask=__lowerCAmelCase			,     mask_labels=__lowerCAmelCase			,     class_labels=__lowerCAmelCase )

       comm_check_on_output(__lowerCAmelCase )

       self.parent.assertTrue(result.loss is not None )
       self.parent.assertEqual(result.loss.shape			,     torch.Size([1] ) )






@require_torch
class     A    (      SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Tuple      =						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
 __a			:						List[str]      =						(
     {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
     if is_torch_available()
     else {}
 )

 __a			:						List[Any]      =						False
 __a			:						int      =						False
 __a			:						List[str]      =						False
 __a			:						Any      =						False
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							MaskFormerModelTester(self )
       UpperCamelCase_				:   Optional[int]					    =							ConfigTester(self			,     config_class=__lowerCAmelCase			,     has_text_modality=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       self.config_tester.run_common_tests()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase			,     **__lowerCAmelCase			,     output_hidden_states=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
 @unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip(reason="""MaskFormer is not a generative model""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip(reason="""MaskFormer does not use token embeddings""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @require_torch_multi_gpu
 @unittest.skip(
     reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
 def   _UpperCAmelCase       (							self ):
       pass
 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   Optional[int]					    =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_				:   List[Any]					    =							model_class(__lowerCAmelCase )
             UpperCamelCase_				:   Union[str, Any]					    =							inspect.signature(model.forward )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase_				:   Optional[Any]					    =							[*signature.parameters.keys()]

             UpperCamelCase_				:   Any					    =							["""pixel_values"""]
             self.assertListEqual(arg_names[:1]			,     __lowerCAmelCase )
 @slow
 def   _UpperCAmelCase       (							self ):
       for model_name in ["facebook/maskformer-swin-small-coco"]:
             UpperCamelCase_				:   List[str]					    =							MaskFormerModel.from_pretrained(__lowerCAmelCase )
             self.assertIsNotNone(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							(self.model_tester.min_size,) * 2
       UpperCamelCase_				:   Optional[int]					    =							{
           """pixel_values""": torch.randn((2, 3, *size)			,     device=__lowerCAmelCase ),
           """mask_labels""": torch.randn((2, 10, *size)			,     device=__lowerCAmelCase ),
           """class_labels""": torch.zeros(2			,     10			,     device=__lowerCAmelCase ).long(),
       }

       UpperCamelCase_				:   Union[str, Any]					    =							MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
       UpperCamelCase_				:   List[str]					    =							model(**__lowerCAmelCase )
       self.assertTrue(outputs.loss is not None )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase			,     **__lowerCAmelCase			,     output_hidden_states=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_				:   Optional[int]					    =							model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
             UpperCamelCase_				:   Optional[int]					    =							model(**__lowerCAmelCase			,     output_attentions=__lowerCAmelCase )
             self.assertTrue(outputs.attentions is not None )
 def   _UpperCAmelCase       (							self ):
       if not self.model_tester.is_training:
             return
       # only MaskFormerForInstanceSegmentation has the loss
       UpperCamelCase_				:   int					    =							self.all_model_classes[1]
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							self.model_tester.prepare_config_and_inputs()

       UpperCamelCase_				:   Dict					    =							model_class(__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.train()

       UpperCamelCase_				:   Union[str, Any]					    =							model(__lowerCAmelCase			,     mask_labels=__lowerCAmelCase			,     class_labels=__lowerCAmelCase ).loss
       loss.backward()





 def   _UpperCAmelCase       (							self ):
       # only MaskFormerForInstanceSegmentation has the loss
       UpperCamelCase_				:   List[Any]					    =							self.all_model_classes[1]
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							self.model_tester.prepare_config_and_inputs()
       UpperCamelCase_				:   Optional[int]					    =							True
       UpperCamelCase_				:   List[str]					    =							True

       UpperCamelCase_				:   Any					    =							model_class(__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.train()

       UpperCamelCase_				:   Dict					    =							model(__lowerCAmelCase			,     mask_labels=__lowerCAmelCase			,     class_labels=__lowerCAmelCase )

       UpperCamelCase_				:   Optional[int]					    =							outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       UpperCamelCase_				:   Optional[int]					    =							outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()
       # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
       UpperCamelCase_				:   List[str]					    =							outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       UpperCamelCase_				:   List[Any]					    =							outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=__lowerCAmelCase )

       self.assertIsNotNone(encoder_hidden_states.grad )
       self.assertIsNotNone(pixel_decoder_hidden_states.grad )
       self.assertIsNotNone(transformer_decoder_hidden_states.grad )
       self.assertIsNotNone(attentions.grad )






UpperCamelCase							=1E-4



def 							snake_case					(     )       ->				Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
      return image




@require_vision
@slow
class     A    (      unittest.TestCase						):






 """simple docstring"""





 @cached_property
 def   _UpperCAmelCase       (							self ):
       return (
           MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
           if is_vision_available()
           else None
       )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							self.default_image_processor
       UpperCamelCase_				:   Optional[int]					    =							prepare_img()
       UpperCamelCase_				:   Optional[int]					    =							image_processor(__lowerCAmelCase			,     return_tensors="""pt""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
       # check size
       self.assertEqual(__lowerCAmelCase			,     (1, 3, 8_00, 10_88) )

       with torch.no_grad():
             UpperCamelCase_				:   Tuple					    =							model(**__lowerCAmelCase )

       UpperCamelCase_				:   str					    =							torch.tensor(
           [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]] ).to(__lowerCAmelCase )
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )

       UpperCamelCase_				:   Any					    =							torch.tensor(
           [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]] ).to(__lowerCAmelCase )
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )

       UpperCamelCase_				:   List[Any]					    =							torch.tensor(
           [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]] ).to(__lowerCAmelCase )
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							(
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
           .to(__lowerCAmelCase )
           .eval()
       )
       UpperCamelCase_				:   int					    =							self.default_image_processor
       UpperCamelCase_				:   Tuple					    =							prepare_img()
       UpperCamelCase_				:   Optional[Any]					    =							image_processor(__lowerCAmelCase			,     return_tensors="""pt""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
       # check size
       self.assertEqual(__lowerCAmelCase			,     (1, 3, 8_00, 10_88) )

       with torch.no_grad():
             UpperCamelCase_				:   List[str]					    =							model(**__lowerCAmelCase )
       # masks_queries_logits
       UpperCamelCase_				:   Any					    =							outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,     )
       UpperCamelCase_				:   Union[str, Any]					    =							[
           [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
           [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
           [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
       ]
       UpperCamelCase_				:   Optional[Any]					    =							torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
       # class_queries_logits
       UpperCamelCase_				:   List[str]					    =							outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
       UpperCamelCase_				:   Optional[Any]					    =							torch.tensor(
           [
               [1.6512E00, -5.2572E00, -3.3519E00],
               [3.6169E-02, -5.9025E00, -2.9313E00],
               [1.0766E-04, -7.7630E00, -5.1263E00],
           ] ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							(
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
           .to(__lowerCAmelCase )
           .eval()
       )
       UpperCamelCase_				:   Optional[int]					    =							self.default_image_processor
       UpperCamelCase_				:   Optional[Any]					    =							prepare_img()
       UpperCamelCase_				:   str					    =							image_processor(__lowerCAmelCase			,     return_tensors="""pt""" ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
       # check size
       self.assertEqual(__lowerCAmelCase			,     (1, 3, 8_00, 10_88) )

       with torch.no_grad():
             UpperCamelCase_				:   List[Any]					    =							model(**__lowerCAmelCase )
       # masks_queries_logits
       UpperCamelCase_				:   Union[str, Any]					    =							outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			,     )
       UpperCamelCase_				:   List[str]					    =							[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
       UpperCamelCase_				:   Any					    =							torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )
       # class_queries_logits
       UpperCamelCase_				:   List[Any]					    =							outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape			,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
       UpperCamelCase_				:   Any					    =							torch.tensor(
           [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]] ).to(__lowerCAmelCase )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,     __lowerCAmelCase			,     atol=__lowerCAmelCase ) )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							(
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
           .to(__lowerCAmelCase )
           .eval()
       )
       UpperCamelCase_				:   Optional[int]					    =							self.default_image_processor

       UpperCamelCase_				:   Tuple					    =							image_processor(
           [np.zeros((3, 8_00, 13_33) ), np.zeros((3, 8_00, 13_33) )]			,     segmentation_maps=[np.zeros((3_84, 3_84) ).astype(np.floataa ), np.zeros((3_84, 3_84) ).astype(np.floataa )]			,     return_tensors="""pt"""			,     )

       UpperCamelCase_				:   List[Any]					    =							inputs["""pixel_values"""].to(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							[el.to(__lowerCAmelCase ) for el in inputs["""mask_labels"""]]
       UpperCamelCase_				:   Tuple					    =							[el.to(__lowerCAmelCase ) for el in inputs["""class_labels"""]]

       with torch.no_grad():
             UpperCamelCase_				:   Union[str, Any]					    =							model(**__lowerCAmelCase )

       self.assertTrue(outputs.loss is not None )






