



"""simple docstring"""





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							SwinConfig.from_pretrained(
          "microsoft/swin-tiny-patch4-window7-224"			,    out_features=["stage1", "stage2", "stage3", "stage4"]   )
      UpperCAmelCase_    				=							MaskFormerConfig(backbone_config=lowerCAmelCase__   )

      UpperCAmelCase_    				=							"huggingface/label-files"
      if "ade20k-full" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							847
            UpperCAmelCase_    				=							"maskformer-ade20k-full-id2label.json"
      elif "ade" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							150
            UpperCAmelCase_    				=							"ade20k-id2label.json"
      elif "coco-stuff" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							171
            UpperCAmelCase_    				=							"maskformer-coco-stuff-id2label.json"
      elif "coco" in model_name:
            # TODO
            UpperCAmelCase_    				=							133
            UpperCAmelCase_    				=							"coco-panoptic-id2label.json"
      elif "cityscapes" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							19
            UpperCAmelCase_    				=							"cityscapes-id2label.json"
      elif "vistas" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							65
            UpperCAmelCase_    				=							"mapillary-vistas-id2label.json"

      UpperCAmelCase_    				=							json.load(open(hf_hub_download(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )			,    "r"   )   )
      UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

      return config
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      # stem
      # fmt: off
      rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")   )
      rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")   )
      rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")   )
      rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.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.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")   )

            if i < 3:
                  rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")   )
            rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")   )
            rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")   )

      # FPN
      rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")   )
      rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")   )
      rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")   )
      for source_index, target_index in zip(range(3			,    0			,    -1   )			,    range(0			,    3   )   ):
            rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")   )
            rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")   )
            rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")   )
            rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")   )
            rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")   )
            rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")   )
      rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")   )
      rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")   )

      # Transformer decoder
      for idx in range(config.decoder_config.decoder_layers   ):
            # self-attention out projection
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")   )
            # cross-attention out projection
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")   )
            # MLP 1
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")   )
            # MLP 2
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")   )
            # layernorm 1 (self-attention layernorm)
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")   )
            # layernorm 2 (cross-attention layernorm)
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")   )
            # layernorm 3 (final layernorm)
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")   )

      rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")   )
      rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")   )

      # heads on top
      rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")   )

      rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")   )
      rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")   )

      rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")   )
      rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")   )

      for i in range(3   ):
            rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")   )
      # fmt: on

      return rename_keys
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dct.pop(lowerCAmelCase__   )
      UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
      for i in range(len(backbone_config.depths   )   ):
            UpperCAmelCase_    				=							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)
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""   )
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""   )
                  # next, add query, keys and values (in that order) to the state dict
                  UpperCAmelCase_    				=							in_proj_weight[:dim, :]
                  UpperCAmelCase_    				=							in_proj_bias[: dim]
                  UpperCAmelCase_    				=							in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_    				=							in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[-dim :]
                  # fmt: on
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # fmt: off
      UpperCAmelCase_    				=							config.decoder_config.hidden_size
      for idx in range(config.decoder_config.decoder_layers   ):
            # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""   )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							in_proj_bias[-hidden_size :]
            # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""   )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							in_proj_bias[-hidden_size :]
      # fmt: on
def      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   )
      return im
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							get_maskformer_config(lowerCAmelCase__   )

      # load original state_dict
      with open(lowerCAmelCase__			,    "rb"   ) as f:
            UpperCAmelCase_    				=							pickle.load(lowerCAmelCase__   )
      UpperCAmelCase_    				=							data["model"]

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

      # rename keys
      UpperCAmelCase_    				=							create_rename_keys(lowerCAmelCase__   )
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      read_in_swin_q_k_v(lowerCAmelCase__			,    config.backbone_config   )
      read_in_decoder_q_k_v(lowerCAmelCase__			,    lowerCAmelCase__   )

      # update to torch tensors
      for key, value in state_dict.items():
            UpperCAmelCase_    				=							torch.from_numpy(lowerCAmelCase__   )

      # load 🤗 model
      UpperCAmelCase_    				=							MaskFormerForInstanceSegmentation(lowerCAmelCase__   )
      model.eval()

      for name, param in model.named_parameters():
            print(lowerCAmelCase__			,    param.shape   )

      UpperCAmelCase_				,					UpperCAmelCase_    				=							model.load_state_dict(lowerCAmelCase__			,    strict=lowerCAmelCase__   )
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(lowerCAmelCase__   ) == 0, f"""Unexpected keys: {unexpected_keys}"""

      # verify results
      UpperCAmelCase_    				=							prepare_img()
      if "vistas" in model_name:
            UpperCAmelCase_    				=							65
      elif "cityscapes" in model_name:
            UpperCAmelCase_    				=							65535
      else:
            UpperCAmelCase_    				=							255
      UpperCAmelCase_    				=							True if "ade" in model_name else False
      UpperCAmelCase_    				=							MaskFormerImageProcessor(ignore_index=lowerCAmelCase__			,    reduce_labels=lowerCAmelCase__   )

      UpperCAmelCase_    				=							image_processor(lowerCAmelCase__			,    return_tensors="pt"   )

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

      print("Logits:"			,    outputs.class_queries_logits[0, :3, :3]   )

      if model_name == "maskformer-swin-tiny-ade":
            UpperCAmelCase_    				=							torch.tensor(
                [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]   )
      assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,    lowerCAmelCase__			,    atol=1e-4   )
      print("Looks ok!"   )

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""   )
            Path(lowerCAmelCase__   ).mkdir(exist_ok=lowerCAmelCase__   )
            model.save_pretrained(lowerCAmelCase__   )
            image_processor.save_pretrained(lowerCAmelCase__   )

      if push_to_hub:
            print("Pushing model and image processor to the hub..."   )
            model.push_to_hub(f"""nielsr/{model_name}"""   )
            image_processor.push_to_hub(f"""nielsr/{model_name}"""   )


if __name__ == "__main__":
  lowerCamelCase      =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""maskformer-swin-tiny-ade""",
      type=str,
      help=("""Name of the MaskFormer model you'd like to convert""",),
  )
  parser.add_argument(
      """--checkpoint_path""",
      default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
      type=str,
      help="""Path to the original state dict (.pth file).""",
  )
  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_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )









"""simple docstring"""





import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
  from transformers.models.mobilevitva.modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      make_divisible,
  )


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						Union[str, Any]					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.config_class(**self.inputs_dict					)
             self.parent.assertTrue(hasattr(_UpperCAmelCase	,       "width_multiplier"					)					)



class    lowercase__     :







       '''simple docstring'''


       def __init__(							self    :						Optional[Any]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Optional[int]=13	,       _UpperCAmelCase    :						Any=64	,       _UpperCAmelCase    :						Union[str, Any]=2	,       _UpperCAmelCase    :						Any=3	,       _UpperCAmelCase    :						Dict="swish"	,       _UpperCAmelCase    :						List[Any]=3	,       _UpperCAmelCase    :						int=32	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Tuple=0.02	,       _UpperCAmelCase    :						List[Any]=True	,       _UpperCAmelCase    :						Any=True	,       _UpperCAmelCase    :						int=10	,       _UpperCAmelCase    :						Optional[int]=None	,       _UpperCAmelCase    :						Any=0.25	,       _UpperCAmelCase    :						Any=0.0	,       _UpperCAmelCase    :						Optional[int]=0.0	,       )				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							parent
             UpperCAmelCase_    				=							batch_size
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							patch_size
             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							make_divisible(512 * width_multiplier	,       divisor=8					)
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							conv_kernel_size
             UpperCAmelCase_    				=							output_stride
             UpperCAmelCase_    				=							classifier_dropout_prob
             UpperCAmelCase_    				=							use_labels
             UpperCAmelCase_    				=							is_training
             UpperCAmelCase_    				=							num_labels
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							scope
             UpperCAmelCase_    				=							width_multiplier
             UpperCAmelCase_    				=							ffn_dropout
             UpperCAmelCase_    				=							attn_dropout



       def   lowercase__       (							self    :						Optional[int]					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

             UpperCAmelCase_    				=							None
             UpperCAmelCase_    				=							None
             if self.use_labels:
                   UpperCAmelCase_    				=							ids_tensor([self.batch_size]	,       self.num_labels					)
                   UpperCAmelCase_    				=							ids_tensor([self.batch_size, self.image_size, self.image_size]	,       self.num_labels					)

             UpperCAmelCase_    				=							self.get_config()

             return config, pixel_values, labels, pixel_labels



       def   lowercase__       (							self    :						Tuple					)				->		Tuple:


             '''simple docstring'''




             return MobileViTVaConfig(
                 image_size=self.image_size	,       patch_size=self.patch_size	,       num_channels=self.num_channels	,       hidden_act=self.hidden_act	,       conv_kernel_size=self.conv_kernel_size	,       output_stride=self.output_stride	,       classifier_dropout_prob=self.classifier_dropout_prob	,       initializer_range=self.initializer_range	,       width_multiplier=self.width_multiplier	,       ffn_dropout=self.ffn_dropout_prob	,       attn_dropout=self.attn_dropout_prob	,       )



       def   lowercase__       (							self    :						List[str]	,       _UpperCAmelCase    :						str	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						int					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							MobileViTVaModel(config=_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()
             UpperCAmelCase_    				=							model(_UpperCAmelCase					)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,       (
                     self.batch_size,
                     self.last_hidden_size,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 )	,       )



       def   lowercase__       (							self    :						Optional[int]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						str	,       _UpperCAmelCase    :						Tuple					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.num_labels
             UpperCAmelCase_    				=							MobileViTVaForImageClassification(_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()
             UpperCAmelCase_    				=							model(_UpperCAmelCase	,       labels=_UpperCAmelCase					)
             self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels)					)



       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Optional[int]	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Tuple					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.num_labels
             UpperCAmelCase_    				=							MobileViTVaForSemanticSegmentation(_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()
             UpperCAmelCase_    				=							model(_UpperCAmelCase					)
             self.parent.assertEqual(
                 result.logits.shape	,       (
                     self.batch_size,
                     self.num_labels,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 )	,       )
             UpperCAmelCase_    				=							model(_UpperCAmelCase	,       labels=_UpperCAmelCase					)
             self.parent.assertEqual(
                 result.logits.shape	,       (
                     self.batch_size,
                     self.num_labels,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 )	,       )







       def   lowercase__       (							self    :						List[str]					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.prepare_config_and_inputs()
             UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							config_and_inputs
             UpperCAmelCase_    				=							{"pixel_values": pixel_values}
             return config, inputs_dict



@require_torch
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  (
           (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
           if is_torch_available()
           else ()
       )

       UpperCamelCase		=  (
           {
               '''feature-extraction''': MobileViTVaModel,
               '''image-classification''': MobileViTVaForImageClassification,
               '''image-segmentation''': MobileViTVaForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False



       def   lowercase__       (							self    :						str					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							MobileViTVaModelTester(self					)
             UpperCAmelCase_    				=							MobileViTVaConfigTester(self	,       config_class=_UpperCAmelCase	,       has_text_modality=_UpperCAmelCase					)



       def   lowercase__       (							self    :						int					)				->		List[str]:


             '''simple docstring'''




             self.config_tester.run_common_tests()



       @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"					)
       def   lowercase__       (							self    :						List[Any]					)				->		List[str]:


             '''simple docstring'''




             pass



       @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"					)
       def   lowercase__       (							self    :						Tuple					)				->		Dict:


             '''simple docstring'''




             pass



       @unittest.skip(reason="MobileViTV2 does not output attentions"					)
       def   lowercase__       (							self    :						Optional[int]					)				->		List[Any]:


             '''simple docstring'''




             pass



       @require_torch_multi_gpu
       @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."					)
       def   lowercase__       (							self    :						int					)				->		int:


             '''simple docstring'''




             pass



       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."					)
       def   lowercase__       (							self    :						Tuple					)				->		Optional[int]:


             '''simple docstring'''




             pass



       def   lowercase__       (							self    :						int					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   UpperCAmelCase_    				=							inspect.signature(model.forward					)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   UpperCAmelCase_    				=							[*signature.parameters.keys()]

                   UpperCAmelCase_    				=							["pixel_values"]
                   self.assertListEqual(arg_names[:1]	,       _UpperCAmelCase					)



       def   lowercase__       (							self    :						Union[str, Any]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_UpperCAmelCase					)



       def   lowercase__       (							self    :						List[Any]					)				->		Optional[int]:


             '''simple docstring'''




             def check_hidden_states_output(_UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Union[str, Any]	,       _UpperCAmelCase    :						int					):
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   model.to(_UpperCAmelCase					)
                   model.eval()

                   with torch.no_grad():
                         UpperCAmelCase_    				=							model(**self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase					)					)

                   UpperCAmelCase_    				=							outputs.hidden_states

                   UpperCAmelCase_    				=							5
                   self.assertEqual(len(_UpperCAmelCase					)	,       _UpperCAmelCase					)

                   # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                   # with the width and height being successively divided by 2.
                   UpperCAmelCase_    				=							2
                   for i in range(len(_UpperCAmelCase					)					):
                         self.assertListEqual(
                             list(hidden_states[i].shape[-2:]					)	,       [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,       )
                         divisor *= 2

                   self.assertEqual(self.model_tester.output_stride	,       divisor // 2					)

             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_    				=							True
                   check_hidden_states_output(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   UpperCAmelCase_    				=							True

                   check_hidden_states_output(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)



       def   lowercase__       (							self    :						int					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase					)



       def   lowercase__       (							self    :						int					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase					)







       @slow
       def   lowercase__       (							self    :						Optional[int]					)				->		Union[str, Any]:


             '''simple docstring'''




             for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   UpperCAmelCase_    				=							MobileViTVaModel.from_pretrained(_UpperCAmelCase					)
                   self.assertIsNotNone(_UpperCAmelCase					)



def      a__			(   ):
      UpperCAmelCase_    				=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
      return image


@require_torch
@require_vision
class    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


       @cached_property
       def   lowercase__       (							self    :						int					)				->		List[Any]:


             '''simple docstring'''




             return (
                 MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"					)
                 if is_vision_available()
                 else None
             )



       @slow
       def   lowercase__       (							self    :						Union[str, Any]					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"					).to(
                 _UpperCAmelCase					)

             UpperCAmelCase_    				=							self.default_image_processor
             UpperCAmelCase_    				=							prepare_img()
             UpperCAmelCase_    				=							image_processor(images=_UpperCAmelCase	,       return_tensors="pt"					).to(_UpperCAmelCase					)

             # forward pass
             with torch.no_grad():
                   UpperCAmelCase_    				=							model(**_UpperCAmelCase					)

             # verify the logits
             UpperCAmelCase_    				=							torch.Size((1, 1000)					)
             self.assertEqual(outputs.logits.shape	,       _UpperCAmelCase					)

             UpperCAmelCase_    				=							torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]					).to(_UpperCAmelCase					)

             self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       _UpperCAmelCase	,       atol=1e-4					)					)



       @slow
       def   lowercase__       (							self    :						List[str]					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"					)
             UpperCAmelCase_    				=							model.to(_UpperCAmelCase					)

             UpperCAmelCase_    				=							MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"					)

             UpperCAmelCase_    				=							prepare_img()
             UpperCAmelCase_    				=							image_processor(images=_UpperCAmelCase	,       return_tensors="pt"					).to(_UpperCAmelCase					)

             # forward pass
             with torch.no_grad():
                   UpperCAmelCase_    				=							model(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							outputs.logits

             # verify the logits
             UpperCAmelCase_    				=							torch.Size((1, 21, 32, 32)					)
             self.assertEqual(logits.shape	,       _UpperCAmelCase					)

             UpperCAmelCase_    				=							torch.tensor(
                 [
                     [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                     [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                     [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
                 ]	,       device=_UpperCAmelCase	,       )

             self.assertTrue(torch.allclose(logits[0, :3, :3, :3]	,       _UpperCAmelCase	,       atol=1e-4					)					)







       @slow
       def   lowercase__       (							self    :						Optional[int]					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"					)
             UpperCAmelCase_    				=							model.to(_UpperCAmelCase					)

             UpperCAmelCase_    				=							MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"					)

             UpperCAmelCase_    				=							prepare_img()
             UpperCAmelCase_    				=							image_processor(images=_UpperCAmelCase	,       return_tensors="pt"					).to(_UpperCAmelCase					)

             # forward pass
             with torch.no_grad():
                   UpperCAmelCase_    				=							model(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							outputs.logits.detach().cpu()

             UpperCAmelCase_    				=							image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase	,       target_sizes=[(50, 60)]					)
             UpperCAmelCase_    				=							torch.Size((50, 60)					)
             self.assertEqual(segmentation[0].shape	,       _UpperCAmelCase					)

             UpperCAmelCase_    				=							image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase					)
             UpperCAmelCase_    				=							torch.Size((32, 32)					)
             self.assertEqual(segmentation[0].shape	,       _UpperCAmelCase					)




