
'''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()
snake_case									=    logging.get_logger(__name__)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	SwinConfig.from_pretrained(
     "microsoft/swin-tiny-patch4-window7-224"      ,			out_features=["stage1", "stage2", "stage3", "stage4"]			)
 lowerCAmelCase__     :   Dict     		=	MaskFormerConfig(backbone_config=lowerCamelCase_			)

 lowerCAmelCase__     :   Any     		=	"huggingface/label-files"
 if "ade20k-full" in model_name:
  # this should be ok
  lowerCAmelCase__     :   Optional[Any]     		=	8_4_7
  lowerCAmelCase__     :   Dict     		=	"maskformer-ade20k-full-id2label.json"
 elif "ade" in model_name:
  # this should be ok
  lowerCAmelCase__     :   Union[str, Any]     		=	1_5_0
  lowerCAmelCase__     :   int     		=	"ade20k-id2label.json"
 elif "coco-stuff" in model_name:
  # this should be ok
  lowerCAmelCase__     :   Union[str, Any]     		=	1_7_1
  lowerCAmelCase__     :   Any     		=	"maskformer-coco-stuff-id2label.json"
 elif "coco" in model_name:
  # TODO
  lowerCAmelCase__     :   List[str]     		=	1_3_3
  lowerCAmelCase__     :   str     		=	"coco-panoptic-id2label.json"
 elif "cityscapes" in model_name:
  # this should be ok
  lowerCAmelCase__     :   str     		=	1_9
  lowerCAmelCase__     :   Any     		=	"cityscapes-id2label.json"
 elif "vistas" in model_name:
  # this should be ok
  lowerCAmelCase__     :   Any     		=	6_5
  lowerCAmelCase__     :   Dict     		=	"mapillary-vistas-id2label.json"

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

 return config


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]
 # 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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   Any     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
 for i in range(len(backbone_config.depths			)			):
  lowerCAmelCase__     :   Tuple     		=	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)
   lowerCAmelCase__     :   Union[str, Any]     		=	state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
   lowerCAmelCase__     :   Optional[int]     		=	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
   lowerCAmelCase__     :   Dict     		=	in_proj_weight[:dim, :]
   lowerCAmelCase__     :   Tuple     		=	in_proj_bias[: dim]
   lowerCAmelCase__     :   Union[str, Any]     		=	in_proj_weight[
       dim : dim * 2, :
   ]
   lowerCAmelCase__     :   int     		=	in_proj_bias[
       dim : dim * 2
   ]
   lowerCAmelCase__     :   str     		=	in_proj_weight[
       -dim :, :
   ]
   lowerCAmelCase__     :   List[Any]     		=	in_proj_bias[-dim :]
   # fmt: on


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	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)
  lowerCAmelCase__     :   Dict     		=	state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
  lowerCAmelCase__     :   List[str]     		=	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
  lowerCAmelCase__     :   Tuple     		=	in_proj_weight[: hidden_size, :]
  lowerCAmelCase__     :   Optional[Any]     		=	in_proj_bias[:config.hidden_size]
  lowerCAmelCase__     :   Optional[Any]     		=	in_proj_weight[hidden_size : hidden_size * 2, :]
  lowerCAmelCase__     :   Dict     		=	in_proj_bias[hidden_size : hidden_size * 2]
  lowerCAmelCase__     :   str     		=	in_proj_weight[-hidden_size :, :]
  lowerCAmelCase__     :   Optional[Any]     		=	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)
  lowerCAmelCase__     :   Dict     		=	state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
  lowerCAmelCase__     :   Optional[int]     		=	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
  lowerCAmelCase__     :   Tuple     		=	in_proj_weight[: hidden_size, :]
  lowerCAmelCase__     :   List[str]     		=	in_proj_bias[:config.hidden_size]
  lowerCAmelCase__     :   str     		=	in_proj_weight[hidden_size : hidden_size * 2, :]
  lowerCAmelCase__     :   Dict     		=	in_proj_bias[hidden_size : hidden_size * 2]
  lowerCAmelCase__     :   int     		=	in_proj_weight[-hidden_size :, :]
  lowerCAmelCase__     :   Tuple     		=	in_proj_bias[-hidden_size :]
 # fmt: on


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCAmelCase__     :   Any     		=	Image.open(requests.get(lowerCamelCase_      ,			stream=lowerCamelCase_			).raw			)
 return im


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_ = False			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	get_maskformer_config(lowerCamelCase_			)

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

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

 # rename keys
 lowerCAmelCase__     :   List[str]     		=	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():
  lowerCAmelCase__     :   Union[str, Any]     		=	torch.from_numpy(lowerCamelCase_			)

 # load 🤗 model
 lowerCAmelCase__     :   int     		=	MaskFormerForInstanceSegmentation(lowerCamelCase_			)
 model.eval()

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

 lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	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
 lowerCAmelCase__     :   Union[str, Any]     		=	prepare_img()
 if "vistas" in model_name:
  lowerCAmelCase__     :   Any     		=	6_5
 elif "cityscapes" in model_name:
  lowerCAmelCase__     :   Optional[int]     		=	6_5_5_3_5
 else:
  lowerCAmelCase__     :   Optional[int]     		=	2_5_5
 lowerCAmelCase__     :   List[Any]     		=	True if "ade" in model_name else False
 lowerCAmelCase__     :   List[str]     		=	MaskFormerImageProcessor(ignore_index=lowerCamelCase_      ,			reduce_labels=lowerCamelCase_			)

 lowerCAmelCase__     :   List[Any]     		=	image_processor(lowerCamelCase_      ,			return_tensors="pt"			)

 lowerCAmelCase__     :   Optional[int]     		=	model(**lowerCamelCase_			)

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

 if model_name == "maskformer-swin-tiny-ade":
  lowerCAmelCase__     :   Union[str, Any]     		=	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__":
     snake_case									=    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."""
     )

     snake_case									=    parser.parse_args()
     convert_maskformer_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )







'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





