



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()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			A__	  =     SwinConfig.from_pretrained(
			    'microsoft/swin-tiny-patch4-window7-224'			,						out_features=['stage1', 'stage2', 'stage3', 'stage4']       )
			A__	  =     MaskFormerConfig(backbone_config=__UpperCamelCase       )

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

			A__	  =     json.load(open(hf_hub_download(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )			,						'r'       )       )
			A__	  =     {int(__UpperCamelCase       ): v for k, v in idalabel.items()}

			return config


def A    (							__UpperCamelCase       )    ->   Dict:
			A__	  =     []
			# 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    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   str:
			A__	  =     [int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
			for i in range(len(backbone_config.depths       )       ):
						A__	  =     num_features[i]
						for j in range(backbone_config.depths[i]       ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									A__	  =     state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''       )
									A__	  =     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
									A__	  =     in_proj_weight[:dim, :]
									A__	  =     in_proj_bias[: dim]
									A__	  =     in_proj_weight[
									    dim : dim * 2, :
									]
									A__	  =     in_proj_bias[
									    dim : dim * 2
									]
									A__	  =     in_proj_weight[
									    -dim :, :
									]
									A__	  =     in_proj_bias[-dim :]
									# fmt: on


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			# fmt: off
			A__	  =     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)
						A__	  =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''       )
						A__	  =     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
						A__	  =     in_proj_weight[: hidden_size, :]
						A__	  =     in_proj_bias[:config.hidden_size]
						A__	  =     in_proj_weight[hidden_size : hidden_size * 2, :]
						A__	  =     in_proj_bias[hidden_size : hidden_size * 2]
						A__	  =     in_proj_weight[-hidden_size :, :]
						A__	  =     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)
						A__	  =     state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''       )
						A__	  =     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
						A__	  =     in_proj_weight[: hidden_size, :]
						A__	  =     in_proj_bias[:config.hidden_size]
						A__	  =     in_proj_weight[hidden_size : hidden_size * 2, :]
						A__	  =     in_proj_bias[hidden_size : hidden_size * 2]
						A__	  =     in_proj_weight[-hidden_size :, :]
						A__	  =     in_proj_bias[-hidden_size :]
			# fmt: on


def A    (							)    ->   torch.Tensor:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase = False       )    ->   List[Any]:
			A__	  =     get_maskformer_config(__UpperCamelCase       )

			# load original state_dict
			with open(__UpperCamelCase			,						'rb'       ) as f:
						A__	  =     pickle.load(__UpperCamelCase       )
			A__	  =     data['model']

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

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

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

			# load 🤗 model
			A__	  =     MaskFormerForInstanceSegmentation(__UpperCamelCase       )
			model.eval()

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

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

			# verify results
			A__	  =     prepare_img()
			if "vistas" in model_name:
						A__	  =     65
			elif "cityscapes" in model_name:
						A__	  =     65_535
			else:
						A__	  =     255
			A__	  =     True if 'ade' in model_name else False
			A__	  =     MaskFormerImageProcessor(ignore_index=__UpperCamelCase			,						reduce_labels=__UpperCamelCase       )

			A__	  =     image_processor(__UpperCamelCase			,						return_tensors='pt'       )

			A__	  =     model(**__UpperCamelCase       )

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

			if model_name == "maskformer-swin-tiny-ade":
						A__	  =     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]			,						__UpperCamelCase			,						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(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
						model.save_pretrained(__UpperCamelCase       )
						image_processor.save_pretrained(__UpperCamelCase       )

			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__":
							SCREAMING_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.'''
							)

							SCREAMING_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 inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import YolosForObjectDetection, YolosModel
  from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=[30, 30]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=32		,      _UpperCAmelCase=5		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=10		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=3		,      _UpperCAmelCase=None		,      _UpperCAmelCase=8		,      _UpperCAmelCase=10		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			parent
          __A		:					Tuple						=			batch_size
          __A		:					List[str]						=			image_size
          __A		:					Dict						=			patch_size
          __A		:					Optional[Any]						=			num_channels
          __A		:					Tuple						=			is_training
          __A		:					Dict						=			use_labels
          __A		:					List[Any]						=			hidden_size
          __A		:					Tuple						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Optional[int]						=			intermediate_size
          __A		:					Tuple						=			hidden_act
          __A		:					Any						=			hidden_dropout_prob
          __A		:					Optional[Any]						=			attention_probs_dropout_prob
          __A		:					List[Any]						=			type_sequence_label_size
          __A		:					List[Any]						=			initializer_range
          __A		:					Optional[int]						=			num_labels
          __A		:					List[Any]						=			scope
          __A		:					Any						=			n_targets
          __A		:					Union[str, Any]						=			num_detection_tokens
          # we set the expected sequence length (which is used in several tests)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
          __A		:					List[str]						=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
          __A		:					int						=			num_patches + 1 + self.num_detection_tokens


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

          __A		:					Tuple						=			None
          if self.use_labels:
               # labels is a list of Dict (each Dict being the labels for a given example in the batch)
               __A		:					List[Any]						=			[]
               for i in range(self.batch_size):
                    __A		:					Optional[int]						=			{}
                    __A		:					Union[str, Any]						=			torch.randint(
                        high=self.num_labels		,      size=(self.n_targets,)		,      device=_UpperCAmelCase)
                    __A		:					str						=			torch.rand(self.n_targets		,      4		,      device=_UpperCAmelCase)
                    labels.append(_UpperCAmelCase)

          __A		:					Any						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return YolosConfig(
              image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      is_decoder=_UpperCAmelCase		,      initializer_range=self.initializer_range		,      num_detection_tokens=self.num_detection_tokens		,      num_labels=self.num_labels		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Dict						=			model(_UpperCAmelCase)
          self.parent.assertEqual(
              result.last_hidden_state.shape		,      (self.batch_size, self.expected_seq_len, self.hidden_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          __A		:					str						=			model(pixel_values=_UpperCAmelCase)
          __A		:					List[str]						=			model(_UpperCAmelCase)

          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
          self.parent.assertEqual(result.pred_boxes.shape		,      (self.batch_size, self.num_detection_tokens, 4))

          __A		:					Union[str, Any]						=			model(pixel_values=_UpperCAmelCase		,      labels=_UpperCAmelCase)

          self.parent.assertEqual(result.loss.shape		,      ())
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
          self.parent.assertEqual(result.pred_boxes.shape		,      (self.batch_size, self.num_detection_tokens, 4))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.prepare_config_and_inputs()
          __A					,__A					,__A		:					Tuple						=			config_and_inputs
          __A		:					Tuple						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     lowerCAmelCase						     =			(
         {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
     )

     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          __A		:					Optional[Any]						=			super()._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)

          if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                    __A		:					Any						=			[]
                    for i in range(self.model_tester.batch_size):
                         __A		:					Tuple						=			{}
                         __A		:					Tuple						=			torch.ones(
                             size=(self.model_tester.n_targets,)		,      device=_UpperCAmelCase		,      dtype=torch.long)
                         __A		:					Optional[Any]						=			torch.ones(
                             self.model_tester.n_targets		,      4		,      device=_UpperCAmelCase		,      dtype=torch.float)
                         labels.append(_UpperCAmelCase)
                    __A		:					str						=			labels

          return inputs_dict


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			YolosModelTester(self)
          __A		:					Dict						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      has_text_modality=_UpperCAmelCase		,      hidden_size=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module))
               __A		:					Any						=			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase		,      nn.Linear))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

               __A		:					List[str]						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Optional[int]						=			True

          # in YOLOS, the seq_len is different
          __A		:					Dict						=			self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
               __A		:					Dict						=			True
               __A		:					Dict						=			False
               __A		:					Union[str, Any]						=			True
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					Any						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					Union[str, Any]						=			outputs.attentions
               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               __A		:					List[Any]						=			True
               __A		:					List[str]						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[Any]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					Optional[Any]						=			outputs.attentions
               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)

               self.assertListEqual(
                   list(attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads, seq_len, seq_len]		,      )
               __A		:					str						=			len(_UpperCAmelCase)

               # Check attention is always last and order is fine
               __A		:					Dict						=			True
               __A		:					Dict						=			True
               __A		:					Dict						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()
               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Union[str, Any]						=			1
               self.assertEqual(out_len + added_hidden_states		,      len(_UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.attentions

               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads, seq_len, seq_len]		,      )


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          def check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.hidden_states

               __A		:					List[str]						=			getattr(
                   self.model_tester		,      'expected_num_hidden_layers'		,      self.model_tester.num_hidden_layers + 1)
               self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

               # YOLOS has a different seq_length
               __A		:					Dict						=			self.model_tester.expected_seq_len

               self.assertListEqual(
                   list(hidden_states[0].shape[-2:])		,      [seq_length, self.model_tester.hidden_size]		,      )

          __A					,__A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					List[str]						=			True
               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __A		:					Optional[int]						=			True

               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A		:					List[Any]						=			YolosModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)






def 			_lowerCAmelCase     (    )	->	int:
     __A		:					Dict						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     return image







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


     @cached_property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_UpperCAmelCase)

          __A		:					Any						=			self.default_image_processor
          __A		:					str						=			prepare_img()
          __A		:					int						=			image_processor(images=_UpperCAmelCase		,      return_tensors='pt').to(_UpperCAmelCase)

          # forward pass
          with torch.no_grad():
               __A		:					str						=			model(inputs.pixel_values)

          # verify outputs
          __A		:					Tuple						=			torch.Size((1, 100, 92))
          self.assertEqual(outputs.logits.shape		,      _UpperCAmelCase)

          __A		:					Dict						=			torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		,      device=_UpperCAmelCase		,      )
          __A		:					int						=			torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]		,      device=_UpperCAmelCase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))

          # verify postprocessing
          __A		:					List[str]						=			image_processor.post_process_object_detection(
              _UpperCAmelCase		,      threshold=0.3		,      target_sizes=[image.size[::-1]])[0]
          __A		:					Optional[int]						=			torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			[75, 75, 17, 63, 17]
          __A		:					Any						=			torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(_UpperCAmelCase)

          self.assertEqual(len(results['scores'])		,      5)
          self.assertTrue(torch.allclose(results['scores']		,      _UpperCAmelCase		,      atol=1e-4))
          self.assertSequenceEqual(results['labels'].tolist()		,      _UpperCAmelCase)
          self.assertTrue(torch.allclose(results['boxes'][0, :]		,      _UpperCAmelCase))