"""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()
UpperCAmelCase       =  logging.get_logger(__name__)






def      lowercase		(						a__	:		str     )						->   List[Any]:
					_UpperCamelCase      						=	SwinConfig.from_pretrained(
					    '''microsoft/swin-tiny-patch4-window7-224''' ,  out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     )
					_UpperCamelCase      						=	MaskFormerConfig(backbone_config=a__     )

					_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(a__ ,  a__ ,  repo_type='''dataset'''     ) ,  '''r'''     )     )
					_UpperCamelCase      						=	{int(a__     ): v for k, v in idalabel.items()}

					return config






def      lowercase		(						a__	:		Any     )						->   List[Any]:
					_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      lowercase		(						a__	:		Tuple ,  a__	:		Tuple ,  a__	:		Optional[Any]     )						->   Any:
					_UpperCamelCase      						=	dct.pop(a__     )
					_UpperCamelCase      						=	val






def      lowercase		(						a__	:		Optional[Any] ,  a__	:		Tuple     )						->   Union[str, Any]:
					_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      lowercase		(						a__	:		List[str] ,  a__	:		List[Any]     )						->   Union[str, Any]:
					# 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      lowercase		(						)						->   Tuple:
					_UpperCamelCase      						=	'http://images.cocodataset.org/val2017/000000039769.jpg'
					_UpperCamelCase      						=	Image.open(requests.get(a__ ,  stream=a__     ).raw     )
					return im






@torch.no_grad()
def      lowercase		(						a__	:		str ,  a__	:		str ,  a__	:		str ,  a__	:		bool = False     )						->   List[Any]:
					_UpperCamelCase      						=	get_maskformer_config(a__     )

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

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

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

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

					# load 🤗 model
					_UpperCamelCase      						=	MaskFormerForInstanceSegmentation(a__     )
					model.eval()

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

					_UpperCamelCase      						=	model.load_state_dict(a__ ,  strict=a__     )
					assert missing_keys == [
					    "model.pixel_level_module.encoder.model.layernorm.weight",
					    "model.pixel_level_module.encoder.model.layernorm.bias",
					]
					assert len(a__     ) == 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=a__ ,  reduce_labels=a__     )

					_UpperCamelCase      						=	image_processor(a__ ,  return_tensors='''pt'''     )

					_UpperCamelCase      						=	model(**a__     )

					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] ,  a__ ,  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(a__     ).mkdir(exist_ok=a__     )
										model.save_pretrained(a__     )
										image_processor.save_pretrained(a__     )

					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__":
		UpperCAmelCase       =  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."""
		)

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





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[int], _snake_case  :  List[Any], _snake_case  :  str=7, _snake_case  :  Tuple=3, _snake_case  :  List[str]=3_0, _snake_case  :  Tuple=4_0_0, _snake_case  :  Any=True, _snake_case  :  List[Any]=None, _snake_case  :  int=0.9, _snake_case  :  Optional[Any]=None, _snake_case  :  str=True, _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], )   ->List[Any]:
            snake_case__							:	int   	=	size if size is not None else {'shortest_edge': 3_0}
            snake_case__							:	Tuple   	=	crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
            snake_case__							:	Union[str, Any]   	=	parent
            snake_case__							:	Dict   	=	batch_size
            snake_case__							:	int   	=	num_channels
            snake_case__							:	Tuple   	=	min_resolution
            snake_case__							:	Any   	=	max_resolution
            snake_case__							:	List[Any]   	=	do_resize_and_center_crop
            snake_case__							:	str   	=	size
            snake_case__							:	str   	=	crop_pct
            snake_case__							:	List[str]   	=	crop_size
            snake_case__							:	Optional[int]   	=	do_normalize
            snake_case__							:	Tuple   	=	image_mean
            snake_case__							:	Tuple   	=	image_std






     def    lowercase_   (       self  :  Optional[int]					)   ->int:
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	PoolFormerImageProcessor if is_vision_available() else None






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Union[str, Any]   	=	PoolFormerImageProcessingTester(self					)






     @property
     def    lowercase_   (       self  :  int					)   ->Dict:
            return self.image_processor_tester.prepare_image_processor_dict()






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[int]:
            snake_case__							:	List[str]   	=	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(_snake_case, 'do_resize_and_center_crop'					)					)
            self.assertTrue(hasattr(_snake_case, 'size'					)					)
            self.assertTrue(hasattr(_snake_case, 'crop_pct'					)					)
            self.assertTrue(hasattr(_snake_case, 'do_normalize'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_mean'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_std'					)					)






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size, {'shortest_edge': 3_0}					)
            self.assertEqual(image_processor.crop_size, {'height': 3_0, 'width': 3_0}					)

            snake_case__							:	int   	=	self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4					)
            self.assertEqual(image_processor.size, {'shortest_edge': 4_2}					)
            self.assertEqual(image_processor.crop_size, {'height': 8_4, 'width': 8_4}					)






     def    lowercase_   (       self  :  List[Any]					)   ->List[Any]:
            pass






     def    lowercase_   (       self  :  List[str]					)   ->str:
            # Initialize image_processing
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, Image.Image					)

            # Test not batched input
            snake_case__							:	Optional[int]   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values

            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	str   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  int					)   ->List[Any]:
            # Initialize image_processing
            snake_case__							:	Optional[int]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            snake_case__							:	Dict   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, numpify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, np.ndarray					)

            # Test not batched input
            snake_case__							:	Dict   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	List[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            # Initialize image_processing
            snake_case__							:	Tuple   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, torchify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, torch.Tensor					)

            # Test not batched input
            snake_case__							:	Tuple   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	Optional[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






