


import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :Union[str, Any]             =							logging.get_logger(__name__)

lowerCAmelCase__  :Tuple             =							{
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

lowerCAmelCase__  :int             =							{
    '''b0''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_2_4,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_4_0,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [1_6],
    },
    '''b2''': {
        '''hidden_dim''': 1_4_0_8,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_6_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 1_6],
    },
    '''b3''': {
        '''hidden_dim''': 1_5_3_6,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_0_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 1_8],
    },
    '''b4''': {
        '''hidden_dim''': 1_7_9_2,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_8_0,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_0_4_8,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_5_6,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [1_3, 2_7],
    },
    '''b6''': {
        '''hidden_dim''': 2_3_0_4,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_2_8,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [3_1],
    },
    '''b7''': {
        '''hidden_dim''': 2_5_6_0,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_0_0,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [1_8],
    },
}

def 	lowerCAmelCase__			(      a__:		Any	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      EfficientNetConfig()
							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""hidden_dim"""]
							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""width_coef"""]
							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""depth_coef"""]
							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""image_size"""]
							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""dropout_rate"""]
							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""dw_padding"""]

							_UpperCAmelCase					=      """huggingface/label-files"""
							_UpperCAmelCase					=      """imagenet-1k-id2label.json"""
							_UpperCAmelCase					=      1_0_0_0
							_UpperCAmelCase					=      json.load(open(hf_hub_download(a__      ,  a__      ,  repo_type='dataset'	)      ,  'r'	)	)
							_UpperCAmelCase					=      {int(a__	): v for k, v in idalabel.items()}

							_UpperCAmelCase					=      idalabel
							_UpperCAmelCase					=      {v: k for k, v in idalabel.items()}
							return config

def 	lowerCAmelCase__			(      )  ->		int:


							'''simple docstring'''





							_UpperCAmelCase					=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
							_UpperCAmelCase					=      Image.open(requests.get(a__      ,  stream=a__	).raw	)
							return im

def 	lowerCAmelCase__			(      a__:		Any	)  ->		str:


							'''simple docstring'''





							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""image_size"""]
							_UpperCAmelCase					=      EfficientNetImageProcessor(
							    size={'height': size, 'width': size}      ,  image_mean=[0.485, 0.456, 0.406]      ,  image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]      ,  do_center_crop=a__      ,  )
							return preprocessor

def 	lowerCAmelCase__			(      a__:		Tuple	)  ->		List[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      [v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
							_UpperCAmelCase					=      sorted(set(a__	)	)
							_UpperCAmelCase					=      len(a__	)
							_UpperCAmelCase					=      {b: str(a__	) for b, i in zip(a__      ,  range(a__	)	)}

							_UpperCAmelCase					=      []
							rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
							rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
							rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
							rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
							rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

							for b in block_names:
														_UpperCAmelCase					=      block_name_mapping[b]
														rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')	)
														rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')	)
														rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')	)
														rename_keys.append(
														    (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')	)
														rename_keys.append(
														    (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')	)
														rename_keys.append(
														    (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')	)
														rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')	)
														rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')	)
														rename_keys.append(
														    (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')	)
														rename_keys.append(
														    (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')	)

														rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')	)
														rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')	)
														rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')	)
														rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')	)
														rename_keys.append(
														    (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')	)
														rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')	)
														rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')	)
														rename_keys.append(
														    (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')	)
														rename_keys.append(
														    (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')	)

							rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')	)
							rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')	)
							rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')	)
							rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')	)
							rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')	)

							_UpperCAmelCase					=      {}
							for item in rename_keys:
														if item[0] in original_param_names:
																					_UpperCAmelCase					=      """efficientnet.""" + item[1]

							_UpperCAmelCase					=      """classifier.weight"""
							_UpperCAmelCase					=      """classifier.bias"""
							return key_mapping

def 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		Optional[Any]      ,  a__:		List[str]	)  ->		str:


							'''simple docstring'''





							for key, value in tf_params.items():
														if "normalization" in key:
																					continue

														_UpperCAmelCase					=      key_mapping[key]
														if "_conv" in key and "kernel" in key:
																					_UpperCAmelCase					=      torch.from_numpy(a__	).permute(3      ,  2      ,  0      ,  1	)
														elif "depthwise_kernel" in key:
																					_UpperCAmelCase					=      torch.from_numpy(a__	).permute(2      ,  3      ,  0      ,  1	)
														elif "kernel" in key:
																					_UpperCAmelCase					=      torch.from_numpy(np.transpose(a__	)	)
														else:
																					_UpperCAmelCase					=      torch.from_numpy(a__	)

														# Replace HF parameters with original TF model parameters
														assert hf_params[hf_key].shape == new_hf_value.shape
														hf_params[hf_key].copy_(a__	)

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		Tuple      ,  a__:		Optional[Any]      ,  a__:		int	)  ->		Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      model_classes[model_name](
							    include_top=a__      ,  weights='imagenet'      ,  input_tensor=a__      ,  input_shape=a__      ,  pooling=a__      ,  classes=1_0_0_0      ,  classifier_activation='softmax'      ,  )

							_UpperCAmelCase					=      original_model.trainable_variables
							_UpperCAmelCase					=      original_model.non_trainable_variables
							_UpperCAmelCase					=      {param.name: param.numpy() for param in tf_params}
							for param in tf_non_train_params:
														_UpperCAmelCase					=      param.numpy()
							_UpperCAmelCase					=      list(tf_params.keys()	)

							# Load HuggingFace model
							_UpperCAmelCase					=      get_efficientnet_config(a__	)
							_UpperCAmelCase					=      EfficientNetForImageClassification(a__	).eval()
							_UpperCAmelCase					=      hf_model.state_dict()

							# Create src-to-dst parameter name mapping dictionary
							print('Converting parameters...'	)
							_UpperCAmelCase					=      rename_keys(a__	)
							replace_params(a__      ,  a__      ,  a__	)

							# Initialize preprocessor and preprocess input image
							_UpperCAmelCase					=      convert_image_processor(a__	)
							_UpperCAmelCase					=      preprocessor(images=prepare_img()      ,  return_tensors='pt'	)

							# HF model inference
							hf_model.eval()
							with torch.no_grad():
														_UpperCAmelCase					=      hf_model(**a__	)
							_UpperCAmelCase					=      outputs.logits.detach().numpy()

							# Original model inference
							_UpperCAmelCase					=      False
							_UpperCAmelCase					=      CONFIG_MAP[model_name]["""image_size"""]
							_UpperCAmelCase					=      prepare_img().resize((image_size, image_size)      ,  resample=PIL.Image.NEAREST	)
							_UpperCAmelCase					=      image.img_to_array(a__	)
							_UpperCAmelCase					=      np.expand_dims(a__      ,  axis=0	)
							_UpperCAmelCase					=      original_model.predict(a__	)

							# Check whether original and HF model outputs match  -> np.allclose
							assert np.allclose(a__      ,  a__      ,  atol=1e-3	), "The predicted logits are not the same."
							print('Model outputs match!'	)

							if save_model:
														# Create folder to save model
														if not os.path.isdir(a__	):
																					os.mkdir(a__	)
														# Save converted model and image processor
														hf_model.save_pretrained(a__	)
														preprocessor.save_pretrained(a__	)

							if push_to_hub:
														# Push model and image processor to hub
														print(F'''Pushing converted {model_name} to the hub...'''	)
														_UpperCAmelCase					=      F'''efficientnet-{model_name}'''
														preprocessor.push_to_hub(a__	)
														hf_model.push_to_hub(a__	)


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''b0''',
		    type=str,
		    help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''hf_model''',
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

		lowerCAmelCase__  :Tuple             =							parser.parse_args()
		convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor

class 		__a				(    unittest.TestCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=30      ,   _SCREAMING_SNAKE_CASE=400      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=1 / 255      ,   _SCREAMING_SNAKE_CASE=True      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      min_resolution
									_UpperCAmelCase					=      max_resolution
									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean
									_UpperCAmelCase					=      image_std
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_pad
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	Optional[Any]:




									"""simple docstring"""





									if not batched:
																_UpperCAmelCase					=      image_inputs[0]
																if isinstance(_SCREAMING_SNAKE_CASE      ,   Image.Image						):
																							_UpperCAmelCase		,	_UpperCAmelCase					=      image.size
																else:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      image.shape[1], image.shape[2]
																if w < h:
																							_UpperCAmelCase					=      int(self.size['shortest_edge'] * h / w						)
																							_UpperCAmelCase					=      self.size['shortest_edge']
																elif w > h:
																							_UpperCAmelCase					=      self.size['shortest_edge']
																							_UpperCAmelCase					=      int(self.size['shortest_edge'] * w / h						)
																else:
																							_UpperCAmelCase					=      self.size['shortest_edge']
																							_UpperCAmelCase					=      self.size['shortest_edge']

									else:
																_UpperCAmelCase					=      []
																for image in image_inputs:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      self.get_expected_values([image]						)
																							expected_values.append((expected_height, expected_width)						)
																_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[0]						)[0]
																_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[1]						)[1]

									return expected_height, expected_width



@require_torch
@require_vision
class 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Tuple           =     ConditionalDetrImageProcessor if is_vision_available() else None
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      ConditionalDetrImageProcessingTester(self						)
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_mean'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_std'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_normalize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_resize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'size'						)						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size      ,   {'shortest_edge': 18, 'longest_edge': 1333}						)
									self.assertEqual(image_processor.do_pad      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.image_processing_class.from_dict(
									    self.image_processor_dict      ,   size=42      ,   max_size=84      ,   pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE						)
									self.assertEqual(image_processor.size      ,   {'shortest_edge': 42, 'longest_edge': 84}						)
									self.assertEqual(image_processor.do_pad      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





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




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   Image.Image						)

									# Test not batched input
									_UpperCAmelCase					=      image_processing(image_inputs[0]      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

									# Test batched
									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE      ,   numpify=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   np.ndarray						)

									# Test not batched input
									_UpperCAmelCase					=      image_processing(image_inputs[0]      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE      ,   torchify=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   torch.Tensor						)

									# Test not batched input
									_UpperCAmelCase					=      image_processing(image_inputs[0]      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,   )

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,   'r'						) as f:
																_UpperCAmelCase					=      json.loads(f.read()						)

									_UpperCAmelCase					=      {'image_id': 39769, 'annotations': target}

									# encode them
									_UpperCAmelCase					=      ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'						)
									_UpperCAmelCase					=      image_processing(images=_SCREAMING_SNAKE_CASE      ,   annotations=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

									# verify pixel values
									_UpperCAmelCase					=      torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding['pixel_values'].shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.tensor([0.2796, 0.3138, 0.3481]						)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)

									# verify area
									_UpperCAmelCase					=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([75, 75, 63, 65, 17, 17]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,   'r'						) as f:
																_UpperCAmelCase					=      json.loads(f.read()						)

									_UpperCAmelCase					=      {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

									_UpperCAmelCase					=      pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

									# encode them
									_UpperCAmelCase					=      ConditionalDetrImageProcessor(format='coco_panoptic'						)
									_UpperCAmelCase					=      image_processing(images=_SCREAMING_SNAKE_CASE      ,   annotations=_SCREAMING_SNAKE_CASE      ,   masks_path=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

									# verify pixel values
									_UpperCAmelCase					=      torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding['pixel_values'].shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.tensor([0.2796, 0.3138, 0.3481]						)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)

									# verify area
									_UpperCAmelCase					=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([17, 17, 63, 75, 75, 93]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify masks
									_UpperCAmelCase					=      822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,   _SCREAMING_SNAKE_CASE						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)
