


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			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''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			=							{
    '''b0''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_24,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_40,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 14_08,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_60,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 15_36,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_00,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 17_92,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_80,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 20_48,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_56,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 23_04,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_28,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 25_60,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_00,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}




def        snake_case_					(  snake_case					)  ->    Any:
       lowercase__:       Optional[int]        =       EfficientNetConfig()
       lowercase__:       Union[str, Any]        =       CONFIG_MAP[model_name]['hidden_dim']
       lowercase__:       Dict        =       CONFIG_MAP[model_name]['width_coef']
       lowercase__:       Any        =       CONFIG_MAP[model_name]['depth_coef']
       lowercase__:       Optional[Any]        =       CONFIG_MAP[model_name]['image_size']
       lowercase__:       List[Any]        =       CONFIG_MAP[model_name]['dropout_rate']
       lowercase__:       Optional[Any]        =       CONFIG_MAP[model_name]['dw_padding']

       lowercase__:       Any        =       'huggingface/label-files'
       lowercase__:       Tuple        =       'imagenet-1k-id2label.json'
       lowercase__:       str        =       10_00
       lowercase__:       List[str]        =       json.load(open(hf_hub_download(snake_case      ,							snake_case      ,							repo_type='dataset'					)      ,							'r'					)					)
       lowercase__:       Optional[Any]        =       {int(snake_case					): v for k, v in idalabel.items()}

       lowercase__:       Tuple        =       idalabel
       lowercase__:       List[str]        =       {v: k for k, v in idalabel.items()}
       return config




def        snake_case_					(  )  ->    Union[str, Any]:
       lowercase__:       int        =       'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:       Union[str, Any]        =       Image.open(requests.get(snake_case      ,							stream=snake_case					).raw					)
       return im




def        snake_case_					(  snake_case					)  ->    List[str]:
       lowercase__:       Any        =       CONFIG_MAP[model_name]['image_size']
       lowercase__:       Any        =       EfficientNetImageProcessor(
           size={'height': size, 'width': size}      ,							image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]      ,							image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]      ,							do_center_crop=snake_case      ,							)
       return preprocessor




def        snake_case_					(  snake_case					)  ->    List[str]:
       lowercase__:       Any        =       [v.split('_'					)[0].split('block'					)[1] for v in original_param_names if v.startswith('block'					)]
       lowercase__:       Optional[Any]        =       sorted(set(snake_case					)					)
       lowercase__:       int        =       len(snake_case					)
       lowercase__:       str        =       {b: str(snake_case					) for b, i in zip(snake_case      ,							range(snake_case					)					)}

       lowercase__:       List[str]        =       []
       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:
              lowercase__:       Optional[Any]        =       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')					)

       lowercase__:       Dict        =       {}
       for item in rename_keys:
              if item[0] in original_param_names:
                     lowercase__:       Dict        =       'efficientnet.' + item[1]

       lowercase__:       Any        =       'classifier.weight'
       lowercase__:       Any        =       'classifier.bias'
       return key_mapping




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Tuple:
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

              lowercase__:       Union[str, Any]        =       key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     lowercase__:       Optional[Any]        =       torch.from_numpy(snake_case					).permute(3      ,							2      ,							0      ,							1					)
              elif "depthwise_kernel" in key:
                     lowercase__:       Any        =       torch.from_numpy(snake_case					).permute(2      ,							3      ,							0      ,							1					)
              elif "kernel" in key:
                     lowercase__:       List[str]        =       torch.from_numpy(np.transpose(snake_case					)					)
              else:
                     lowercase__:       Any        =       torch.from_numpy(snake_case					)

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




@torch.no_grad()
def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    int:
       lowercase__:       int        =       model_classes[model_name](
           include_top=snake_case      ,							weights='imagenet'      ,							input_tensor=snake_case      ,							input_shape=snake_case      ,							pooling=snake_case      ,							classes=10_00      ,							classifier_activation='softmax'      ,							)

       lowercase__:       Dict        =       original_model.trainable_variables
       lowercase__:       List[str]        =       original_model.non_trainable_variables
       lowercase__:       str        =       {param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              lowercase__:       Any        =       param.numpy()
       lowercase__:       Dict        =       list(tf_params.keys()					)

       # Load HuggingFace model
       lowercase__:       Optional[Any]        =       get_efficientnet_config(snake_case					)
       lowercase__:       str        =       EfficientNetForImageClassification(snake_case					).eval()
       lowercase__:       Optional[Any]        =       hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print('Converting parameters...'					)
       lowercase__:       Tuple        =       rename_keys(snake_case					)
       replace_params(snake_case      ,							snake_case      ,							snake_case					)

       # Initialize preprocessor and preprocess input image
       lowercase__:       int        =       convert_image_processor(snake_case					)
       lowercase__:       List[str]        =       preprocessor(images=prepare_img()      ,							return_tensors='pt'					)

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              lowercase__:       str        =       hf_model(**snake_case					)
       lowercase__:       List[Any]        =       outputs.logits.detach().numpy()

       # Original model inference
       lowercase__:       Tuple        =       False
       lowercase__:       Tuple        =       CONFIG_MAP[model_name]['image_size']
       lowercase__:       str        =       prepare_img().resize((image_size, image_size)      ,							resample=PIL.Image.NEAREST					)
       lowercase__:       Union[str, Any]        =       image.img_to_array(snake_case					)
       lowercase__:       Tuple        =       np.expand_dims(snake_case      ,							axis=0					)
       lowercase__:       Optional[int]        =       original_model.predict(snake_case					)

       # Check whether original and HF model outputs match  -> np.allclose
       assert np.allclose(snake_case      ,							snake_case      ,							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(snake_case					):
                     os.mkdir(snake_case					)
              # Save converted model and image processor
              hf_model.save_pretrained(snake_case					)
              preprocessor.save_pretrained(snake_case					)

       if push_to_hub:
              # Push model and image processor to hub
              print(f'Pushing converted {model_name} to the hub...'					)
              lowercase__:       Optional[Any]        =       f'efficientnet-{model_name}'
              preprocessor.push_to_hub(snake_case					)
              hf_model.push_to_hub(snake_case					)


if __name__ == "__main__":
 __lowerCAmelCase			=							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			=							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 DetrImageProcessor





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




        '''simple docstring'''
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        lowercase__:       Dict        =       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
        lowercase__:       Tuple        =       parent
        lowercase__:       Optional[Any]        =       batch_size
        lowercase__:       Any        =       num_channels
        lowercase__:       str        =       min_resolution
        lowercase__:       Dict        =       max_resolution
        lowercase__:       Any        =       do_resize
        lowercase__:       str        =       size
        lowercase__:       Any        =       do_rescale
        lowercase__:       Union[str, Any]        =       rescale_factor
        lowercase__:       Optional[int]        =       do_normalize
        lowercase__:       Union[str, Any]        =       image_mean
        lowercase__:       List[str]        =       image_std
        lowercase__:       Optional[Any]        =       do_pad
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False			) ->    int:




        '''simple docstring'''
        if not batched:
               lowercase__:       List[Any]        =       image_inputs[0]
               if isinstance(lowerCAmelCase__    ,			Image.Image			):
                      lowercase__      ,						lowercase__:       List[str]        =       image.size
               else:
                      lowercase__      ,						lowercase__:       str        =       image.shape[1], image.shape[2]
               if w < h:
                      lowercase__:       Optional[int]        =       int(self.size['shortest_edge'] * h / w			)
                      lowercase__:       int        =       self.size['shortest_edge']
               elif w > h:
                      lowercase__:       Tuple        =       self.size['shortest_edge']
                      lowercase__:       int        =       int(self.size['shortest_edge'] * w / h			)
               else:
                      lowercase__:       Tuple        =       self.size['shortest_edge']
                      lowercase__:       Optional[Any]        =       self.size['shortest_edge']

        else:
               lowercase__:       str        =       []
               for image in image_inputs:
                      lowercase__      ,						lowercase__:       Any        =       self.get_expected_values([image]			)
                      expected_values.append((expected_height, expected_width)			)
               lowercase__:       Union[str, Any]        =       max(lowerCAmelCase__    ,			key=lambda lowerCAmelCase__			: item[0]			)[0]
               lowercase__:       Any        =       max(lowerCAmelCase__    ,			key=lambda lowerCAmelCase__			: item[1]			)[1]

        return expected_height, expected_width





@require_torch
@require_vision
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Tuple       					=			DetrImageProcessor if is_vision_available() else None
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       DetrImageProcessingTester(self			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        return self.image_processor_tester.prepare_image_processor_dict()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       Dict        =       self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'image_mean'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'image_std'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_normalize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_rescale'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'rescale_factor'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_resize'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'size'			)			)
        self.assertTrue(hasattr(lowerCAmelCase__    ,			'do_pad'			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       int        =       self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 18, 'longest_edge': 1_333}			)
        self.assertEqual(image_processor.do_pad    ,			lowerCAmelCase__			)

        lowercase__:       str        =       self.image_processing_class.from_dict(
            self.image_processor_dict    ,			size=42    ,			max_size=84    ,			pad_and_return_pixel_mask=lowerCAmelCase__			)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 42, 'longest_edge': 84}			)
        self.assertEqual(image_processor.do_pad    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Dict        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        lowercase__:       str        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			Image.Image			)

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

        lowercase__      ,						lowercase__:       Tuple        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

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

        # Test batched
        lowercase__      ,						lowercase__:       Union[str, Any]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

        lowercase__:       Dict        =       image_processing(lowerCAmelCase__    ,			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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Union[str, Any]        =       self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        lowercase__:       Optional[int]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			numpify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			np.ndarray			)

        # Test not batched input
        lowercase__:       Union[str, Any]        =       image_processing(image_inputs[0]    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Optional[int]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

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

        # Test batched
        lowercase__:       Union[str, Any]        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Dict        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

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




        '''simple docstring'''
        # Initialize image_processing
        lowercase__:       Tuple        =       self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        lowercase__:       Union[str, Any]        =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowerCAmelCase__    ,			torchify=lowerCAmelCase__			)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__    ,			torch.Tensor			)

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

        lowercase__      ,						lowercase__:       Dict        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__			)

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

        # Test batched
        lowercase__:       Optional[Any]        =       image_processing(lowerCAmelCase__    ,			return_tensors='pt'			).pixel_values

        lowercase__      ,						lowercase__:       Optional[Any]        =       self.image_processor_tester.get_expected_values(lowerCAmelCase__    ,			batched=lowerCAmelCase__			)

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




        '''simple docstring'''
        # prepare image and target
        lowercase__:       Any        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
        with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,			'r'			) as f:
               lowercase__:       Optional[int]        =       json.loads(f.read()			)

        lowercase__:       Optional[Any]        =       {'image_id': 39_769, 'annotations': target}

        # encode them
        lowercase__:       Optional[Any]        =       DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'			)
        lowercase__:       List[Any]        =       image_processing(images=lowerCAmelCase__    ,			annotations=lowerCAmelCase__    ,			return_tensors='pt'			)

        # verify pixel values
        lowercase__:       Optional[int]        =       torch.Size([1, 3, 800, 1_066]			)
        self.assertEqual(encoding['pixel_values'].shape    ,			lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)

        # verify area
        lowercase__:       List[Any]        =       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowerCAmelCase__			)			)
        # verify boxes
        lowercase__:       Union[str, Any]        =       torch.Size([6, 4]			)
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowerCAmelCase__			)
        lowercase__:       int        =       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowerCAmelCase__    ,			atol=1E-3			)			)
        # verify image_id
        lowercase__:       List[Any]        =       torch.tensor([39_769]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowerCAmelCase__			)			)
        # verify is_crowd
        lowercase__:       Union[str, Any]        =       torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowerCAmelCase__			)			)
        # verify class_labels
        lowercase__:       List[Any]        =       torch.tensor([75, 75, 63, 65, 17, 17]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowerCAmelCase__			)			)
        # verify orig_size
        lowercase__:       Tuple        =       torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowerCAmelCase__			)			)
        # verify size
        lowercase__:       Tuple        =       torch.tensor([800, 1_066]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowerCAmelCase__			)			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        # prepare image, target and masks_path
        lowercase__:       List[str]        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
        with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,			'r'			) as f:
               lowercase__:       Tuple        =       json.loads(f.read()			)

        lowercase__:       Tuple        =       {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

        lowercase__:       List[str]        =       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

        # encode them
        lowercase__:       Dict        =       DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'			)
        lowercase__:       Dict        =       image_processing(images=lowerCAmelCase__    ,			annotations=lowerCAmelCase__    ,			masks_path=lowerCAmelCase__    ,			return_tensors='pt'			)

        # verify pixel values
        lowercase__:       Any        =       torch.Size([1, 3, 800, 1_066]			)
        self.assertEqual(encoding['pixel_values'].shape    ,			lowerCAmelCase__			)

        lowercase__:       Any        =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)

        # verify area
        lowercase__:       str        =       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowerCAmelCase__			)			)
        # verify boxes
        lowercase__:       Dict        =       torch.Size([6, 4]			)
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowerCAmelCase__			)
        lowercase__:       str        =       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowerCAmelCase__    ,			atol=1E-3			)			)
        # verify image_id
        lowercase__:       Optional[int]        =       torch.tensor([39_769]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowerCAmelCase__			)			)
        # verify is_crowd
        lowercase__:       List[Any]        =       torch.tensor([0, 0, 0, 0, 0, 0]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowerCAmelCase__			)			)
        # verify class_labels
        lowercase__:       Optional[int]        =       torch.tensor([17, 17, 63, 75, 75, 93]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowerCAmelCase__			)			)
        # verify masks
        lowercase__:       str        =       822_873
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,			lowerCAmelCase__			)
        # verify orig_size
        lowercase__:       Optional[int]        =       torch.tensor([480, 640]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowerCAmelCase__			)			)
        # verify size
        lowercase__:       Optional[int]        =       torch.tensor([800, 1_066]			)
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowerCAmelCase__			)			)


