

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 DeformableDetrImageProcessor



class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[Any]=7       ,						lowerCAmelCase__  :					Tuple=3       ,						lowerCAmelCase__  :					Optional[int]=30       ,						lowerCAmelCase__  :					Dict=400       ,						lowerCAmelCase__  :					int=True       ,						lowerCAmelCase__  :					Dict=None       ,						lowerCAmelCase__  :					Union[str, Any]=True       ,						lowerCAmelCase__  :					Any=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					Optional[Any]=[0.5, 0.5, 0.5]       ,						lowerCAmelCase__  :					List[Any]=True       ,						lowerCAmelCase__  :					Tuple=1 / 255       ,						lowerCAmelCase__  :					int=True       ,						):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
									SCREAMING_SNAKE_CASE_:			str  		=	parent
									SCREAMING_SNAKE_CASE_:			Tuple  		=	batch_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	num_channels
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	min_resolution
									SCREAMING_SNAKE_CASE_:			Tuple  		=	max_resolution
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Any  		=	image_mean
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_std
									SCREAMING_SNAKE_CASE_:			Tuple  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			int  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			int  		=	do_pad



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									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 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					int=False):
									if not batched:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	image_inputs[0]
															if isinstance(lowerCAmelCase__       ,						Image.Image):
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image.size
															else:
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image.shape[1], image.shape[2]
															if w < h:
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	int(self.size["shortest_edge"] * h / w)
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.size["shortest_edge"]
															elif w > h:
																					SCREAMING_SNAKE_CASE_:			Any  		=	self.size["shortest_edge"]
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	int(self.size["shortest_edge"] * w / h)
															else:
																					SCREAMING_SNAKE_CASE_:			int  		=	self.size["shortest_edge"]
																					SCREAMING_SNAKE_CASE_:			Dict  		=	self.size["shortest_edge"]

									else:
															SCREAMING_SNAKE_CASE_:			int  		=	[]
															for image in image_inputs:
																					SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.get_expected_values([image])
																					expected_values.append((expected_height, expected_width))
															SCREAMING_SNAKE_CASE_:			Tuple  		=	max(lowerCAmelCase__       ,						key=lambda lowerCAmelCase__: item[0])[0]
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max(lowerCAmelCase__       ,						key=lambda lowerCAmelCase__: item[1])[1]

									return expected_height, expected_width







@require_torch
@require_vision
class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					DeformableDetrImageProcessor if is_vision_available() else None



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	DeformableDetrImageProcessingTester(self)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self.image_processor_tester.prepare_image_processor_dict()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			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_resize"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_rescale"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"do_pad"))
									self.assertTrue(hasattr(lowerCAmelCase__       ,						"size"))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Dict  		=	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       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	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  :					Optional[Any]):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCAmelCase__)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase__       ,						Image.Image)

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

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	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
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,						batched=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	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  :					str):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	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
									SCREAMING_SNAKE_CASE_:			str  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	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
									SCREAMING_SNAKE_CASE_:			Any  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	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  :					List[Any]):
									# Initialize image_processing
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE_:			int  		=	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
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_processing(image_inputs[0]       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	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
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(lowerCAmelCase__       ,						return_tensors="pt").pixel_values

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			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  :					Tuple):
									# prepare image and target
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
									with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,						"r") as f:
															SCREAMING_SNAKE_CASE_:			str  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{"image_id": 3_9769, "annotations": target}

									# encode them
									SCREAMING_SNAKE_CASE_:			str  		=	DeformableDetrImageProcessor()
									SCREAMING_SNAKE_CASE_:			Dict  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						return_tensors="pt")

									# verify pixel values
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.Size([1, 3, 800, 1066])
									self.assertEqual(encoding["pixel_values"].shape       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			int  		=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			str  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			int  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.tensor([75, 75, 63, 65, 17, 17])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# prepare image, target and masks_path
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
									with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,						"r") as f:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	json.loads(f.read())

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

									SCREAMING_SNAKE_CASE_:			int  		=	pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

									# encode them
									SCREAMING_SNAKE_CASE_:			Any  		=	DeformableDetrImageProcessor(format="coco_panoptic")
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_processing(images=lowerCAmelCase__       ,						annotations=lowerCAmelCase__       ,						masks_path=lowerCAmelCase__       ,						return_tensors="pt")

									# verify pixel values
									SCREAMING_SNAKE_CASE_:			Dict  		=	torch.Size([1, 3, 800, 1066])
									self.assertEqual(encoding["pixel_values"].shape       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([0.2796, 0.3138, 0.3481])
									self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCAmelCase__       ,						atol=1E-4))

									# verify area
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
									self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCAmelCase__))
									# verify boxes
									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.Size([6, 4])
									self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
									self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCAmelCase__       ,						atol=1E-3))
									# verify image_id
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.tensor([3_9769])
									self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCAmelCase__))
									# verify is_crowd
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCAmelCase__))
									# verify class_labels
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.tensor([17, 17, 63, 75, 75, 93])
									self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCAmelCase__))
									# verify masks
									SCREAMING_SNAKE_CASE_:			Tuple  		=	82_2873
									self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,						lowerCAmelCase__)
									# verify orig_size
									SCREAMING_SNAKE_CASE_:			str  		=	torch.tensor([480, 640])
									self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCAmelCase__))
									# verify size
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.tensor([800, 1066])
									self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCAmelCase__))









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							:  Tuple            =						logging.get_logger(__name__)

lowerCAmelCase							:  Any            =						{
    """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							:  str            =						{
    """b0""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Dict  		=	EfficientNetConfig()
						SCREAMING_SNAKE_CASE_:			Any  		=	CONFIG_MAP[model_name]["hidden_dim"]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	CONFIG_MAP[model_name]["width_coef"]
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	CONFIG_MAP[model_name]["depth_coef"]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CONFIG_MAP[model_name]["dropout_rate"]
						SCREAMING_SNAKE_CASE_:			Dict  		=	CONFIG_MAP[model_name]["dw_padding"]

						SCREAMING_SNAKE_CASE_:			str  		=	"huggingface/label-files"
						SCREAMING_SNAKE_CASE_:			str  		=	"imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE_:			int  		=	10_00
						SCREAMING_SNAKE_CASE_:			int  		=	json.load(open(hf_hub_download(_UpperCAmelCase    ,     _UpperCAmelCase    ,     repo_type="dataset"   )    ,     "r"   )   )
						SCREAMING_SNAKE_CASE_:			int  		=	{int(_UpperCAmelCase   ): v for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE_:			Any  		=	idalabel
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{v: k for k, v in idalabel.items()}
						return config







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







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			Optional[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=_UpperCAmelCase    ,     )
						return preprocessor







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[v.split("_"   )[0].split("block"   )[1] for v in original_param_names if v.startswith("block"   )]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	sorted(set(_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			int  		=	len(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{b: str(_UpperCAmelCase   ) for b, i in zip(_UpperCAmelCase    ,     range(_UpperCAmelCase   )   )}

						SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
						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:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	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")   )

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{}
						for item in rename_keys:
												if item[0] in original_param_names:
																		SCREAMING_SNAKE_CASE_:			str  		=	"efficientnet." + item[1]

						SCREAMING_SNAKE_CASE_:			List[str]  		=	"classifier.weight"
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"classifier.bias"
						return key_mapping







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

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

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







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	model_classes[model_name](
						    include_top=_UpperCAmelCase    ,     weights="imagenet"    ,     input_tensor=_UpperCAmelCase    ,     input_shape=_UpperCAmelCase    ,     pooling=_UpperCAmelCase    ,     classes=10_00    ,     classifier_activation="softmax"    ,     )

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

						# Load HuggingFace model
						SCREAMING_SNAKE_CASE_:			Any  		=	get_efficientnet_config(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	EfficientNetForImageClassification(_UpperCAmelCase   ).eval()
						SCREAMING_SNAKE_CASE_:			str  		=	hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print("Converting parameters..."   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	rename_keys(_UpperCAmelCase   )
						replace_params(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

						# Initialize preprocessor and preprocess input image
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	convert_image_processor(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	preprocessor(images=prepare_img()    ,     return_tensors="pt"   )

						# HF model inference
						hf_model.eval()
						with torch.no_grad():
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hf_model(**_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	outputs.logits.detach().numpy()

						# Original model inference
						SCREAMING_SNAKE_CASE_:			Dict  		=	False
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			int  		=	prepare_img().resize((image_size, image_size)    ,     resample=PIL.Image.NEAREST   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	image.img_to_array(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	np.expand_dims(_UpperCAmelCase    ,     axis=0   )
						SCREAMING_SNAKE_CASE_:			str  		=	original_model.predict(_UpperCAmelCase   )

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

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


if __name__ == "__main__":
						lowerCAmelCase							:  List[str]            =						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							:  Any            =						parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






