
'''simple docstring'''
import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor


class        SCREAMING_SNAKE_CASE__	(			unittest.TestCase):







				def __init__(    self		,							A_		,							A_=3		,							A_=32		,							A_=3		,							A_=10		,							A_=[10, 20, 30, 40]		,							A_=[1, 1, 2, 1]		,							A_=True		,							A_=True		,							A_="relu"		,							A_=3		,							A_=None		,							)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			parent
											UpperCamelCase					       =			batch_size
											UpperCamelCase					       =			image_size
											UpperCamelCase					       =			num_channels
											UpperCamelCase					       =			embeddings_size
											UpperCamelCase					       =			hidden_sizes
											UpperCamelCase					       =			depths
											UpperCamelCase					       =			is_training
											UpperCamelCase					       =			use_labels
											UpperCamelCase					       =			hidden_act
											UpperCamelCase					       =			num_labels
											UpperCamelCase					       =			scope
											UpperCamelCase					       =			len(A_			)







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

											UpperCamelCase					       =			self.get_config()

											return config, pixel_values







				def 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											return ResNetConfig(
											    num_channels=self.num_channels		,							embeddings_size=self.embeddings_size		,							hidden_sizes=self.hidden_sizes		,							depths=self.depths		,							hidden_act=self.hidden_act		,							num_labels=self.num_labels		,							image_size=self.image_size		,							)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			FlaxResNetModel(config=A_			)
											UpperCamelCase					       =			model(A_			)

											# Output shape (b, c, h, w)
											self.parent.assertEqual(
											    result.last_hidden_state.shape		,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,							)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						Optional[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.num_labels
											UpperCamelCase					       =			FlaxResNetForImageClassification(config=A_			)
											UpperCamelCase					       =			model(A_			)
											self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels)			)







				def 		UpperCAmelCase_  (    self			)->						Any:







											'''simple docstring'''






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


@require_flax
class        SCREAMING_SNAKE_CASE__	(			snake_case_				,    unittest.TestCase):
				lowerCAmelCase_			       =				(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

				lowerCAmelCase_			       =				False
				lowerCAmelCase_			       =				False
				lowerCAmelCase_			       =				False







				def 		UpperCAmelCase_  (    self			)->						None:







											'''simple docstring'''






											UpperCamelCase					       =			FlaxResNetModelTester(self			)
											UpperCamelCase					       =			ConfigTester(self		,							config_class=A_		,							has_text_modality=A_			)







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()







				def 		UpperCAmelCase_  (    self			)->						str:







											'''simple docstring'''






											return







				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_			)







				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*A_			)







				@unittest.skip(reason='ResNet does not use inputs_embeds'			)
				def 		UpperCAmelCase_  (    self			)->						str:







											'''simple docstring'''






											pass







				@unittest.skip(reason='ResNet does not support input and output embeddings'			)
				def 		UpperCAmelCase_  (    self			)->						str:







											'''simple docstring'''






											pass







				def 		UpperCAmelCase_  (    self			)->						Optional[int]:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		UpperCamelCase					       =			model_class(A_			)
																		UpperCamelCase					       =			inspect.signature(model.__call__			)
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		UpperCamelCase					       =			[*signature.parameters.keys()]

																		UpperCamelCase					       =			['pixel_values']
																		self.assertListEqual(arg_names[:1]		,							A_			)







				def 		UpperCAmelCase_  (    self			)->						Any:







											'''simple docstring'''






											def check_hidden_states_output(A_		,							A_		,							A_			):
																		UpperCamelCase					       =			model_class(A_			)

																		UpperCamelCase					       =			model(**self._prepare_for_class(A_		,							A_			)			)

																		UpperCamelCase					       =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		UpperCamelCase					       =			self.model_tester.num_stages
																		self.assertEqual(len(A_			)		,							expected_num_stages + 1			)







				@unittest.skip(reason='ResNet does not use feedforward chunking'			)
				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											pass







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase       ,     UpperCamelCase					       =			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__			):
																									UpperCamelCase					       =			self._prepare_for_class(A_		,							A_			)
																									UpperCamelCase					       =			model_class(A_			)

																									@jax.jit
																									def model_jitted(A_		,							**A_			):
																																return model(pixel_values=A_		,							**A_			)

																									with self.subTest('JIT Enabled'			):
																																UpperCamelCase					       =			model_jitted(**A_			).to_tuple()

																									with self.subTest('JIT Disabled'			):
																																with jax.disable_jit():
																																							UpperCamelCase					       =			model_jitted(**A_			).to_tuple()

																									self.assertEqual(len(A_			)		,							len(A_			)			)
																									for jitted_output, output in zip(A_		,							A_			):
																																self.assertEqual(jitted_output.shape		,							output.shape			)







def 						A_(			):
							UpperCamelCase					       =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
							return image





@require_flax
class        SCREAMING_SNAKE_CASE__	(			unittest.TestCase):







				@cached_property
				def 		UpperCAmelCase_  (    self			)->						Any:







											'''simple docstring'''






											return AutoImageProcessor.from_pretrained('microsoft/resnet-50'			) if is_vision_available() else None







				@slow
				def 		UpperCAmelCase_  (    self			)->						str:







											'''simple docstring'''






											UpperCamelCase					       =			FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'			)

											UpperCamelCase					       =			self.default_image_processor
											UpperCamelCase					       =			prepare_img()
											UpperCamelCase					       =			image_processor(images=A_		,							return_tensors='np'			)

											UpperCamelCase					       =			model(**A_			)

											# verify the logits
											UpperCamelCase					       =			(1, 1000)
											self.assertEqual(outputs.logits.shape		,							A_			)

											UpperCamelCase					       =			jnp.array([-11.1_069, -9.7_877, -8.3_777]			)

											self.assertTrue(jnp.allclose(outputs.logits[0, :3]		,							A_		,							atol=1e-4			)			)






'''simple docstring'''
import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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







def 						A_(			A		:						int):
							UpperCamelCase					       =			ConvNextVaConfig()

							if "atto" in checkpoint_url:
														UpperCamelCase					       =			[2, 2, 6, 2]
														UpperCamelCase					       =			[40, 80, 160, 320]
							if "femto" in checkpoint_url:
														UpperCamelCase					       =			[2, 2, 6, 2]
														UpperCamelCase					       =			[48, 96, 192, 384]
							if "pico" in checkpoint_url:
														UpperCamelCase					       =			[2, 2, 6, 2]
														UpperCamelCase					       =			[64, 128, 256, 512]
							if "nano" in checkpoint_url:
														UpperCamelCase					       =			[2, 2, 8, 2]
														UpperCamelCase					       =			[80, 160, 320, 640]
							if "tiny" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 9, 3]
														UpperCamelCase					       =			[96, 192, 384, 768]
							if "base" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 27, 3]
														UpperCamelCase					       =			[128, 256, 512, 1024]
							if "large" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 27, 3]
														UpperCamelCase					       =			[192, 384, 768, 1536]
							if "huge" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 27, 3]
														UpperCamelCase					       =			[352, 704, 1408, 2816]

							UpperCamelCase					       =			1000
							UpperCamelCase					       =			'imagenet-1k-id2label.json'
							UpperCamelCase					       =			(1, 1000)

							UpperCamelCase					       =			'huggingface/label-files'
							UpperCamelCase					       =			num_labels
							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()}
							UpperCamelCase					       =			hidden_sizes
							UpperCamelCase					       =			depths

							return config, expected_shape







def 						A_(			A		:						int):
							if "downsample_layers.0.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.0.0'						,			'embeddings.patch_embeddings')
							if "downsample_layers.0.1" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.0.1'						,			'embeddings.norm')  # we rename to layernorm later on
							if "downsample_layers.1.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.1.0'						,			'stages.1.downsampling_layer.0')
							if "downsample_layers.1.1" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.1.1'						,			'stages.1.downsampling_layer.1')
							if "downsample_layers.2.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.2.0'						,			'stages.2.downsampling_layer.0')
							if "downsample_layers.2.1" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.2.1'						,			'stages.2.downsampling_layer.1')
							if "downsample_layers.3.0" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.3.0'						,			'stages.3.downsampling_layer.0')
							if "downsample_layers.3.1" in name:
														UpperCamelCase					       =			name.replace('downsample_layers.3.1'						,			'stages.3.downsampling_layer.1')
							if "stages" in name and "downsampling_layer" not in name:
														# stages.0.0. for instance should be renamed to stages.0.layers.0.
														UpperCamelCase					       =			name[: len('stages.0')] + '.layers' + name[len('stages.0') :]
							if "gamma" in name:
														UpperCamelCase					       =			name.replace('gamma'						,			'weight')
							if "beta" in name:
														UpperCamelCase					       =			name.replace('beta'						,			'bias')
							if "stages" in name:
														UpperCamelCase					       =			name.replace('stages'						,			'encoder.stages')
							if "norm" in name:
														UpperCamelCase					       =			name.replace('norm'						,			'layernorm')
							if "head" in name:
														UpperCamelCase					       =			name.replace('head'						,			'classifier')

							return name







def 						A_(			):
							UpperCamelCase					       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
							UpperCamelCase					       =			Image.open(requests.get(A						,			stream=A).raw)
							return im







def 						A_(			A		:						Tuple):
							if "224" in checkpoint_url:
														UpperCamelCase					       =			224
														UpperCamelCase					       =			224 / 256
							elif "384" in checkpoint_url:
														UpperCamelCase					       =			384
														UpperCamelCase					       =			None
							else:
														UpperCamelCase					       =			512
														UpperCamelCase					       =			None

							return ConvNextImageProcessor(
							    size=A						,			crop_pct=A						,			image_mean=[0.485, 0.456, 0.406]						,			image_std=[0.229, 0.224, 0.225]						,			resample=PILImageResampling.BICUBIC						,			)







@torch.no_grad()
def 						A_(			A		:						List[str]						,			A		:						List[str]						,			A		:						Any						,			A		:						int):
							print('Downloading original model from checkpoint...')
							# define ConvNeXTV2 configuration based on URL
							UpperCamelCase       ,     UpperCamelCase					       =			get_convnextva_config(A)
							# load original state_dict from URL
							UpperCamelCase					       =			torch.hub.load_state_dict_from_url(A)['model']

							print('Converting model parameters...')
							# rename keys
							for key in state_dict.copy().keys():
														UpperCamelCase					       =			state_dict.pop(A)
														UpperCamelCase					       =			val
							# add prefix to all keys expect classifier head
							for key in state_dict.copy().keys():
														UpperCamelCase					       =			state_dict.pop(A)
														if not key.startswith('classifier'):
																					UpperCamelCase					       =			'convnextv2.' + key
														UpperCamelCase					       =			val

							# load HuggingFace model
							UpperCamelCase					       =			ConvNextVaForImageClassification(A)
							model.load_state_dict(A)
							model.eval()

							# Check outputs on an image, prepared by ConvNextImageProcessor
							UpperCamelCase					       =			convert_preprocessor(A)
							UpperCamelCase					       =			preprocessor(images=prepare_img()						,			return_tensors='pt')
							UpperCamelCase					       =			model(**A).logits

							# note: the logits below were obtained without center cropping
							if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
														UpperCamelCase					       =			torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826])
							else:
														raise ValueError(f'''Unknown URL: {checkpoint_url}''')

							assert torch.allclose(logits[0, :5]						,			A						,			atol=1E-3)
							assert logits.shape == expected_shape
							print('Model outputs match the original results!')

							if save_model:
														print('Saving model to local...')
														# Create folder to save model
														if not os.path.isdir(A):
																					os.mkdir(A)

														model.save_pretrained(A)
														preprocessor.save_pretrained(A)

							UpperCamelCase					       =			'convnextv2'
							if "atto" in checkpoint_url:
														model_name += "-atto"
							if "femto" in checkpoint_url:
														model_name += "-femto"
							if "pico" in checkpoint_url:
														model_name += "-pico"
							if "nano" in checkpoint_url:
														model_name += "-nano"
							elif "tiny" in checkpoint_url:
														model_name += "-tiny"
							elif "base" in checkpoint_url:
														model_name += "-base"
							elif "large" in checkpoint_url:
														model_name += "-large"
							elif "huge" in checkpoint_url:
														model_name += "-huge"
							if "22k" in checkpoint_url and "1k" not in checkpoint_url:
														model_name += "-22k"
							elif "22k" in checkpoint_url and "1k" in checkpoint_url:
														model_name += "-22k-1k"
							elif "1k" in checkpoint_url:
														model_name += "-1k"
							if "224" in checkpoint_url:
														model_name += "-224"
							elif "384" in checkpoint_url:
														model_name += "-384"
							elif "512" in checkpoint_url:
														model_name += "-512"

							if push_to_hub:
														print(f'''Pushing {model_name} to the hub...''')
														model.push_to_hub(A)
														preprocessor.push_to_hub(A)


if __name__ == "__main__":
						lowerCAmelCase :  List[Any]      =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
						    type=str,
						    help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='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 preprocessor to the hub')

						lowerCAmelCase :  int      =      parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)




