


'''simple docstring'''


import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


lowerCamelCase__					=   get_tests_dir('fixtures/dummy-config.json')






class 		lowerCAmelCase__	(	unittest.TestCase		):




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					0




			def 							lowerCAmelCase__			(						self     :   Any       )      ->str:



					'''simple docstring'''







					self.assertIsNotNone(transformers.models.auto.__spec__       )
					self.assertIsNotNone(importlib.util.find_spec("transformers.models.auto"       )       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					AutoConfig.from_pretrained("bert-base-uncased"       )
					self.assertIsInstance(lowerCamelCase__		,     lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
					self.assertIsInstance(lowerCamelCase__		,     lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
					self.assertIsInstance(lowerCamelCase__		,     lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					AutoConfig.for_model("roberta"       )
					self.assertIsInstance(lowerCamelCase__		,     lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   int       )      ->str:



					'''simple docstring'''







					with tempfile.TemporaryDirectory() as tmp_dir:
							# This model name contains bert and roberta, but roberta ends up being picked.
							_UpperCAmelCase						:				List[str]	    =					os.path.join(lowerCamelCase__		,     "fake-roberta"       )
							os.makedirs(lowerCamelCase__		,     exist_ok=lowerCamelCase__       )
							with open(os.path.join(lowerCamelCase__		,     "config.json"       )		,     "w"       ) as f:
									f.write(json.dumps({}       )       )
							_UpperCAmelCase						:				int	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
							self.assertEqual(type(lowerCamelCase__       )		,     lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Optional[Any]:



					'''simple docstring'''







					try:
							AutoConfig.register("custom"		,     lowerCamelCase__       )
							# Wrong model type will raise an error
							with self.assertRaises(lowerCamelCase__       ):
									AutoConfig.register("model"		,     lowerCamelCase__       )
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(lowerCamelCase__       ):
									AutoConfig.register("bert"		,     lowerCamelCase__       )

							# Now that the config is registered, it can be used as any other config with the auto-API
							_UpperCAmelCase						:				Union[str, Any]	    =					CustomConfig()
							with tempfile.TemporaryDirectory() as tmp_dir:
									config.save_pretrained(lowerCamelCase__       )
									_UpperCAmelCase						:				Tuple	    =					AutoConfig.from_pretrained(lowerCamelCase__       )
									self.assertIsInstance(lowerCamelCase__		,     lowerCamelCase__       )

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Tuple:



					'''simple docstring'''







					with self.assertRaisesRegex(
					    lowerCamelCase__		,     "bert-base is not a local folder and is not a valid model identifier"       ):
							_UpperCAmelCase						:				Tuple	    =					AutoConfig.from_pretrained("bert-base"       )




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Any:



					'''simple docstring'''







					with self.assertRaisesRegex(
					    lowerCamelCase__		,     R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"       ):
							_UpperCAmelCase						:				str	    =					AutoConfig.from_pretrained(lowerCamelCase__		,     revision="aaaaaa"       )




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Optional[Any]:



					'''simple docstring'''







					with self.assertRaisesRegex(
					    lowerCamelCase__		,     "hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."		,     ):
							_UpperCAmelCase						:				Tuple	    =					AutoConfig.from_pretrained("hf-internal-testing/no-config-test-repo"       )




			def 							lowerCAmelCase__			(						self     :   int       )      ->Any:



					'''simple docstring'''







					with self.assertRaises(lowerCamelCase__       ):
							_UpperCAmelCase						:				List[str]	    =					AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"       )
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(lowerCamelCase__       ):
							_UpperCAmelCase						:				List[str]	    =					AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"		,     trust_remote_code=lowerCamelCase__       )

					_UpperCAmelCase						:				Dict	    =					AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"		,     trust_remote_code=lowerCamelCase__       )
					self.assertEqual(config.__class__.__name__		,     "NewModelConfig"       )

					# Test config can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
							config.save_pretrained(lowerCamelCase__       )
							_UpperCAmelCase						:				List[Any]	    =					AutoConfig.from_pretrained(lowerCamelCase__		,     trust_remote_code=lowerCamelCase__       )
					self.assertEqual(reloaded_config.__class__.__name__		,     "NewModelConfig"       )






			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Tuple:



					'''simple docstring'''







					class 		lowerCAmelCase__	(	UpperCAmelCase__		):
								lowerCAmelCase       :		int				      =   "new-model"

					try:
							AutoConfig.register("new-model"		,     lowerCamelCase__       )
							# If remote code is not set, the default is to use local
							_UpperCAmelCase						:				Optional[Any]	    =					AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"       )
							self.assertEqual(config.__class__.__name__		,     "NewModelConfigLocal"       )

							# If remote code is disabled, we load the local one.
							_UpperCAmelCase						:				Dict	    =					AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"		,     trust_remote_code=lowerCamelCase__       )
							self.assertEqual(config.__class__.__name__		,     "NewModelConfigLocal"       )

							# If remote is enabled, we load from the Hub
							_UpperCAmelCase						:				Optional[int]	    =					AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"		,     trust_remote_code=lowerCamelCase__       )
							self.assertEqual(config.__class__.__name__		,     "NewModelConfig"       )

					finally:
							if "new-model" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["new-model"]







'''simple docstring'''


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					old_name

		if "patch_embed" in old_name:
				_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					old_name.split("."      )

				if layer == "0":
						_UpperCAmelCase						:				List[str]	    =					old_name.replace("0"					,       "convolution1"      )
				elif layer == "1":
						_UpperCAmelCase						:				Dict	    =					old_name.replace("1"					,       "batchnorm_before"      )
				elif layer == "3":
						_UpperCAmelCase						:				Tuple	    =					old_name.replace("3"					,       "convolution2"      )
				else:
						_UpperCAmelCase						:				Tuple	    =					old_name.replace("4"					,       "batchnorm_after"      )

		if "network" in old_name and re.search(R"\d\.\d"					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				List[Any]	    =					R"\b\d{2}\b"
				if bool(re.search(__lowerCAmelCase					,       __lowerCAmelCase      )      ):
						_UpperCAmelCase						:				Optional[int]	    =					re.search(R"\d\.\d\d."					,       __lowerCAmelCase      ).group()
				else:
						_UpperCAmelCase						:				Any	    =					re.search(R"\d\.\d."					,       __lowerCAmelCase      ).group()
				if int(match[0]      ) < 6:
						_UpperCAmelCase						:				str	    =					old_name.replace(__lowerCAmelCase					,       ""      )
						_UpperCAmelCase						:				Optional[Any]	    =					trimmed_name.replace("network"					,       match[0] + ".meta4D_layers.blocks." + match[2:-1]      )
						_UpperCAmelCase						:				Union[str, Any]	    =					"intermediate_stages." + trimmed_name
				else:
						_UpperCAmelCase						:				Tuple	    =					old_name.replace(__lowerCAmelCase					,       ""      )
						if int(match[2]      ) < num_meta4D_last_stage:
								_UpperCAmelCase						:				Any	    =					trimmed_name.replace("network"					,       "meta4D_layers.blocks." + match[2]      )
						else:
								_UpperCAmelCase						:				List[str]	    =					str(int(match[2]      ) - num_meta4D_last_stage      )
								_UpperCAmelCase						:				int	    =					trimmed_name.replace("network"					,       "meta3D_layers.blocks." + layer_index      )
								if "norm1" in old_name:
										_UpperCAmelCase						:				Tuple	    =					trimmed_name.replace("norm1"					,       "layernorm1"      )
								elif "norm2" in old_name:
										_UpperCAmelCase						:				int	    =					trimmed_name.replace("norm2"					,       "layernorm2"      )
								elif "fc1" in old_name:
										_UpperCAmelCase						:				Optional[int]	    =					trimmed_name.replace("fc1"					,       "linear_in"      )
								elif "fc2" in old_name:
										_UpperCAmelCase						:				List[str]	    =					trimmed_name.replace("fc2"					,       "linear_out"      )

						_UpperCAmelCase						:				Optional[Any]	    =					"last_stage." + trimmed_name

		elif "network" in old_name and re.search(R".\d."					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Optional[Any]	    =					old_name.replace("network"					,       "intermediate_stages"      )

		if "fc" in new_name:
				_UpperCAmelCase						:				Union[str, Any]	    =					new_name.replace("fc"					,       "convolution"      )
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				_UpperCAmelCase						:				List[Any]	    =					new_name.replace("norm1"					,       "batchnorm_before"      )
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				_UpperCAmelCase						:				List[Any]	    =					new_name.replace("norm2"					,       "batchnorm_after"      )
		if "proj" in new_name:
				_UpperCAmelCase						:				Union[str, Any]	    =					new_name.replace("proj"					,       "projection"      )
		if "dist_head" in new_name:
				_UpperCAmelCase						:				List[Any]	    =					new_name.replace("dist_head"					,       "distillation_classifier"      )
		elif "head" in new_name:
				_UpperCAmelCase						:				str	    =					new_name.replace("head"					,       "classifier"      )
		elif "patch_embed" in new_name:
				_UpperCAmelCase						:				List[str]	    =					"efficientformer." + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				_UpperCAmelCase						:				List[Any]	    =					new_name.replace("norm"					,       "layernorm"      )
				_UpperCAmelCase						:				Any	    =					"efficientformer." + new_name
		else:
				_UpperCAmelCase						:				Dict	    =					"efficientformer.encoder." + new_name

		return new_name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		for key in checkpoint.copy().keys():
				_UpperCAmelCase						:				List[Any]	    =					checkpoint.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val

		return checkpoint






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase						:				Tuple	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      )

		return image






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					torch.load(__lowerCAmelCase					,       map_location="cpu"      )["model"]
		_UpperCAmelCase						:				Dict	    =					EfficientFormerConfig.from_json_file(__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[Any]	    =					EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase      )
		_UpperCAmelCase						:				Tuple	    =					"_".join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

		_UpperCAmelCase						:				Union[str, Any]	    =					config.depths[-1] - config.num_metaad_blocks + 1
		_UpperCAmelCase						:				Optional[int]	    =					convert_torch_checkpoint(__lowerCAmelCase					,       __lowerCAmelCase      )

		model.load_state_dict(__lowerCAmelCase      )
		model.eval()

		_UpperCAmelCase						:				Optional[Any]	    =					{
		    "bilinear": PILImageResampling.BILINEAR,
		    "bicubic": PILImageResampling.BICUBIC,
		    "nearest": PILImageResampling.NEAREST,
		}

		# prepare image
		_UpperCAmelCase						:				int	    =					prepare_img()
		_UpperCAmelCase						:				List[str]	    =					256
		_UpperCAmelCase						:				Optional[int]	    =					224
		_UpperCAmelCase						:				Tuple	    =					EfficientFormerImageProcessor(
		    size={"shortest_edge": image_size}					,       crop_size={"height": crop_size, "width": crop_size}					,       resample=pillow_resamplings["bicubic"]					,       )
		_UpperCAmelCase						:				Any	    =					processor(images=__lowerCAmelCase					,       return_tensors="pt"      ).pixel_values

		# original processing pipeline
		_UpperCAmelCase						:				int	    =					Compose(
		    [
		        Resize(__lowerCAmelCase					,       interpolation=pillow_resamplings["bicubic"]      ),
		        CenterCrop(__lowerCAmelCase      ),
		        ToTensor(),
		        Normalize(__lowerCAmelCase					,       __lowerCAmelCase      ),
		    ]      )
		_UpperCAmelCase						:				Any	    =					image_transforms(__lowerCAmelCase      ).unsqueeze(0      )

		assert torch.allclose(__lowerCAmelCase					,       __lowerCAmelCase      )

		_UpperCAmelCase						:				Union[str, Any]	    =					model(__lowerCAmelCase      )
		_UpperCAmelCase						:				Dict	    =					outputs.logits

		_UpperCAmelCase						:				Optional[int]	    =					(1, 1_000)

		if "l1" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					torch.Tensor(
				    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]      )
				assert torch.allclose(logits[0, :10]					,       __lowerCAmelCase					,       atol=1e-3      )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					torch.Tensor(
				    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]      )
				assert torch.allclose(logits[0, :10]					,       __lowerCAmelCase					,       atol=1e-3      )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_UpperCAmelCase						:				List[Any]	    =					torch.Tensor(
				    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]      )
				assert logits.shape == expected_shape
		else:
				raise ValueError(
				    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""      )

		# Save Checkpoints
		Path(__lowerCAmelCase      ).mkdir(exist_ok=__lowerCAmelCase      )
		model.save_pretrained(__lowerCAmelCase      )
		print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )
		processor.save_pretrained(__lowerCAmelCase      )
		print(F"""Processor successfuly saved at {pytorch_dump_path}"""      )

		if push_to_hub:
				print("Pushing model to the hub..."      )

				model.push_to_hub(
				    repo_id=F"""Bearnardd/{pytorch_dump_path}"""					,       commit_message="Add model"					,       use_temp_dir=__lowerCAmelCase					,       )
				processor.push_to_hub(
				    repo_id=F"""Bearnardd/{pytorch_dump_path}"""					,       commit_message="Add image processor"					,       use_temp_dir=__lowerCAmelCase					,       )


if __name__ == "__main__":
						lowerCamelCase__					=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--pytorch_model_path',
						    default=None,
						    type=str,
						    required=True,
						    help='Path to EfficientFormer pytorch checkpoint.',
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The json file for EfficientFormer model config.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)

						parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
						parser.add_argument(
						    '--no-push_to_hub',
						    dest='push_to_hub',
						    action='store_false',
						    help='Do not push model and image processor to the hub',
						)
						parser.set_defaults(push_to_hub=True)

						lowerCamelCase__					=   parser.parse_args()
						convert_efficientformer_checkpoint(
						    checkpoint_path=args.pytorch_model_path,
						    efficientformer_config_file=args.config_file,
						    pytorch_dump_path=args.pytorch_dump_path,
						    push_to_hub=args.push_to_hub,
						)



