


"""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        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Any			) ->      str:
		__a           =			old_name

		if "patch_embed" in old_name:
				__a      ,     __a      ,     __a           =			old_name.split('''.'''			)

				if layer == "0":
						__a           =			old_name.replace('''0'''    ,					'''convolution1'''			)
				elif layer == "1":
						__a           =			old_name.replace('''1'''    ,					'''batchnorm_before'''			)
				elif layer == "3":
						__a           =			old_name.replace('''3'''    ,					'''convolution2'''			)
				else:
						__a           =			old_name.replace('''4'''    ,					'''batchnorm_after'''			)

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

						__a           =			'''last_stage.''' + trimmed_name

		elif "network" in old_name and re.search(r'''.\d.'''    ,					lowerCAmelCase__			):
				__a           =			old_name.replace('''network'''    ,					'''intermediate_stages'''			)

		if "fc" in new_name:
				__a           =			new_name.replace('''fc'''    ,					'''convolution'''			)
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				__a           =			new_name.replace('''norm1'''    ,					'''batchnorm_before'''			)
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				__a           =			new_name.replace('''norm2'''    ,					'''batchnorm_after'''			)
		if "proj" in new_name:
				__a           =			new_name.replace('''proj'''    ,					'''projection'''			)
		if "dist_head" in new_name:
				__a           =			new_name.replace('''dist_head'''    ,					'''distillation_classifier'''			)
		elif "head" in new_name:
				__a           =			new_name.replace('''head'''    ,					'''classifier'''			)
		elif "patch_embed" in new_name:
				__a           =			'''efficientformer.''' + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				__a           =			new_name.replace('''norm'''    ,					'''layernorm'''			)
				__a           =			'''efficientformer.''' + new_name
		else:
				__a           =			'''efficientformer.encoder.''' + new_name

		return new_name

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Tuple			) ->      Tuple:
		for key in checkpoint.copy().keys():
				__a           =			checkpoint.pop(lowerCAmelCase__			)
				__a           =			val

		return checkpoint

def        lowercase							(		) ->      Any:
		__a           =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__a           =			Image.open(requests.get(lowerCAmelCase__    ,					stream=lowerCAmelCase__			).raw			)

		return image

def        lowercase							(		lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     bool			) ->      Any:
		__a           =			torch.load(lowerCAmelCase__    ,					map_location='''cpu'''			)['''model''']
		__a           =			EfficientFormerConfig.from_json_file(lowerCAmelCase__			)
		__a           =			EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__			)
		__a           =			'''_'''.join(checkpoint_path.split('''/'''			)[-1].split('''.'''			)[0].split('''_'''			)[:-1]			)

		__a           =			config.depths[-1] - config.num_metaad_blocks + 1
		__a           =			convert_torch_checkpoint(lowerCAmelCase__    ,					lowerCAmelCase__			)

		model.load_state_dict(lowerCAmelCase__			)
		model.eval()

		__a           =			{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		# prepare image
		__a           =			prepare_img()
		__a           =			256
		__a           =			224
		__a           =			EfficientFormerImageProcessor(
		    size={'''shortest_edge''': image_size}    ,					crop_size={'''height''': crop_size, '''width''': crop_size}    ,					resample=pillow_resamplings['''bicubic''']    ,					)
		__a           =			processor(images=lowerCAmelCase__    ,					return_tensors='''pt'''			).pixel_values

		# original processing pipeline
		__a           =			Compose(
		    [
		        Resize(lowerCAmelCase__    ,					interpolation=pillow_resamplings['''bicubic''']			),
		        CenterCrop(lowerCAmelCase__			),
		        ToTensor(),
		        Normalize(lowerCAmelCase__    ,					lowerCAmelCase__			),
		    ]			)
		__a           =			image_transforms(lowerCAmelCase__			).unsqueeze(0			)

		assert torch.allclose(lowerCAmelCase__    ,					lowerCAmelCase__			)

		__a           =			model(lowerCAmelCase__			)
		__a           =			outputs.logits

		__a           =			(1, 1000)

		if "l1" in model_name:
				__a           =			torch.Tensor(
				    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]			)
				assert torch.allclose(logits[0, :10]    ,					lowerCAmelCase__    ,					atol=1e-3			)
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				__a           =			torch.Tensor(
				    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]			)
				assert torch.allclose(logits[0, :10]    ,					lowerCAmelCase__    ,					atol=1e-3			)
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				__a           =			torch.Tensor(
				    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]			)
				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__":
				lowercase_			     =  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)

				lowercase_			     =  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,
				)








import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.config_class(**self.inputs_dict       )
				self.parent.assertTrue(hasattr(A_				,      'embed_dim'       )       )
				self.parent.assertTrue(hasattr(A_				,      'num_heads'       )       )






class 				lowercase					:



		def __init__( self				,      A_				,      A_=13				,      A_=64				,      A_=3				,      A_=[16, 48, 96]				,      A_=[1, 3, 6]				,      A_=[1, 2, 10]				,      A_=[7, 3, 3]				,      A_=[4, 2, 2]				,      A_=[2, 1, 1]				,      A_=[2, 2, 2]				,      A_=[False, False, True]				,      A_=[0.0, 0.0, 0.0]				,      A_=0.02				,      A_=1e-12				,      A_=True				,      A_=True				,      A_=2				,      )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     image_size
				UpperCamelCase		=     patch_sizes
				UpperCamelCase		=     patch_stride
				UpperCamelCase		=     patch_padding
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     num_labels
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     embed_dim
				UpperCamelCase		=     num_heads
				UpperCamelCase		=     stride_kv
				UpperCamelCase		=     depth
				UpperCamelCase		=     cls_token
				UpperCamelCase		=     attention_drop_rate
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

				UpperCamelCase		=     None
				if self.use_labels:
						UpperCamelCase		=     ids_tensor([self.batch_size]				,      self.num_labels       )

				UpperCamelCase		=     self.get_config()
				return config, pixel_values, labels



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				return CvtConfig(
				    image_size=self.image_size				,      num_labels=self.num_labels				,      num_channels=self.num_channels				,      embed_dim=self.embed_dim				,      num_heads=self.num_heads				,      patch_sizes=self.patch_sizes				,      patch_padding=self.patch_padding				,      patch_stride=self.patch_stride				,      stride_kv=self.stride_kv				,      depth=self.depth				,      cls_token=self.cls_token				,      attention_drop_rate=self.attention_drop_rate				,      initializer_range=self.initializer_range				,      )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     CvtModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				UpperCamelCase		=     (self.image_size, self.image_size)
				UpperCamelCase	,  UpperCamelCase		=     image_size[0], image_size[1]
				for i in range(len(self.depth       )       ):
						UpperCamelCase		=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
						UpperCamelCase		=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
				self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.embed_dim[-1], height, width)       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     CvtForImageClassification(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     config_and_inputs
				UpperCamelCase		=     {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       List[str]	     =				(CvtModel, CvtForImageClassification) if is_torch_available() else ()
		__lowercase       :       Tuple	     =				(
		    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowercase       :       Tuple	     =				False
		__lowercase       :       Union[str, Any]	     =				False
		__lowercase       :       Optional[Any]	     =				False
		__lowercase       :       List[str]	     =				False
		__lowercase       :       Dict	     =				False



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     CvtModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      has_text_modality=A_				,      hidden_size=37       )



		def __UpperCamelCase ( self       )  ->					str:
				"""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       )  ->					Any:
				"""simple docstring"""
				return



		@unittest.skip(reason='Cvt does not output attentions'       )
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not use inputs_embeds'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not support input and output embeddings'       )
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""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.forward       )
						# 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"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				def check_hidden_states_output(A_				,      A_				,      A_       ):
						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.eval()

						with torch.no_grad():
								UpperCamelCase		=     model(**self._prepare_for_class(A_				,      A_       )       )

						UpperCamelCase		=     outputs.hidden_states

						UpperCamelCase		=     len(self.model_tester.depth       )
						self.assertEqual(len(A_       )				,      A_       )

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]       )				,      [
						        self.model_tester.embed_dim[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]				,      )

				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase		=     True
						check_hidden_states_output(A_				,      A_				,      A_       )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase		=     True

						check_hidden_states_output(A_				,      A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_       )



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase		=     CvtModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )






def       A       (					)   ->  Tuple:
		'''simple docstring'''

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


@require_torch
@require_vision
class 				lowercase					(		unittest.TestCase   ):



		@cached_property
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )



		@slow
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(A_       )

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

				# forward pass
				with torch.no_grad():
						UpperCamelCase		=     model(**A_       )

				# verify the logits
				UpperCamelCase		=     torch.Size((1, 1_000)       )
				self.assertEqual(outputs.logits.shape				,      A_       )

				UpperCamelCase		=     torch.tensor([0.9285, 0.9015, -0.3150]       ).to(A_       )

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