






"""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 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   old_name

			if "patch_embed" in old_name:
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   old_name.split("."		)

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

			if "network" in old_name and re.search(r"\d\.\d"      ,A__		):
						UpperCAmelCase_	:					int     	=   r"\b\d{2}\b"
						if bool(re.search(A__      ,A__		)		):
									UpperCAmelCase_	:					List[Any]     	=   re.search(r"\d\.\d\d."      ,A__		).group()
						else:
									UpperCAmelCase_	:					Tuple     	=   re.search(r"\d\.\d."      ,A__		).group()
						if int(match[0]		) < 6:
									UpperCAmelCase_	:					List[Any]     	=   old_name.replace(A__      ,""		)
									UpperCAmelCase_	:					str     	=   trimmed_name.replace("network"      ,match[0] + ".meta4D_layers.blocks." + match[2:-1]		)
									UpperCAmelCase_	:					List[Any]     	=   "intermediate_stages." + trimmed_name
						else:
									UpperCAmelCase_	:					Dict     	=   old_name.replace(A__      ,""		)
									if int(match[2]		) < num_meta4D_last_stage:
												UpperCAmelCase_	:					List[Any]     	=   trimmed_name.replace("network"      ,"meta4D_layers.blocks." + match[2]		)
									else:
												UpperCAmelCase_	:					Dict     	=   str(int(match[2]		) - num_meta4D_last_stage		)
												UpperCAmelCase_	:					List[Any]     	=   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_	:					Dict     	=   trimmed_name.replace("norm2"      ,"layernorm2"		)
												elif "fc1" in old_name:
															UpperCAmelCase_	:					Tuple     	=   trimmed_name.replace("fc1"      ,"linear_in"		)
												elif "fc2" in old_name:
															UpperCAmelCase_	:					str     	=   trimmed_name.replace("fc2"      ,"linear_out"		)

									UpperCAmelCase_	:					str     	=   "last_stage." + trimmed_name

			elif "network" in old_name and re.search(r".\d."      ,A__		):
						UpperCAmelCase_	:					Any     	=   old_name.replace("network"      ,"intermediate_stages"		)

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

			return new_name

def 							snake_case (  A__      ,A__		):
			for key in checkpoint.copy().keys():
						UpperCAmelCase_	:					int     	=   checkpoint.pop(A__		)
						UpperCAmelCase_	:					Tuple     	=   val

			return checkpoint

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					int     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)

			return image

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					List[Any]     	=   torch.load(A__      ,map_location="cpu"		)["model"]
			UpperCAmelCase_	:					Any     	=   EfficientFormerConfig.from_json_file(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   EfficientFormerForImageClassificationWithTeacher(A__		)
			UpperCAmelCase_	:					Optional[int]     	=   "_".join(checkpoint_path.split("/"		)[-1].split("."		)[0].split("_"		)[:-1]		)

			UpperCAmelCase_	:					Optional[Any]     	=   config.depths[-1] - config.num_metaad_blocks + 1
			UpperCAmelCase_	:					List[str]     	=   convert_torch_checkpoint(A__      ,A__		)

			model.load_state_dict(A__		)
			model.eval()

			UpperCAmelCase_	:					Any     	=   {
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			# prepare image
			UpperCAmelCase_	:					Tuple     	=   prepare_img()
			UpperCAmelCase_	:					Tuple     	=   2_56
			UpperCAmelCase_	:					Any     	=   2_24
			UpperCAmelCase_	:					str     	=   EfficientFormerImageProcessor(
			    size={"shortest_edge": image_size}      ,crop_size={"height": crop_size, "width": crop_size}      ,resample=pillow_resamplings["bicubic"]      ,)
			UpperCAmelCase_	:					List[str]     	=   processor(images=A__      ,return_tensors="pt"		).pixel_values

			# original processing pipeline
			UpperCAmelCase_	:					List[Any]     	=   Compose(
			    [
			        Resize(A__      ,interpolation=pillow_resamplings["bicubic"]		),
			        CenterCrop(A__		),
			        ToTensor(),
			        Normalize(A__      ,A__		),
			    ]		)
			UpperCAmelCase_	:					str     	=   image_transforms(A__		).unsqueeze(0		)

			assert torch.allclose(A__      ,A__		)

			UpperCAmelCase_	:					Optional[Any]     	=   model(A__		)
			UpperCAmelCase_	:					int     	=   outputs.logits

			UpperCAmelCase_	:					Tuple     	=   (1, 10_00)

			if "l1" in model_name:
						UpperCAmelCase_	:					Dict     	=   torch.Tensor(
						    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]		)
						assert torch.allclose(logits[0, :10]      ,A__      ,atol=1e-3		)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						UpperCAmelCase_	:					Optional[Any]     	=   torch.Tensor(
						    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]		)
						assert torch.allclose(logits[0, :10]      ,A__      ,atol=1e-3		)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						UpperCAmelCase_	:					int     	=   torch.Tensor(
						    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]		)
						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(A__		).mkdir(exist_ok=A__		)
			model.save_pretrained(A__		)
			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""		)
			processor.save_pretrained(A__		)
			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=A__      ,)
						processor.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""      ,commit_message="Add image processor"      ,use_temp_dir=A__      ,)


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,
			)








import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image
else:


   class 	lowerCamelCase__	:



    """simple docstring"""

    @staticmethod
    def     snake_case_      (    *__lowerCAmelCase					:					Optional[Any]			,						**__lowerCAmelCase					:					Any      )   ->	Any:
       pass


@is_pipeline_test
@require_vision
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 @require_torch
 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						)
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''a''', '''b''', '''c''']      )

    # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
    # python and torch versions.
    self.assertIn(
        nested_simplify(__lowerCAmelCase      )			,						[
            [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
            [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
        ]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''A''', '''B''', '''C''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
        ]			,						)

 @require_tf
 def     snake_case_      (    self					:					int      )   ->	Optional[int]:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						framework='''tf'''      )
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''a''', '''b''', '''c''']      )

    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''A''', '''B''', '''C''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
            [
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
                {'''score''': 0.333, '''label''': ANY(__lowerCAmelCase      )},
            ],
        ]			,						)

 @slow
 @require_torch
 def     snake_case_      (    self					:					Optional[int]      )   ->	int:
    _A      =       pipeline(
        task='''zero-shot-image-classification'''			,						model='''openai/clip-vit-base-patch32'''			,						)
    # This is an image of 2 cats with remotes and no planes
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''cat''', '''plane''', '''remote''']      )

    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            {'''score''': 0.511, '''label''': '''remote'''},
            {'''score''': 0.485, '''label''': '''cat'''},
            {'''score''': 0.004, '''label''': '''plane'''},
        ]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''cat''', '''plane''', '''remote''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.511, '''label''': '''remote'''},
                {'''score''': 0.485, '''label''': '''cat'''},
                {'''score''': 0.004, '''label''': '''plane'''},
            ],
        ]
        * 5			,						)

 @slow
 @require_tf
 def     snake_case_      (    self					:					Optional[int]      )   ->	Dict:
    _A      =       pipeline(
        task='''zero-shot-image-classification'''			,						model='''openai/clip-vit-base-patch32'''			,						framework='''tf'''      )
    # This is an image of 2 cats with remotes and no planes
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       image_classifier(__lowerCAmelCase			,						candidate_labels=['''cat''', '''plane''', '''remote''']      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            {'''score''': 0.511, '''label''': '''remote'''},
            {'''score''': 0.485, '''label''': '''cat'''},
            {'''score''': 0.004, '''label''': '''plane'''},
        ]			,						)

    _A      =       image_classifier([image] * 5			,						candidate_labels=['''cat''', '''plane''', '''remote''']			,						batch_size=2      )
    self.assertEqual(
        nested_simplify(__lowerCAmelCase      )			,						[
            [
                {'''score''': 0.511, '''label''': '''remote'''},
                {'''score''': 0.485, '''label''': '''cat'''},
                {'''score''': 0.004, '''label''': '''plane'''},
            ],
        ]
        * 5			,						)



