




'''simple docstring'''



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      lowerCAmelCase (			__lowerCAmelCase):


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.config_class(**self.inputs_dict	)
            self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''embed_dim'''	)	)
            self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''num_heads'''	)	)





class      lowerCAmelCase :


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=64     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=[16, 48, 96]     ,	__SCREAMING_SNAKE_CASE=[1, 3, 6]     ,	__SCREAMING_SNAKE_CASE=[1, 2, 10]     ,	__SCREAMING_SNAKE_CASE=[7, 3, 3]     ,	__SCREAMING_SNAKE_CASE=[4, 2, 2]     ,	__SCREAMING_SNAKE_CASE=[2, 1, 1]     ,	__SCREAMING_SNAKE_CASE=[2, 2, 2]     ,	__SCREAMING_SNAKE_CASE=[False, False, True]     ,	__SCREAMING_SNAKE_CASE=[0.0, 0.0, 0.0]     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-12     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=2     ,	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	image_size
            __snake_case            =	patch_sizes
            __snake_case            =	patch_stride
            __snake_case            =	patch_padding
            __snake_case            =	is_training
            __snake_case            =	use_labels
            __snake_case            =	num_labels
            __snake_case            =	num_channels
            __snake_case            =	embed_dim
            __snake_case            =	num_heads
            __snake_case            =	stride_kv
            __snake_case            =	depth
            __snake_case            =	cls_token
            __snake_case            =	attention_drop_rate
            __snake_case            =	initializer_range
            __snake_case            =	layer_norm_eps


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




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

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

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


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



            '''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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	CvtModel(config=__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	(self.image_size, self.image_size)
            __snake_case       ,     __snake_case            =	image_size[0], image_size[1]
            for i in range(len(self.depth	)	):
                 __snake_case            =	floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1	)
                 __snake_case            =	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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.num_labels
            __snake_case            =	CvtForImageClassification(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.eval()
            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE	)
            self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)	)




       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




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





@require_torch
class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      (CvtModel, CvtForImageClassification) if is_torch_available() else ()
       __lowercase	:   Union[str, Any]										=      (
           {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
           if is_torch_available()
           else {}
       )

       __lowercase	:   Union[str, Any]										=      False
       __lowercase	:   Optional[int]										=      False
       __lowercase	:   Union[str, Any]										=      False
       __lowercase	:   Optional[int]										=      False
       __lowercase	:   str										=      False


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	CvtModelTester(self	)
            __snake_case            =	ConfigTester(self     ,	config_class=__SCREAMING_SNAKE_CASE     ,	has_text_modality=__SCREAMING_SNAKE_CASE     ,	hidden_size=37	)


       def    lowerCAmelCase   ( 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    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            return


       @unittest.skip(reason='''Cvt does not output attentions'''	)
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            pass


       @unittest.skip(reason='''Cvt does not use inputs_embeds'''	)
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            pass


       @unittest.skip(reason='''Cvt does not support input and output embeddings'''	)
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            pass


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



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 __snake_case            =	['''pixel_values''']
                 self.assertListEqual(arg_names[:1]     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            def check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
                 model.to(__SCREAMING_SNAKE_CASE	)
                 model.eval()

                 with torch.no_grad():
                      __snake_case            =	model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	)

                 __snake_case            =	outputs.hidden_states

                 __snake_case            =	len(self.model_tester.depth	)
                 self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)

                 # 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,
                     ]     ,	)

            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case            =	True
                 check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

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

                 check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE	)


       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            pass




       @slow
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case            =	CvtModel.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)







def       _UpperCamelCase			()-> str:




     '''simple docstring'''





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







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


       @cached_property
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.default_image_processor
            __snake_case            =	prepare_img()
            __snake_case            =	image_processor(images=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)

            # forward pass
            with torch.no_grad():
                 __snake_case            =	model(**__SCREAMING_SNAKE_CASE	)

            # verify the logits
            __snake_case            =	torch.Size((1, 1000)	)
            self.assertEqual(outputs.logits.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.tensor([0.9_285, 0.9_015, -0.3_150]	).to(__SCREAMING_SNAKE_CASE	)

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












'''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       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  str						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	old_name

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

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

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

               __snake_case            =	'''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(R'''.\d.''' ,						_lowerCamelCase						):
          __snake_case            =	old_name.replace('''network''' ,						'''intermediate_stages'''						)

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

     return new_name







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     for key in checkpoint.copy().keys():
          __snake_case            =	checkpoint.pop(_lowerCamelCase						)
          __snake_case            =	val

     return checkpoint







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __snake_case            =	Image.open(requests.get(_lowerCamelCase ,						stream=_lowerCamelCase						).raw						)

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  bool						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	torch.load(_lowerCamelCase ,						map_location='''cpu'''						)['''model''']
     __snake_case            =	EfficientFormerConfig.from_json_file(_lowerCamelCase						)
     __snake_case            =	EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase						)
     __snake_case            =	'''_'''.join(checkpoint_path.split('''/'''						)[-1].split('''.'''						)[0].split('''_'''						)[:-1]						)

     __snake_case            =	config.depths[-1] - config.num_metaad_blocks + 1
     __snake_case            =	convert_torch_checkpoint(_lowerCamelCase ,						_lowerCamelCase						)

     model.load_state_dict(_lowerCamelCase						)
     model.eval()

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

     # prepare image
     __snake_case            =	prepare_img()
     __snake_case            =	2_56
     __snake_case            =	2_24
     __snake_case            =	EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size} ,						crop_size={'''height''': crop_size, '''width''': crop_size} ,						resample=pillow_resamplings['''bicubic'''] ,						)
     __snake_case            =	processor(images=_lowerCamelCase ,						return_tensors='''pt'''						).pixel_values

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

     assert torch.allclose(_lowerCamelCase ,						_lowerCamelCase						)

     __snake_case            =	model(_lowerCamelCase						)
     __snake_case            =	outputs.logits

     __snake_case            =	(1, 10_00)

     if "l1" in model_name:
          __snake_case            =	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] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          __snake_case            =	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] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          __snake_case            =	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(_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__":
     UpperCAmelCase_							:	Optional[int]	     =		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)

     UpperCAmelCase_							:	Union[str, Any]	     =		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,
     )






