





'''simple docstring'''






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin




class 					UpperCAmelCase	(     UpperCAmelCase__  ):
      _lowercase:     Tuple			=  None
      _lowercase:     Tuple			=  None




      @property
      def 							lowercase__       (     self     :					Any    )							-> Optional[int]:
            return self.feat_extract_tester.prepare_feat_extract_dict()




      def 							lowercase__       (     self     :					Any    )							-> List[Any]:
            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            self.assertTrue(hasattr(__lowercase     ,     """feature_size"""    )    )
            self.assertTrue(hasattr(__lowercase     ,     """sampling_rate"""    )    )
            self.assertTrue(hasattr(__lowercase     ,     """padding_value"""    )    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common()
            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}    )

            self.assertTrue(all(len(__lowercase    ) == len(__lowercase    ) for x, y in zip(__lowercase     ,     processed_features[input_name]    )    )    )

            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase    )
            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}     ,     tensor_type="""np"""    )

            _lowerCAmelCase					=				processed_features[input_name]

            if len(batch_features_input.shape    ) < 3:
                  _lowerCAmelCase					=				batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )




      @require_torch
      def 							lowercase__       (     self     :					int    )							-> List[Any]:
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase    )
            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}     ,     tensor_type="""pt"""    )

            _lowerCAmelCase					=				processed_features[input_name]

            if len(batch_features_input.shape    ) < 3:
                  _lowerCAmelCase					=				batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )




      @require_tf
      def 							lowercase__       (     self     :					Tuple    )							-> List[Any]:
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase    )
            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}     ,     tensor_type="""tf"""    )

            _lowerCAmelCase					=				processed_features[input_name]

            if len(batch_features_input.shape    ) < 3:
                  _lowerCAmelCase					=				batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					Dict=False    )							-> int:






            def _inputs_have_equal_length(__snake_case     :					Dict    ):
                  _lowerCAmelCase					=				len(input[0]    )
                  for input_slice in input[1:]:
                        if len(__lowercase    ) != length:
                              return False
                  return True

            def _inputs_are_equal(__snake_case     :					Dict     ,     __snake_case     :					Optional[int]    ):
                  if len(__lowercase    ) != len(__lowercase    ):
                        return False

                  for input_slice_a, input_slice_a in zip(__lowercase     ,     __lowercase    ):
                        if not np.allclose(np.asarray(__lowercase    )     ,     np.asarray(__lowercase    )     ,     atol=1E-3    ):
                              return False
                  return True

            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase    )
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}    )

            _lowerCAmelCase					=				self.feat_extract_tester.seq_length_diff
            _lowerCAmelCase					=				self.feat_extract_tester.max_seq_length + pad_diff
            _lowerCAmelCase					=				self.feat_extract_tester.min_seq_length
            _lowerCAmelCase					=				self.feat_extract_tester.batch_size
            _lowerCAmelCase					=				self.feat_extract_tester.feature_size

            # test padding for List[int] + numpy
            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding=__lowercase    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[-1]    )    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				input_a[input_name]

            # max_length parameter has to be provided when setting `padding="max_length"`
            with self.assertRaises(__lowercase    ):
                  feat_extract.pad(__lowercase     ,     padding="""max_length"""    )[input_name]

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=__lowercase     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				input_a[input_name]

            self.assertFalse(_inputs_have_equal_length(__lowercase    )    )
            self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
            self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
            self.assertTrue(_inputs_are_equal(__lowercase     ,     __lowercase    )    )
            self.assertTrue(len(input_a[0]    ) == pad_min_length    )
            self.assertTrue(len(input_a[1]    ) == pad_min_length + pad_diff    )
            self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]    ))    )
            self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)    )

            if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size    )

            # test padding for `pad_to_multiple_of` for List[int] + numpy
            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     pad_to_multiple_of=10    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     pad_to_multiple_of=10    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     pad_to_multiple_of=10     ,     max_length=__lowercase    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     pad_to_multiple_of=10     ,     max_length=__lowercase     ,     return_tensors="""np"""     ,     )
            _lowerCAmelCase					=				input_a[input_name]

            self.assertTrue(all(len(__lowercase    ) % 10 == 0 for x in input_a    )    )
            self.assertTrue(_inputs_are_equal(__lowercase     ,     __lowercase    )    )

            _lowerCAmelCase					=				pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
            self.assertTrue(all(len(__lowercase    ) == expected_mult_pad_length for x in input_a    )    )
            self.assertEqual(input_a.shape[:2]     ,     (batch_size, expected_mult_pad_length)    )

            if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == feature_size    )

            # Check padding value is correct
            _lowerCAmelCase					=				(np.ones(self.feat_extract_tester.feature_size    ) * feat_extract.padding_value).sum()
            self.assertTrue(
                abs(np.asarray(input_a[0]    )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    )
                < 1E-3    )
            self.assertTrue(
                abs(
                    np.asarray(input_a[1]    )[pad_min_length + pad_diff :].sum()
                    - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)    )
                < 1E-3    )
            self.assertTrue(
                abs(
                    np.asarray(input_a[2]    )[pad_min_length + 2 * pad_diff :].sum()
                    - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)    )
                < 1E-3    )
            self.assertTrue(
                abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    ) < 1E-3    )
            self.assertTrue(
                abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)    )
                < 1E-3    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Union[str, Any]=False    )							-> Dict:






            def _inputs_have_equal_length(__snake_case     :					Union[str, Any]    ):
                  _lowerCAmelCase					=				len(input[0]    )
                  for input_slice in input[1:]:
                        if len(__lowercase    ) != length:
                              return False
                  return True

            def _inputs_are_equal(__snake_case     :					str     ,     __snake_case     :					str    ):
                  if len(__lowercase    ) != len(__lowercase    ):
                        return False

                  for input_slice_a, input_slice_a in zip(__lowercase     ,     __lowercase    ):
                        if not np.allclose(np.asarray(__lowercase    )     ,     np.asarray(__lowercase    )     ,     atol=1E-3    ):
                              return False
                  return True

            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase    )
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}    )

            # truncate to smallest
            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[0]    )     ,     truncation=__lowercase    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[0]    )    )
            _lowerCAmelCase					=				input_a[input_name]

            self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
            self.assertFalse(_inputs_have_equal_length(__lowercase    )    )

            # truncate to smallest with np
            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[0]    )     ,     return_tensors="""np"""     ,     truncation=__lowercase     ,     )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[0]    )     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				input_a[input_name]

            self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
            self.assertTrue(input_a.shape[1] == len(speech_inputs[0]    )    )

            # since truncation forces padding to be smaller than longest input
            # function can't return `np.ndarray`, but has to return list
            self.assertFalse(_inputs_have_equal_length(__lowercase    )    )

            # truncate to middle
            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[1]    )     ,     truncation=__lowercase     ,     return_tensors="""np"""     ,     )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[1]    )     ,     truncation=__lowercase    )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[1]    )     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				input_a[input_name]

            self.assertTrue(input_a.shape[1] == len(speech_inputs[1]    )    )
            self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
            self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
            self.assertTrue(_inputs_are_equal(__lowercase     ,     __lowercase    )    )

            # since truncation forces padding to be smaller than longest input
            # function can't return `np.ndarray`, but has to return list
            self.assertFalse(_inputs_have_equal_length(__lowercase    )    )
            self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__lowercase    ):
                  feat_extract.pad(__lowercase     ,     truncation=__lowercase    )[input_name]

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__lowercase    ):
                  feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     truncation=__lowercase    )[input_name]

            # padding has to be max_length when setting `truncation=True`
            with self.assertRaises(__lowercase    ):
                  feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     truncation=__lowercase    )[input_name]

            # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
            with self.assertRaises(__lowercase    ):
                  feat_extract.pad(__lowercase     ,     padding="""max_length"""     ,     truncation=__lowercase    )[input_name]

            # test truncation for `pad_to_multiple_of` for List[int] + numpy
            _lowerCAmelCase					=				12
            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[0]    )     ,     pad_to_multiple_of=__lowercase     ,     truncation=__lowercase     ,     )
            _lowerCAmelCase					=				input_a[input_name]

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=len(speech_inputs[0]    )     ,     pad_to_multiple_of=__lowercase     ,     )
            _lowerCAmelCase					=				input_a[input_name]

            # retrieve expected_length as multiple of pad_to_multiple_of
            _lowerCAmelCase					=				len(speech_inputs[0]    )
            if expected_length % pad_to_multiple_of != 0:
                  _lowerCAmelCase					=				((len(speech_inputs[0]    ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

            self.assertTrue(len(input_a[0]    ) == expected_length    )
            self.assertTrue(_inputs_have_equal_length(__lowercase    )    )
            self.assertFalse(_inputs_have_equal_length(__lowercase    )    )




      def 							lowercase__       (     self     :					Tuple    )							-> Optional[Any]:
            self._check_padding(numpify=__lowercase    )




      def 							lowercase__       (     self     :					List[str]    )							-> Tuple:
            self._check_padding(numpify=__lowercase    )




      def 							lowercase__       (     self     :					List[str]    )							-> Optional[Any]:
            self._check_truncation(numpify=__lowercase    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Union[str, Any]:
            self._check_truncation(numpify=__lowercase    )




      @require_torch
      def 							lowercase__       (     self     :					Tuple    )							-> Dict:
            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common()
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}    )

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     return_tensors="""np"""    )[input_name]
            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     return_tensors="""pt"""    )[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )




      @require_tf
      def 							lowercase__       (     self     :					List[str]    )							-> List[Any]:
            _lowerCAmelCase					=				self.feature_extraction_class(**self.feat_extract_dict    )
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common()
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}    )

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     return_tensors="""np"""    )[input_name]
            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     return_tensors="""tf"""    )[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_tf.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )




      def 							lowercase__       (     self     :					int    )							-> Dict:
            _lowerCAmelCase					=				self.feat_extract_dict
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				self.feature_extraction_class(**__lowercase    )
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common()
            _lowerCAmelCase					=				[len(__lowercase    ) for x in speech_inputs]
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}    )

            _lowerCAmelCase					=				feat_extract.pad(__lowercase     ,     padding="""longest"""     ,     return_tensors="""np"""    )
            self.assertIn("""attention_mask"""     ,     __lowercase    )
            self.assertListEqual(list(processed.attention_mask.shape    )     ,     list(processed[input_name].shape[:2]    )    )
            self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()     ,     __lowercase    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Optional[Any]:
            _lowerCAmelCase					=				self.feat_extract_dict
            _lowerCAmelCase					=				True
            _lowerCAmelCase					=				self.feature_extraction_class(**__lowercase    )
            _lowerCAmelCase					=				self.feat_extract_tester.prepare_inputs_for_common()
            _lowerCAmelCase					=				[len(__lowercase    ) for x in speech_inputs]
            _lowerCAmelCase					=				feat_extract.model_input_names[0]

            _lowerCAmelCase					=				BatchFeature({input_name: speech_inputs}    )
            _lowerCAmelCase					=				min(__lowercase    )

            _lowerCAmelCase					=				feat_extract.pad(
                __lowercase     ,     padding="""max_length"""     ,     max_length=__lowercase     ,     truncation=__lowercase     ,     return_tensors="""np"""    )
            self.assertIn("""attention_mask"""     ,     __lowercase    )
            self.assertListEqual(
                list(processed_pad.attention_mask.shape    )     ,     [processed_pad[input_name].shape[0], max_length]    )
            self.assertListEqual(
                processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()     ,     [max_length for x in speech_inputs]    )












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__       (						_A	,      _A				):







	'''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"	,      _A				):
		snake_case_						       =	R"\b\d{2}\b"
		if bool(re.search(_A	,      _A				)				):
			snake_case_						       =	re.search(R"\d\.\d\d."	,      _A				).group()
		else:
			snake_case_						       =	re.search(R"\d\.\d."	,      _A				).group()
		if int(match[0]				) < 6:
			snake_case_						       =	old_name.replace(_A	,      ""				)
			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(_A	,      ""				)
			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."	,      _A				):
		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 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	for key in checkpoint.copy().keys():
		snake_case_						       =	checkpoint.pop(_A				)
		snake_case_						       =	val

	return checkpoint


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	snake_case_						       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_						       =	Image.open(requests.get(_A	,      stream=_A				).raw				)

	return image


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	torch.load(_A	,      map_location="cpu"				)["model"]
	snake_case_						       =	EfficientFormerConfig.from_json_file(_A				)
	snake_case_						       =	EfficientFormerForImageClassificationWithTeacher(_A				)
	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(_A	,      _A				)

	model.load_state_dict(_A				)
	model.eval()

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

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

	# original processing pipeline
	snake_case_						       =	Compose(
	    [
	        Resize(_A	,      interpolation=pillow_resamplings["bicubic"]				),
	        CenterCrop(_A				),
	        ToTensor(),
	        Normalize(_A	,      _A				),
	    ]				)
	snake_case_						       =	image_transforms(_A				).unsqueeze(0				)

	assert torch.allclose(_A	,      _A				)

	snake_case_						       =	model(_A				)
	snake_case_						       =	outputs.logits

	snake_case_						       =	(1, 1000)

	if "l1" in model_name:
		snake_case_						       =	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]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		snake_case_						       =	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]	,      _A	,      atol=1E-3				)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		snake_case_						       =	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(_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__":
		lowercase__							:	Tuple											=		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__							:	Optional[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,
		)
