
'''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 		A_							(						_lowerCAmelCase :     List[Any]					,						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = old_name

      if "patch_embed" in old_name:
            _lowerCamelCase					: Tuple		    = old_name.split("."				)

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

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

                  _lowerCamelCase					: Dict		    = "last_stage." + trimmed_name

      elif "network" in old_name and re.search(r".\d."					,						_lowerCAmelCase				):
            _lowerCamelCase					: Union[str, Any]		    = old_name.replace("network"					,						"intermediate_stages"				)

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

      return new_name







def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      for key in checkpoint.copy().keys():
            _lowerCamelCase					: List[str]		    = checkpoint.pop(_lowerCAmelCase				)
            _lowerCamelCase					: int		    = val

      return checkpoint







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: str		    = "http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase					: Optional[Any]		    = Image.open(requests.get(_lowerCAmelCase					,						stream=_lowerCAmelCase				).raw				)

      return image







def 		A_							(						_lowerCAmelCase :     Optional[int]					,						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     Union[str, Any]				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = torch.load(_lowerCAmelCase					,						map_location="cpu"				)["model"]
      _lowerCamelCase					: int		    = EfficientFormerConfig.from_json_file(_lowerCAmelCase				)
      _lowerCamelCase					: Tuple		    = EfficientFormerForImageClassificationWithTeacher(_lowerCAmelCase				)
      _lowerCamelCase					: int		    = "_".join(checkpoint_path.split("/"				)[-1].split("."				)[0].split("_"				)[:-1]				)

      _lowerCamelCase					: Optional[int]		    = config.depths[-1] - config.num_metaad_blocks + 1
      _lowerCamelCase					: int		    = convert_torch_checkpoint(_lowerCAmelCase					,						_lowerCAmelCase				)

      model.load_state_dict(_lowerCAmelCase				)
      model.eval()

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

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

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

      assert torch.allclose(_lowerCAmelCase					,						_lowerCAmelCase				)

      _lowerCamelCase					: Union[str, Any]		    = model(_lowerCAmelCase				)
      _lowerCamelCase					: Any		    = outputs.logits

      _lowerCamelCase					: List[str]		    = (1, 1000)

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

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

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

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


if __name__ == "__main__":
 UpperCAmelCase_							: 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)

 UpperCAmelCase_							: Optional[int] =							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,
 )



'''simple docstring'''
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=4							,      )    ->  Tuple:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		List[str]   					=		is_training
					lowercase						:		str   					=		use_attention_mask
					lowercase						:		List[str]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Union[str, Any]   					=		hidden_size
					lowercase						:		int   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		int   					=		hidden_act
					lowercase						:		Dict   					=		hidden_dropout_prob
					lowercase						:		Optional[Any]   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		Tuple   					=		type_vocab_size
					lowercase						:		int   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		num_choices

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Any   					=		None
					if self.use_attention_mask:
						lowercase						:		str   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		Optional[Any]   					=		DistilBertConfig(
					    vocab_size=self.vocab_size							,      dim=self.hidden_size							,      n_layers=self.num_hidden_layers							,      n_heads=self.num_attention_heads							,      hidden_dim=self.intermediate_size							,      hidden_act=self.hidden_act							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      initializer_range=self.initializer_range							,      tie_weights_=a_							,      )

					return config, input_ids, attention_mask


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		Dict   					=		config_and_inputs
					lowercase						:		Any   					=		{"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		FlaxDistilBertModelTester(self							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("distilbert-base-uncased"							)
						lowercase						:		Tuple   					=		model(np.ones((1, 1)							)							)
						self.assertIsNotNone(a_							)




@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					lowercase						:		int   					=		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
					lowercase						:		int   					=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					lowercase						:		List[str]   					=		model(a_							,      attention_mask=a_							)[0]
					lowercase						:		int   					=		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,      a_							)
					lowercase						:		List[str]   					=		np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,      a_							,      atol=1e-4							)							)


