





'''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_      (		lowercase__				,  lowercase__						)   ->Optional[Any]:
						_snake_case:   List[Any]  					=		old_name

						if "patch_embed" in old_name:
												_snake_case					,						_snake_case					,						_snake_case:   List[Any]  					=		old_name.split('.'						)

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

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

																		_snake_case:   List[Any]  					=		'last_stage.' + trimmed_name

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

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

						return new_name





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Dict:
						for key in checkpoint.copy().keys():
												_snake_case:   List[Any]  					=		checkpoint.pop(lowercase__						)
												_snake_case:   int  					=		val

						return checkpoint





def       lowercase_      (		)   ->Optional[int]:
						_snake_case:   Any  					=		'http://images.cocodataset.org/val2017/000000039769.jpg'
						_snake_case:   int  					=		Image.open(requests.get(lowercase__				,  stream=lowercase__						).raw						)

						return image





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Optional[int]:
						_snake_case:   Optional[Any]  					=		torch.load(lowercase__				,  map_location='cpu'						)['model']
						_snake_case:   Tuple  					=		EfficientFormerConfig.from_json_file(lowercase__						)
						_snake_case:   List[str]  					=		EfficientFormerForImageClassificationWithTeacher(lowercase__						)
						_snake_case:   Optional[Any]  					=		'_'.join(checkpoint_path.split('/'						)[-1].split('.'						)[0].split('_'						)[:-1]						)

						_snake_case:   Optional[Any]  					=		config.depths[-1] - config.num_metaad_blocks + 1
						_snake_case:   Dict  					=		convert_torch_checkpoint(lowercase__				,  lowercase__						)

						model.load_state_dict(lowercase__						)
						model.eval()

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

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

						# original processing pipeline
						_snake_case:   Any  					=		Compose(
						    [
						        Resize(lowercase__				,  interpolation=pillow_resamplings['bicubic']						),
						        CenterCrop(lowercase__						),
						        ToTensor(),
						        Normalize(lowercase__				,  lowercase__						),
						    ]						)
						_snake_case:   Optional[Any]  					=		image_transforms(lowercase__						).unsqueeze(0						)

						assert torch.allclose(lowercase__				,  lowercase__						)

						_snake_case:   str  					=		model(lowercase__						)
						_snake_case:   List[str]  					=		outputs.logits

						_snake_case:   Optional[int]  					=		(1, 1000)

						if "l1" in model_name:
												_snake_case:   Union[str, 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]				,  lowercase__				,  atol=1e-3						)
												assert logits.shape == expected_shape
						elif "l3" in model_name:
												_snake_case:   Optional[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]				,  lowercase__				,  atol=1e-3						)
												assert logits.shape == expected_shape
						elif "l7" in model_name:
												_snake_case:   List[Any]  					=		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(lowercase__						).mkdir(exist_ok=lowercase__						)
						model.save_pretrained(lowercase__						)
						print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''						)
						processor.save_pretrained(lowercase__						)
						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=lowercase__				,  )
												processor.push_to_hub(
												    repo_id=F'''Bearnardd/{pytorch_dump_path}'''				,  commit_message='Add image processor'				,  use_temp_dir=lowercase__				,  )


if __name__ == "__main__":
		A   : List[str]             =			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)

		A   : Dict             =			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
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


A   : Tuple             =			['bert-base-uncased', 'bert-base-cased']
A   : str             =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







		class 			lowerCamelCase					(						tf.keras.Model  ):


							def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ):





													'''simple docstring'''





													super().__init__()
													_snake_case:   List[Any]  					=		tokenizer
													_snake_case:   str  					=		AutoConfig.from_pretrained(__snake_case       )
													_snake_case:   List[Any]  					=		TFAutoModel.from_config(__snake_case       )




							def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  List[str]       ):





													'''simple docstring'''





													_snake_case:   Optional[int]  					=		self.tokenizer(__snake_case       )
													_snake_case:   Tuple  					=		self.bert(**__snake_case       )
													return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											super().setUp()

											_snake_case:   List[Any]  					=		[
											    BertTokenizer.from_pretrained(__snake_case       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											_snake_case:   List[Any]  					=		[TFBertTokenizer.from_pretrained(__snake_case       ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
											    TFBertTokenizer.from_pretrained(__snake_case       ,     use_fast_bert_tokenizer=__snake_case       )
											    for checkpoint in TOKENIZER_CHECKPOINTS
											]
											assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

											_snake_case:   int  					=		[
											    'This is a straightforward English test sentence.',
											    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
											    'Now we\'re going to add some Chinese: 一 二 三 一二三',
											    'And some much more rare Chinese: 齉 堃 齉堃',
											    'Je vais aussi écrire en français pour tester les accents',
											    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
											]
											_snake_case:   str  					=		list(zip(self.test_sentences       ,     self.test_sentences[::-1]       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											for tokenizer, tf_tokenizer in zip(self.tokenizers       ,     self.tf_tokenizers       ):
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   List[Any]  					=		tokenizer(__snake_case       ,     return_tensors='tf'       ,     padding='longest'       )
																							_snake_case:   Optional[Any]  					=		tf_tokenizer(__snake_case       )

																							for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape       )       )
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]       ,     tf.intaa       ) == tf_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   str  					=		tf_tokenizer(self.paired_sentences       )
																	_snake_case:   Any  					=		tf_tokenizer(
																	    text=[sentence[0] for sentence in self.paired_sentences]       ,     text_pair=[sentence[1] for sentence in self.paired_sentences]       ,     )
																	for key in merged_outputs.keys():
																							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]       ,     tf.intaa       ) == separated_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   List[str]  					=		tf.function(__snake_case       )
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   Union[str, Any]  					=		tf.constant(__snake_case       )
																							_snake_case:   Any  					=		compiled_tokenizer(__snake_case       )
																							_snake_case:   Union[str, Any]  					=		tf_tokenizer(__snake_case       )

																							for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   Optional[Any]  					=		ModelToSave(tokenizer=__snake_case       )
																	_snake_case:   Tuple  					=		tf.convert_to_tensor(self.test_sentences       )
																	_snake_case:   List[str]  					=		model(__snake_case       )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							_snake_case:   List[str]  					=		Path(__snake_case       ) / 'saved.model'
																							model.save(__snake_case       )
																							_snake_case:   int  					=		tf.keras.models.load_model(__snake_case       )
																	_snake_case:   int  					=		loaded_model(__snake_case       )
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output       )       )       ,     1e-5       )





