



"""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     (		A	:						List[Any]     ,				A	:						Dict       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		old_name

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

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

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

						_UpperCAmelCase							 =		'last_stage.' + trimmed_name

		elif "network" in old_name and re.search(r'.\d.'     ,				A       ):
				_UpperCAmelCase							 =		old_name.replace('network'     ,				'intermediate_stages'       )

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

		return new_name




def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						List[str]       ):

		'''simple docstring'''


		for key in checkpoint.copy().keys():
				_UpperCAmelCase							 =		checkpoint.pop(A       )
				_UpperCAmelCase							 =		val

		return checkpoint




def 	UpperCAmelCase     (		):

		'''simple docstring'''


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

		return image




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						Path     ,				A	:						Path     ,				A	:						bool       ):

		'''simple docstring'''


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

		_UpperCAmelCase							 =		config.depths[-1] - config.num_metaad_blocks + 1
		_UpperCAmelCase							 =		convert_torch_checkpoint(A     ,				A       )

		model.load_state_dict(A       )
		model.eval()

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

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

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

		assert torch.allclose(A     ,				A       )

		_UpperCAmelCase							 =		model(A       )
		_UpperCAmelCase							 =		outputs.logits

		_UpperCAmelCase							 =		(1, 1000)

		if "l1" in model_name:
				_UpperCAmelCase							 =		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							 =		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							 =		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__":
					lowercase  							=					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  							=					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"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  							=					{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''RobertaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaForCausalLM''',
					    '''RobertaForMaskedLM''',
					    '''RobertaForMultipleChoice''',
					    '''RobertaForQuestionAnswering''',
					    '''RobertaForSequenceClassification''',
					    '''RobertaForTokenClassification''',
					    '''RobertaModel''',
					    '''RobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaForCausalLM''',
					    '''TFRobertaForMaskedLM''',
					    '''TFRobertaForMultipleChoice''',
					    '''TFRobertaForQuestionAnswering''',
					    '''TFRobertaForSequenceClassification''',
					    '''TFRobertaForTokenClassification''',
					    '''TFRobertaMainLayer''',
					    '''TFRobertaModel''',
					    '''TFRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''FlaxRobertaForCausalLM''',
					    '''FlaxRobertaForMaskedLM''',
					    '''FlaxRobertaForMultipleChoice''',
					    '''FlaxRobertaForQuestionAnswering''',
					    '''FlaxRobertaForSequenceClassification''',
					    '''FlaxRobertaForTokenClassification''',
					    '''FlaxRobertaModel''',
					    '''FlaxRobertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


