




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_ :     Tuple  ,							lowerCamelCase_ :     Optional[int]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      old_name

			if "patch_embed" in old_name:
						SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      old_name.split('.'  )

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

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

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

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

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

			return new_name





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     str  )       ->  Dict:







			"""simple docstring"""





			for key in checkpoint.copy().keys():
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      checkpoint.pop(lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      val

			return checkpoint





def        __UpperCAmelCase						(							)       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      Image.open(requests.get(lowerCamelCase_  ,							stream=lowerCamelCase_  ).raw  )

			return image





def        __UpperCAmelCase						(							lowerCamelCase_ :     Path  ,							lowerCamelCase_ :     Path  ,							lowerCamelCase_ :     Path  ,							lowerCamelCase_ :     bool  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      torch.load(lowerCamelCase_  ,							map_location='cpu'  )['model']
			SCREAMING_SNAKE_CASE_   : Any					  =      EfficientFormerConfig.from_json_file(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      EfficientFormerForImageClassificationWithTeacher(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      '_'.join(checkpoint_path.split('/'  )[-1].split('.'  )[0].split('_'  )[:-1]  )

			SCREAMING_SNAKE_CASE_   : Dict					  =      config.depths[-1] - config.num_metaad_blocks + 1
			SCREAMING_SNAKE_CASE_   : Tuple					  =      convert_torch_checkpoint(lowerCamelCase_  ,							lowerCamelCase_  )

			model.load_state_dict(lowerCamelCase_  )
			model.eval()

			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      {
			    'bilinear': PILImageResampling.BILINEAR,
			    'bicubic': PILImageResampling.BICUBIC,
			    'nearest': PILImageResampling.NEAREST,
			}

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

			# original processing pipeline
			SCREAMING_SNAKE_CASE_   : List[str]					  =      Compose(
			    [
			        Resize(lowerCamelCase_  ,							interpolation=pillow_resamplings['bicubic']  ),
			        CenterCrop(lowerCamelCase_  ),
			        ToTensor(),
			        Normalize(lowerCamelCase_  ,							lowerCamelCase_  ),
			    ]  )
			SCREAMING_SNAKE_CASE_   : Any					  =      image_transforms(lowerCamelCase_  ).unsqueeze(0  )

			assert torch.allclose(lowerCamelCase_  ,							lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Dict					  =      model(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      outputs.logits

			SCREAMING_SNAKE_CASE_   : Any					  =      (1, 10_00)

			if "l1" in model_name:
						SCREAMING_SNAKE_CASE_   : str					  =      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:
						SCREAMING_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]  ,							lowerCamelCase_  ,							atol=1E-3  )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						SCREAMING_SNAKE_CASE_   : Optional[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(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__ :   List[str]						=							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,
						)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :   Optional[int]						=							{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Tuple						=							['''BloomTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :   Any						=							[
						    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BloomForCausalLM''',
						    '''BloomModel''',
						    '''BloomPreTrainedModel''',
						    '''BloomForSequenceClassification''',
						    '''BloomForTokenClassification''',
						    '''BloomForQuestionAnswering''',
						]

if TYPE_CHECKING:
						from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bloom_fast import BloomTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bloom import (
												    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BloomForCausalLM,
												    BloomForQuestionAnswering,
												    BloomForSequenceClassification,
												    BloomForTokenClassification,
												    BloomModel,
												    BloomPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :   Tuple						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



