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"""


							_lowercase					=						old_name

							if "patch_embed" in old_name:
														_lowercase					=						old_name.split("""."""       )

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

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

																					_lowercase					=						'''last_stage.''' + trimmed_name

							elif "network" in old_name and re.search(r""".\d."""			,_A       ):
														_lowercase					=						old_name.replace("""network"""			,"""intermediate_stages"""       )

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

							return new_name


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


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

							return checkpoint


def        __lowerCAmelCase      (	):



							"""simple docstring"""


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

							return image


def        __lowerCAmelCase      (	_A			,_A			,_A			,_A       ):



							"""simple docstring"""


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

							_lowercase					=						config.depths[-1] - config.num_metaad_blocks + 1
							_lowercase					=						convert_torch_checkpoint(_A			,_A       )

							model.load_state_dict(_A       )
							model.eval()

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

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

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

							assert torch.allclose(_A			,_A       )

							_lowercase					=						model(_A       )
							_lowercase					=						outputs.logits

							_lowercase					=						(1, 1_000)

							if "l1" in model_name:
														_lowercase					=						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]			,_A			,atol=1E-3       )
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														_lowercase					=						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]			,_A			,atol=1E-3       )
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														_lowercase					=						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(_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__":
				A_:   Any   =      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_:   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


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
					from .tokenization_herbert import HerbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_herbert_fast import HerbertTokenizerFast

else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






