
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_		,       a_						)		->     int:







							"""simple docstring"""



							__A             =			old_name

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

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

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

																					__A             =			'last_stage.' + trimmed_name

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

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

							return new_name


def 						UpperCAmelCase      (     a_		,       a_						)		->     Union[str, Any]:







							"""simple docstring"""



							for key in checkpoint.copy().keys():
														__A             =			checkpoint.pop(lowerCAmelCase_						)
														__A             =			val

							return checkpoint


def 						UpperCAmelCase      (     )		->     Any:







							"""simple docstring"""



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

							return image


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_						)		->     List[Any]:







							"""simple docstring"""



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

							__A             =			config.depths[-1] - config.num_metaad_blocks + 1
							__A             =			convert_torch_checkpoint(lowerCAmelCase_		,       lowerCAmelCase_						)

							model.load_state_dict(lowerCAmelCase_						)
							model.eval()

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

							# prepare image
							__A             =			prepare_img()
							__A             =			2_5_6
							__A             =			2_2_4
							__A             =			EfficientFormerImageProcessor(
							    size={"shortest_edge": image_size}		,       crop_size={"height": crop_size, "width": crop_size}		,       resample=pillow_resamplings["bicubic"]		,       )
							__A             =			processor(images=lowerCAmelCase_		,       return_tensors="pt"						).pixel_values

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

							assert torch.allclose(lowerCAmelCase_		,       lowerCAmelCase_						)

							__A             =			model(lowerCAmelCase_						)
							__A             =			outputs.logits

							__A             =			(1, 1_0_0_0)

							if "l1" in model_name:
														__A             =			torch.Tensor(
														    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]						)
														assert torch.allclose(logits[0, :1_0]		,       lowerCAmelCase_		,       atol=1E-3						)
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														__A             =			torch.Tensor(
														    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]						)
														assert torch.allclose(logits[0, :1_0]		,       lowerCAmelCase_		,       atol=1E-3						)
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														__A             =			torch.Tensor(
														    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]						)
														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__":
					SCREAMING_SNAKE_CASE							:List[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)

					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    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_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
