






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_		:						int             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= original_name.split('.')[0]
			SCREAMING_SNAKE_CASE						= key.split('.')
			SCREAMING_SNAKE_CASE						= int(key_list[key_list.index(_UpperCAmelCase) - 2])
			SCREAMING_SNAKE_CASE						= int(key_list[key_list.index(_UpperCAmelCase) - 1])
			SCREAMING_SNAKE_CASE						= orig_block_num - offset

			SCREAMING_SNAKE_CASE						= key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''							,       F'''block.{new_block_num}.{layer_num}.{new_name}''')
			return key






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= OrderedDict()
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 0, 0
			for key, value in state_dict.items():
						if key.startswith('network'):
									SCREAMING_SNAKE_CASE						= key.replace('network'							,       'poolformer.encoder')
						if "proj" in key:
									# Works for the first embedding as well as the internal embedding layers
									if key.endswith('bias') and "patch_embed" not in key:
												patch_emb_offset += 1
									SCREAMING_SNAKE_CASE						= key[: key.find('proj')]
									SCREAMING_SNAKE_CASE						= key.replace(_UpperCAmelCase							,       F'''patch_embeddings.{total_embed_found}.''')
									SCREAMING_SNAKE_CASE						= key.replace('proj'							,       'projection')
									if key.endswith('bias'):
												total_embed_found += 1
						if "patch_embeddings" in key:
									SCREAMING_SNAKE_CASE						= 'poolformer.encoder.' + key
						if "mlp.fc1" in key:
									SCREAMING_SNAKE_CASE						= replace_key_with_offset(_UpperCAmelCase							,       _UpperCAmelCase							,       'mlp.fc1'							,       'output.conv1')
						if "mlp.fc2" in key:
									SCREAMING_SNAKE_CASE						= replace_key_with_offset(_UpperCAmelCase							,       _UpperCAmelCase							,       'mlp.fc2'							,       'output.conv2')
						if "norm1" in key:
									SCREAMING_SNAKE_CASE						= replace_key_with_offset(_UpperCAmelCase							,       _UpperCAmelCase							,       'norm1'							,       'before_norm')
						if "norm2" in key:
									SCREAMING_SNAKE_CASE						= replace_key_with_offset(_UpperCAmelCase							,       _UpperCAmelCase							,       'norm2'							,       'after_norm')
						if "layer_scale_1" in key:
									SCREAMING_SNAKE_CASE						= replace_key_with_offset(_UpperCAmelCase							,       _UpperCAmelCase							,       'layer_scale_1'							,       'layer_scale_1')
						if "layer_scale_2" in key:
									SCREAMING_SNAKE_CASE						= replace_key_with_offset(_UpperCAmelCase							,       _UpperCAmelCase							,       'layer_scale_2'							,       'layer_scale_2')
						if "head" in key:
									SCREAMING_SNAKE_CASE						= key.replace('head'							,       'classifier')
						SCREAMING_SNAKE_CASE						= value
			return new_state_dict






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

			return image






@torch.no_grad()
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= PoolFormerConfig()

			# set attributes based on model_name
			SCREAMING_SNAKE_CASE						= 'huggingface/label-files'
			SCREAMING_SNAKE_CASE						= model_name[-3:]
			SCREAMING_SNAKE_CASE						= 1000
			SCREAMING_SNAKE_CASE						= 'imagenet-1k-id2label.json'
			SCREAMING_SNAKE_CASE						= (1, 1000)

			# set config attributes
			SCREAMING_SNAKE_CASE						= json.load(open(hf_hub_download(_UpperCAmelCase							,       _UpperCAmelCase							,       repo_type='dataset')							,       'r'))
			SCREAMING_SNAKE_CASE						= {int(_UpperCAmelCase): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE						= idalabel
			SCREAMING_SNAKE_CASE						= {v: k for k, v in idalabel.items()}
			if size == "s12":
						SCREAMING_SNAKE_CASE						= [2, 2, 6, 2]
						SCREAMING_SNAKE_CASE						= [64, 128, 320, 512]
						SCREAMING_SNAKE_CASE						= 4.0
						SCREAMING_SNAKE_CASE						= 0.9
			elif size == "s24":
						SCREAMING_SNAKE_CASE						= [4, 4, 12, 4]
						SCREAMING_SNAKE_CASE						= [64, 128, 320, 512]
						SCREAMING_SNAKE_CASE						= 4.0
						SCREAMING_SNAKE_CASE						= 0.9
			elif size == "s36":
						SCREAMING_SNAKE_CASE						= [6, 6, 18, 6]
						SCREAMING_SNAKE_CASE						= [64, 128, 320, 512]
						SCREAMING_SNAKE_CASE						= 4.0
						SCREAMING_SNAKE_CASE						= 1e-6
						SCREAMING_SNAKE_CASE						= 0.9
			elif size == "m36":
						SCREAMING_SNAKE_CASE						= [6, 6, 18, 6]
						SCREAMING_SNAKE_CASE						= [96, 192, 384, 768]
						SCREAMING_SNAKE_CASE						= 4.0
						SCREAMING_SNAKE_CASE						= 1e-6
						SCREAMING_SNAKE_CASE						= 0.95
			elif size == "m48":
						SCREAMING_SNAKE_CASE						= [8, 8, 24, 8]
						SCREAMING_SNAKE_CASE						= [96, 192, 384, 768]
						SCREAMING_SNAKE_CASE						= 4.0
						SCREAMING_SNAKE_CASE						= 1e-6
						SCREAMING_SNAKE_CASE						= 0.95
			else:
						raise ValueError(F'''Size {size} not supported''')

			# load image processor
			SCREAMING_SNAKE_CASE						= PoolFormerImageProcessor(crop_pct=_UpperCAmelCase)

			# Prepare image
			SCREAMING_SNAKE_CASE						= prepare_img()
			SCREAMING_SNAKE_CASE						= image_processor(images=_UpperCAmelCase							,       return_tensors='pt').pixel_values

			logger.info(F'''Converting model {model_name}...''')

			# load original state dict
			SCREAMING_SNAKE_CASE						= torch.load(_UpperCAmelCase							,       map_location=torch.device('cpu'))

			# rename keys
			SCREAMING_SNAKE_CASE						= rename_keys(_UpperCAmelCase)

			# create HuggingFace model and load state dict
			SCREAMING_SNAKE_CASE						= PoolFormerForImageClassification(_UpperCAmelCase)
			model.load_state_dict(_UpperCAmelCase)
			model.eval()

			# Define image processor
			SCREAMING_SNAKE_CASE						= PoolFormerImageProcessor(crop_pct=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= image_processor(images=prepare_img()							,       return_tensors='pt').pixel_values

			# forward pass
			SCREAMING_SNAKE_CASE						= model(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= outputs.logits

			# define expected logit slices for different models
			if size == "s12":
						SCREAMING_SNAKE_CASE						= torch.tensor([-0.30_45, -0.67_58, -0.48_69])
			elif size == "s24":
						SCREAMING_SNAKE_CASE						= torch.tensor([0.44_02, -0.13_74, -0.80_45])
			elif size == "s36":
						SCREAMING_SNAKE_CASE						= torch.tensor([-0.60_80, -0.51_33, -0.58_98])
			elif size == "m36":
						SCREAMING_SNAKE_CASE						= torch.tensor([0.39_52, 0.22_63, -1.26_68])
			elif size == "m48":
						SCREAMING_SNAKE_CASE						= torch.tensor([0.11_67, -0.06_56, -0.34_23])
			else:
						raise ValueError(F'''Size {size} not supported''')

			# verify logits
			assert logits.shape == expected_shape
			assert torch.allclose(logits[0, :3]							,       _UpperCAmelCase							,       atol=1e-2)

			# finally, save model and image processor
			logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
			Path(_UpperCAmelCase).mkdir(exist_ok=_UpperCAmelCase)
			model.save_pretrained(_UpperCAmelCase)
			print(F'''Saving image processor to {pytorch_dump_folder_path}''')
			image_processor.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
				a_		:						Tuple             =    argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='poolformer_s12',
				    type=str,
				    help='Name of the model you\'d like to convert.',
				)
				parser.add_argument(
				    '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				a_		:						int             =    parser.parse_args()
				convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from ..image_utils import load_image


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

a_		:						str             =    logging.get_logger(__name__)


a_		:						List[Any]             =    Dict[str, Any]
a_		:						Optional[int]             =    List[Prediction]

@add_end_docstrings(A__					)
class 		_snake_case  (  A__					):






			def __init__(       self		,					*a		,					**a)      ->       int:
						super().__init__(*a		,					**a)

						if self.framework == "tf":
									raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')

						requires_backends(self		,					'vision')
						self.check_model_type(
						    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       int:
						SCREAMING_SNAKE_CASE						= {}
						if "threshold" in kwargs:
									SCREAMING_SNAKE_CASE						= kwargs['threshold']
						return {}, {}, postprocess_kwargs






			def __call__(       self		,					*a		,					**a)      ->       Union[Predictions, List[Prediction]]:
						return super().__call__(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= load_image(a)
						SCREAMING_SNAKE_CASE						= torch.IntTensor([[image.height, image.width]])
						SCREAMING_SNAKE_CASE						= self.image_processor(images=[image]		,					return_tensors='pt')
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= self.tokenizer(text=inputs['words']		,					boxes=inputs['boxes']		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= target_size
						return inputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= model_inputs.pop('target_size')
						SCREAMING_SNAKE_CASE						= self.model(**a)
						SCREAMING_SNAKE_CASE						= outputs.__class__({'target_size': target_size, **outputs})
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= model_inputs['bbox']
						return model_outputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0.9)      ->       Dict:
						SCREAMING_SNAKE_CASE						= model_outputs['target_size']
						if self.tokenizer is not None:
									# This is a LayoutLMForTokenClassification variant.
									# The OCR got the boxes and the model classified the words.
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= target_size[0].tolist()

									def unnormalize(a):
												return self._get_bounding_box(
												    torch.Tensor(
												        [
												            (width * bbox[0] / 1000),
												            (height * bbox[1] / 1000),
												            (width * bbox[2] / 1000),
												            (height * bbox[3] / 1000),
												        ]))

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
									SCREAMING_SNAKE_CASE						= [unnormalize(a) for bbox in model_outputs['bbox'].squeeze(0)]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [dict(zip(a		,					a)) for vals in zip(scores.tolist()		,					a		,					a) if vals[0] > threshold]
						else:
									# This is a regular ForObjectDetectionModel
									SCREAMING_SNAKE_CASE						= self.image_processor.post_process_object_detection(a		,					a		,					a)
									SCREAMING_SNAKE_CASE						= raw_annotations[0]
									SCREAMING_SNAKE_CASE						= raw_annotation['scores']
									SCREAMING_SNAKE_CASE						= raw_annotation['labels']
									SCREAMING_SNAKE_CASE						= raw_annotation['boxes']

									SCREAMING_SNAKE_CASE						= scores.tolist()
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[label.item()] for label in labels]
									SCREAMING_SNAKE_CASE						= [self._get_bounding_box(a) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [
									    dict(zip(a		,					a))
									    for vals in zip(raw_annotation['scores']		,					raw_annotation['labels']		,					raw_annotation['boxes'])
									]

						return annotation







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict[str, int]:
						if self.framework != "pt":
									raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= box.int().tolist()
						SCREAMING_SNAKE_CASE						= {
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox




