



'''simple docstring'''




from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

UpperCamelCase__ :			Union[str, Any]												=				logging.get_logger(__name__)






@add_end_docstrings(lowerCamelCase__					)
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				str							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					super().__init__(**lowerCAmelCase__		)
					requires_backends(self							,  """vision"""		)
					requires_backends(self							,  """torch"""		)

					if self.framework != "pt":
							raise ValueError(F"The {self.__class__} is only available in PyTorch."		)

					self.check_model_type(lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[Any]							,  **lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	{}
					__SCREAMING_SNAKE_CASE :	str					 =	{}
					__SCREAMING_SNAKE_CASE :	Tuple					 =	{}
					# preprocess args
					if "points_per_batch" in kwargs:
							__SCREAMING_SNAKE_CASE :	Any					 =	kwargs["""points_per_batch"""]
					if "points_per_crop" in kwargs:
							__SCREAMING_SNAKE_CASE :	Dict					 =	kwargs["""points_per_crop"""]
					if "crops_n_layers" in kwargs:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	kwargs["""crops_n_layers"""]
					if "crop_overlap_ratio" in kwargs:
							__SCREAMING_SNAKE_CASE :	Dict					 =	kwargs["""crop_overlap_ratio"""]
					if "crop_n_points_downscale_factor" in kwargs:
							__SCREAMING_SNAKE_CASE :	int					 =	kwargs["""crop_n_points_downscale_factor"""]
					# postprocess args
					if "pred_iou_thresh" in kwargs:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	kwargs["""pred_iou_thresh"""]
					if "stability_score_offset" in kwargs:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	kwargs["""stability_score_offset"""]
					if "mask_threshold" in kwargs:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	kwargs["""mask_threshold"""]
					if "stability_score_thresh" in kwargs:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	kwargs["""stability_score_thresh"""]
					if "crops_nms_thresh" in kwargs:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	kwargs["""crops_nms_thresh"""]
					if "output_rle_mask" in kwargs:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	kwargs["""output_rle_mask"""]
					if "output_bboxes_mask" in kwargs:
							__SCREAMING_SNAKE_CASE :	Any					 =	kwargs["""output_bboxes_mask"""]
					return preprocess_kwargs, forward_params, postprocess_kwargs

			def __call__(							self  :				Tuple							,  lowerCAmelCase__  :				int							,  *lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				Optional[int]=None							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					return super().__call__(lowerCAmelCase__							,  *lowerCAmelCase__							,  num_workers=lowerCAmelCase__							,  batch_size=lowerCAmelCase__							,  **lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[Any]=6_4							,  lowerCAmelCase__  :				int = 0							,  lowerCAmelCase__  :				float = 5_1_2 / 1_5_0_0							,  lowerCAmelCase__  :				Optional[int] = 3_2							,  lowerCAmelCase__  :				Optional[int] = 1							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	load_image(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.image_processor.size["""longest_edge"""]
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.image_processor.generate_crop_boxes(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.image_processor(images=lowerCAmelCase__							,  return_tensors="""pt"""		)

					with self.device_placement():
							if self.framework == "pt":
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.get_inference_context()
									with inference_context():
											__SCREAMING_SNAKE_CASE :	Any					 =	self._ensure_tensor_on_device(lowerCAmelCase__							,  device=self.device		)
											__SCREAMING_SNAKE_CASE :	int					 =	self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
											__SCREAMING_SNAKE_CASE :	List[Any]					 =	image_embeddings

					__SCREAMING_SNAKE_CASE :	List[str]					 =	grid_points.shape[1]
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	points_per_batch if points_per_batch is not None else n_points

					if points_per_batch <= 0:
							raise ValueError(
							    """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
							    """To return all points at once, set points_per_batch to None"""		)

					for i in range(0							,  lowerCAmelCase__							,  lowerCAmelCase__		):
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	grid_points[:, i : i + points_per_batch, :, :]
							__SCREAMING_SNAKE_CASE :	Dict					 =	input_labels[:, i : i + points_per_batch]
							__SCREAMING_SNAKE_CASE :	Any					 =	i == n_points - points_per_batch
							yield {
							    "input_points": batched_points,
							    "input_labels": labels,
							    "input_boxes": crop_boxes,
							    "is_last": is_last,
							    **model_inputs,
							}

			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]=0.88							,  lowerCAmelCase__  :				Dict=0.95							,  lowerCAmelCase__  :				List[str]=0							,  lowerCAmelCase__  :				Optional[int]=1							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	model_inputs.pop("""input_boxes"""		)
					__SCREAMING_SNAKE_CASE :	int					 =	model_inputs.pop("""is_last"""		)
					__SCREAMING_SNAKE_CASE :	Any					 =	model_inputs.pop("""original_sizes"""		).tolist()
					__SCREAMING_SNAKE_CASE :	str					 =	model_inputs.pop("""reshaped_input_sizes"""		).tolist()

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.model(**lowerCAmelCase__		)

					# post processing happens here in order to avoid CPU GPU copies of ALL the masks
					__SCREAMING_SNAKE_CASE :	Any					 =	model_outputs["""pred_masks"""]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.image_processor.post_process_masks(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  binarize=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	model_outputs["""iou_scores"""]
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.image_processor.filter_masks(
					    masks[0]							,  iou_scores[0]							,  original_sizes[0]							,  input_boxes[0]							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  )
					return {
					    "masks": masks,
					    "is_last": is_last,
					    "boxes": boxes,
					    "iou_scores": iou_scores,
					}



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				int=False							,  lowerCAmelCase__  :				List[str]=False							,  lowerCAmelCase__  :				Optional[Any]=0.7							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[]
					__SCREAMING_SNAKE_CASE :	int					 =	[]
					__SCREAMING_SNAKE_CASE :	Any					 =	[]
					for model_output in model_outputs:
							all_scores.append(model_output.pop("""iou_scores"""		)		)
							all_masks.extend(model_output.pop("""masks"""		)		)
							all_boxes.append(model_output.pop("""boxes"""		)		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.cat(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	torch.cat(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.image_processor.post_process_for_mask_generation(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	defaultdict(lowerCAmelCase__		)
					for output in model_outputs:
							for k, v in output.items():
									extra[k].append(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	{}
					if output_rle_mask:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	rle_mask

					if output_bboxes_mask:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	bounding_boxes

					return {"masks": output_masks, "scores": iou_scores, **optional, **extra}




'''simple docstring'''




import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	SwinvaConfig()
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	swinva_name.split("""_"""      )

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	name_split[1]
		if "to" in name_split[3]:
				__SCREAMING_SNAKE_CASE :	Dict					 =	int(name_split[3][-3:]      )
		else:
				__SCREAMING_SNAKE_CASE :	str					 =	int(name_split[3]      )
		if "to" in name_split[2]:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	int(name_split[2][-2:]      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	int(name_split[2][6:]      )

		if model_size == "tiny":
				__SCREAMING_SNAKE_CASE :	Dict					 =	96
				__SCREAMING_SNAKE_CASE :	List[str]					 =	(2, 2, 6, 2)
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(3, 6, 12, 24)
		elif model_size == "small":
				__SCREAMING_SNAKE_CASE :	List[str]					 =	96
				__SCREAMING_SNAKE_CASE :	int					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	int					 =	(3, 6, 12, 24)
		elif model_size == "base":
				__SCREAMING_SNAKE_CASE :	int					 =	1_28
				__SCREAMING_SNAKE_CASE :	str					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(4, 8, 16, 32)
		else:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	1_92
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Dict					 =	(6, 12, 24, 48)

		if "to" in swinva_name:
				__SCREAMING_SNAKE_CASE :	int					 =	(12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				__SCREAMING_SNAKE_CASE :	int					 =	2_18_41
				__SCREAMING_SNAKE_CASE :	str					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	"""imagenet-22k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	str					 =	{v: k for k, v in idalabel.items()}

		else:
				__SCREAMING_SNAKE_CASE :	str					 =	10_00
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	Any					 =	"""imagenet-1k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	int					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	Dict					 =	{v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE :	Any					 =	img_size
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_classes
		__SCREAMING_SNAKE_CASE :	int					 =	embed_dim
		__SCREAMING_SNAKE_CASE :	Dict					 =	depths
		__SCREAMING_SNAKE_CASE :	str					 =	num_heads
		__SCREAMING_SNAKE_CASE :	int					 =	window_size

		return config


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if "patch_embed.proj" in name:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	name.replace("""patch_embed.proj""" ,   """embeddings.patch_embeddings.projection"""      )
		if "patch_embed.norm" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""patch_embed.norm""" ,   """embeddings.norm"""      )
		if "layers" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""encoder.""" + name
		if "attn.proj" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""attn.proj""" ,   """attention.output.dense"""      )
		if "attn" in name:
				__SCREAMING_SNAKE_CASE :	Any					 =	name.replace("""attn""" ,   """attention.self"""      )
		if "norm1" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	name.replace("""norm1""" ,   """layernorm_before"""      )
		if "norm2" in name:
				__SCREAMING_SNAKE_CASE :	Dict					 =	name.replace("""norm2""" ,   """layernorm_after"""      )
		if "mlp.fc1" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""mlp.fc1""" ,   """intermediate.dense"""      )
		if "mlp.fc2" in name:
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	name.replace("""mlp.fc2""" ,   """output.dense"""      )
		if "q_bias" in name:
				__SCREAMING_SNAKE_CASE :	Tuple					 =	name.replace("""q_bias""" ,   """query.bias"""      )
		if "k_bias" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	name.replace("""k_bias""" ,   """key.bias"""      )
		if "v_bias" in name:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	name.replace("""v_bias""" ,   """value.bias"""      )
		if "cpb_mlp" in name:
				__SCREAMING_SNAKE_CASE :	str					 =	name.replace("""cpb_mlp""" ,   """continuous_position_bias_mlp"""      )
		if name == "norm.weight":
				__SCREAMING_SNAKE_CASE :	Tuple					 =	"""layernorm.weight"""
		if name == "norm.bias":
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""layernorm.bias"""

		if "head" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""head""" ,   """classifier"""      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""swinv2.""" + name

		return name


def        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     Optional[Any]      ):
		for key in orig_state_dict.copy().keys():
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	orig_state_dict.pop(_lowerCamelCase      )

				if "mask" in key:
						continue
				elif "qkv" in key:
						__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	key.split("""."""      )
						__SCREAMING_SNAKE_CASE :	List[str]					 =	int(key_split[1]      )
						__SCREAMING_SNAKE_CASE :	Dict					 =	int(key_split[3]      )
						__SCREAMING_SNAKE_CASE :	str					 =	model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								__SCREAMING_SNAKE_CASE :	Optional[int]					 =	val[:dim, :]
								__SCREAMING_SNAKE_CASE :	str					 =	val[dim : dim * 2, :]
								__SCREAMING_SNAKE_CASE :	Dict					 =	val[-dim:, :]
						else:
								__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val[:dim]
								__SCREAMING_SNAKE_CASE :	int					 =	val[
								    dim : dim * 2
								]
								__SCREAMING_SNAKE_CASE :	int					 =	val[-dim:]
				else:
						__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val

		return orig_state_dict


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     int      ):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	timm.create_model(_lowerCamelCase ,   pretrained=_lowerCamelCase      )
		timm_model.eval()

		__SCREAMING_SNAKE_CASE :	int					 =	get_swinva_config(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwinvaForImageClassification(_lowerCamelCase      )
		model.eval()

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	convert_state_dict(timm_model.state_dict() ,   _lowerCamelCase      )
		model.load_state_dict(_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	int					 =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_""" ,   """-"""      )      )      )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase      ).raw      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	image_processor(images=_lowerCamelCase ,   return_tensors="""pt"""      )

		__SCREAMING_SNAKE_CASE :	int					 =	timm_model(inputs["""pixel_values"""]      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model(**_lowerCamelCase      ).logits

		assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase ,   atol=1E-3      )

		print(F"Saving model {swinva_name} to {pytorch_dump_folder_path}"      )
		model.save_pretrained(_lowerCamelCase      )

		print(F"Saving image processor to {pytorch_dump_folder_path}"      )
		image_processor.save_pretrained(_lowerCamelCase      )

		model.push_to_hub(
		    repo_path_or_name=Path(_lowerCamelCase ,   _lowerCamelCase      ) ,   organization="""nandwalritik""" ,   commit_message="""Add model""" ,   )


if __name__ == "__main__":
						UpperCamelCase__ :			Union[str, Any]												=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swinv2_name''',
						    default='''swinv2_tiny_patch4_window8_256''',
						    type=str,
						    help='''Name of the Swinv2 timm model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						UpperCamelCase__ :			Optional[int]												=				parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)