

import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case							:	Optional[int]											=       logging.get_logger(__name__)

__snake_case							:	List[str]											=       torch.device("""cpu""")


def  _UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Optional[int]      				=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_		:		Optional[int]      				=      Image.open(requests.get(a__   ,     stream=a__).raw)
			return im


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01])

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01])

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02])

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02])


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Any      				=      dct.pop(a__)
			a_		:		Optional[int]      				=      val


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Tuple      				=      []
			for k in state_dict.keys():
						a_		:		Tuple      				=      k
						if ".pwconv" in k:
									a_		:		List[Any]      				=      k_new.replace(""".pwconv"""   ,     """.point_wise_conv""")
						if ".dwconv" in k:
									a_		:		Optional[Any]      				=      k_new.replace(""".dwconv"""   ,     """.depth_wise_conv""")
						if ".Proj." in k:
									a_		:		List[str]      				=      k_new.replace(""".Proj."""   ,     """.proj.""")
						if "patch_embed" in k_new:
									a_		:		Any      				=      k_new.replace("""patch_embed"""   ,     """swiftformer.patch_embed.patch_embedding""")
						if "network" in k_new:
									a_		:		List[str]      				=      k_new.split(""".""")
									if ls[2].isdigit():
												a_		:		Union[str, Any]      				=      """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:])
									else:
												a_		:		List[str]      				=      k_new.replace("""network"""   ,     """swiftformer.encoder.network""")
						rename_keys.append((k, k_new))
			return rename_keys


@torch.no_grad()
def  _UpperCAmelCase							(		a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Union[str, Any]      				=      SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			a_		:		int      				=      1_0_0_0
			a_		:		Optional[Any]      				=      """huggingface/label-files"""
			a_		:		str      				=      """imagenet-1k-id2label.json"""
			a_		:		Optional[Any]      				=      json.load(open(hf_hub_download(a__   ,     a__   ,     repo_type="""dataset""")   ,     """r"""))
			a_		:		List[Any]      				=      {int(a__): v for k, v in idalabel.items()}
			a_		:		Union[str, Any]      				=      idalabel
			a_		:		str      				=      {v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						a_		:		str      				=      [3, 3, 6, 4]
						a_		:		Optional[int]      				=      [4_8, 5_6, 1_1_2, 2_2_0]

			elif swiftformer_name == "swiftformer_s":
						a_		:		Dict      				=      [3, 3, 9, 6]
						a_		:		Dict      				=      [4_8, 6_4, 1_6_8, 2_2_4]

			elif swiftformer_name == "swiftformer_l1":
						a_		:		List[str]      				=      [4, 3, 1_0, 5]
						a_		:		int      				=      [4_8, 9_6, 1_9_2, 3_8_4]

			elif swiftformer_name == "swiftformer_l3":
						a_		:		Dict      				=      [4, 4, 1_2, 6]
						a_		:		Optional[Any]      				=      [6_4, 1_2_8, 3_2_0, 5_1_2]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith("""https"""):
									a_		:		Any      				=      torch.hub.load_state_dict_from_url(a__   ,     map_location="""cpu"""   ,     check_hash=a__)
						else:
									a_		:		Tuple      				=      torch.load(a__   ,     map_location="""cpu""")
			a_		:		str      				=      checkpoint

			a_		:		Union[str, Any]      				=      create_rename_keys(a__)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(a__   ,     a__   ,     a__)

			# load HuggingFace model
			a_		:		Optional[Any]      				=      SwiftFormerForImageClassification(a__).eval()
			hf_model.load_state_dict(a__)

			# prepare test inputs
			a_		:		Any      				=      prepare_img()
			a_		:		Any      				=      ViTImageProcessor.from_pretrained("""preprocessor_config""")
			a_		:		str      				=      processor(images=a__   ,     return_tensors="""pt""")

			# compare outputs from both models
			a_		:		Optional[int]      				=      get_expected_output(a__)
			a_		:		Dict      				=      hf_model(inputs["""pixel_values"""]).logits

			assert hf_logits.shape == torch.Size([1, 1_0_0_0])
			assert torch.allclose(hf_logits[0, 0:5]   ,     a__   ,     atol=1e-3)

			Path(a__).mkdir(exist_ok=a__)
			print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}''')
			hf_model.save_pretrained(a__)


if __name__ == "__main__":
	__snake_case							:	Optional[int]											=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--swiftformer_name""",
	    default="""swiftformer_xs""",
	    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
	    type=str,
	    help="""Name of the SwiftFormer model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""./converted_outputs/""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

	__snake_case							:	List[Any]											=       parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







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

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""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		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}
						# preprocess args
						if "points_per_batch" in kwargs:
											a					     =   kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
											a					     =   kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
											a					     =   kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
											a					     =   kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
											a					     =   kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
											a					     =   kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
											a					     =   kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
											a					     =   kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
											a					     =   kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
											a					     =   kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
											a					     =   kwargs["output_rle_mask"]
						if "output_bboxes_mask" in kwargs:
											a					     =   kwargs["output_bboxes_mask"]
						return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

						with self.device_placement():
											if self.framework == "pt":
																a					     =   self.get_inference_context()
																with inference_context():
																					a					     =   self._ensure_tensor_on_device(lowerCamelCase_		,       device=self.device		)
																					a					     =   self.model.get_image_embeddings(model_inputs.pop("pixel_values"		)		)
																					a					     =   image_embeddings

						a					     =   grid_points.shape[1]
						a					     =   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_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   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		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   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		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						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"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   defaultdict(lowerCamelCase_		)
						for output in model_outputs:
											for k, v in output.items():
																extra[k].append(lowerCamelCase_		)

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

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