

'''simple docstring'''

import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
       import torch

if is_vision_available():
       import PIL


UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	np.ndarray,       lowercase__    :	Union[int, Iterable[int]],       lowercase__    :	bool,       lowercase__    :	int			):






     '''simple docstring'''





     def constraint_to_multiple_of(lowercase__    :	Optional[Any],       lowercase__    :	str,       lowercase__    :	int=0,       lowercase__    :	List[str]=None			):
          __lowercase      		=round(val / multiple			) * multiple

          if max_val is not None and x > max_val:
               __lowercase      		=math.floor(val / multiple			) * multiple

          if x < min_val:
               __lowercase      		=math.ceil(val / multiple			) * multiple

          return x

     __lowercase      		=(output_size, output_size) if isinstance(lowercase__,       lowercase__			) else output_size

     __lowercase		,  __lowercase      		=get_image_size(lowercase__			)
     __lowercase		,  __lowercase      		=output_size

     # determine new height and width
     __lowercase      		=output_height / input_height
     __lowercase      		=output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width			) < abs(1 - scale_height			):
               # fit width
               __lowercase      		=scale_width
          else:
               # fit height
               __lowercase      		=scale_height

     __lowercase      		=constraint_to_multiple_of(scale_height * input_height,       multiple=lowercase__			)
     __lowercase      		=constraint_to_multiple_of(scale_width * input_width,       multiple=lowercase__			)

     return (new_height, new_width)







class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			["pixel_values"]







       def __init__(				self      :			Dict    ,   __lowercase      :			bool = True    ,   __lowercase      :			Dict[str, int] = None    ,   __lowercase      :			PILImageResampling = PILImageResampling.BILINEAR    ,   __lowercase      :			bool = False    ,   __lowercase      :			int = 1    ,   __lowercase      :			bool = True    ,   __lowercase      :			Union[int, float] = 1 / 255    ,   __lowercase      :			bool = True    ,   __lowercase      :			Optional[Union[float, List[float]]] = None    ,   __lowercase      :			Optional[Union[float, List[float]]] = None    ,   **__lowercase      :			Optional[int]    ,   ):







            """simple docstring"""







            super().__init__(**__lowercase   )
            __lowercase      		=size if size is not None else {'height': 384, 'width': 384}
            __lowercase      		=get_size_dict(__lowercase   )
            __lowercase      		=do_resize
            __lowercase      		=size
            __lowercase      		=keep_aspect_ratio
            __lowercase      		=ensure_multiple_of
            __lowercase      		=resample
            __lowercase      		=do_rescale
            __lowercase      		=rescale_factor
            __lowercase      		=do_normalize
            __lowercase      		=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase      		=image_std if image_std is not None else IMAGENET_STANDARD_STD







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			Dict[str, int]    ,   __lowercase      :			bool = False    ,   __lowercase      :			int = 1    ,   __lowercase      :			PILImageResampling = PILImageResampling.BICUBIC    ,   __lowercase      :			Optional[Union[str, ChannelDimension]] = None    ,   **__lowercase      :			Optional[int]    ,   ):







            """simple docstring"""







            __lowercase      		=get_size_dict(__lowercase   )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''   )
            __lowercase      		=get_resize_output_image_size(
                __lowercase    ,   output_size=(size['height'], size['width'])    ,   keep_aspect_ratio=__lowercase    ,   multiple=__lowercase    ,   )
            return resize(__lowercase    ,   size=__lowercase    ,   resample=__lowercase    ,   data_format=__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			Any    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			Union[int, float]    ,   __lowercase      :			Optional[Union[str, ChannelDimension]] = None    ,   **__lowercase      :			str    ,   ):







            """simple docstring"""







            return rescale(__lowercase    ,   scale=__lowercase    ,   data_format=__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			int    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			Union[float, List[float]]    ,   __lowercase      :			Union[float, List[float]]    ,   __lowercase      :			Optional[Union[str, ChannelDimension]] = None    ,   **__lowercase      :			Optional[Any]    ,   ):







            """simple docstring"""







            return normalize(__lowercase    ,   mean=__lowercase    ,   std=__lowercase    ,   data_format=__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			ImageInput    ,   __lowercase      :			bool = None    ,   __lowercase      :			int = None    ,   __lowercase      :			bool = None    ,   __lowercase      :			int = None    ,   __lowercase      :			PILImageResampling = None    ,   __lowercase      :			bool = None    ,   __lowercase      :			float = None    ,   __lowercase      :			bool = None    ,   __lowercase      :			Optional[Union[float, List[float]]] = None    ,   __lowercase      :			Optional[Union[float, List[float]]] = None    ,   __lowercase      :			Optional[Union[str, TensorType]] = None    ,   __lowercase      :			ChannelDimension = ChannelDimension.FIRST    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            __lowercase      		=do_resize if do_resize is not None else self.do_resize
            __lowercase      		=size if size is not None else self.size
            __lowercase      		=get_size_dict(__lowercase   )
            __lowercase      		=keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
            __lowercase      		=ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
            __lowercase      		=resample if resample is not None else self.resample
            __lowercase      		=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase      		=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase      		=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase      		=image_mean if image_mean is not None else self.image_mean
            __lowercase      		=image_std if image_std is not None else self.image_std

            __lowercase      		=make_list_of_images(__lowercase   )

            if not valid_images(__lowercase   ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'   )

            if do_resize and size is None or resample is None:
                 raise ValueError('Size and resample must be specified if do_resize is True.'   )

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

            # All transformations expect numpy arrays.
            __lowercase      		=[to_numpy_array(__lowercase   ) for image in images]

            if do_resize:
                 __lowercase      		=[self.resize(image=__lowercase    ,   size=__lowercase    ,   resample=__lowercase   ) for image in images]

            if do_rescale:
                 __lowercase      		=[self.rescale(image=__lowercase    ,   scale=__lowercase   ) for image in images]

            if do_normalize:
                 __lowercase      		=[self.normalize(image=__lowercase    ,   mean=__lowercase    ,   std=__lowercase   ) for image in images]

            __lowercase      		=[to_channel_dimension_format(__lowercase    ,   __lowercase   ) for image in images]

            __lowercase      		={'pixel_values': images}
            return BatchFeature(data=__lowercase    ,   tensor_type=__lowercase   )







       def       snake_case (				self      :			str    ,   __lowercase      :			int    ,   __lowercase      :			List[Tuple] = None   ):







            """simple docstring"""







            __lowercase      		=outputs.logits

            # Resize logits and compute semantic segmentation maps
            if target_sizes is not None:
                 if len(__lowercase   ) != len(__lowercase   ):
                      raise ValueError(
                          'Make sure that you pass in as many target sizes as the batch dimension of the logits'   )

                 if is_torch_tensor(__lowercase   ):
                      __lowercase      		=target_sizes.numpy()

                 __lowercase      		=[]

                 for idx in range(len(__lowercase   )   ):
                      __lowercase      		=torch.nn.functional.interpolate(
                          logits[idx].unsqueeze(dim=0   )    ,   size=target_sizes[idx]    ,   mode='bilinear'    ,   align_corners=__lowercase   )
                      __lowercase      		=resized_logits[0].argmax(dim=0   )
                      semantic_segmentation.append(__lowercase   )
            else:
                 __lowercase      		=logits.argmax(dim=1   )
                 __lowercase      		=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

            return semantic_segmentation





'''simple docstring'''

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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=YolosConfig()

     # size of the architecture
     if "yolos_ti" in yolos_name:
          __lowercase      		=1_92
          __lowercase      		=7_68
          __lowercase      		=12
          __lowercase      		=3
          __lowercase      		=[8_00, 13_33]
          __lowercase      		=False
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=3_30
          __lowercase      		=14
          __lowercase      		=6
          __lowercase      		=13_20
     elif "yolos_s" in yolos_name:
          __lowercase      		=3_84
          __lowercase      		=15_36
          __lowercase      		=12
          __lowercase      		=6
     elif "yolos_b" in yolos_name:
          __lowercase      		=[8_00, 13_44]

     __lowercase      		=91
     __lowercase      		='huggingface/label-files'
     __lowercase      		='coco-detection-id2label.json'
     __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
     __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
     __lowercase      		=idalabel
     __lowercase      		={v: k for k, v in idalabel.items()}

     return config

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosConfig,       lowercase__    :	bool = False			):






     '''simple docstring'''





     for i in range(config.num_hidden_layers			):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[-config.hidden_size :, :]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     if "backbone" in name:
          __lowercase      		=name.replace('backbone',       'vit'			)
     if "cls_token" in name:
          __lowercase      		=name.replace('cls_token',       'embeddings.cls_token'			)
     if "det_token" in name:
          __lowercase      		=name.replace('det_token',       'embeddings.detection_tokens'			)
     if "mid_pos_embed" in name:
          __lowercase      		=name.replace('mid_pos_embed',       'encoder.mid_position_embeddings'			)
     if "pos_embed" in name:
          __lowercase      		=name.replace('pos_embed',       'embeddings.position_embeddings'			)
     if "patch_embed.proj" in name:
          __lowercase      		=name.replace('patch_embed.proj',       'embeddings.patch_embeddings.projection'			)
     if "blocks" in name:
          __lowercase      		=name.replace('blocks',       'encoder.layer'			)
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "attn" in name:
          __lowercase      		=name.replace('attn',       'attention.self'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "class_embed" in name:
          __lowercase      		=name.replace('class_embed',       'class_labels_classifier'			)
     if "bbox_embed" in name:
          __lowercase      		=name.replace('bbox_embed',       'bbox_predictor'			)
     if "vit.norm" in name:
          __lowercase      		=name.replace('vit.norm',       'vit.layernorm'			)

     return name

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosForObjectDetection			):






     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __lowercase      		=orig_state_dict.pop(lowercase__			)

          if "qkv" in key:
               __lowercase      		=key.split('.'			)
               __lowercase      		=int(key_split[2]			)
               __lowercase      		=model.vit.encoder.layer[layer_num].attention.attention.all_head_size
               if "weight" in key:
                    __lowercase      		=val[:dim, :]
                    __lowercase      		=val[
                        dim : dim * 2, :
                    ]
                    __lowercase      		=val[-dim:, :]
               else:
                    __lowercase      		=val[:dim]
                    __lowercase      		=val[dim : dim * 2]
                    __lowercase      		=val[-dim:]
          else:
               __lowercase      		=val

     return orig_state_dict

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		='http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowercase      		=Image.open(requests.get(lowercase__,       stream=lowercase__			).raw			)
     return im

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	bool = False			):






     '''simple docstring'''





     __lowercase      		=get_yolos_config(lowercase__			)

     # load original state_dict
     __lowercase      		=torch.load(lowercase__,       map_location='cpu'			)['model']

     # load 🤗 model
     __lowercase      		=YolosForObjectDetection(lowercase__			)
     model.eval()
     __lowercase      		=convert_state_dict(lowercase__,       lowercase__			)
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by YolosImageProcessor
     __lowercase      		=8_00 if yolos_name != 'yolos_ti' else 5_12
     __lowercase      		=YolosImageProcessor(format='coco_detection',       size=lowercase__			)
     __lowercase      		=image_processor(images=prepare_img(),       return_tensors='pt'			)
     __lowercase      		=model(**lowercase__			)
     __lowercase		,  __lowercase      		=outputs.logits, outputs.pred_boxes

     __lowercase		,  __lowercase      		=None, None
     if yolos_name == "yolos_ti":
          __lowercase      		=torch.tensor(
              [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]			)
          __lowercase      		=torch.tensor(
              [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]			)
     elif yolos_name == "yolos_s_200_pre":
          __lowercase      		=torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			)
          __lowercase      		=torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]			)
     elif yolos_name == "yolos_s_300_pre":
          __lowercase      		=torch.tensor(
              [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]			)
          __lowercase      		=torch.tensor(
              [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]			)
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=torch.tensor(
              [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]			)
          __lowercase      		=torch.tensor(
              [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]			)
     elif yolos_name == "yolos_base":
          __lowercase      		=torch.tensor(
              [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]			)
          __lowercase      		=torch.tensor(
              [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]			)
     else:
          raise ValueError(F'''Unknown yolos_name: {yolos_name}'''			)

     assert torch.allclose(logits[0, :3, :3],       lowercase__,       atol=1E-4			)
     assert torch.allclose(pred_boxes[0, :3, :3],       lowercase__,       atol=1E-4			)

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)

     if push_to_hub:
          __lowercase      		={
              'yolos_ti': 'yolos-tiny',
              'yolos_s_200_pre': 'yolos-small',
              'yolos_s_300_pre': 'yolos-small-300',
              'yolos_s_dWr': 'yolos-small-dwr',
              'yolos_base': 'yolos-base',
          }

          print('Pushing to the hub...'			)
          __lowercase      		=model_mapping[yolos_name]
          image_processor.push_to_hub(lowercase__,       organization='hustvl'			)
          model.push_to_hub(lowercase__,       organization='hustvl'			)


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--yolos_name''',
           default='''yolos_s_200_pre''',
           type=str,
           help=(
               '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
               ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
           ),
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )
       parser.add_argument(
           '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
       )

       UpperCAmelCase					 =	parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


