




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()
_lowerCamelCase			:	Any							 =     logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> YolosConfig:



							"""simple docstring"""

							A__   				=					YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														A__   				=					192
														A__   				=					768
														A__   				=					12
														A__   				=					3
														A__   				=					[800, 1_333]
														A__   				=					False
							elif yolos_name == "yolos_s_dWr":
														A__   				=					330
														A__   				=					14
														A__   				=					6
														A__   				=					1_320
							elif "yolos_s" in yolos_name:
														A__   				=					384
														A__   				=					1_536
														A__   				=					12
														A__   				=					6
							elif "yolos_b" in yolos_name:
														A__   				=					[800, 1_344]

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

							return config







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_ = False		)     -> Tuple:



							"""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)
														A__   				=					state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""		)
														A__   				=					state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""		)
														# next, add query, keys and values (in that order) to the state dict
														A__   				=					in_proj_weight[: config.hidden_size, :]
														A__   				=					in_proj_bias[: config.hidden_size]
														A__   				=					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														A__   				=					in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														A__   				=					in_proj_weight[-config.hidden_size :, :]
														A__   				=					in_proj_bias[-config.hidden_size :]







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



							"""simple docstring"""

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

							return name







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> dict:



							"""simple docstring"""

							for key in orig_state_dict.copy().keys():
														A__   				=					orig_state_dict.pop(lowercase_		)

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

							return orig_state_dict







def SCREAMING_SNAKE_CASE       ( )     -> torch.Tensor:



							"""simple docstring"""

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







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = False		)     -> int:



							"""simple docstring"""

							A__   				=					get_yolos_config(lowercase_		)

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

							# load 🤗 model
							A__   				=					YolosForObjectDetection(lowercase_		)
							model.eval()
							A__   				=					convert_state_dict(lowercase_				,       lowercase_		)
							model.load_state_dict(lowercase_		)

							# Check outputs on an image, prepared by YolosImageProcessor
							A__   				=					800 if yolos_name != '''yolos_ti''' else 512
							A__   				=					YolosImageProcessor(format='''coco_detection'''				,       size=lowercase_		)
							A__   				=					image_processor(images=prepare_img()				,       return_tensors='''pt'''		)
							A__   				=					model(**lowercase_		)
							A__				,							A__   				=					outputs.logits, outputs.pred_boxes

							A__				,							A__   				=					None, None
							if yolos_name == "yolos_ti":
														A__   				=					torch.tensor(
														    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]		)
														A__   				=					torch.tensor(
														    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]		)
							elif yolos_name == "yolos_s_200_pre":
														A__   				=					torch.tensor(
														    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]		)
														A__   				=					torch.tensor(
														    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]		)
							elif yolos_name == "yolos_s_300_pre":
														A__   				=					torch.tensor(
														    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]		)
														A__   				=					torch.tensor(
														    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]		)
							elif yolos_name == "yolos_s_dWr":
														A__   				=					torch.tensor(
														    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]		)
														A__   				=					torch.tensor(
														    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]		)
							elif yolos_name == "yolos_base":
														A__   				=					torch.tensor(
														    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]		)
														A__   				=					torch.tensor(
														    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]		)
							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:
														A__   				=					{
														    '''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...'''		)
														A__   				=					model_mapping[yolos_name]
														image_processor.push_to_hub(lowercase_				,       organization='''hustvl'''		)
														model.push_to_hub(lowercase_				,       organization='''hustvl'''		)


if __name__ == "__main__":
		_lowerCamelCase			:	List[Any]							 =     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."""
		)

		_lowerCamelCase			:	List[str]							 =     parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   IFInpaintingPipeline
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase__													=   PipelineTesterMixin.required_optional_params - {'''latents'''}


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           return self._get_dummy_components()


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int=0)		->int:





           '''simple docstring'''

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__   				=					torch.manual_seed(UpperCAmelCase__)
           else:
                  A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)

           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)

           A__   				=					{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''mask_image''': mask_image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''output_type''': '''numpy''',
           }

           return inputs


    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()      ,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,				)
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != '''cuda'''      ,				reason='''float16 requires CUDA''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           super().test_save_load_floataa(expected_max_diff=1e-1)


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[Any]:





           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Any:





           '''simple docstring'''

           self._test_save_load_local()





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Union[str, Any]:





           '''simple docstring'''

           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2      ,				)


