





"""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()
SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      lowerCAmelCase  						=       YolosConfig()

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

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

      return config


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						dict							,						SCREAMING_SNAKE_CASE     :						YolosConfig							,						SCREAMING_SNAKE_CASE     :						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)
            lowerCAmelCase  						=       state_dict.pop(F'blocks.{i}.attn.qkv.weight'			)
            lowerCAmelCase  						=       state_dict.pop(F'blocks.{i}.attn.qkv.bias'			)
            # next, add query, keys and values (in that order) to the state dict
            lowerCAmelCase  						=       in_proj_weight[: config.hidden_size, :]
            lowerCAmelCase  						=       in_proj_bias[: config.hidden_size]
            lowerCAmelCase  						=       in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCAmelCase  						=       in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCAmelCase  						=       in_proj_weight[-config.hidden_size :, :]
            lowerCAmelCase  						=       in_proj_bias[-config.hidden_size :]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




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

      return name


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						dict							,						SCREAMING_SNAKE_CASE     :						YolosForObjectDetection			):







      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCAmelCase  						=       orig_state_dict.pop(SCREAMING_SNAKE_CASE			)

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

      return orig_state_dict


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE			).raw			)
      return im


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						bool = False			):







      '''simple docstring'''




      lowerCAmelCase  						=       get_yolos_config(SCREAMING_SNAKE_CASE			)

      # load original state_dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location="""cpu"""			)["""model"""]

      # load 🤗 model
      lowerCAmelCase  						=       YolosForObjectDetection(SCREAMING_SNAKE_CASE			)
      model.eval()
      lowerCAmelCase  						=       convert_state_dict(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)

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

      lowerCAmelCase						,		lowerCAmelCase  						=       None, None
      if yolos_name == "yolos_ti":
            lowerCAmelCase  						=       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]]			)
            lowerCAmelCase  						=       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":
            lowerCAmelCase  						=       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]]			)
            lowerCAmelCase  						=       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":
            lowerCAmelCase  						=       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]]			)
            lowerCAmelCase  						=       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":
            lowerCAmelCase  						=       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]]			)
            lowerCAmelCase  						=       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":
            lowerCAmelCase  						=       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]]			)
            lowerCAmelCase  						=       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]							,						SCREAMING_SNAKE_CASE							,						atol=1e-4			)
      assert torch.allclose(pred_boxes[0, :3, :3]							,						SCREAMING_SNAKE_CASE							,						atol=1e-4			)

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

      if push_to_hub:
            lowerCAmelCase  						=       {
                """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..."""			)
            lowerCAmelCase  						=       model_mapping[yolos_name]
            image_processor.push_to_hub(SCREAMING_SNAKE_CASE							,						organization="""hustvl"""			)
            model.push_to_hub(SCREAMING_SNAKE_CASE							,						organization="""hustvl"""			)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   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."
       )

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













"""simple docstring"""





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 		lowercase					:





      def     _snake_case			(       self )		->     List[Any]:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       UNetaDConditionModel(
                sample_size=32      ,						layers_per_block=1      ,						block_out_channels=[32, 64]      ,						down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]      ,						mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""      ,						up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]      ,						in_channels=3      ,						out_channels=6      ,						cross_attention_dim=32      ,						encoder_hid_dim=32      ,						attention_head_dim=8      ,						addition_embed_type="""text"""      ,						addition_embed_type_num_heads=2      ,						cross_attention_norm="""group_norm"""      ,						resnet_time_scale_shift="""scale_shift"""      ,						act_fn="""gelu"""      ,						)
            unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						thresholding=lowercase      ,						dynamic_thresholding_ratio=0.95      ,						sample_max_value=1.0      ,						prediction_type="""epsilon"""      ,						variance_type="""learned_range"""      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }





      def     _snake_case			(       self )		->     int:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       UNetaDConditionModel(
                sample_size=32      ,						layers_per_block=[1, 2]      ,						block_out_channels=[32, 64]      ,						down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]      ,						mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""      ,						up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]      ,						in_channels=6      ,						out_channels=6      ,						cross_attention_dim=32      ,						encoder_hid_dim=32      ,						attention_head_dim=8      ,						addition_embed_type="""text"""      ,						addition_embed_type_num_heads=2      ,						cross_attention_norm="""group_norm"""      ,						resnet_time_scale_shift="""scale_shift"""      ,						act_fn="""gelu"""      ,						class_embed_type="""timestep"""      ,						mid_block_scale_factor=1.414      ,						time_embedding_act_fn="""gelu"""      ,						time_embedding_dim=32      ,						)
            unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						thresholding=lowercase      ,						dynamic_thresholding_ratio=0.95      ,						sample_max_value=1.0      ,						prediction_type="""epsilon"""      ,						variance_type="""learned_range"""      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "image_noising_scheduler": image_noising_scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.get_dummy_components()
            lowerCAmelCase  						=       self.pipeline_class(**lowercase )
            pipe.to(lowercase )
            pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )

            lowerCAmelCase  						=       inputs["""prompt"""]
            lowerCAmelCase  						=       inputs["""generator"""]
            lowerCAmelCase  						=       inputs["""num_inference_steps"""]
            lowerCAmelCase  						=       inputs["""output_type"""]

            if "image" in inputs:
                  lowerCAmelCase  						=       inputs["""image"""]
            else:
                  lowerCAmelCase  						=       None

            if "mask_image" in inputs:
                  lowerCAmelCase  						=       inputs["""mask_image"""]
            else:
                  lowerCAmelCase  						=       None

            if "original_image" in inputs:
                  lowerCAmelCase  						=       inputs["""original_image"""]
            else:
                  lowerCAmelCase  						=       None

            lowerCAmelCase						,		lowerCAmelCase  						=       pipe.encode_prompt(lowercase )

            # inputs with prompt converted to embeddings
            lowerCAmelCase  						=       {
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowerCAmelCase  						=       image

            if mask_image is not None:
                  lowerCAmelCase  						=       mask_image

            if original_image is not None:
                  lowerCAmelCase  						=       original_image

            # set all optional components to None
            for optional_component in pipe._optional_components:
                  setattr(lowercase      ,						lowercase      ,						lowercase )

            lowerCAmelCase  						=       pipe(**lowercase )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(lowercase )
                  lowerCAmelCase  						=       self.pipeline_class.from_pretrained(lowercase )
                  pipe_loaded.to(lowercase )
                  pipe_loaded.set_progress_bar_config(disable=lowercase )

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            for optional_component in pipe._optional_components:
                  self.assertTrue(
                      getattr(lowercase      ,						lowercase ) is None      ,						f'`{optional_component}` did not stay set to None after loading.'      ,						)

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )

            lowerCAmelCase  						=       inputs["""generator"""]
            lowerCAmelCase  						=       inputs["""num_inference_steps"""]
            lowerCAmelCase  						=       inputs["""output_type"""]

            # inputs with prompt converted to embeddings
            lowerCAmelCase  						=       {
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowerCAmelCase  						=       image

            if mask_image is not None:
                  lowerCAmelCase  						=       mask_image

            if original_image is not None:
                  lowerCAmelCase  						=       original_image

            lowerCAmelCase  						=       pipe_loaded(**lowercase )[0]

            lowerCAmelCase  						=       np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
            self.assertLess(lowercase      ,						1e-4 )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.get_dummy_components()
            lowerCAmelCase  						=       self.pipeline_class(**lowercase )
            pipe.to(lowercase )
            pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )
            lowerCAmelCase  						=       pipe(**lowercase )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(lowercase )
                  lowerCAmelCase  						=       self.pipeline_class.from_pretrained(lowercase )
                  pipe_loaded.to(lowercase )
                  pipe_loaded.set_progress_bar_config(disable=lowercase )

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )
            lowerCAmelCase  						=       pipe_loaded(**lowercase )[0]

            lowerCAmelCase  						=       np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
            self.assertLess(lowercase      ,						1e-4 )






