
import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


__magic_name__            =  {
    '''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
    '''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
    '''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
    '''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
    '''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
    '''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
    '''mask_downscaling.0''': '''mask_embed.conv1''',
    '''mask_downscaling.1''': '''mask_embed.layer_norm1''',
    '''mask_downscaling.3''': '''mask_embed.conv2''',
    '''mask_downscaling.4''': '''mask_embed.layer_norm2''',
    '''mask_downscaling.6''': '''mask_embed.conv3''',
    '''point_embeddings''': '''point_embed''',
    '''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
    '''image_encoder''': '''vision_encoder''',
    '''neck.0''': '''neck.conv1''',
    '''neck.1''': '''neck.layer_norm1''',
    '''neck.2''': '''neck.conv2''',
    '''neck.3''': '''neck.layer_norm2''',
    '''patch_embed.proj''': '''patch_embed.projection''',
    '''.norm''': '''.layer_norm''',
    '''blocks''': '''layers''',
}





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				{}
    state_dict.pop("pixel_mean"    ,		__lowerCAmelCase							)
    state_dict.pop("pixel_std"    ,		__lowerCAmelCase							)

    snake_case__						=				R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

    for key, value in state_dict.items():
        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
            if key_to_modify in key:
                snake_case__						=				key.replace(__lowerCAmelCase    ,		__lowerCAmelCase							)

        if re.match(__lowerCAmelCase    ,		__lowerCAmelCase							):
            snake_case__						=				int(re.match(__lowerCAmelCase    ,		__lowerCAmelCase							).group(2							)							)
            if layer_nb == 0:
                snake_case__						=				key.replace("layers.0"    ,		"proj_in"							)
            elif layer_nb == 1:
                snake_case__						=				key.replace("layers.1"    ,		"layers.0"							)
            elif layer_nb == 2:
                snake_case__						=				key.replace("layers.2"    ,		"proj_out"							)

        snake_case__						=				value

    snake_case__						=				model_state_dict[
        "prompt_encoder.shared_embedding.positional_embedding"
    ]

    return model_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase="ybelkada/segment-anything"							):
    snake_case__						=				hf_hub_download(__lowerCAmelCase    ,		F"""checkpoints/{model_name}.pth"""							)

    if "sam_vit_b" in model_name:
        snake_case__						=				SamConfig()
    elif "sam_vit_l" in model_name:
        snake_case__						=				SamVisionConfig(
            hidden_size=1_024    ,		num_hidden_layers=24    ,		num_attention_heads=16    ,		global_attn_indexes=[5, 11, 17, 23]    ,		)

        snake_case__						=				SamConfig(
            vision_config=__lowerCAmelCase    ,		)
    elif "sam_vit_h" in model_name:
        snake_case__						=				SamVisionConfig(
            hidden_size=1_280    ,		num_hidden_layers=32    ,		num_attention_heads=16    ,		global_attn_indexes=[7, 15, 23, 31]    ,		)

        snake_case__						=				SamConfig(
            vision_config=__lowerCAmelCase    ,		)

    snake_case__						=				torch.load(__lowerCAmelCase    ,		map_location="cpu"							)
    snake_case__						=				replace_keys(__lowerCAmelCase							)

    snake_case__						=				SamImageProcessor()

    snake_case__						=				SamProcessor(image_processor=__lowerCAmelCase							)
    snake_case__						=				SamModel(__lowerCAmelCase							)

    hf_model.load_state_dict(__lowerCAmelCase							)
    snake_case__						=				hf_model.to("cuda"							)

    snake_case__						=				"https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							).convert("RGB"							)

    snake_case__						=				[[[400, 650]]]
    snake_case__						=				[[1]]

    snake_case__						=				processor(images=np.array(__lowerCAmelCase							)    ,		return_tensors="pt"							).to("cuda"							)

    with torch.no_grad():
        snake_case__						=				hf_model(**__lowerCAmelCase							)
    snake_case__						=				output.iou_scores.squeeze()

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579_890_251_159_668

        snake_case__						=				processor(
            images=np.array(__lowerCAmelCase							)    ,		input_points=__lowerCAmelCase    ,		input_labels=__lowerCAmelCase    ,		return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            snake_case__						=				hf_model(**__lowerCAmelCase							)
        snake_case__						=				output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9_712_603_092_193_604

        snake_case__						=				((75, 275, 1_725, 850),)

        snake_case__						=				processor(images=np.array(__lowerCAmelCase							)    ,		input_boxes=__lowerCAmelCase    ,		return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            snake_case__						=				hf_model(**__lowerCAmelCase							)
        snake_case__						=				output.iou_scores.squeeze()

        assert scores[-1].item() == 0.8_686_015_605_926_514

        # Test with 2 points and 1 image.
        snake_case__						=				[[[400, 650], [800, 650]]]
        snake_case__						=				[[1, 1]]

        snake_case__						=				processor(
            images=np.array(__lowerCAmelCase							)    ,		input_points=__lowerCAmelCase    ,		input_labels=__lowerCAmelCase    ,		return_tensors="pt"							).to("cuda"							)

        with torch.no_grad():
            snake_case__						=				hf_model(**__lowerCAmelCase							)
        snake_case__						=				output.iou_scores.squeeze()

        assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     __magic_name__            =  ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
     parser.add_argument(
         '''--model_name''',
         default='''sam_vit_h_4b8939''',
         choices=choices,
         type=str,
         help='''Path to hf config.json of model to convert''',
     )
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     parser.add_argument(
         '''--push_to_hub''',
         action='''store_true''',
         help='''Whether to push the model and processor to the hub after converting''',
     )
     parser.add_argument(
         '''--model_hub_id''',
         default='''ybelkada/segment-anything''',
         choices=choices,
         type=str,
         help='''Path to hf config.json of model to convert''',
     )

     __magic_name__            =  parser.parse_args()

     convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     (KDPMaDiscreteScheduler,)
      _A						:		Dict    	=     10
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase )
          return config
      def A_			(			self ):
          for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def A_			(			self ):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
              self.check_over_configs(beta_start=lowerCamelCase		,	beta_end=lowerCamelCase )
      def A_			(			self ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowerCamelCase )
      def A_			(			self ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config(prediction_type="v_prediction" )
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
              assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
              assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3
      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__						=				sample.to(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps		,	device=lowerCamelCase )

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter.to(lowerCamelCase ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if str(lowerCamelCase ).startswith("cpu" ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



