


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,
)


SCREAMING_SNAKE_CASE							    =							{
    '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  a   (lowerCAmelCase__   ):
   __a					=  {}
   state_dict.pop("""pixel_mean"""		,				lowerCAmelCase__   )
   state_dict.pop("""pixel_std"""		,				lowerCAmelCase__   )

   __a					=  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:
            __a					=  key.replace(lowerCAmelCase__		,				lowerCAmelCase__   )

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

      __a					=  value

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

   return model_state_dict




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__="ybelkada/segment-anything"   ):
   __a					=  hf_hub_download(lowerCAmelCase__		,				f'''checkpoints/{model_name}.pth'''   )

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

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

      __a					=  SamConfig(
          vision_config=lowerCAmelCase__		,				)

   __a					=  torch.load(lowerCAmelCase__		,				map_location="""cpu"""   )
   __a					=  replace_keys(lowerCAmelCase__   )

   __a					=  SamImageProcessor()

   __a					=  SamProcessor(image_processor=lowerCAmelCase__   )
   __a					=  SamModel(lowerCAmelCase__   )

   hf_model.load_state_dict(lowerCAmelCase__   )
   __a					=  hf_model.to("""cuda"""   )

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

   __a					=  [[[400, 650]]]
   __a					=  [[1]]

   __a					=  processor(images=np.array(lowerCAmelCase__   )		,				return_tensors="""pt"""   ).to("""cuda"""   )

   with torch.no_grad():
      __a					=  hf_model(**lowerCAmelCase__   )
   __a					=  output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

      with torch.no_grad():
         __a					=  hf_model(**lowerCAmelCase__   )
      __a					=  output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

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

      __a					=  processor(images=np.array(lowerCAmelCase__   )		,				input_boxes=lowerCAmelCase__		,				return_tensors="""pt"""   ).to("""cuda"""   )

      with torch.no_grad():
         __a					=  hf_model(**lowerCAmelCase__   )
      __a					=  output.iou_scores.squeeze()

      assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

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

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

      with torch.no_grad():
         __a					=  hf_model(**lowerCAmelCase__   )
      __a					=  output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							argparse.ArgumentParser()
  SCREAMING_SNAKE_CASE							    =							['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',
  )

  SCREAMING_SNAKE_CASE							    =							parser.parse_args()

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







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0  ,       """run_glue_deebert.py"""    )
           with patch.object(__A  ,       """argv"""  ,       __A    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )



