





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(SCREAMING_SNAKE_CASE_  :					Union[str, Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      {}
    state_dict.pop("pixel_mean"		,       lowerCAmelCase__    )
    state_dict.pop("pixel_std"		,       lowerCAmelCase__    )

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

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

        _lowerCAmelCase				=      value

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

    return model_state_dict


def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					Optional[int]		,       SCREAMING_SNAKE_CASE_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					List[str]="ybelkada/segment-anything"    ):



    '''simple docstring'''





    _lowerCAmelCase				=      hf_hub_download(lowerCAmelCase__		,       F'''checkpoints/{model_name}.pth'''    )

    if "sam_vit_b" in model_name:
        _lowerCAmelCase				=      SamConfig()
    elif "sam_vit_l" in model_name:
        _lowerCAmelCase				=      SamVisionConfig(
            hidden_size=1024		,       num_hidden_layers=24		,       num_attention_heads=16		,       global_attn_indexes=[5, 11, 17, 23]		,       )

        _lowerCAmelCase				=      SamConfig(
            vision_config=lowerCAmelCase__		,       )
    elif "sam_vit_h" in model_name:
        _lowerCAmelCase				=      SamVisionConfig(
            hidden_size=1280		,       num_hidden_layers=32		,       num_attention_heads=16		,       global_attn_indexes=[7, 15, 23, 31]		,       )

        _lowerCAmelCase				=      SamConfig(
            vision_config=lowerCAmelCase__		,       )

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

    _lowerCAmelCase				=      SamImageProcessor()

    _lowerCAmelCase				=      SamProcessor(image_processor=lowerCAmelCase__    )
    _lowerCAmelCase				=      SamModel(lowerCAmelCase__    )

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

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

    _lowerCAmelCase				=      [[[400, 650]]]
    _lowerCAmelCase				=      [[1]]

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

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

    if model_name == "sam_vit_h_4b8939":
        assert scores[-1].item() == 0.579_8902_5115_9668

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

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

        assert scores[-1].item() == 0.9712_6030_9219_3604

        _lowerCAmelCase				=      ((75, 275, 1725, 850),)

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

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

        assert scores[-1].item() == 0.8686_0156_0592_6514

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

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

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

        assert scores[-1].item() == 0.9936_0477_9243_4692


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)









'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_SCREAMING_SNAKE_CASE	 =							{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class    lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase						:							Optional[Any]         =					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __lowerCamelCase						:							str         =					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  __lowerCamelCase						:							Optional[int]         =					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  __lowerCamelCase						:							List[Any]         =					{
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }




 @require_torch
 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "This is bad"]			,     top_k=2		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"			,     top_k=1		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     # Legacy behavior
     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)

     _lowerCAmelCase				=      text_classifier("This is great !"			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]		)

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
             [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
         ]			,     )

     _lowerCAmelCase				=      text_classifier(["This is great !", "Something else"]			,     return_all_scores=_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [
             {"label": "LABEL_0", "score": 0.504},
             {"label": "LABEL_0", "score": 0.504},
         ]			,     )




 @require_torch
 def 							_snake_case   (	self		)    ->							Tuple:
     import torch

     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="pt"			,     device=torch.device("cpu"		)			,     )

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline(
         task="text-classification"			,     model="hf-internal-testing/tiny-random-distilbert"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "LABEL_0", "score": 0.504}]		)




 @slow
 @require_torch
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      pipeline("text-classification"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 @slow
 @require_tf
 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      pipeline("text-classification"			,     framework="tf"		)

     _lowerCAmelCase				=      text_classifier("This is great !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("This is bad !"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "NEGATIVE", "score": 1.0}]		)
     _lowerCAmelCase				=      text_classifier("Birds are a type of animal"		)
     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": "POSITIVE", "score": 0.988}]		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      TextClassificationPipeline(model=_lowerCAmelCase			,     tokenizer=_lowerCAmelCase		)
     return text_classifier, ["HuggingFace is in", "This is another test"]





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      text_classifier.model
     # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
     _lowerCAmelCase				=      "HuggingFace is in"
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)

     self.assertEqual(nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]		)
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)

     _lowerCAmelCase				=      ["HuggingFace is in ", "Paris is in France"]
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}, {"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]			,     )
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)
     self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()		)

     # Forcing to get all results with `top_k=None`
     # This is NOT the legacy format
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase			,     top_k=_lowerCAmelCase		)
     _lowerCAmelCase				=      len(model.config.idalabel.values()		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [[{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N, [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}] * N]			,     )

     _lowerCAmelCase				=      {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
     _lowerCAmelCase				=      text_classifier(_lowerCAmelCase		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     {"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}			,     )
     self.assertTrue(outputs["label"] in model.config.idalabel.values()		)

     # This might be used a text pair, but tokenizer + pipe interaction
     # makes it hard to understand that it's not using the pair properly
     # https://github.com/huggingface/transformers/issues/17305
     # We disabled this usage instead as it was outputting wrong outputs.
     _lowerCAmelCase				=      [["HuggingFace is in ", "Paris is in France"]]
     with self.assertRaises(_lowerCAmelCase		):
         text_classifier(_lowerCAmelCase		)

     # This used to be valid for doing text pairs
     # We're keeping it working because of backward compatibility
     _lowerCAmelCase				=      text_classifier([[["HuggingFace is in ", "Paris is in France"]]]		)
     self.assertEqual(
         nested_simplify(_lowerCAmelCase		)			,     [{"label": ANY(_lowerCAmelCase		), "score": ANY(_lowerCAmelCase		)}]			,     )
     self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)


