





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,
)


lowercase      :       List[str]									=  {
    """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_	(		A__				)  ->		int:
       a__						:	str =				{}
       state_dict.pop('pixel_mean'		,      A__				)
       state_dict.pop('pixel_std'		,      A__				)

       a__						:	Any =				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__						:	str =				key.replace(A__		,      A__				)

              if re.match(A__		,      A__				):
                     a__						:	Any =				int(re.match(A__		,      A__				).group(2				)				)
                     if layer_nb == 0:
                            a__						:	List[str] =				key.replace('layers.0'		,      'proj_in'				)
                     elif layer_nb == 1:
                            a__						:	Optional[int] =				key.replace('layers.1'		,      'layers.0'				)
                     elif layer_nb == 2:
                            a__						:	List[str] =				key.replace('layers.2'		,      'proj_out'				)

              a__						:	Optional[Any] =				value

       a__						:	List[Any] =				model_state_dict[
           'prompt_encoder.shared_embedding.positional_embedding'
       ]

       return model_state_dict



def A_	(		A__		,      A__		,      A__		,      A__="ybelkada/segment-anything"				)  ->		str:
       a__						:	int =				hf_hub_download(A__		,      F'checkpoints/{model_name}.pth'				)

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

              a__						:	List[str] =				SamConfig(
                  vision_config=A__		,      )
       elif "sam_vit_h" in model_name:
              a__						:	Tuple =				SamVisionConfig(
                  hidden_size=1280		,      num_hidden_layers=32		,      num_attention_heads=16		,      global_attn_indexes=[7, 15, 23, 31]		,      )

              a__						:	Optional[int] =				SamConfig(
                  vision_config=A__		,      )

       a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)
       a__						:	Optional[int] =				replace_keys(A__				)

       a__						:	Union[str, Any] =				SamImageProcessor()

       a__						:	Optional[Any] =				SamProcessor(image_processor=A__				)
       a__						:	List[Any] =				SamModel(A__				)

       hf_model.load_state_dict(A__				)
       a__						:	Optional[Any] =				hf_model.to('cuda'				)

       a__						:	List[Any] =				'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
       a__						:	str =				Image.open(requests.get(A__		,      stream=A__				).raw				).convert('RGB'				)

       a__						:	int =				[[[400, 650]]]
       a__						:	List[str] =				[[1]]

       a__						:	str =				processor(images=np.array(A__				)		,      return_tensors='pt'				).to('cuda'				)

       with torch.no_grad():
              a__						:	Union[str, Any] =				hf_model(**A__				)
       a__						:	Any =				output.iou_scores.squeeze()

       if model_name == "sam_vit_h_4b8939":
              assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

              a__						:	Any =				processor(
                  images=np.array(A__				)		,      input_points=A__		,      input_labels=A__		,      return_tensors='pt'				).to('cuda'				)

              with torch.no_grad():
                     a__						:	Union[str, Any] =				hf_model(**A__				)
              a__						:	Dict =				output.iou_scores.squeeze()

              assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

              a__						:	str =				((75, 275, 1725, 850),)

              a__						:	Union[str, Any] =				processor(images=np.array(A__				)		,      input_boxes=A__		,      return_tensors='pt'				).to('cuda'				)

              with torch.no_grad():
                     a__						:	List[Any] =				hf_model(**A__				)
              a__						:	Any =				output.iou_scores.squeeze()

              assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

              # Test with 2 points and 1 image.
              a__						:	Dict =				[[[400, 650], [800, 650]]]
              a__						:	List[str] =				[[1, 1]]

              a__						:	Any =				processor(
                  images=np.array(A__				)		,      input_points=A__		,      input_labels=A__		,      return_tensors='pt'				).to('cuda'				)

              with torch.no_grad():
                     a__						:	Optional[Any] =				hf_model(**A__				)
              a__						:	str =				output.iou_scores.squeeze()

              assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
    lowercase      :       Dict									=  argparse.ArgumentParser()
    lowercase      :       List[str]									=  ["""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""",
    )

    lowercase      :       List[Any]									=  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 os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase      :       List[str]									=  """src/transformers"""
lowercase      :       Optional[int]									=  """docs/source/en/tasks"""



def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	Any =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	int =				start_index
       while not lines[end_index].startswith(A__				):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]				) <= 1:
              start_index += 1
       while len(lines[end_index]				) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)

lowercase      :       Optional[Any]									=  {
    """asr.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    """audio_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    """language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    """image_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    """masked_language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    """multiple_choice.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    """object_detection.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    """question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    """semantic_segmentation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    """sequence_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    """summarization.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """token_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    """translation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """video_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    """document_question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    """monocular_depth_estimation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase      :       Optional[Any]									=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[Any] =				TASK_GUIDE_TO_MODELS[task_guide]
       a__						:	int =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__		,      set()				)
       a__						:	Optional[Any] =				{
           code: name
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if (code in model_maping_names or code in special_model_types)
       }
       return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]				) + "\n"



def A_	(		A__		,      A__=False				)  ->		Optional[int]:
       a__ ,							a__ ,							a__ ,							a__						:	Dict =				_find_text_in_file(
           filename=os.path.join(A__		,      A__				)		,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'		,      end_prompt='<!--End of the generated tip-->'		,      )

       a__						:	List[Any] =				get_model_list_for_task(A__				)

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(A__		,      A__				)		,      'w'		,      encoding='utf-8'		,      newline='\n'				) as f:
                            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]				)
              else:
                     raise ValueError(
                         F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
                         ' to fix this.'				)


if __name__ == "__main__":
    lowercase      :       Dict									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    for task_guide in TASK_GUIDE_TO_MODELS.keys():
        check_model_list_for_task(task_guide, args.fix_and_overwrite)


