


"""simple docstring"""

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 __lowercase    (							_a					):
    snake_case_				:  List[str]       =			{}
    state_dict.pop('''pixel_mean'''	,    _a					)
    state_dict.pop('''pixel_std'''	,    _a					)

    snake_case_				:  Union[str, 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:
                snake_case_				:  Optional[int]       =			key.replace(_a	,    _a					)

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

        snake_case_				:  Optional[Any]       =			value

    snake_case_				:  Tuple       =			model_state_dict[
        '''prompt_encoder.shared_embedding.positional_embedding'''
    ]

    return model_state_dict




def __lowercase    (							_a	,    _a	,    _a	,    _a="ybelkada/segment-anything"					):
    snake_case_				:  Optional[Any]       =			hf_hub_download(_a	,    f"checkpoints/{model_name}.pth"					)

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

        snake_case_				:  Union[str, Any]       =			SamConfig(
            vision_config=_a	,    )
    elif "sam_vit_h" in model_name:
        snake_case_				:  Tuple       =			SamVisionConfig(
            hidden_size=1_280	,    num_hidden_layers=32	,    num_attention_heads=16	,    global_attn_indexes=[7, 15, 23, 31]	,    )

        snake_case_				:  List[str]       =			SamConfig(
            vision_config=_a	,    )

    snake_case_				:  Tuple       =			torch.load(_a	,    map_location='''cpu'''					)
    snake_case_				:  Optional[Any]       =			replace_keys(_a					)

    snake_case_				:  Any       =			SamImageProcessor()

    snake_case_				:  Optional[Any]       =			SamProcessor(image_processor=_a					)
    snake_case_				:  Tuple       =			SamModel(_a					)

    hf_model.load_state_dict(_a					)
    snake_case_				:  Tuple       =			hf_model.to('''cuda'''					)

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

    snake_case_				:  Tuple       =			[[[400, 650]]]
    snake_case_				:  List[str]       =			[[1]]

    snake_case_				:  Optional[int]       =			processor(images=np.array(_a					)	,    return_tensors='''pt'''					).to('''cuda'''					)

    with torch.no_grad():
        snake_case_				:  Optional[Any]       =			hf_model(**_a					)
    snake_case_				:  Any       =			output.iou_scores.squeeze()

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

        snake_case_				:  Optional[Any]       =			processor(
            images=np.array(_a					)	,    input_points=_a	,    input_labels=_a	,    return_tensors='''pt'''					).to('''cuda'''					)

        with torch.no_grad():
            snake_case_				:  Optional[Any]       =			hf_model(**_a					)
        snake_case_				:  Tuple       =			output.iou_scores.squeeze()

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

        snake_case_				:  Tuple       =			((75, 275, 1_725, 850),)

        snake_case_				:  Optional[Any]       =			processor(images=np.array(_a					)	,    input_boxes=_a	,    return_tensors='''pt'''					).to('''cuda'''					)

        with torch.no_grad():
            snake_case_				:  Dict       =			hf_model(**_a					)
        snake_case_				:  Any       =			output.iou_scores.squeeze()

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

        # Test with 2 points and 1 image.
        snake_case_				:  Union[str, Any]       =			[[[400, 650], [800, 650]]]
        snake_case_				:  Optional[int]       =			[[1, 1]]

        snake_case_				:  Tuple       =			processor(
            images=np.array(_a					)	,    input_points=_a	,    input_labels=_a	,    return_tensors='''pt'''					).to('''cuda'''					)

        with torch.no_grad():
            snake_case_				:  Dict       =			hf_model(**_a					)
        snake_case_				:  Dict       =			output.iou_scores.squeeze()

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


if __name__ == "__main__":
     lowercase__					:						Optional[int]												=			argparse.ArgumentParser()
     lowercase__					:						Any												=			['''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__					:						Tuple												=			parser.parse_args()

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










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





