



"""simple docstring"""

import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()




def __lowercase    (							_a	,    _a	,    _a					):
    def get_masked_lm_array(_a					):
        snake_case_				:  Tuple       =			f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  Dict       =			tf.train.load_variable(_a	,    _a					)

        if "kernel" in name:
            snake_case_				:  Optional[int]       =			array.transpose()

        return torch.from_numpy(_a					)

    def get_encoder_array(_a					):
        snake_case_				:  Tuple       =			f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  str       =			tf.train.load_variable(_a	,    _a					)

        if "kernel" in name:
            snake_case_				:  Union[str, Any]       =			array.transpose()

        return torch.from_numpy(_a					)

    def get_encoder_layer_array(_a	,    _a					):
        snake_case_				:  Any       =			f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  Union[str, Any]       =			tf.train.load_variable(_a	,    _a					)

        if "kernel" in name:
            snake_case_				:  int       =			array.transpose()

        return torch.from_numpy(_a					)

    def get_encoder_attention_layer_array(_a	,    _a	,    _a					):
        snake_case_				:  int       =			f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        snake_case_				:  Optional[int]       =			tf.train.load_variable(_a	,    _a					)
        snake_case_				:  int       =			array.reshape(_a					)

        if "kernel" in name:
            snake_case_				:  int       =			array.transpose()

        return torch.from_numpy(_a					)

    print(f"Loading model based on config from {config_path}..."					)
    snake_case_				:  List[Any]       =			BertConfig.from_json_file(_a					)
    snake_case_				:  List[str]       =			BertForMaskedLM(_a					)

    # Layers
    for layer_index in range(0	,    config.num_hidden_layers					):
        snake_case_				:  BertLayer       =			model.bert.encoder.layer[layer_index]

        # Self-attention
        snake_case_				:  BertSelfAttention       =			layer.attention.self

        snake_case_				:  str       =			get_encoder_attention_layer_array(
            _a	,    '''_query_dense/kernel'''	,    self_attn.query.weight.data.shape					)
        snake_case_				:  int       =			get_encoder_attention_layer_array(
            _a	,    '''_query_dense/bias'''	,    self_attn.query.bias.data.shape					)
        snake_case_				:  List[Any]       =			get_encoder_attention_layer_array(
            _a	,    '''_key_dense/kernel'''	,    self_attn.key.weight.data.shape					)
        snake_case_				:  Tuple       =			get_encoder_attention_layer_array(
            _a	,    '''_key_dense/bias'''	,    self_attn.key.bias.data.shape					)
        snake_case_				:  Optional[Any]       =			get_encoder_attention_layer_array(
            _a	,    '''_value_dense/kernel'''	,    self_attn.value.weight.data.shape					)
        snake_case_				:  Union[str, Any]       =			get_encoder_attention_layer_array(
            _a	,    '''_value_dense/bias'''	,    self_attn.value.bias.data.shape					)

        # Self-attention Output
        snake_case_				:  BertSelfOutput       =			layer.attention.output

        snake_case_				:  int       =			get_encoder_attention_layer_array(
            _a	,    '''_output_dense/kernel'''	,    self_output.dense.weight.data.shape					)
        snake_case_				:  List[Any]       =			get_encoder_attention_layer_array(
            _a	,    '''_output_dense/bias'''	,    self_output.dense.bias.data.shape					)

        snake_case_				:  Union[str, Any]       =			get_encoder_layer_array(_a	,    '''_attention_layer_norm/gamma'''					)
        snake_case_				:  List[str]       =			get_encoder_layer_array(_a	,    '''_attention_layer_norm/beta'''					)

        # Intermediate
        snake_case_				:  BertIntermediate       =			layer.intermediate

        snake_case_				:  str       =			get_encoder_layer_array(_a	,    '''_intermediate_dense/kernel'''					)
        snake_case_				:  Any       =			get_encoder_layer_array(_a	,    '''_intermediate_dense/bias'''					)

        # Output
        snake_case_				:  BertOutput       =			layer.output

        snake_case_				:  int       =			get_encoder_layer_array(_a	,    '''_output_dense/kernel'''					)
        snake_case_				:  Dict       =			get_encoder_layer_array(_a	,    '''_output_dense/bias'''					)

        snake_case_				:  Dict       =			get_encoder_layer_array(_a	,    '''_output_layer_norm/gamma'''					)
        snake_case_				:  Optional[Any]       =			get_encoder_layer_array(_a	,    '''_output_layer_norm/beta'''					)

    # Embeddings
    snake_case_				:  Optional[int]       =			get_encoder_array('''_position_embedding_layer/embeddings'''					)
    snake_case_				:  List[str]       =			get_encoder_array('''_type_embedding_layer/embeddings'''					)
    snake_case_				:  Any       =			get_encoder_array('''_embedding_norm_layer/gamma'''					)
    snake_case_				:  List[Any]       =			get_encoder_array('''_embedding_norm_layer/beta'''					)

    # LM Head
    snake_case_				:  Any       =			model.cls.predictions.transform

    snake_case_				:  Optional[Any]       =			get_masked_lm_array('''dense/kernel'''					)
    snake_case_				:  str       =			get_masked_lm_array('''dense/bias'''					)

    snake_case_				:  List[str]       =			get_masked_lm_array('''layer_norm/gamma'''					)
    snake_case_				:  Any       =			get_masked_lm_array('''layer_norm/beta'''					)

    snake_case_				:  Dict       =			get_masked_lm_array('''embedding_table'''					)

    # Pooling
    snake_case_				:  Optional[int]       =			BertPooler(config=_a					)
    snake_case_				:  BertPooler       =			get_encoder_array('''_pooler_layer/kernel'''					)
    snake_case_				:  BertPooler       =			get_encoder_array('''_pooler_layer/bias'''					)

    # Export final model
    model.save_pretrained(_a					)

    # Integration test - should load without any errors ;)
    snake_case_				:  int       =			BertForMaskedLM.from_pretrained(_a					)
    print(new_model.eval()					)

    print('''Model conversion was done sucessfully!'''					)


if __name__ == "__main__":
     lowercase__					:						str												=			argparse.ArgumentParser()
     parser.add_argument(
         '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
     )
     parser.add_argument(
         '''--bert_config_file''',
         type=str,
         required=True,
         help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''',
         type=str,
         required=True,
         help='''Path to the output PyTorch model.''',
     )
     lowercase__					:						int												=			parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










"""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)





