



"""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"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''					)

    # Encoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(_a					)
    print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





