
"""simple docstring"""

import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD




def     lowercase__						(					) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
    UpperCAmelCase									=					Image.open(requests.get(lowerCAmelCase ,				stream=lowerCAmelCase ).raw ).convert('RGB' )

    return image




def     lowercase__						(					lowerCAmelCase				:					Tuple ) ->      int:






    """simple docstring"""




    UpperCAmelCase									=					[]
    # fmt: off

    # vision encoder
    rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
    rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
    rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
    rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
    rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
    rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )

    for i in range(config.vision_config.num_hidden_layers ):
        rename_keys.append((F"visual_encoder.blocks.{i}.norm1.weight", F"vision_model.encoder.layers.{i}.layer_norm1.weight") )
        rename_keys.append((F"visual_encoder.blocks.{i}.norm1.bias", F"vision_model.encoder.layers.{i}.layer_norm1.bias") )
        rename_keys.append((F"visual_encoder.blocks.{i}.norm2.weight", F"vision_model.encoder.layers.{i}.layer_norm2.weight") )
        rename_keys.append((F"visual_encoder.blocks.{i}.norm2.bias", F"vision_model.encoder.layers.{i}.layer_norm2.bias") )
        rename_keys.append((F"visual_encoder.blocks.{i}.attn.qkv.weight", F"vision_model.encoder.layers.{i}.self_attn.qkv.weight") )
        rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.weight", F"vision_model.encoder.layers.{i}.self_attn.projection.weight",) )
        rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.bias", F"vision_model.encoder.layers.{i}.self_attn.projection.bias") )
        rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.weight", F"vision_model.encoder.layers.{i}.mlp.fc1.weight") )
        rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.bias", F"vision_model.encoder.layers.{i}.mlp.fc1.bias") )
        rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.weight", F"vision_model.encoder.layers.{i}.mlp.fc2.weight") )
        rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.bias", F"vision_model.encoder.layers.{i}.mlp.fc2.bias") )

    # QFormer
    rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight') )
    rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias') )

    # fmt: on
    return rename_keys




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					List[Any] ) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					dct.pop(lowerCAmelCase )
    UpperCAmelCase									=					val




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					List[str] ) ->      Optional[Any]:






    """simple docstring"""




    for i in range(config.vision_config.num_hidden_layers ):
        # read in original q and v biases
        UpperCAmelCase									=					state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias" )
        UpperCAmelCase									=					state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias" )

        # next, set bias in the state dict
        UpperCAmelCase									=					torch.cat((q_bias, torch.zeros_like(lowerCAmelCase ,				requires_grad=lowerCAmelCase ), v_bias) )
        UpperCAmelCase									=					qkv_bias




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					364 if 'coco' in model_name else 224
    UpperCAmelCase									=					InstructBlipVisionConfig(image_size=lowerCAmelCase ).to_dict()

    # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
    # seems like flan-T5 models don't have bos_token_id properly set?
    if "t5-xl" in model_name:
        UpperCAmelCase									=					TaConfig.from_pretrained('google/flan-t5-xl' ,				dense_act_fn='gelu' ,				bos_token_id=1 ).to_dict()
    elif "t5-xxl" in model_name:
        UpperCAmelCase									=					TaConfig.from_pretrained('google/flan-t5-xxl' ,				dense_act_fn='gelu' ,				bos_token_id=1 ).to_dict()
    elif "vicuna-7b" in model_name:
        UpperCAmelCase									=					LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' ,				vocab_size=32_001 ).to_dict()
    elif "vicuna-13b" in model_name:
        UpperCAmelCase									=					LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' ,				vocab_size=32_001 ).to_dict()
    else:
        raise ValueError('Model name not supported' )

    # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
    UpperCAmelCase									=					InstructBlipQFormerConfig(vocab_size=30_523 ).to_dict()
    UpperCAmelCase									=					InstructBlipConfig(vision_config=lowerCAmelCase ,				text_config=lowerCAmelCase ,				qformer_config=lowerCAmelCase )

    return config, image_size




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Tuple=None ,				lowerCAmelCase				:					Any=False ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					AutoTokenizer.from_pretrained('bert-base-uncased' ,				truncation_side='left' )
    qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'} )

    if "t5" in model_name:
        UpperCAmelCase									=					TaTokenizerFast.from_pretrained('google/flan-t5-xl' ,				truncation_side='left' )
    elif "vicuna" in model_name:
        # the following was used in the original implementation:
        # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
        # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
        # tokenizer.add_special_tokens({"bos_token": "</s>"})
        # tokenizer.add_special_tokens({"eos_token": "</s>"})
        # tokenizer.add_special_tokens({"unk_token": "</s>"})
        UpperCAmelCase									=					LlamaTokenizerFast.from_pretrained(
            'huggyllama/llama-7b' ,				truncation_side='left' ,				bos_token='</s>' ,				unk_token='</s>' )
        tokenizer.add_special_tokens({'pad_token': '[PAD]'} )

    UpperCAmelCase			,							UpperCAmelCase									=					get_blipa_config(lowerCAmelCase )
    UpperCAmelCase									=					InstructBlipForConditionalGeneration(lowerCAmelCase ).eval()

    UpperCAmelCase									=					{
        'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
        'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
        'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
        'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
    }

    UpperCAmelCase			,							UpperCAmelCase									=					model_name_to_original[model_name]

    # load original model
    print('Loading original model...' )
    UpperCAmelCase									=					'cuda:1' if torch.cuda.is_available() else 'cpu'
    UpperCAmelCase									=					'cuda:2' if torch.cuda.is_available() else 'cpu'
    UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					load_model_and_preprocess(
        name=lowerCAmelCase ,				model_type=lowerCAmelCase ,				is_eval=lowerCAmelCase ,				device=lowerCAmelCase )
    original_model.eval()
    print('Done!' )

    # update state dict keys
    UpperCAmelCase									=					original_model.state_dict()
    UpperCAmelCase									=					create_rename_keys(lowerCAmelCase )
    for src, dest in rename_keys:
        rename_key(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )

    # some keys can be renamed efficiently
    for key, val in state_dict.copy().items():
        UpperCAmelCase									=					state_dict.pop(lowerCAmelCase )
        if key.startswith('Qformer.bert' ):
            UpperCAmelCase									=					key.replace('Qformer.bert' ,				'qformer' )
        if "attention.self" in key:
            UpperCAmelCase									=					key.replace('self' ,				'attention' )
        if "llm_proj" in key:
            UpperCAmelCase									=					key.replace('llm_proj' ,				'language_projection' )
        if "t5_proj" in key:
            UpperCAmelCase									=					key.replace('t5_proj' ,				'language_projection' )
        if key.startswith('llm_model' ):
            UpperCAmelCase									=					key.replace('llm_model' ,				'language_model' )
        if key.startswith('t5' ):
            UpperCAmelCase									=					key.replace('t5' ,				'language' )
        UpperCAmelCase									=					val

    # read in qv biases
    read_in_q_v_bias(lowerCAmelCase ,				lowerCAmelCase )

    # note: weights get loaded in torch.float32 by default
    hf_model.load_state_dict(lowerCAmelCase ,				strict=lowerCAmelCase )

    UpperCAmelCase									=					load_demo_image()
    UpperCAmelCase									=					'What is unusual about this image?'

    # create processor
    UpperCAmelCase									=					BlipImageProcessor(
        size={'height': image_size, 'width': image_size} ,				image_mean=lowerCAmelCase ,				image_std=lowerCAmelCase )
    UpperCAmelCase									=					InstructBlipProcessor(
        image_processor=lowerCAmelCase ,				tokenizer=lowerCAmelCase ,				qformer_tokenizer=lowerCAmelCase ,				)
    UpperCAmelCase									=					processor(images=lowerCAmelCase ,				text=lowerCAmelCase ,				return_tensors='pt' ).to(lowerCAmelCase )

    # make sure processor creates exact same pixel values
    UpperCAmelCase									=					vis_processors['eval'](lowerCAmelCase ).unsqueeze(0 ).to(lowerCAmelCase )
    UpperCAmelCase									=					inputs.pixel_values
    assert torch.allclose(original_pixel_values.to(pixel_values.device ) ,				lowerCAmelCase )

    original_model.to(lowerCAmelCase )
    hf_model.to(lowerCAmelCase )
    with torch.no_grad():
        if "vicuna" in model_name:
            UpperCAmelCase									=					original_model({'image': original_pixel_values, 'text_input': [prompt]} ).logits
            UpperCAmelCase									=					hf_model(**lowerCAmelCase ).logits
        else:
            UpperCAmelCase									=					original_model(
                {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']} ).logits
            UpperCAmelCase									=					tokenizer('\n' ,				return_tensors='pt' ).input_ids.to(lowerCAmelCase )
            UpperCAmelCase									=					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id ,				-100 )
            UpperCAmelCase									=					hf_model(**lowerCAmelCase ,				labels=lowerCAmelCase ).logits

    print('First values of original logits:' ,				original_logits[0, :3, :3] )
    print('First values of HF logits:' ,				logits[0, :3, :3] )

    # assert values
    assert original_logits.shape == logits.shape
    UpperCAmelCase									=					1E-4 if 'vicuna' in model_name else 1E-5
    assert torch.allclose(original_logits.to(logits.device ) ,				lowerCAmelCase ,				atol=lowerCAmelCase )
    print('Looks ok!' )

    print('Generating with original model...' )
    UpperCAmelCase									=					original_model.generate({'image': original_pixel_values, 'prompt': prompt} ,				num_beams=5 )

    # important: we need to cast the weights of the HF model to the appropriate type
    print('Generating with HF model...' )
    UpperCAmelCase									=					hf_model.generate(
        **lowerCAmelCase ,				do_sample=lowerCAmelCase ,				num_beams=5 ,				max_length=256 ,				min_length=1 ,				top_p=0.9 ,				repetition_penalty=1.5 ,				length_penalty=1.0 ,				temperature=1 ,				)
    if "vicuna" in model_name:
        # convert output id 0 to 2 (eos_token_id)
        # TODO add this in the generate method?
        UpperCAmelCase									=					2
    print('Original generation:' ,				lowerCAmelCase )
    UpperCAmelCase									=					processor.batch_decode(lowerCAmelCase ,				skip_special_tokens=lowerCAmelCase )
    UpperCAmelCase									=					[text.strip() for text in output_text]
    print('HF generation:' ,				lowerCAmelCase )

    if pytorch_dump_folder_path is not None:
        processor.save_pretrained(lowerCAmelCase )
        hf_model.save_pretrained(lowerCAmelCase )

    if push_to_hub:
        processor.push_to_hub(F"Salesforce/{model_name}" )
        hf_model.push_to_hub(F"Salesforce/{model_name}" )


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    					=						argparse.ArgumentParser()
   SCREAMING_SNAKE_CASE_    					=						[
       '''instructblip-vicuna-7b''',
       '''instructblip-vicuna-13b''',
       '''instructblip-flan-t5-xl''',
       '''instructblip-flan-t5-xxl''',
   ]
   parser.add_argument(
       '''--model_name''',
       default='''instructblip-flan-t5-xl''',
       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''',
   )

   SCREAMING_SNAKE_CASE_    					=						parser.parse_args()

   convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					int ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					DPTConfig()

    if "large" in checkpoint_url:
        UpperCAmelCase									=					1_024
        UpperCAmelCase									=					4_096
        UpperCAmelCase									=					24
        UpperCAmelCase									=					16
        UpperCAmelCase									=					[5, 11, 17, 23]
        UpperCAmelCase									=					[256, 512, 1_024, 1_024]
        UpperCAmelCase									=					(1, 384, 384)

    if "ade" in checkpoint_url:
        UpperCAmelCase									=					True

        UpperCAmelCase									=					150
        UpperCAmelCase									=					'huggingface/label-files'
        UpperCAmelCase									=					'ade20k-id2label.json'
        UpperCAmelCase									=					json.load(open(cached_download(hf_hub_url(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ) ,				'r' ) )
        UpperCAmelCase									=					{int(lowerCAmelCase ): v for k, v in idalabel.items()}
        UpperCAmelCase									=					idalabel
        UpperCAmelCase									=					{v: k for k, v in idalabel.items()}
        UpperCAmelCase									=					[1, 150, 480, 480]

    return config, expected_shape




def     lowercase__						(					lowerCAmelCase				:					str ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[Any]:






    """simple docstring"""




    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        UpperCAmelCase									=					name.replace('pretrained.model' ,				'dpt.encoder' )
    if "pretrained.model" in name:
        UpperCAmelCase									=					name.replace('pretrained.model' ,				'dpt.embeddings' )
    if "patch_embed" in name:
        UpperCAmelCase									=					name.replace('patch_embed' ,				'patch_embeddings' )
    if "pos_embed" in name:
        UpperCAmelCase									=					name.replace('pos_embed' ,				'position_embeddings' )
    if "attn.proj" in name:
        UpperCAmelCase									=					name.replace('attn.proj' ,				'attention.output.dense' )
    if "proj" in name and "project" not in name:
        UpperCAmelCase									=					name.replace('proj' ,				'projection' )
    if "blocks" in name:
        UpperCAmelCase									=					name.replace('blocks' ,				'layer' )
    if "mlp.fc1" in name:
        UpperCAmelCase									=					name.replace('mlp.fc1' ,				'intermediate.dense' )
    if "mlp.fc2" in name:
        UpperCAmelCase									=					name.replace('mlp.fc2' ,				'output.dense' )
    if "norm1" in name:
        UpperCAmelCase									=					name.replace('norm1' ,				'layernorm_before' )
    if "norm2" in name:
        UpperCAmelCase									=					name.replace('norm2' ,				'layernorm_after' )
    if "scratch.output_conv" in name:
        UpperCAmelCase									=					name.replace('scratch.output_conv' ,				'head' )
    if "scratch" in name:
        UpperCAmelCase									=					name.replace('scratch' ,				'neck' )
    if "layer1_rn" in name:
        UpperCAmelCase									=					name.replace('layer1_rn' ,				'convs.0' )
    if "layer2_rn" in name:
        UpperCAmelCase									=					name.replace('layer2_rn' ,				'convs.1' )
    if "layer3_rn" in name:
        UpperCAmelCase									=					name.replace('layer3_rn' ,				'convs.2' )
    if "layer4_rn" in name:
        UpperCAmelCase									=					name.replace('layer4_rn' ,				'convs.3' )
    if "refinenet" in name:
        UpperCAmelCase									=					int(name[len('neck.refinenet' ) : len('neck.refinenet' ) + 1] )
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        UpperCAmelCase									=					name.replace(F"refinenet{layer_idx}" ,				F"fusion_stage.layers.{abs(layer_idx-4 )}" )
    if "out_conv" in name:
        UpperCAmelCase									=					name.replace('out_conv' ,				'projection' )
    if "resConfUnit1" in name:
        UpperCAmelCase									=					name.replace('resConfUnit1' ,				'residual_layer1' )
    if "resConfUnit2" in name:
        UpperCAmelCase									=					name.replace('resConfUnit2' ,				'residual_layer2' )
    if "conv1" in name:
        UpperCAmelCase									=					name.replace('conv1' ,				'convolution1' )
    if "conv2" in name:
        UpperCAmelCase									=					name.replace('conv2' ,				'convolution2' )
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess1.0.project.0' ,				'neck.reassemble_stage.readout_projects.0.0' )
    if "pretrained.act_postprocess2.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess2.0.project.0' ,				'neck.reassemble_stage.readout_projects.1.0' )
    if "pretrained.act_postprocess3.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess3.0.project.0' ,				'neck.reassemble_stage.readout_projects.2.0' )
    if "pretrained.act_postprocess4.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess4.0.project.0' ,				'neck.reassemble_stage.readout_projects.3.0' )
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess1.3' ,				'neck.reassemble_stage.layers.0.projection' )
    if "pretrained.act_postprocess1.4" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess1.4' ,				'neck.reassemble_stage.layers.0.resize' )
    if "pretrained.act_postprocess2.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess2.3' ,				'neck.reassemble_stage.layers.1.projection' )
    if "pretrained.act_postprocess2.4" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess2.4' ,				'neck.reassemble_stage.layers.1.resize' )
    if "pretrained.act_postprocess3.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess3.3' ,				'neck.reassemble_stage.layers.2.projection' )
    if "pretrained.act_postprocess4.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess4.3' ,				'neck.reassemble_stage.layers.3.projection' )
    if "pretrained.act_postprocess4.4" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess4.4' ,				'neck.reassemble_stage.layers.3.resize' )
    if "pretrained" in name:
        UpperCAmelCase									=					name.replace('pretrained' ,				'dpt' )
    if "bn" in name:
        UpperCAmelCase									=					name.replace('bn' ,				'batch_norm' )
    if "head" in name:
        UpperCAmelCase									=					name.replace('head' ,				'head.head' )
    if "encoder.norm" in name:
        UpperCAmelCase									=					name.replace('encoder.norm' ,				'layernorm' )
    if "auxlayer" in name:
        UpperCAmelCase									=					name.replace('auxlayer' ,				'auxiliary_head.head' )

    return name




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    for i in range(config.num_hidden_layers ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCAmelCase									=					state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
        UpperCAmelCase									=					state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias" )
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase									=					in_proj_weight[: config.hidden_size, :]
        UpperCAmelCase									=					in_proj_bias[: config.hidden_size]
        UpperCAmelCase									=					in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase									=					in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCAmelCase									=					in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase									=					in_proj_bias[-config.hidden_size :]




def     lowercase__						(					) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase									=					Image.open(requests.get(lowerCAmelCase ,				stream=lowerCAmelCase ).raw )
    return im




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Union[str, Any] ) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase			,							UpperCAmelCase									=					get_dpt_config(lowerCAmelCase )
    # load original state_dict from URL
    UpperCAmelCase									=					torch.hub.load_state_dict_from_url(lowerCAmelCase ,				map_location='cpu' )
    # remove certain keys
    remove_ignore_keys_(lowerCAmelCase )
    # rename keys
    for key in state_dict.copy().keys():
        UpperCAmelCase									=					state_dict.pop(lowerCAmelCase )
        UpperCAmelCase									=					val
    # read in qkv matrices
    read_in_q_k_v(lowerCAmelCase ,				lowerCAmelCase )

    # load HuggingFace model
    UpperCAmelCase									=					DPTForSemanticSegmentation(lowerCAmelCase ) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase )
    model.load_state_dict(lowerCAmelCase )
    model.eval()

    # Check outputs on an image
    UpperCAmelCase									=					480 if 'ade' in checkpoint_url else 384
    UpperCAmelCase									=					DPTImageProcessor(size=lowerCAmelCase )

    UpperCAmelCase									=					prepare_img()
    UpperCAmelCase									=					image_processor(lowerCAmelCase ,				return_tensors='pt' )

    # forward pass
    UpperCAmelCase									=					model(**lowerCAmelCase ).logits if 'ade' in checkpoint_url else model(**lowerCAmelCase ).predicted_depth

    # Assert logits
    UpperCAmelCase									=					torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
    if "ade" in checkpoint_url:
        UpperCAmelCase									=					torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
    assert outputs.shape == torch.Size(lowerCAmelCase )
    assert (
        torch.allclose(outputs[0, 0, :3, :3] ,				lowerCAmelCase ,				atol=1E-4 )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3] ,				lowerCAmelCase )
    )

    Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
    print(F"Saving model to {pytorch_dump_folder_path}" )
    model.save_pretrained(lowerCAmelCase )
    print(F"Saving image processor to {pytorch_dump_folder_path}" )
    image_processor.save_pretrained(lowerCAmelCase )

    if push_to_hub:
        print('Pushing model to hub...' )
        model.push_to_hub(
            repo_path_or_name=Path(lowerCAmelCase ,				lowerCAmelCase ) ,				organization='nielsr' ,				commit_message='Add model' ,				use_temp_dir=lowerCAmelCase ,				)
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowerCAmelCase ,				lowerCAmelCase ) ,				organization='nielsr' ,				commit_message='Add image processor' ,				use_temp_dir=lowerCAmelCase ,				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    					=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
       type=str,
       help='''URL of the original DPT checkpoint you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
   )
   parser.add_argument(
       '''--model_name''',
       default='''dpt-large''',
       type=str,
       help='''Name of the model, in case you\'re pushing to the hub.''',
   )

   SCREAMING_SNAKE_CASE_    					=						parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



