
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()
lowercase						:      int									= logging.get_logger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Any:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      DPTConfig()

							if "large" in checkpoint_url:
														__UpperCamelCase			:     Dict         =      1_024
														__UpperCamelCase			:     Optional[Any]         =      4_096
														__UpperCamelCase			:     Dict         =      24
														__UpperCamelCase			:     Dict         =      16
														__UpperCamelCase			:     List[str]         =      [5, 11, 17, 23]
														__UpperCamelCase			:     Optional[Any]         =      [256, 512, 1_024, 1_024]
														__UpperCamelCase			:     List[Any]         =      (1, 384, 384)

							if "ade" in checkpoint_url:
														__UpperCamelCase			:     Tuple         =      True

														__UpperCamelCase			:     List[Any]         =      150
														__UpperCamelCase			:     Optional[int]         =      """huggingface/label-files"""
														__UpperCamelCase			:     Union[str, Any]         =      """ade20k-id2label.json"""
														__UpperCamelCase			:     Dict         =      json.load(open(cached_download(hf_hub_url(_lowerCamelCase		,						_lowerCamelCase		,						repo_type="dataset"))		,						"r"))
														__UpperCamelCase			:     Optional[int]         =      {int(_lowerCamelCase): v for k, v in idalabel.items()}
														__UpperCamelCase			:     str         =      idalabel
														__UpperCamelCase			:     Dict         =      {v: k for k, v in idalabel.items()}
														__UpperCamelCase			:     Union[str, Any]         =      [1, 150, 480, 480]

							return config, expected_shape




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					int:






							'''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			:     str         =      name.replace("pretrained.model"		,						"dpt.encoder")
							if "pretrained.model" in name:
														__UpperCamelCase			:     Dict         =      name.replace("pretrained.model"		,						"dpt.embeddings")
							if "patch_embed" in name:
														__UpperCamelCase			:     Optional[int]         =      name.replace("patch_embed"		,						"patch_embeddings")
							if "pos_embed" in name:
														__UpperCamelCase			:     Dict         =      name.replace("pos_embed"		,						"position_embeddings")
							if "attn.proj" in name:
														__UpperCamelCase			:     str         =      name.replace("attn.proj"		,						"attention.output.dense")
							if "proj" in name and "project" not in name:
														__UpperCamelCase			:     Optional[int]         =      name.replace("proj"		,						"projection")
							if "blocks" in name:
														__UpperCamelCase			:     Dict         =      name.replace("blocks"		,						"layer")
							if "mlp.fc1" in name:
														__UpperCamelCase			:     List[str]         =      name.replace("mlp.fc1"		,						"intermediate.dense")
							if "mlp.fc2" in name:
														__UpperCamelCase			:     Any         =      name.replace("mlp.fc2"		,						"output.dense")
							if "norm1" in name:
														__UpperCamelCase			:     List[str]         =      name.replace("norm1"		,						"layernorm_before")
							if "norm2" in name:
														__UpperCamelCase			:     Dict         =      name.replace("norm2"		,						"layernorm_after")
							if "scratch.output_conv" in name:
														__UpperCamelCase			:     str         =      name.replace("scratch.output_conv"		,						"head")
							if "scratch" in name:
														__UpperCamelCase			:     Dict         =      name.replace("scratch"		,						"neck")
							if "layer1_rn" in name:
														__UpperCamelCase			:     Tuple         =      name.replace("layer1_rn"		,						"convs.0")
							if "layer2_rn" in name:
														__UpperCamelCase			:     List[str]         =      name.replace("layer2_rn"		,						"convs.1")
							if "layer3_rn" in name:
														__UpperCamelCase			:     Union[str, Any]         =      name.replace("layer3_rn"		,						"convs.2")
							if "layer4_rn" in name:
														__UpperCamelCase			:     List[str]         =      name.replace("layer4_rn"		,						"convs.3")
							if "refinenet" in name:
														__UpperCamelCase			:     List[Any]         =      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			:     int         =      name.replace(F'refinenet{layer_idx}'		,						F'fusion_stage.layers.{abs(layer_idx-4)}')
							if "out_conv" in name:
														__UpperCamelCase			:     str         =      name.replace("out_conv"		,						"projection")
							if "resConfUnit1" in name:
														__UpperCamelCase			:     str         =      name.replace("resConfUnit1"		,						"residual_layer1")
							if "resConfUnit2" in name:
														__UpperCamelCase			:     Any         =      name.replace("resConfUnit2"		,						"residual_layer2")
							if "conv1" in name:
														__UpperCamelCase			:     str         =      name.replace("conv1"		,						"convolution1")
							if "conv2" in name:
														__UpperCamelCase			:     str         =      name.replace("conv2"		,						"convolution2")
							# readout blocks
							if "pretrained.act_postprocess1.0.project.0" in name:
														__UpperCamelCase			:     Any         =      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			:     Any         =      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			:     int         =      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			:     List[Any]         =      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			:     Union[str, Any]         =      name.replace("pretrained.act_postprocess1.3"		,						"neck.reassemble_stage.layers.0.projection")
							if "pretrained.act_postprocess1.4" in name:
														__UpperCamelCase			:     Union[str, Any]         =      name.replace("pretrained.act_postprocess1.4"		,						"neck.reassemble_stage.layers.0.resize")
							if "pretrained.act_postprocess2.3" in name:
														__UpperCamelCase			:     Optional[int]         =      name.replace("pretrained.act_postprocess2.3"		,						"neck.reassemble_stage.layers.1.projection")
							if "pretrained.act_postprocess2.4" in name:
														__UpperCamelCase			:     Union[str, Any]         =      name.replace("pretrained.act_postprocess2.4"		,						"neck.reassemble_stage.layers.1.resize")
							if "pretrained.act_postprocess3.3" in name:
														__UpperCamelCase			:     List[Any]         =      name.replace("pretrained.act_postprocess3.3"		,						"neck.reassemble_stage.layers.2.projection")
							if "pretrained.act_postprocess4.3" in name:
														__UpperCamelCase			:     List[str]         =      name.replace("pretrained.act_postprocess4.3"		,						"neck.reassemble_stage.layers.3.projection")
							if "pretrained.act_postprocess4.4" in name:
														__UpperCamelCase			:     Dict         =      name.replace("pretrained.act_postprocess4.4"		,						"neck.reassemble_stage.layers.3.resize")
							if "pretrained" in name:
														__UpperCamelCase			:     List[str]         =      name.replace("pretrained"		,						"dpt")
							if "bn" in name:
														__UpperCamelCase			:     Optional[Any]         =      name.replace("bn"		,						"batch_norm")
							if "head" in name:
														__UpperCamelCase			:     str         =      name.replace("head"		,						"head.head")
							if "encoder.norm" in name:
														__UpperCamelCase			:     Optional[Any]         =      name.replace("encoder.norm"		,						"layernorm")
							if "auxlayer" in name:
														__UpperCamelCase			:     Tuple         =      name.replace("auxlayer"		,						"auxiliary_head.head")

							return name




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Optional[int])      ->					str:






							'''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			:     Dict         =      state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight')
														__UpperCamelCase			:     List[str]         =      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			:     str         =      in_proj_weight[: config.hidden_size, :]
														__UpperCamelCase			:     Dict         =      in_proj_bias[: config.hidden_size]
														__UpperCamelCase			:     int         =      in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase			:     Union[str, Any]         =      in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__UpperCamelCase			:     int         =      in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase			:     Tuple         =      in_proj_bias[-config.hidden_size :]




def 			_SCREAMING_SNAKE_CASE      (	)      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
							__UpperCamelCase			:     Tuple         =      Image.open(requests.get(_lowerCamelCase		,						stream=_lowerCamelCase).raw)
							return im




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






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      get_dpt_config(_lowerCamelCase)
							# load original state_dict from URL
							__UpperCamelCase			:     Optional[Any]         =      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			:     Dict         =      state_dict.pop(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      val
							# read in qkv matrices
							read_in_q_k_v(_lowerCamelCase		,						_lowerCamelCase)

							# load HuggingFace model
							__UpperCamelCase			:     int         =      DPTForSemanticSegmentation(_lowerCamelCase) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase)
							model.load_state_dict(_lowerCamelCase)
							model.eval()

							# Check outputs on an image
							__UpperCamelCase			:     int         =      480 if """ade""" in checkpoint_url else 384
							__UpperCamelCase			:     str         =      DPTImageProcessor(size=_lowerCamelCase)

							__UpperCamelCase			:     Dict         =      prepare_img()
							__UpperCamelCase			:     List[Any]         =      image_processor(_lowerCamelCase		,						return_tensors="pt")

							# forward pass
							__UpperCamelCase			:     Union[str, Any]         =      model(**_lowerCamelCase).logits if """ade""" in checkpoint_url else model(**_lowerCamelCase).predicted_depth

							# Assert logits
							__UpperCamelCase			:     Optional[Any]         =      torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]])
							if "ade" in checkpoint_url:
														__UpperCamelCase			:     Optional[Any]         =      torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]])
							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__":
		lowercase						:      Tuple									= 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.',
		)

		lowercase						:      int									= parser.parse_args()
		convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
		from .deepspeed import (
		    DeepSpeedEngineWrapper,
		    DeepSpeedOptimizerWrapper,
		    DeepSpeedSchedulerWrapper,
		    DummyOptim,
		    DummyScheduler,
		    HfDeepSpeedConfig,
		)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers