






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()
lowerCamelCase__       		=  logging.get_logger(__name__)






def 	UpperCamelCase	(							snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :Dict 							=    DPTConfig(embedding_type="""hybrid"""	)

     if "large" in checkpoint_url:
          __snake_case      :Optional[Any] 							=    1024
          __snake_case      :str 							=    4096
          __snake_case      :Optional[Any] 							=    24
          __snake_case      :Tuple 							=    16
          __snake_case      :Optional[int] 							=    [5, 11, 17, 23]
          __snake_case      :int 							=    [256, 512, 1024, 1024]
          __snake_case      :List[str] 							=    (1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          __snake_case      :Optional[Any] 							=    768
          __snake_case      :str 							=    [1, 1, 1, 0.5]
          __snake_case      :Tuple 							=    [256, 512, 768, 768]
          __snake_case      :Union[str, Any] 							=    150
          __snake_case      :List[Any] 							=    16
          __snake_case      :Union[str, Any] 							=    (1, 384, 384)
          __snake_case      :str 							=    False
          __snake_case      :Optional[Any] 							=    """project"""

     if "ade" in checkpoint_url:
          __snake_case      :Union[str, Any] 							=    True
          __snake_case      :Optional[int] 							=    768
          __snake_case      :Any 							=    [1, 1, 1, 0.5]
          __snake_case      :Tuple 							=    150
          __snake_case      :Optional[Any] 							=    16
          __snake_case      :List[str] 							=    """huggingface/label-files"""
          __snake_case      :Tuple 							=    """ade20k-id2label.json"""
          __snake_case      :List[Any] 							=    json.load(open(cached_download(hf_hub_url(snake_case__						,snake_case__						,repo_type="""dataset"""	)	)						,"""r"""	)	)
          __snake_case      :str 							=    {int(snake_case__	): v for k, v in idalabel.items()}
          __snake_case      :List[str] 							=    idalabel
          __snake_case      :Union[str, Any] 							=    {v: k for k, v in idalabel.items()}
          __snake_case      :List[Any] 							=    [1, 150, 480, 480]

     return config, expected_shape






def 	UpperCamelCase	(							snake_case__  : Tuple	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
     for k in ignore_keys:
          state_dict.pop(snake_case__						,snake_case__	)






def 	UpperCamelCase	(							snake_case__  : Union[str, 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
     ):
          __snake_case      :List[str] 							=    name.replace("""pretrained.model"""						,"""dpt.encoder"""	)
     if "pretrained.model" in name:
          __snake_case      :List[str] 							=    name.replace("""pretrained.model"""						,"""dpt.embeddings"""	)
     if "patch_embed" in name:
          __snake_case      :List[Any] 							=    name.replace("""patch_embed"""						,""""""	)
     if "pos_embed" in name:
          __snake_case      :Optional[int] 							=    name.replace("""pos_embed"""						,"""position_embeddings"""	)
     if "attn.proj" in name:
          __snake_case      :Union[str, Any] 							=    name.replace("""attn.proj"""						,"""attention.output.dense"""	)
     if "proj" in name and "project" not in name:
          __snake_case      :Union[str, Any] 							=    name.replace("""proj"""						,"""projection"""	)
     if "blocks" in name:
          __snake_case      :List[Any] 							=    name.replace("""blocks"""						,"""layer"""	)
     if "mlp.fc1" in name:
          __snake_case      :List[str] 							=    name.replace("""mlp.fc1"""						,"""intermediate.dense"""	)
     if "mlp.fc2" in name:
          __snake_case      :List[str] 							=    name.replace("""mlp.fc2"""						,"""output.dense"""	)
     if "norm1" in name and "backbone" not in name:
          __snake_case      :Optional[Any] 							=    name.replace("""norm1"""						,"""layernorm_before"""	)
     if "norm2" in name and "backbone" not in name:
          __snake_case      :List[Any] 							=    name.replace("""norm2"""						,"""layernorm_after"""	)
     if "scratch.output_conv" in name:
          __snake_case      :Union[str, Any] 							=    name.replace("""scratch.output_conv"""						,"""head"""	)
     if "scratch" in name:
          __snake_case      :Optional[int] 							=    name.replace("""scratch"""						,"""neck"""	)
     if "layer1_rn" in name:
          __snake_case      :List[str] 							=    name.replace("""layer1_rn"""						,"""convs.0"""	)
     if "layer2_rn" in name:
          __snake_case      :Any 							=    name.replace("""layer2_rn"""						,"""convs.1"""	)
     if "layer3_rn" in name:
          __snake_case      :Optional[Any] 							=    name.replace("""layer3_rn"""						,"""convs.2"""	)
     if "layer4_rn" in name:
          __snake_case      :Optional[Any] 							=    name.replace("""layer4_rn"""						,"""convs.3"""	)
     if "refinenet" in name:
          __snake_case      :Tuple 							=    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
          __snake_case      :str 							=    name.replace(f'''refinenet{layer_idx}'''						,f'''fusion_stage.layers.{abs(layer_idx-4	)}'''	)
     if "out_conv" in name:
          __snake_case      :Dict 							=    name.replace("""out_conv"""						,"""projection"""	)
     if "resConfUnit1" in name:
          __snake_case      :int 							=    name.replace("""resConfUnit1"""						,"""residual_layer1"""	)
     if "resConfUnit2" in name:
          __snake_case      :Optional[Any] 							=    name.replace("""resConfUnit2"""						,"""residual_layer2"""	)
     if "conv1" in name:
          __snake_case      :Tuple 							=    name.replace("""conv1"""						,"""convolution1"""	)
     if "conv2" in name:
          __snake_case      :List[str] 							=    name.replace("""conv2"""						,"""convolution2"""	)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          __snake_case      :Union[str, 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:
          __snake_case      :int 							=    name.replace("""pretrained.act_postprocess2.0.project.0"""						,"""neck.reassemble_stage.readout_projects.1.0"""	)
     if "pretrained.act_postprocess3.0.project.0" in name:
          __snake_case      :Optional[Any] 							=    name.replace("""pretrained.act_postprocess3.0.project.0"""						,"""neck.reassemble_stage.readout_projects.2.0"""	)
     if "pretrained.act_postprocess4.0.project.0" in name:
          __snake_case      :Optional[int] 							=    name.replace("""pretrained.act_postprocess4.0.project.0"""						,"""neck.reassemble_stage.readout_projects.3.0"""	)

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __snake_case      :str 							=    name.replace("""pretrained.act_postprocess1.3"""						,"""neck.reassemble_stage.layers.0.projection"""	)
     if "pretrained.act_postprocess1.4" in name:
          __snake_case      :Optional[Any] 							=    name.replace("""pretrained.act_postprocess1.4"""						,"""neck.reassemble_stage.layers.0.resize"""	)
     if "pretrained.act_postprocess2.3" in name:
          __snake_case      :Dict 							=    name.replace("""pretrained.act_postprocess2.3"""						,"""neck.reassemble_stage.layers.1.projection"""	)
     if "pretrained.act_postprocess2.4" in name:
          __snake_case      :Union[str, Any] 							=    name.replace("""pretrained.act_postprocess2.4"""						,"""neck.reassemble_stage.layers.1.resize"""	)
     if "pretrained.act_postprocess3.3" in name:
          __snake_case      :Optional[int] 							=    name.replace("""pretrained.act_postprocess3.3"""						,"""neck.reassemble_stage.layers.2.projection"""	)
     if "pretrained.act_postprocess4.3" in name:
          __snake_case      :Optional[Any] 							=    name.replace("""pretrained.act_postprocess4.3"""						,"""neck.reassemble_stage.layers.3.projection"""	)
     if "pretrained.act_postprocess4.4" in name:
          __snake_case      :Any 							=    name.replace("""pretrained.act_postprocess4.4"""						,"""neck.reassemble_stage.layers.3.resize"""	)
     if "pretrained" in name:
          __snake_case      :Dict 							=    name.replace("""pretrained"""						,"""dpt"""	)
     if "bn" in name:
          __snake_case      :List[Any] 							=    name.replace("""bn"""						,"""batch_norm"""	)
     if "head" in name:
          __snake_case      :Any 							=    name.replace("""head"""						,"""head.head"""	)
     if "encoder.norm" in name:
          __snake_case      :str 							=    name.replace("""encoder.norm"""						,"""layernorm"""	)
     if "auxlayer" in name:
          __snake_case      :int 							=    name.replace("""auxlayer"""						,"""auxiliary_head.head"""	)
     if "backbone" in name:
          __snake_case      :Tuple 							=    name.replace("""backbone"""						,"""backbone.bit.encoder"""	)

     if ".." in name:
          __snake_case      :List[Any] 							=    name.replace(""".."""						,"""."""	)

     if "stem.conv" in name:
          __snake_case      :Union[str, Any] 							=    name.replace("""stem.conv"""						,"""bit.embedder.convolution"""	)
     if "blocks" in name:
          __snake_case      :int 							=    name.replace("""blocks"""						,"""layers"""	)
     if "convolution" in name and "backbone" in name:
          __snake_case      :Dict 							=    name.replace("""convolution"""						,"""conv"""	)
     if "layer" in name and "backbone" in name:
          __snake_case      :Union[str, Any] 							=    name.replace("""layer"""						,"""layers"""	)
     if "backbone.bit.encoder.bit" in name:
          __snake_case      :List[str] 							=    name.replace("""backbone.bit.encoder.bit"""						,"""backbone.bit"""	)
     if "embedder.conv" in name:
          __snake_case      :Tuple 							=    name.replace("""embedder.conv"""						,"""embedder.convolution"""	)
     if "backbone.bit.encoder.stem.norm" in name:
          __snake_case      :Dict 							=    name.replace("""backbone.bit.encoder.stem.norm"""						,"""backbone.bit.embedder.norm"""	)
     return name






def 	UpperCamelCase	(							snake_case__  : Union[str, Any]						,snake_case__  : Optional[Any]	):




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






def 	UpperCamelCase	(							):




     '''simple docstring'''






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






@torch.no_grad()
def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : Any						,snake_case__  : str						,snake_case__  : Union[str, Any]						,snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case							,			__snake_case      :List[Any] 							=    get_dpt_config(snake_case__	)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     __snake_case      :Optional[int] 							=    torch.load(snake_case__						,map_location="""cpu"""	)
     # remove certain keys
     remove_ignore_keys_(snake_case__	)
     # rename keys
     for key in state_dict.copy().keys():
          __snake_case      :List[str] 							=    state_dict.pop(snake_case__	)
          __snake_case      :Optional[Any] 							=    val
     # read in qkv matrices
     read_in_q_k_v(snake_case__						,snake_case__	)

     # load HuggingFace model
     __snake_case      :Optional[Any] 							=    DPTForSemanticSegmentation(snake_case__	) if """ade""" in checkpoint_url else DPTForDepthEstimation(snake_case__	)
     model.load_state_dict(snake_case__	)
     model.eval()

     # Check outputs on an image
     __snake_case      :Union[str, Any] 							=    480 if """ade""" in checkpoint_url else 384
     __snake_case      :Optional[int] 							=    DPTImageProcessor(size=snake_case__	)

     __snake_case      :Tuple 							=    prepare_img()
     __snake_case      :Optional[Any] 							=    image_processor(snake_case__						,return_tensors="""pt"""	)

     # forward pass
     __snake_case      :List[str] 							=    model(**snake_case__	).logits if """ade""" in checkpoint_url else model(**snake_case__	).predicted_depth

     if show_prediction:
          __snake_case      :List[Any] 							=    (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1	)						,size=(image.size[1], image.size[0])						,mode="""bicubic"""						,align_corners=snake_case__						,)
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255	).show()

     if pytorch_dump_folder_path is not None:
          Path(snake_case__	).mkdir(exist_ok=snake_case__	)
          print(f'''Saving model to {pytorch_dump_folder_path}'''	)
          model.save_pretrained(snake_case__	)
          print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
          image_processor.save_pretrained(snake_case__	)

     if push_to_hub:
          model.push_to_hub("""ybelkada/dpt-hybrid-midas"""	)
          image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""	)


if __name__ == "__main__":
     lowerCamelCase__       		=  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=False,
         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.""",
     )
     parser.add_argument(
         """--show_prediction""",
         action="""store_true""",
     )

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










from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :int 							=    []
     for part_id in partition_order:
          __snake_case      :int 							=    df.where(f'''SPARK_PARTITION_ID() = {part_id}'''	).collect()
          for row_idx, row in enumerate(snake_case__	):
               expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict())	)
     return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Any 							=    spark.range(100	).repartition(1	)
     __snake_case      :int 							=    Spark(snake_case__	)
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16	)
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Tuple 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :int 							=    spark.range(10	).repartition(2	)
     __snake_case      :str 							=    [1, 0]
     __snake_case      :List[Any] 							=    _generate_iterable_examples(snake_case__						,snake_case__	)  # Reverse the partitions.
     __snake_case      :Union[str, Any] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,snake_case__	)

     for i, (row_id, row_dict) in enumerate(generate_fn()	):
          __snake_case							,			__snake_case      :Union[str, Any] 							=    expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(10	).repartition(1	)
     __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Union[str, Any] 							=    spark.range(30	).repartition(3	)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch("""numpy.random.Generator"""	) as generator_mock:
          __snake_case      :Dict 							=    lambda snake_case__	: x.reverse()
          __snake_case      :int 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[2, 1, 0]	)

          __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	).shuffle_data_sources(snake_case__	)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(snake_case__	):
               __snake_case							,			__snake_case      :List[str] 							=    expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(20	).repartition(4	)

     # Partitions 0 and 2
     __snake_case      :List[Any] 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=0						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Dict 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[0, 2]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Tuple 							=    expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __snake_case      :str 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=1						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Optional[int] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[1, 3]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Dict 							=    expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(100	).repartition(1	)
     __snake_case      :Dict 							=    Spark(snake_case__	)
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1	)
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 100


