
'''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()
_UpperCAmelCase :   List[str]			    =	logging.get_logger(__name__)







def     snake_case__						(   UpperCamelCase						)			->   Optional[Any]:
		_UpperCamelCase							:			Dict     =   DPTConfig(embedding_type='''hybrid'''						)

		if "large" in checkpoint_url:
				_UpperCamelCase							:			Optional[Any]     =   10_24
				_UpperCamelCase							:			str     =   40_96
				_UpperCamelCase							:			Optional[Any]     =   24
				_UpperCamelCase							:			Tuple     =   16
				_UpperCamelCase							:			Optional[int]     =   [5, 11, 17, 23]
				_UpperCamelCase							:			int     =   [2_56, 5_12, 10_24, 10_24]
				_UpperCamelCase							:			List[str]     =   (1, 3_84, 3_84)

		if "nyu" or "midas" in checkpoint_url:
				_UpperCamelCase							:			Optional[Any]     =   7_68
				_UpperCamelCase							:			str     =   [1, 1, 1, 0.5]
				_UpperCamelCase							:			Tuple     =   [2_56, 5_12, 7_68, 7_68]
				_UpperCamelCase							:			Union[str, Any]     =   1_50
				_UpperCamelCase							:			List[Any]     =   16
				_UpperCamelCase							:			Union[str, Any]     =   (1, 3_84, 3_84)
				_UpperCamelCase							:			str     =   False
				_UpperCamelCase							:			Optional[Any]     =   """project"""

		if "ade" in checkpoint_url:
				_UpperCamelCase							:			Union[str, Any]     =   True
				_UpperCamelCase							:			Optional[int]     =   7_68
				_UpperCamelCase							:			Any     =   [1, 1, 1, 0.5]
				_UpperCamelCase							:			Tuple     =   1_50
				_UpperCamelCase							:			Optional[Any]     =   16
				_UpperCamelCase							:			List[str]     =   """huggingface/label-files"""
				_UpperCamelCase							:			Tuple     =   """ade20k-id2label.json"""
				_UpperCamelCase							:			List[Any]     =   json.load(open(cached_download(hf_hub_url(snake_case__      ,snake_case__      ,repo_type='''dataset'''						)						)      ,'''r'''						)						)
				_UpperCamelCase							:			str     =   {int(snake_case__						): v for k, v in idalabel.items()}
				_UpperCamelCase							:			List[str]     =   idalabel
				_UpperCamelCase							:			Union[str, Any]     =   {v: k for k, v in idalabel.items()}
				_UpperCamelCase							:			List[Any]     =   [1, 1_50, 4_80, 4_80]

		return config, expected_shape







def     snake_case__						(   UpperCamelCase						)			->   int:
		_UpperCamelCase							:			List[str]     =   ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
		for k in ignore_keys:
				state_dict.pop(snake_case__      ,snake_case__						)







def     snake_case__						(   UpperCamelCase						)			->   str:






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

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

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







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase						)			->   Optional[int]:






		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							:			int     =   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							:			Any     =   in_proj_bias[: config.hidden_size]
				_UpperCamelCase							:			int     =   in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_UpperCamelCase							:			str     =   in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_UpperCamelCase							:			List[str]     =   in_proj_weight[
				    -config.hidden_size :, :
				]
				_UpperCamelCase							:			int     =   in_proj_bias[-config.hidden_size :]







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







@torch.no_grad()
def     snake_case__						(   UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase      ,UpperCamelCase						)			->   Dict:
		_UpperCamelCase							:			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")
		_UpperCamelCase							:			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():
				_UpperCamelCase							:			List[str]     =   state_dict.pop(snake_case__						)
				_UpperCamelCase							:			Optional[Any]     =   val
		# read in qkv matrices
		read_in_q_k_v(snake_case__      ,snake_case__						)

		# load HuggingFace model
		_UpperCamelCase							:			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
		_UpperCamelCase							:			Union[str, Any]     =   4_80 if """ade""" in checkpoint_url else 3_84
		_UpperCamelCase							:			Optional[int]     =   DPTImageProcessor(size=snake_case__						)

		_UpperCamelCase							:			Tuple     =   prepare_img()
		_UpperCamelCase							:			Optional[Any]     =   image_processor(snake_case__      ,return_tensors='''pt'''						)

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

		if show_prediction:
				_UpperCamelCase							:			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()) * 2_55						).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__":
	_UpperCAmelCase :   int			    =	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""",
	)

	_UpperCAmelCase :   Optional[Any]			    =	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


