"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     OrderedDict()
						for key, value in state_dict.items():
												if key.startswith('module.encoder'   ):
																		snake_case        =     key.replace('module.encoder'	,     'glpn.encoder'   )
												if key.startswith('module.decoder'   ):
																		snake_case        =     key.replace('module.decoder'	,     'decoder.stages'   )
												if "patch_embed" in key:
																		# replace for example patch_embed1 by patch_embeddings.0
																		snake_case        =     key[key.find('patch_embed'   ) + len('patch_embed'   )]
																		snake_case        =     key.replace(f"""patch_embed{idx}"""	,     f"""patch_embeddings.{int(_UpperCamelCase   )-1}"""   )
												if "norm" in key:
																		snake_case        =     key.replace('norm'	,     'layer_norm'   )
												if "glpn.encoder.layer_norm" in key:
																		# replace for example layer_norm1 by layer_norm.0
																		snake_case        =     key[key.find('glpn.encoder.layer_norm'   ) + len('glpn.encoder.layer_norm'   )]
																		snake_case        =     key.replace(f"""layer_norm{idx}"""	,     f"""layer_norm.{int(_UpperCamelCase   )-1}"""   )
												if "layer_norm1" in key:
																		snake_case        =     key.replace('layer_norm1'	,     'layer_norm_1'   )
												if "layer_norm2" in key:
																		snake_case        =     key.replace('layer_norm2'	,     'layer_norm_2'   )
												if "block" in key:
																		# replace for example block1 by block.0
																		snake_case        =     key[key.find('block'   ) + len('block'   )]
																		snake_case        =     key.replace(f"""block{idx}"""	,     f"""block.{int(_UpperCamelCase   )-1}"""   )
												if "attn.q" in key:
																		snake_case        =     key.replace('attn.q'	,     'attention.self.query'   )
												if "attn.proj" in key:
																		snake_case        =     key.replace('attn.proj'	,     'attention.output.dense'   )
												if "attn" in key:
																		snake_case        =     key.replace('attn'	,     'attention.self'   )
												if "fc1" in key:
																		snake_case        =     key.replace('fc1'	,     'dense1'   )
												if "fc2" in key:
																		snake_case        =     key.replace('fc2'	,     'dense2'   )
												if "linear_pred" in key:
																		snake_case        =     key.replace('linear_pred'	,     'classifier'   )
												if "linear_fuse" in key:
																		snake_case        =     key.replace('linear_fuse.conv'	,     'linear_fuse'   )
																		snake_case        =     key.replace('linear_fuse.bn'	,     'batch_norm'   )
												if "linear_c" in key:
																		# replace for example linear_c4 by linear_c.3
																		snake_case        =     key[key.find('linear_c'   ) + len('linear_c'   )]
																		snake_case        =     key.replace(f"""linear_c{idx}"""	,     f"""linear_c.{int(_UpperCamelCase   )-1}"""   )
												if "bot_conv" in key:
																		snake_case        =     key.replace('bot_conv'	,     '0.convolution'   )
												if "skip_conv1" in key:
																		snake_case        =     key.replace('skip_conv1'	,     '1.convolution'   )
												if "skip_conv2" in key:
																		snake_case        =     key.replace('skip_conv2'	,     '2.convolution'   )
												if "fusion1" in key:
																		snake_case        =     key.replace('fusion1'	,     '1.fusion'   )
												if "fusion2" in key:
																		snake_case        =     key.replace('fusion2'	,     '2.fusion'   )
												if "fusion3" in key:
																		snake_case        =     key.replace('fusion3'	,     '3.fusion'   )
												if "fusion" in key and "conv" in key:
																		snake_case        =     key.replace('conv'	,     'convolutional_layer'   )
												if key.startswith('module.last_layer_depth'   ):
																		snake_case        =     key.replace('module.last_layer_depth'	,     'head.head'   )
												snake_case        =     value

						return new_state_dict





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			str   )   ->			Any:





						"""simple docstring"""






						for i in range(config.num_encoder_blocks   ):
												for j in range(config.depths[i]   ):
																		# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																		snake_case        =     state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""   )
																		snake_case        =     state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""   )
																		# next, add keys and values (in that order) to the state dict
																		snake_case        =     kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		snake_case        =     kv_bias[: config.hidden_sizes[i]]
																		snake_case        =     kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		snake_case        =     kv_bias[config.hidden_sizes[i] :]





def 				lowerCAmelCase__      (     )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     'http://images.cocodataset.org/val2017/000000039769.jpg'
						snake_case        =     Image.open(requests.get(_UpperCamelCase	,     stream=_UpperCamelCase   ).raw   )

						return image





@torch.no_grad()
def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			Union[str, Any]=False	,     _UpperCamelCase    :			Any=None   )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]	,     decoder_hidden_size=6_4	,     depths=[3, 8, 2_7, 3]   )

						# load image processor (only resize + rescale)
						snake_case        =     GLPNImageProcessor()

						# prepare image
						snake_case        =     prepare_img()
						snake_case        =     image_processor(images=_UpperCamelCase	,     return_tensors='pt'   ).pixel_values

						logger.info('Converting model...'   )

						# load original state dict
						snake_case        =     torch.load(_UpperCamelCase	,     map_location=torch.device('cpu'   )   )

						# rename keys
						snake_case        =     rename_keys(_UpperCamelCase   )

						# key and value matrices need special treatment
						read_in_k_v(_UpperCamelCase	,     _UpperCamelCase   )

						# create HuggingFace model and load state dict
						snake_case        =     GLPNForDepthEstimation(_UpperCamelCase   )
						model.load_state_dict(_UpperCamelCase   )
						model.eval()

						# forward pass
						snake_case        =     model(_UpperCamelCase   )
						snake_case        =     outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		snake_case        =     torch.tensor(
																		    [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]   )
												elif "kitti" in model_name:
																		snake_case        =     torch.tensor(
																		    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]   )
												else:
																		raise ValueError(f"""Unknown model name: {model_name}"""   )

												snake_case        =     torch.Size([1, 4_8_0, 6_4_0]   )

												assert predicted_depth.shape == expected_shape
												assert torch.allclose(predicted_depth[0, :3, :3]	,     _UpperCamelCase	,     atol=1e-4   )
												print('Looks ok!'   )

						# finally, push to hub if required
						if push_to_hub:
												logger.info('Pushing model and image processor to the hub...'   )
												model.push_to_hub(
												    repo_path_or_name=Path(_UpperCamelCase	,     _UpperCamelCase   )	,     organization='nielsr'	,     commit_message='Add model'	,     use_temp_dir=_UpperCamelCase	,     )
												image_processor.push_to_hub(
												    repo_path_or_name=Path(_UpperCamelCase	,     _UpperCamelCase   )	,     organization='nielsr'	,     commit_message='Add image processor'	,     use_temp_dir=_UpperCamelCase	,     )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()

					parser.add_argument(
					    "--checkpoint_path",
					    default=None,
					    type=str,
					    help="Path to the original PyTorch checkpoint (.pth file).",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
					)
					parser.add_argument(
					    "--model_name",
					    default="glpn-kitti",
					    type=str,
					    help="Name of the model in case you're pushing to the hub.",
					)
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


SCREAMING_SNAKE_CASE__   =					datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
					import pyspark



@dataclass
class 					lowerCAmelCase_      (     datasets.BuilderConfig   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[datasets.Features]     					=  None





def 				lowerCAmelCase__      (     _UpperCamelCase    :			"pyspark.sql.DataFrame"	,     _UpperCamelCase    :			List[int]	,     )   ->			Dict:





						"""simple docstring"""






						import pyspark

						def generate_fn():
												snake_case        =     df.select('*'	,     pyspark.sql.functions.spark_partition_id().alias('part_id'   )   )
												for partition_id in partition_order:
																		snake_case        =     df_with_partition_id.select('*'   ).where(f"""part_id = {partition_id}"""   ).drop('part_id'   )
																		snake_case        =     partition_df.collect()
																		snake_case        =     0
																		for row in rows:
																								yield f"""{partition_id}_{row_id}""", row.asDict()
																								row_id += 1

						return generate_fn



class 					lowerCAmelCase_      (     _BaseExamplesIterable   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										snake_case        =     df
										snake_case        =     partition_order or range(self.df.rdd.getNumPartitions()      )
										snake_case        =     _generate_iterable_examples(self.df						,      self.partition_order      )

				def __iter__(			self      ):






										"""simple docstring"""

										yield from self.generate_examples_fn()

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     list(range(self.df.rdd.getNumPartitions()      )      )
										generator.shuffle(lowerCAmelCase      )
										return SparkExamplesIterable(self.df						,      partition_order=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.split_shard_indices_by_worker(lowerCAmelCase						,      lowerCAmelCase      )
										return SparkExamplesIterable(self.df						,      partition_order=lowerCAmelCase      )

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return len(self.partition_order      )





class 					lowerCAmelCase_      (     datasets.DatasetBuilder   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[str]     					=  SparkConfig

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										import pyspark

										snake_case        =     pyspark.sql.SparkSession.builder.getOrCreate()
										snake_case        =     df
										snake_case        =     working_dir

										super().__init__(
										    cache_dir=lowerCAmelCase						,      config_name=str(self.df.semanticHash()      )						,      **lowerCAmelCase						,      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										def create_cache_and_write_probe(lowerCAmelCase      ):
																# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
																# already exist.
																os.makedirs(self._cache_dir						,      exist_ok=lowerCAmelCase      )
																snake_case        =     os.path.join(self._cache_dir						,      'fs_test' + uuid.uuida().hex      )
																# Opening the file in append mode will create a new file unless it already exists, in which case it will not
																# change the file contents.
																open(lowerCAmelCase						,      'a'      )
																return [probe_file]

										if self._spark.conf.get('spark.master'						,      ''      ).startswith('local'      ):
																return

										# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
										# accessible to the driver.
										# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
										if self._cache_dir:
																snake_case        =     (
																    self._spark.sparkContext.parallelize(range(1      )						,      1      ).mapPartitions(lowerCAmelCase      ).collect()
																)
																if os.path.isfile(probe[0]      ):
																						return

										raise ValueError(
										    'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										return datasets.DatasetInfo(features=self.config.features      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return [datasets.SplitGenerator(name=datasets.Split.TRAIN      )]

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										import pyspark

										def get_arrow_batch_size(lowerCAmelCase      ):
																for batch in it:
																						yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]}      )

										snake_case        =     self.df.count()
										snake_case        =     df_num_rows if df_num_rows <= 1_00 else 1_00
										# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
										snake_case        =     (
										    self.df.limit(lowerCAmelCase      )
										    .repartition(1      )
										    .mapInArrow(lowerCAmelCase						,      'batch_bytes: long'      )
										    .agg(pyspark.sql.functions.sum('batch_bytes'      ).alias('sample_bytes'      )      )
										    .collect()[0]
										    .sample_bytes
										    / sample_num_rows
										)
										snake_case        =     approx_bytes_per_row * df_num_rows
										if approx_total_size > max_shard_size:
																# Make sure there is at least one row per partition.
																snake_case        =     min(lowerCAmelCase						,      int(approx_total_size / max_shard_size      )      )
																snake_case        =     self.df.repartition(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										import pyspark

										snake_case        =     ParquetWriter if file_format == 'parquet' else ArrowWriter
										snake_case        =     os.path.join(self._working_dir						,      os.path.basename(lowerCAmelCase      )      ) if self._working_dir else fpath
										snake_case        =     file_format == 'parquet'

										# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
										# pickling the SparkContext.
										snake_case        =     self.config.features
										snake_case        =     self._writer_batch_size
										snake_case        =     self._fs.storage_options

										def write_arrow(lowerCAmelCase      ):
																# Within the same SparkContext, no two task attempts will share the same attempt ID.
																snake_case        =     pyspark.TaskContext().taskAttemptId()
																snake_case        =     next(lowerCAmelCase						,      lowerCAmelCase      )
																if first_batch is None:
																						# Some partitions might not receive any data.
																						return pa.RecordBatch.from_arrays(
																						    [[task_id], [0], [0]]						,      names=['task_id', 'num_examples', 'num_bytes']						,      )
																snake_case        =     0
																snake_case        =     writer_class(
																    features=lowerCAmelCase						,      path=working_fpath.replace('SSSSS'						,      F"""{shard_id:05d}"""      ).replace('TTTTT'						,      F"""{task_id:05d}"""      )						,      writer_batch_size=lowerCAmelCase						,      storage_options=lowerCAmelCase						,      embed_local_files=lowerCAmelCase						,      )
																snake_case        =     pa.Table.from_batches([first_batch]      )
																writer.write_table(lowerCAmelCase      )
																for batch in it:
																						if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																												snake_case   ,snake_case        =     writer.finalize()
																												writer.close()
																												yield pa.RecordBatch.from_arrays(
																												    [[task_id], [num_examples], [num_bytes]]						,      names=['task_id', 'num_examples', 'num_bytes']						,      )
																												shard_id += 1
																												snake_case        =     writer_class(
																												    features=writer._features						,      path=working_fpath.replace('SSSSS'						,      F"""{shard_id:05d}"""      ).replace('TTTTT'						,      F"""{task_id:05d}"""      )						,      writer_batch_size=lowerCAmelCase						,      storage_options=lowerCAmelCase						,      embed_local_files=lowerCAmelCase						,      )
																						snake_case        =     pa.Table.from_batches([batch]      )
																						writer.write_table(lowerCAmelCase      )

																if writer._num_bytes > 0:
																						snake_case   ,snake_case        =     writer.finalize()
																						writer.close()
																						yield pa.RecordBatch.from_arrays(
																						    [[task_id], [num_examples], [num_bytes]]						,      names=['task_id', 'num_examples', 'num_bytes']						,      )

																if working_fpath != fpath:
																						for file in os.listdir(os.path.dirname(lowerCAmelCase      )      ):
																												snake_case        =     os.path.join(os.path.dirname(lowerCAmelCase      )						,      os.path.basename(lowerCAmelCase      )      )
																												shutil.move(lowerCAmelCase						,      lowerCAmelCase      )

										snake_case        =     (
										    self.df.mapInArrow(lowerCAmelCase						,      'task_id: long, num_examples: long, num_bytes: long'      )
										    .groupBy('task_id'      )
										    .agg(
										        pyspark.sql.functions.sum('num_examples'      ).alias('total_num_examples'      )						,      pyspark.sql.functions.sum('num_bytes'      ).alias('total_num_bytes'      )						,      pyspark.sql.functions.count('num_bytes'      ).alias('num_shards'      )						,      pyspark.sql.functions.collect_list('num_examples'      ).alias('shard_lengths'      )						,      )
										    .collect()
										)
										for row in stats:
																yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = "arrow"						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										self._validate_cache_dir()

										snake_case        =     convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE      )
										self._repartition_df_if_needed(lowerCAmelCase      )
										snake_case        =     not is_remote_filesystem(self._fs      )
										snake_case        =     os.path.join if is_local else posixpath.join

										snake_case        =     '-TTTTT-SSSSS-of-NNNNN'
										snake_case        =     F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
										snake_case        =     path_join(self._output_dir						,      lowerCAmelCase      )

										snake_case        =     0
										snake_case        =     0
										snake_case        =     0
										snake_case        =     []
										snake_case        =     []

										for task_id, content in self._prepare_split_single(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):
																(
																    (
																    snake_case
																)   ,(
																    snake_case
																)   ,(
																    snake_case
																)   ,(
																    snake_case
																)   ,
																)        =     content
																if num_bytes > 0:
																						total_num_examples += num_examples
																						total_num_bytes += num_bytes
																						total_shards += num_shards
																						task_id_and_num_shards.append((task_id, num_shards)      )
																						all_shard_lengths.extend(lowerCAmelCase      )

										snake_case        =     total_num_examples
										snake_case        =     total_num_bytes

										# should rename everything at the end
										logger.debug(F"""Renaming {total_shards} shards."""      )
										if total_shards > 1:
																snake_case        =     all_shard_lengths

																# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
																# pickling error due to pickling the SparkContext.
																snake_case        =     self._fs

																# use the -SSSSS-of-NNNNN pattern
																def _rename_shard(
																    lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):
																						rename(
																						    lowerCAmelCase						,      fpath.replace('SSSSS'						,      F"""{shard_id:05d}"""      ).replace('TTTTT'						,      F"""{task_id:05d}"""      )						,      fpath.replace('TTTTT-SSSSS'						,      F"""{global_shard_id:05d}"""      ).replace('NNNNN'						,      F"""{total_shards:05d}"""      )						,      )

																snake_case        =     []
																snake_case        =     0
																for i in range(len(lowerCAmelCase      )      ):
																						snake_case   ,snake_case        =     task_id_and_num_shards[i]
																						for shard_id in range(lowerCAmelCase      ):
																												args.append([task_id, shard_id, global_shard_id]      )
																												global_shard_id += 1
																self._spark.sparkContext.parallelize(lowerCAmelCase						,      len(lowerCAmelCase      )      ).map(lambda lowerCAmelCase      : _rename_shard(*lowerCAmelCase      )      ).collect()
										else:
																# don't use any pattern
																snake_case        =     0
																snake_case        =     task_id_and_num_shards[0][0]
																self._rename(
																    fpath.replace('SSSSS'						,      F"""{shard_id:05d}"""      ).replace('TTTTT'						,      F"""{task_id:05d}"""      )						,      fpath.replace(lowerCAmelCase						,      ''      )						,      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										return SparkExamplesIterable(self.df      )


