




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


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







@dataclass
class 						lowercase							(   datasets.BuilderConfig		):
     _a							 =						None
     _a							 =						"utf-8"
     _a							 =						None
     _a							 =						None
     _a							 =						True  # deprecated
     _a							 =						None  # deprecated
     _a							 =						1_0 << 2_0  # 10MB
     _a							 =						None







class 						lowercase							(   datasets.ArrowBasedBuilder		):
     _a							 =						JsonConfig

     def 					a__	(  self				)						->							Optional[int]:
           if self.config.block_size is not None:
                 logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""				)
                 _A							:     Optional[Any]			=						self.config.block_size
           if self.config.use_threads is not True:
                 logger.warning(
                     """The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore."""				)
           if self.config.newlines_in_values is not None:
                 raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""				)
           return datasets.DatasetInfo(features=self.config.features				)

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           if not self.config.data_files:
                 raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
           _A							:     str			=						dl_manager.download_and_extract(self.config.data_files				)
           if isinstance(_a		,     (str, list, tuple)				):
                 _A							:     Optional[Any]			=						data_files
                 if isinstance(_a		,     _a				):
                       _A							:     List[Any]			=						[files]
                 _A							:     Optional[Any]			=						[dl_manager.iter_files(_a				) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={"""files""": files}				)]
           _A							:     Any			=						[]
           for split_name, files in data_files.items():
                 if isinstance(_a		,     _a				):
                       _A							:     Tuple			=						[files]
                 _A							:     Any			=						[dl_manager.iter_files(_a				) for file in files]
                 splits.append(datasets.SplitGenerator(name=_a		,     gen_kwargs={"""files""": files}				)				)
           return splits

     def 					a__	(  self		,     _a				)						->							pa.Table:
           if self.config.features is not None:
                 # adding missing columns
                 for column_name in set(self.config.features				) - set(pa_table.column_names				):
                       _A							:     str			=						self.config.features.arrow_schema.field(_a				).type
                       _A							:     int			=						pa_table.append_column(_a		,     pa.array([None] * len(_a				)		,     type=_a				)				)
                 # more expensive cast to support nested structures with keys in a different order
                 # allows str <-> int/float or str to Audio for example
                 _A							:     int			=						table_cast(_a		,     self.config.features.arrow_schema				)
           return pa_table



     def 					a__	(  self		,     _a				)						->							Dict:
           for file_idx, file in enumerate(itertools.chain.from_iterable(_a				)				):
                 # If the file is one json object and if we need to look at the list of items in one specific field
                 if self.config.field is not None:
                       with open(_a		,     encoding=self.config.encoding		,     errors=self.config.encoding_errors				) as f:
                             _A							:     Optional[Any]			=						json.load(_a				)

                       # We keep only the field we are interested in
                       _A							:     Optional[int]			=						dataset[self.config.field]

                       # We accept two format: a list of dicts or a dict of lists
                       if isinstance(_a		,     (list, tuple)				):
                             _A							:     Any			=						set().union(*[row.keys() for row in dataset]				)
                             _A							:     List[Any]			=						{col: [row.get(_a				) for row in dataset] for col in keys}
                       else:
                             _A							:     int			=						dataset
                       _A							:     Union[str, Any]			=						pa.Table.from_pydict(_a				)
                       yield file_idx, self._cast_table(_a				)

                 # If the file has one json object per line
                 else:
                       with open(_a		,     """rb"""				) as f:
                             _A							:     Any			=						0
                             # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                             # Set a default minimum value of 16kB if the chunk size is really small
                             _A							:     Optional[int]			=						max(self.config.chunksize // 32		,     16 << 10				)
                             _A							:     List[str]			=						(
                                 self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
                             )
                             while True:
                                   _A							:     Optional[int]			=						f.read(self.config.chunksize				)
                                   if not batch:
                                         break
                                   # Finish current line
                                   try:
                                         batch += f.readline()
                                   except (AttributeError, io.UnsupportedOperation):
                                         batch += readline(_a				)
                                   # PyArrow only accepts utf-8 encoded bytes
                                   if self.config.encoding != "utf-8":
                                         _A							:     Union[str, Any]			=						batch.decode(self.config.encoding		,     errors=_a				).encode("""utf-8"""				)
                                   try:
                                         while True:
                                               try:
                                                     _A							:     List[str]			=						paj.read_json(
                                                         io.BytesIO(_a				)		,     read_options=paj.ReadOptions(block_size=_a				)				)
                                                     break
                                               except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                     if (
                                                         isinstance(_a		,     pa.ArrowInvalid				)
                                                         and "straddling" not in str(_a				)
                                                         or block_size > len(_a				)
                                                     ):
                                                           raise
                                                     else:
                                                           # Increase the block size in case it was too small.
                                                           # The block size will be reset for the next file.
                                                           logger.debug(
                                                               F'''Batch of {len(_a				)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''				)
                                                           block_size *= 2
                                   except pa.ArrowInvalid as e:
                                         try:
                                               with open(
                                                   _a		,     encoding=self.config.encoding		,     errors=self.config.encoding_errors				) as f:
                                                     _A							:     str			=						json.load(_a				)
                                         except json.JSONDecodeError:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                               raise e
                                         # If possible, parse the file as a list of json objects and exit the loop
                                         if isinstance(_a		,     _a				):  # list is the only sequence type supported in JSON
                                               try:
                                                     _A							:     List[str]			=						set().union(*[row.keys() for row in dataset]				)
                                                     _A							:     Optional[Any]			=						{col: [row.get(_a				) for row in dataset] for col in keys}
                                                     _A							:     int			=						pa.Table.from_pydict(_a				)
                                               except (pa.ArrowInvalid, AttributeError) as e:
                                                     logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                                     raise ValueError(F'''Not able to read records in the JSON file at {file}.'''				) from None
                                               yield file_idx, self._cast_table(_a				)
                                               break
                                         else:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                               raise ValueError(
                                                   F'''Not able to read records in the JSON file at {file}. '''
                                                   F'''You should probably indicate the field of the JSON file containing your records. '''
                                                   F'''This JSON file contain the following fields: {str(list(dataset.keys()				)				)}. '''
                                                   F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''				) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                   yield (file_idx, batch_idx), self._cast_table(_a				)
                                   batch_idx += 1








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()
_UpperCAmelCase				:       Dict	   =				logging.get_logger(__name__)


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

    return new_state_dict


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    # for each of the encoder blocks:
    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)
            lowerCamelCase__    :      Any					 =		state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
            lowerCamelCase__    :      Optional[Any]					 =		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
            lowerCamelCase__    :      Optional[int]					 =		kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowerCamelCase__    :      Optional[int]					 =		kv_bias[: config.hidden_sizes[i]]
            lowerCamelCase__    :      Any					 =		kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowerCamelCase__    :      Dict					 =		kv_bias[config.hidden_sizes[i] :]


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      List[str]					 =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__    :      Tuple					 =		Image.open(requests.get(_UpperCAmelCase						,	stream=_UpperCAmelCase ).raw )

    return image


@torch.no_grad()
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase=False						,	_UpperCAmelCase=None )			->     Optional[int]:
    lowerCamelCase__    :      str					 =		GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,	decoder_hidden_size=64						,	depths=[3, 8, 27, 3] )

    # load image processor (only resize + rescale)
    lowerCamelCase__    :      Union[str, Any]					 =		GLPNImageProcessor()

    # prepare image
    lowerCamelCase__    :      str					 =		prepare_img()
    lowerCamelCase__    :      Tuple					 =		image_processor(images=_UpperCAmelCase						,	return_tensors='pt' ).pixel_values

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

    # load original state dict
    lowerCamelCase__    :      Any					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )

    # rename keys
    lowerCamelCase__    :      str					 =		rename_keys(_UpperCAmelCase )

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

    # create HuggingFace model and load state dict
    lowerCamelCase__    :      Dict					 =		GLPNForDepthEstimation(_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )
    model.eval()

    # forward pass
    lowerCamelCase__    :      List[str]					 =		model(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowerCamelCase__    :      List[Any]					 =		torch.tensor(
                [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] )
        elif "kitti" in model_name:
            lowerCamelCase__    :      List[str]					 =		torch.tensor(
                [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] )
        else:
            raise ValueError(F"""Unknown model name: {model_name}""" )

        lowerCamelCase__    :      Tuple					 =		torch.Size([1, 480, 640] )

        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__":
    _UpperCAmelCase				:       Tuple	   =				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.""",
    )
    _UpperCAmelCase				:       int	   =				parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





