




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_snake_case										=       re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_snake_case										=       10
_snake_case										=       256







def 				lowerCAmelCase_	(     snake_case_     ):
      if len(snake_case_     ) < MIN_NUM_TOKENS:
            return None
      _A							:     Tuple			=						MinHash(num_perm=snake_case_     )
      for token in set(snake_case_     ):
            min_hash.update(token.encode()     )
      return min_hash







def 				lowerCAmelCase_	(     snake_case_     ):
      return {t for t in NON_ALPHA.split(snake_case_     ) if len(t.strip()     ) > 0}







class 						lowercase							:

     def __init__(  self		,     *,
         _a = 0.85		,     )						->							List[Any]:
           _A							:     Any			=						duplication_jaccard_threshold
           _A							:     int			=						NUM_PERM
           _A							:     List[str]			=						MinHashLSH(threshold=self._duplication_jaccard_threshold		,     num_perm=self._num_perm				)

           _A							:     str			=						defaultdict(_a				)

     def 					a__	(  self		,     _a		,     _a				)						->							None:
           _A							:     Any			=						self._index.query(_a				)
           if code_key in self._index.keys:
                 print(F'''Duplicate key {code_key}'''				)
                 return

           self._index.insert(_a		,     _a				)
           if len(_a				) > 0:
                 for base_duplicate in close_duplicates:
                       if base_duplicate in self._duplicate_clusters:
                             self._duplicate_clusters[base_duplicate].add(_a				)
                             break
                 else:
                       self._duplicate_clusters[close_duplicates[0]].add(_a				)

     def 					a__	(  self				)						->							List[List[Dict]]:
           _A							:     List[Any]			=						[]
           for base, duplicates in self._duplicate_clusters.items():
                 _A							:     Dict			=						[base] + list(_a				)
                 # reformat the cluster to be a list of dict
                 _A							:     Tuple			=						[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
                 duplicate_clusters.append(_a				)
           return duplicate_clusters



     def 					a__	(  self		,     _a				)						->							None:
           _A							:     str			=						self.get_duplicate_clusters()
           with open(_a		,     """w"""				) as f:
                 json.dump(_a		,     _a				)







def 				lowerCAmelCase_	(     snake_case_     ):
      _A			,   _A							:     Tuple			=						element
      _A							:     Any			=						get_min_hash([t for t in NON_ALPHA.split(data["""content"""]     ) if len(t.strip()     ) > 0]     )
      if min_hash is not None:
            return (index, data["repo_name"], data["path"]), min_hash







def 				lowerCAmelCase_	(     snake_case_     ):
      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash,ThreadedIterator(snake_case_,max_queue_size=10000     ),chunksize=100,):
                  if data is not None:
                        yield data







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     int			=						DuplicationIndex(duplication_jaccard_threshold=snake_case_     )

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(snake_case_     )     ),max_queue_size=100     )     ):
            di.add(snake_case_,snake_case_     )

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Any			=						get_tokens(snake_case_     )
      _A							:     Dict			=						get_tokens(snake_case_     )
      return len(tokensa & tokensa     ) / len(tokensa | tokensa     )


_snake_case										=       None







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						[]
      for elementa in cluster:
            _A							:     List[str]			=						_shared_dataset[elementa["""base_index"""]]["""content"""]
            for elementa in extremes:
                  _A							:     Union[str, Any]			=						_shared_dataset[elementa["""base_index"""]]["""content"""]
                  if jaccard_similarity(snake_case_,snake_case_     ) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  _A							:     int			=						1
                  extremes.append(snake_case_     )
      return extremes







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      global _shared_dataset
      _A							:     Any			=						dataset
      _A							:     List[Any]			=						[]
      _A							:     Optional[int]			=						partial(_find_cluster_extremes_shared,jaccard_threshold=snake_case_     )
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    snake_case_,snake_case_,),total=len(snake_case_     ),):
                  extremes_list.append(snake_case_     )
      return extremes_list







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = 0.85     ):
      _A							:     List[Any]			=						make_duplicate_clusters(snake_case_,snake_case_     )
      _A							:     str			=						{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
      _A							:     List[str]			=						{}
      _A							:     Optional[Any]			=						find_extremes(snake_case_,snake_case_,snake_case_     )
      for extremes in extremes_clusters:
            for element in extremes:
                  _A							:     int			=						element
      _A							:     Union[str, Any]			=						duplicate_indices - set(extreme_dict.keys()     )
      _A							:     int			=						dataset.filter(lambda snake_case_,snake_case_     : idx not in remove_indices,with_indices=snake_case_     )

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  _A							:     int			=						element["""base_index"""] in extreme_dict
                  if element["is_extreme"]:
                        _A							:     Any			=						extreme_dict[element["""base_index"""]]["""copies"""]

      print(f'''Original dataset size: {len(snake_case_     )}'''     )
      print(f'''Number of duplicate clusters: {len(snake_case_     )}'''     )
      print(f'''Files in duplicate cluster: {len(snake_case_     )}'''     )
      print(f'''Unique files in duplicate cluster: {len(snake_case_     )}'''     )
      print(f'''Filtered dataset size: {len(snake_case_     )}'''     )

      return ds_filter, duplicate_clusters








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)





