

"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		# Load configuration defined in the metadata file
		with open(__SCREAMING_SNAKE_CASE			) as metadata_file:
				__lowerCAmelCase:	List[Any]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      LukeConfig(use_entity_aware_attention=__SCREAMING_SNAKE_CASE ,			**metadata["model_config"]			)

		# Load in the weights from the checkpoint_path
		__lowerCAmelCase:	Optional[Any]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["module"]

		# Load the entity vocab file
		__lowerCAmelCase:	List[Any]   				=      load_original_entity_vocab(__SCREAMING_SNAKE_CASE			)
		# add an entry for [MASK2]
		__lowerCAmelCase:	Any   				=      max(entity_vocab.values()			) + 1
		config.entity_vocab_size += 1

		__lowerCAmelCase:	Union[str, Any]   				=      XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

		# Add special tokens to the token vocabulary for downstream tasks
		__lowerCAmelCase:	str   				=      AddedToken("<ent>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Optional[int]   				=      AddedToken("<ent2>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}			)
		config.vocab_size += 2

		print(F"Saving tokenizer to {pytorch_dump_folder_path}"			)
		tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE			)
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"r"			) as f:
				__lowerCAmelCase:	Optional[int]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      "MLukeTokenizer"
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"w"			) as f:
				json.dump(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			MLukeTokenizer.vocab_files_names["entity_vocab_file"]			) ,			"w"			) as f:
				json.dump(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# Initialize the embeddings of the special tokens
		__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.convert_tokens_to_ids(["@"]			)[0]
		__lowerCAmelCase:	Optional[int]   				=      tokenizer.convert_tokens_to_ids(["#"]			)[0]

		__lowerCAmelCase:	Dict   				=      state_dict["embeddings.word_embeddings.weight"]
		__lowerCAmelCase:	Optional[int]   				=      word_emb[ent_init_index].unsqueeze(0			)
		__lowerCAmelCase:	int   				=      word_emb[enta_init_index].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      torch.cat([word_emb, ent_emb, enta_emb]			)
		# add special tokens for 'entity_predictions.bias'
		for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
				__lowerCAmelCase:	Dict   				=      state_dict[bias_name]
				__lowerCAmelCase:	Union[str, Any]   				=      decoder_bias[ent_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      decoder_bias[enta_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

		# Initialize the query layers of the entity-aware self-attention mechanism
		for layer_index in range(config.num_hidden_layers			):
				for matrix_name in ["query.weight", "query.bias"]:
						__lowerCAmelCase:	Optional[int]   				=      F"encoder.layer.{layer_index}.attention.self."
						__lowerCAmelCase:	Tuple   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Dict   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Optional[Any]   				=      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
		__lowerCAmelCase:	int   				=      state_dict["entity_embeddings.entity_embeddings.weight"]
		__lowerCAmelCase:	Dict   				=      entity_emb[entity_vocab["[MASK]"]].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      torch.cat([entity_emb, entity_mask_emb]			)
		# add [MASK2] for 'entity_predictions.bias'
		__lowerCAmelCase:	List[str]   				=      state_dict["entity_predictions.bias"]
		__lowerCAmelCase:	Dict   				=      entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      torch.cat([entity_prediction_bias, entity_mask_bias]			)

		__lowerCAmelCase:	Optional[int]   				=      LukeForMaskedLM(config=__SCREAMING_SNAKE_CASE			).eval()

		state_dict.pop("entity_predictions.decoder.weight"			)
		state_dict.pop("lm_head.decoder.weight"			)
		state_dict.pop("lm_head.decoder.bias"			)
		__lowerCAmelCase:	Tuple   				=      OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith("lm_head"			) or key.startswith("entity_predictions"			)):
						__lowerCAmelCase:	Any   				=      state_dict[key]
				else:
						__lowerCAmelCase:	Tuple   				=      state_dict[key]

		__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      model.load_state_dict(__SCREAMING_SNAKE_CASE ,			strict=__SCREAMING_SNAKE_CASE			)

		if set(__SCREAMING_SNAKE_CASE			) != {"luke.embeddings.position_ids"}:
				raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"			)
		if set(__SCREAMING_SNAKE_CASE			) != {
		    "lm_head.decoder.weight",
		    "lm_head.decoder.bias",
		    "entity_predictions.decoder.weight",
		}:
				raise ValueError(F"Unexpected missing_keys: {missing_keys}"			)

		model.tie_weights()
		assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
		assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

		# Check outputs
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ,			task="entity_classification"			)

		__lowerCAmelCase:	Tuple   				=      "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
		__lowerCAmelCase:	Optional[Any]   				=      (0, 9)
		__lowerCAmelCase:	Optional[int]   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			entity_spans=[span] ,			return_tensors="pt"			)

		__lowerCAmelCase:	int   				=      model(**__SCREAMING_SNAKE_CASE			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Dict   				=      torch.Size((1, 3_3, 7_6_8)			)
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]			)

		if not (outputs.last_hidden_state.shape == expected_shape):
				raise ValueError(
				    F"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"			)
		if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Union[str, Any]   				=      torch.Size((1, 1, 7_6_8)			)
				__lowerCAmelCase:	Any   				=      torch.tensor([[-0.1482, 0.0609, 0.0322]]			)

		if not (outputs.entity_last_hidden_state.shape == expected_shape):
				raise ValueError(
				    F"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
				    F" {expected_shape}"			)
		if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify masked word/entity prediction
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[Any]   				=      "Tokyo is the capital of <mask>."
		__lowerCAmelCase:	List[str]   				=      (2_4, 3_0)
		__lowerCAmelCase:	int   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			entity_spans=[span] ,			return_tensors="pt"			)

		__lowerCAmelCase:	Union[str, Any]   				=      model(**__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Optional[int]   				=      encoding["input_ids"][0].tolist()
		__lowerCAmelCase:	int   				=      input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
		__lowerCAmelCase:	Optional[Any]   				=      outputs.logits[0][mask_position_id].argmax(dim=-1			)
		assert "Japan" == tokenizer.decode(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Any   				=      outputs.entity_logits[0][0].argmax().item()
		__lowerCAmelCase:	Union[str, Any]   				=      [
		    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
		]
		assert [e for e in multilingual_predicted_entities if e.startswith("en:"			)][0] == "en:Japan"

		# Finally, save our PyTorch model and tokenizer
		print("Saving PyTorch model to {}".format(__SCREAMING_SNAKE_CASE			)			)
		model.save_pretrained(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	Tuple   				=      ["[MASK]", "[PAD]", "[UNK]"]

		__lowerCAmelCase:	Optional[Any]   				=      [json.loads(__SCREAMING_SNAKE_CASE			) for line in open(__SCREAMING_SNAKE_CASE			)]

		__lowerCAmelCase:	str   				=      {}
		for entry in data:
				__lowerCAmelCase:	Tuple   				=      entry["id"]
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								__lowerCAmelCase:	Optional[int]   				=      entity_id
								break
						__lowerCAmelCase:	Optional[Any]   				=      F"{language}:{entity_name}"
						__lowerCAmelCase:	Optional[int]   				=      entity_id
		return new_mapping


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
			parser.add_argument(
			    "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
			)
			parser.add_argument(
			    "--entity_vocab_path",
			    default=None,
			    type=str,
			    help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
			)
			parser.add_argument(
			    "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
			)
			__A						       =      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)





"""simple docstring"""


# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


__A						       =      get_logger(__name__)





class    snake_case     :
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  """dummy_data"""
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  """datasets"""
				SCREAMING_SNAKE_CASE_       :				Any       				=  False

				def __init__(     self							:    List[str]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Union[Version, str]					,  UpperCamelCase__							:    Optional[str] = None					,  UpperCamelCase__							:    bool = False					,  UpperCamelCase__							:    bool = True					,  UpperCamelCase__							:    Optional[List[Callable]] = None					,  )->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      0
						__lowerCAmelCase:	Tuple   				=      dataset_name
						__lowerCAmelCase:	Optional[Any]   				=      cache_dir
						__lowerCAmelCase:	Optional[int]   				=      use_local_dummy_data
						__lowerCAmelCase:	Optional[Any]   				=      config
						# download_callbacks take a single url as input
						__lowerCAmelCase:	List[Callable]   				=      download_callbacks or []
						# if False, it doesn't load existing files and it returns the paths of the dummy files relative
						# to the dummy_data zip file root
						__lowerCAmelCase:	Union[str, Any]   				=      load_existing_dummy_data

						# TODO(PVP, QL) might need to make this more general
						__lowerCAmelCase:	List[str]   				=      str(UpperCamelCase__)
						# to be downloaded
						__lowerCAmelCase:	Dict   				=      None
						__lowerCAmelCase:	Dict   				=      None

				@property
				def 							lowercase_					(     self							:    List[str])->       str:



						'''simple docstring'''


						if self._dummy_file is None:
								__lowerCAmelCase:	Tuple   				=      self.download_dummy_data()
						return self._dummy_file

				@property
				def 							lowercase_					(     self							:    Dict)->       Optional[Any]:



						'''simple docstring'''


						if self.config is not None:
								# structure is dummy / config_name / version_name
								return os.path.join("dummy"					,  self.config.name					,  self.version_name)
						# structure is dummy / version_name
						return os.path.join("dummy"					,  self.version_name)

				@property
				def 							lowercase_					(     self							:    List[str])->       Any:



						'''simple docstring'''


						return os.path.join(self.dummy_data_folder					,  "dummy_data.zip")

				def 							lowercase_					(     self							:    Optional[Any])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      (
						    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
						)

						__lowerCAmelCase:	str   				=      cached_path(
						    UpperCamelCase__					,  cache_dir=self.cache_dir					,  extract_compressed_file=UpperCamelCase__					,  force_extract=UpperCamelCase__)

						return os.path.join(UpperCamelCase__					,  self.dummy_file_name)

				@property
				def 							lowercase_					(     self							:    Dict)->       List[Any]:



						'''simple docstring'''


						return os.path.join(self.datasets_scripts_dir					,  self.dataset_name					,  self.dummy_zip_file)

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       Tuple:



						'''simple docstring'''


						if self._bucket_url is None:
								__lowerCAmelCase:	int   				=      hf_github_url(self.dataset_name					,  self.dummy_zip_file.replace(os.sep					,  "/"))
						return self._bucket_url

				@property
				def 							lowercase_					(     self							:    str)->       Optional[int]:



						'''simple docstring'''


						if os.path.isdir(self.dummy_file):
								return self.dummy_file
						# else cut off path to file -> example `xsum`.
						return "/".join(self.dummy_file.replace(os.sep					,  "/").split("/")[:-1])

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    int					,  *UpperCamelCase__							:    List[str])->       Optional[int]:



						'''simple docstring'''


						if self.load_existing_dummy_data:
								# dummy data is downloaded and tested
								__lowerCAmelCase:	List[Any]   				=      self.dummy_file
						else:
								# dummy data cannot be downloaded and only the path to dummy file is returned
								__lowerCAmelCase:	str   				=      self.dummy_file_name

						# special case when data_url is a dict
						if isinstance(UpperCamelCase__					,  UpperCamelCase__):
								return self.create_dummy_data_dict(UpperCamelCase__					,  UpperCamelCase__)
						elif isinstance(UpperCamelCase__					,  (list, tuple)):
								return self.create_dummy_data_list(UpperCamelCase__					,  UpperCamelCase__)
						else:
								return self.create_dummy_data_single(UpperCamelCase__					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    Dict					,  *UpperCamelCase__							:    int)->       Dict:



						'''simple docstring'''


						return self.download_and_extract(UpperCamelCase__)

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Optional[Any])->       str:



						'''simple docstring'''


						return self.download_and_extract(UpperCamelCase__)

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    List[Any]					,  *UpperCamelCase__							:    int					,  **UpperCamelCase__							:    str)->       List[str]:



						'''simple docstring'''


						return path

				def 							lowercase_					(     self							:    Optional[Any])->       Any:



						'''simple docstring'''


						return {}

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      {}
						for key, single_urls in data_url.items():
								for download_callback in self.download_callbacks:
										if isinstance(UpperCamelCase__					,  UpperCamelCase__):
												for single_url in single_urls:
														download_callback(UpperCamelCase__)
										else:
												__lowerCAmelCase:	Union[str, Any]   				=      single_urls
												download_callback(UpperCamelCase__)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
								if isinstance(UpperCamelCase__					,  UpperCamelCase__):
										__lowerCAmelCase:	Dict   				=      [os.path.join(UpperCamelCase__					,  urllib.parse.quote_plus(Path(UpperCamelCase__).name)) for x in single_urls]
								else:
										__lowerCAmelCase:	Any   				=      single_urls
										__lowerCAmelCase:	Optional[int]   				=      os.path.join(UpperCamelCase__					,  urllib.parse.quote_plus(Path(UpperCamelCase__).name))
								__lowerCAmelCase:	Dict   				=      value

						# make sure that values are unique
						if all(isinstance(UpperCamelCase__					,  UpperCamelCase__) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
						    dummy_data_dict.values()):
								# append key to value to make its name unique
								__lowerCAmelCase:	Any   				=      {key: value + key for key, value in dummy_data_dict.items()}

						return dummy_data_dict

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    List[Any])->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      []
						# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
						__lowerCAmelCase:	Any   				=      all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"					,  UpperCamelCase__)) for url in data_url)
						__lowerCAmelCase:	str   				=      all(
						    url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed") for url in data_url)
						if data_url and (is_tf_records or is_pubmed_records):
								__lowerCAmelCase:	Optional[int]   				=      [data_url[0]] * len(UpperCamelCase__)
						for single_url in data_url:
								for download_callback in self.download_callbacks:
										download_callback(UpperCamelCase__)
								# we force the name of each key to be the last file / folder name of the url path
								# if the url has arguments, we need to encode them with urllib.parse.quote_plus
								__lowerCAmelCase:	Optional[Any]   				=      os.path.join(UpperCamelCase__					,  urllib.parse.quote_plus(single_url.split("/")[-1]))
								dummy_data_list.append(UpperCamelCase__)
						return dummy_data_list

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Union[str, Any])->       Optional[int]:



						'''simple docstring'''


						for download_callback in self.download_callbacks:
								download_callback(UpperCamelCase__)
						# we force the name of each key to be the last file / folder name of the url path
						# if the url has arguments, we need to encode them with urllib.parse.quote_plus
						__lowerCAmelCase:	List[Any]   				=      os.path.join(UpperCamelCase__					,  urllib.parse.quote_plus(data_url.split("/")[-1]))
						if os.path.exists(UpperCamelCase__) or not self.load_existing_dummy_data:
								return value
						else:
								# Backward compatibility, maybe deprecate at one point.
								# For many datasets with single url calls to dl_manager.download_and_extract,
								# the dummy_data.zip file is actually the zipped downloaded file
								# while now we expected the dummy_data.zip file to be a directory containing
								# the downloaded file.
								return path_to_dummy_data

				def 							lowercase_					(     self							:    List[str])->       Dict:



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Union[str, Any])->       Tuple:



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    str)->       int:



						'''simple docstring'''


						def _iter_archive_members(UpperCamelCase__							:    str):
								# this preserves the order of the members inside the ZIP archive
								__lowerCAmelCase:	Optional[Any]   				=      Path(self.dummy_file).parent
								__lowerCAmelCase:	Optional[int]   				=      path.relative_to(UpperCamelCase__)
								with ZipFile(self.local_path_to_dummy_data) as zip_file:
										__lowerCAmelCase:	Optional[int]   				=      zip_file.namelist()
								for member in members:
										if member.startswith(relative_path.as_posix()):
												yield dummy_parent_path.joinpath(UpperCamelCase__)

						__lowerCAmelCase:	str   				=      Path(UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      _iter_archive_members(UpperCamelCase__) if self.use_local_dummy_data else path.rglob("*")
						for file_path in file_paths:
								if file_path.is_file() and not file_path.name.startswith((".", "__")):
										yield file_path.relative_to(UpperCamelCase__).as_posix(), file_path.open("rb")




				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    str)->       str:



						'''simple docstring'''


						if not isinstance(UpperCamelCase__					,  UpperCamelCase__):
								__lowerCAmelCase:	Dict   				=      [paths]
						for path in paths:
								if os.path.isfile(UpperCamelCase__):
										if os.path.basename(UpperCamelCase__).startswith((".", "__")):
												return
										yield path
								else:
										for dirpath, dirnames, filenames in os.walk(UpperCamelCase__):
												if os.path.basename(UpperCamelCase__).startswith((".", "__")):
														continue
												dirnames.sort()
												for filename in sorted(UpperCamelCase__):
														if filename.startswith((".", "__")):
																continue
														yield os.path.join(UpperCamelCase__					,  UpperCamelCase__)


