
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


_A     =  logging.get_logger(__name__)


_A     =  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
_A     =  {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

_A     =  {
    """RUCAIBox/mvp""": 10_24,
}





class 			_lowerCAmelCase      (			UpperCamelCase_ ):
    _lowercase    	=VOCAB_FILES_NAMES
    _lowercase    	=PRETRAINED_VOCAB_FILES_MAP
    _lowercase    	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowercase    	=['''input_ids''', '''attention_mask''']
    _lowercase    	=MvpTokenizer




    def __init__(      self       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase="replace"       ,       _UpperCamelCase="<s>"       ,       _UpperCamelCase="</s>"       ,       _UpperCamelCase="</s>"       ,       _UpperCamelCase="<s>"       ,       _UpperCamelCase="<unk>"       ,       _UpperCamelCase="<pad>"       ,       _UpperCamelCase="<mask>"       ,       _UpperCamelCase=False       ,       _UpperCamelCase=True       ,       **_UpperCamelCase       ,       )       ->   Optional[Any]:
       super().__init__(
           __A       ,       __A       ,       tokenizer_file=__A       ,       errors=__A       ,       bos_token=__A       ,       eos_token=__A       ,       sep_token=__A       ,       cls_token=__A       ,       unk_token=__A       ,       pad_token=__A       ,       mask_token=__A       ,       add_prefix_space=__A       ,       trim_offsets=__A       ,       **__A       ,       )

       lowerCAmelCase_      			=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
       if pre_tok_state.get("add_prefix_space"       ,       __A  ) != add_prefix_space:
          lowerCAmelCase_      			=			getattr(__A       ,       pre_tok_state.pop("type"  )  )
          lowerCAmelCase_      			=			add_prefix_space
          lowerCAmelCase_      			=			pre_tok_class(**__A  )

       lowerCAmelCase_      			=			add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       lowerCAmelCase_      			=			"post_processor"
       lowerCAmelCase_      			=			getattr(self.backend_tokenizer       ,       __A       ,       __A  )
       if tokenizer_component_instance:
          lowerCAmelCase_      			=			json.loads(tokenizer_component_instance.__getstate__()  )

          # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
          if "sep" in state:
             lowerCAmelCase_      			=			tuple(state["sep"]  )
          if "cls" in state:
             lowerCAmelCase_      			=			tuple(state["cls"]  )

          lowerCAmelCase_      			=			False

          if state.get("add_prefix_space"       ,       __A  ) != add_prefix_space:
             lowerCAmelCase_      			=			add_prefix_space
             lowerCAmelCase_      			=			True

          if state.get("trim_offsets"       ,       __A  ) != trim_offsets:
             lowerCAmelCase_      			=			trim_offsets
             lowerCAmelCase_      			=			True

          if changes_to_apply:
             lowerCAmelCase_      			=			getattr(__A       ,       state.pop("type"  )  )
             lowerCAmelCase_      			=			component_class(**__A  )
             setattr(self.backend_tokenizer       ,       __A       ,       __A  )




    @property
    def   __a		(      self  )       ->   Any:
       if self._mask_token is None:
          if self.verbose:
             logger.error("Using mask_token, but it is not set yet."  )
          return None
       return str(self._mask_token  )




    @mask_token.setter
    def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
       lowerCAmelCase_      			=			AddedToken(__A       ,       lstrip=__A       ,       rstrip=__A  ) if isinstance(__A       ,       __A  ) else value
       lowerCAmelCase_      			=			value




    def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   int:
       lowerCAmelCase_      			=			kwargs.get("is_split_into_words"       ,       __A  )

       if is_split_into_words and not self.add_prefix_space:
          raise ValueError(
              f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."  )

       return super()._batch_encode_plus(*__A       ,       **__A  )




    def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Any:
       lowerCAmelCase_      			=			kwargs.get("is_split_into_words"       ,       __A  )

       if is_split_into_words and not self.add_prefix_space:
          raise ValueError(
              f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."  )

       return super()._encode_plus(*__A       ,       **__A  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   int:
       lowerCAmelCase_      			=			self._tokenizer.model.save(__A       ,       name=__A  )
       return tuple(__A  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   int:
       lowerCAmelCase_      			=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
          return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Optional[int]:
       lowerCAmelCase_      			=			[self.sep_token_id]
       lowerCAmelCase_      			=			[self.cls_token_id]

       if token_ids_a is None:
          return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]





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


_A     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_A     =  10
_A     =  2_56






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			return {t for t in NON_ALPHA.split(__lowerCAmelCase       ) if len(t.strip()       ) > 0}





class 			_lowerCAmelCase      :




				def __init__(      self       ,       *,
				    _UpperCamelCase = 0.85       ,       )       ->   Dict:
							lowerCAmelCase_      			=			duplication_jaccard_threshold
							lowerCAmelCase_      			=			NUM_PERM
							lowerCAmelCase_      			=			MinHashLSH(threshold=self._duplication_jaccard_threshold       ,       num_perm=self._num_perm  )

							lowerCAmelCase_      			=			defaultdict(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self._index.query(_UpperCamelCase  )
							if code_key in self._index.keys:
										print(f"""Duplicate key {code_key}"""  )
										return

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




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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.get_duplicate_clusters()
							with open(_UpperCamelCase       ,       "w"  ) as f:
										json.dump(_UpperCamelCase       ,       _UpperCamelCase  )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			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__       (       __lowerCAmelCase						:						Type[Dataset]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase       )

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

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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_A     =  None






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			for elementa in cluster:
						lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
						for elementa in extremes:
									lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
									if jaccard_similarity(__lowerCAmelCase					,							__lowerCAmelCase       ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									lowerCAmelCase_      			=			1
									extremes.append(__lowerCAmelCase       )
			return extremes






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			global _shared_dataset
			lowerCAmelCase_      			=			dataset
			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			partial(_find_cluster_extremes_shared					,							jaccard_threshold=__lowerCAmelCase       )
			with mp.Pool() as pool:
						for extremes in tqdm(
						    pool.imap_unordered(
						        __lowerCAmelCase					,							__lowerCAmelCase					,							)					,							total=len(__lowerCAmelCase       )					,							):
									extremes_list.append(__lowerCAmelCase       )
			return extremes_list






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float = 0.85       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			make_duplicate_clusters(__lowerCAmelCase					,							__lowerCAmelCase       )
			lowerCAmelCase_      			=			{x["base_index"] for cluster in duplicate_clusters for x in cluster}
			lowerCAmelCase_      			=			{}
			lowerCAmelCase_      			=			find_extremes(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			for extremes in extremes_clusters:
						for element in extremes:
									lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			duplicate_indices - set(extreme_dict.keys()       )
			lowerCAmelCase_      			=			dataset.filter(lambda __lowerCAmelCase					,							__lowerCAmelCase       : idx not in remove_indices					,							with_indices=__lowerCAmelCase       )

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

			print(F"""Original dataset size: {len(__lowerCAmelCase       )}"""       )
			print(F"""Number of duplicate clusters: {len(__lowerCAmelCase       )}"""       )
			print(F"""Files in duplicate cluster: {len(__lowerCAmelCase       )}"""       )
			print(F"""Unique files in duplicate cluster: {len(__lowerCAmelCase       )}"""       )
			print(F"""Filtered dataset size: {len(__lowerCAmelCase       )}"""       )

			return ds_filter, duplicate_clusters



