

import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =['image_processor', 'tokenizer']
			lowerCamelCase__           ='OwlViTImageProcessor'
			lowerCamelCase__           =('CLIPTokenizer', 'CLIPTokenizerFast')
			def __init__(			self							: Optional[Any]      ,      a							: int=None      ,      a							: Optional[Any]=None      ,      **a							: str	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."      ,      a      ,      )
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       kwargs.pop("feature_extractor"	)

				SCREAMING_SNAKE_CASE   :      int				     =       image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."	)
				if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."	)

				super().__init__(a      ,      a	)
			def __call__(			self							: Optional[Any]      ,      a							: Optional[Any]=None      ,      a							: List[Any]=None      ,      a							: Tuple=None      ,      a							: int="max_length"      ,      a							: str="np"      ,      **a							: Any	)							->       Dict:

				"""simple docstring"""

				if text is None and query_images is None and images is None:
					raise ValueError(
					    "You have to specify at least one text or query image or image. All three cannot be none."	)

				if text is not None:
					if isinstance(a      ,      a	) or (isinstance(a      ,      a	) and not isinstance(text[0]      ,      a	)):
						SCREAMING_SNAKE_CASE   :      Tuple				     =       [self.tokenizer(a      ,      padding=a      ,      return_tensors=a      ,      **a	)]

					elif isinstance(a      ,      a	) and isinstance(text[0]      ,      a	):
						SCREAMING_SNAKE_CASE   :      int				     =       []

						# Maximum number of queries across batch
						SCREAMING_SNAKE_CASE   :      int				     =       max([len(a	) for t in text]	)

						# Pad all batch samples to max number of text queries
						for t in text:
							if len(a	) != max_num_queries:
								SCREAMING_SNAKE_CASE   :      Dict				     =       t + [" "] * (max_num_queries - len(a	))

							SCREAMING_SNAKE_CASE   :      List[str]				     =       self.tokenizer(a      ,      padding=a      ,      return_tensors=a      ,      **a	)
							encodings.append(a	)
					else:
						raise TypeError("Input text should be a string, a list of strings or a nested list of strings"	)

					if return_tensors == "np":
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       np.concatenate([encoding["input_ids"] for encoding in encodings]      ,      axis=0	)
						SCREAMING_SNAKE_CASE   :      Dict				     =       np.concatenate([encoding["attention_mask"] for encoding in encodings]      ,      axis=0	)

					elif return_tensors == "jax" and is_flax_available():
						import jax.numpy as jnp

						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       jnp.concatenate([encoding["input_ids"] for encoding in encodings]      ,      axis=0	)
						SCREAMING_SNAKE_CASE   :      int				     =       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]      ,      axis=0	)

					elif return_tensors == "pt" and is_torch_available():
						import torch

						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.cat([encoding["input_ids"] for encoding in encodings]      ,      dim=0	)
						SCREAMING_SNAKE_CASE   :      Any				     =       torch.cat([encoding["attention_mask"] for encoding in encodings]      ,      dim=0	)

					elif return_tensors == "tf" and is_tf_available():
						import tensorflow as tf

						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tf.stack([encoding["input_ids"] for encoding in encodings]      ,      axis=0	)
						SCREAMING_SNAKE_CASE   :      int				     =       tf.stack([encoding["attention_mask"] for encoding in encodings]      ,      axis=0	)

					else:
						raise ValueError("Target return tensor type could not be returned"	)

					SCREAMING_SNAKE_CASE   :      Any				     =       BatchEncoding()
					SCREAMING_SNAKE_CASE   :      str				     =       input_ids
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       attention_mask

				if query_images is not None:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       BatchEncoding()
					SCREAMING_SNAKE_CASE   :      Any				     =       self.image_processor(
					    a      ,      return_tensors=a      ,      **a	).pixel_values
					SCREAMING_SNAKE_CASE   :      int				     =       query_pixel_values

				if images is not None:
					SCREAMING_SNAKE_CASE   :      str				     =       self.image_processor(a      ,      return_tensors=a      ,      **a	)

				if text is not None and images is not None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       image_features.pixel_values
					return encoding
				elif query_images is not None and images is not None:
					SCREAMING_SNAKE_CASE   :      int				     =       image_features.pixel_values
					return encoding
				elif text is not None or query_images is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**a	)      ,      tensor_type=a	)
			def 							__UpperCamelCase (			self							: Any      ,      *a							: List[Any]      ,      **a							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

				return self.image_processor.post_process(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      *a							: List[str]      ,      **a							: int	)							->       List[Any]:

				"""simple docstring"""

				return self.image_processor.post_process_object_detection(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      *a							: Any      ,      **a							: Tuple	)							->       int:

				"""simple docstring"""

				return self.image_processor.post_process_image_guided_detection(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      *a							: Dict      ,      **a							: List[Any]	)							->       Any:

				"""simple docstring"""

				return self.tokenizer.batch_decode(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: str      ,      *a							: Optional[int]      ,      **a							: Union[str, Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				return self.tokenizer.decode(*a      ,      **a	)
			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Any:

				"""simple docstring"""

				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,      a      ,      )
				return self.image_processor_class






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

				"""simple docstring"""

				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,      a      ,      )
				return self.image_processor


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_						     =    256

def  lowerCamelCase__ (					_a):
	if len(_a) < MIN_NUM_TOKENS:
		return None
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MinHash(num_perm=_a)
	for token in set(_a):
		min_hash.update(token.encode())
	return min_hash

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






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      *,
			    a							: float = 0.85      ,      )							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       duplication_jaccard_threshold
				SCREAMING_SNAKE_CASE   :      Any				     =       NUM_PERM
				SCREAMING_SNAKE_CASE   :      int				     =       MinHashLSH(threshold=self._duplication_jaccard_threshold      ,      num_perm=self._num_perm	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       defaultdict(a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Tuple      ,      a							: MinHash	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       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 							__UpperCamelCase (			self							: Optional[int]	)							->       List[List[Dict]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
				for base, duplicates in self._duplicate_clusters.items():
					SCREAMING_SNAKE_CASE   :      int				     =       [base] + list(a	)
					# reformat the cluster to be a list of dict
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
					duplicate_clusters.append(a	)
				return duplicate_clusters






			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: List[str]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_duplicate_clusters()
				with open(a      ,      "w"	) as f:
					json.dump(a      ,      a	)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       element
	SCREAMING_SNAKE_CASE   :      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__ (					_a):
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,			ThreadedIterator(_a							,			max_queue_size=10000)							,			chunksize=100							,			):
			if data is not None:
				yield data

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DuplicationIndex(duplication_jaccard_threshold=_a)

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

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

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       get_tokens(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       get_tokens(_a)
	return len(tokensa & tokensa) / len(tokensa | tokensa)


a_						     =    None

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
	for elementa in cluster:
		SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(_a							,			_a) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1
			extremes.append(_a)
	return extremes

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	global _shared_dataset
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dataset
	SCREAMING_SNAKE_CASE   :      str				     =       []
	SCREAMING_SNAKE_CASE   :      Dict				     =       partial(_find_cluster_extremes_shared							,			jaccard_threshold=_a)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        _a							,			_a							,			)							,			total=len(_a)							,			):
			extremes_list.append(_a)
	return extremes_list


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

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			SCREAMING_SNAKE_CASE   :      Dict				     =       element["base_index"] in extreme_dict
			if element["is_extreme"]:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       extreme_dict[element["base_index"]]["copies"]

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

	return ds_filter, duplicate_clusters