

from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


a_						     =    logging.get_logger(__name__)


# General docstring
a_						     =    'MobileNetV1Config'

# Base docstring
a_						     =    'google/mobilenet_v1_1.0_224'
a_						     =    [1, 1024, 7, 7]

# Image classification docstring
a_						     =    'google/mobilenet_v1_1.0_224'
a_						     =    'tabby, tabby cat'


a_						     =    [
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]

def  lowerCamelCase__ (					_a							,			_a							,			_a=None):
	SCREAMING_SNAKE_CASE   :      str				     =       {}

	if isinstance(_a							,			_a):
		SCREAMING_SNAKE_CASE   :      str				     =       model.mobilenet_va
	else:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model

	SCREAMING_SNAKE_CASE   :      Dict				     =       "MobilenetV1/Conv2d_0/"
	SCREAMING_SNAKE_CASE   :      Any				     =       backbone.conv_stem.convolution.weight
	SCREAMING_SNAKE_CASE   :      Tuple				     =       backbone.conv_stem.normalization.bias
	SCREAMING_SNAKE_CASE   :      Tuple				     =       backbone.conv_stem.normalization.weight
	SCREAMING_SNAKE_CASE   :      Dict				     =       backbone.conv_stem.normalization.running_mean
	SCREAMING_SNAKE_CASE   :      List[str]				     =       backbone.conv_stem.normalization.running_var

	for i in range(13):
		SCREAMING_SNAKE_CASE   :      Tuple				     =       i + 1
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       i * 2

		SCREAMING_SNAKE_CASE   :      Dict				     =       backbone.layer[pt_index]
		SCREAMING_SNAKE_CASE   :      Any				     =       f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
		SCREAMING_SNAKE_CASE   :      str				     =       pointer.convolution.weight
		SCREAMING_SNAKE_CASE   :      Dict				     =       pointer.normalization.bias
		SCREAMING_SNAKE_CASE   :      int				     =       pointer.normalization.weight
		SCREAMING_SNAKE_CASE   :      List[str]				     =       pointer.normalization.running_mean
		SCREAMING_SNAKE_CASE   :      int				     =       pointer.normalization.running_var

		SCREAMING_SNAKE_CASE   :      str				     =       backbone.layer[pt_index + 1]
		SCREAMING_SNAKE_CASE   :      Tuple				     =       f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pointer.convolution.weight
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pointer.normalization.bias
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pointer.normalization.weight
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pointer.normalization.running_mean
		SCREAMING_SNAKE_CASE   :      Tuple				     =       pointer.normalization.running_var

	if isinstance(_a							,			_a):
		SCREAMING_SNAKE_CASE   :      Any				     =       "MobilenetV1/Logits/Conv2d_1c_1x1/"
		SCREAMING_SNAKE_CASE   :      str				     =       model.classifier.weight
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model.classifier.bias

	return tf_to_pt_map

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	try:
		import numpy as np
		import tensorflow as tf
	except ImportError:
		logger.error(
		    "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
		    "https://www.tensorflow.org/install/ for installation instructions.")
		raise

	# Load weights from TF model
	SCREAMING_SNAKE_CASE   :      Dict				     =       tf.train.list_variables(_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       {}
	for name, shape in init_vars:
		logger.info(f"Loading TF weight {name} with shape {shape}")
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tf.train.load_variable(_a							,			_a)
		SCREAMING_SNAKE_CASE   :      Any				     =       array

	# Build TF to PyTorch weights loading map
	SCREAMING_SNAKE_CASE   :      Any				     =       _build_tf_to_pytorch_map(_a							,			_a							,			_a)

	for name, pointer in tf_to_pt_map.items():
		logger.info(f"Importing {name}")
		if name not in tf_weights:
			logger.info(f"{name} not in tf pre-trained weights, skipping")
			continue

		SCREAMING_SNAKE_CASE   :      List[str]				     =       tf_weights[name]

		if "depthwise_weights" in name:
			logger.info("Transposing depthwise")
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       np.transpose(_a							,			(2, 3, 0, 1))
		elif "weights" in name:
			logger.info("Transposing")
			if len(pointer.shape) == 2:  # copying into linear layer
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       array.squeeze().transpose()
			else:
				SCREAMING_SNAKE_CASE   :      List[str]				     =       np.transpose(_a							,			(3, 2, 0, 1))

		if pointer.shape != array.shape:
			raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")

		logger.info(f"Initialize PyTorch weight {name} {array.shape}")
		SCREAMING_SNAKE_CASE   :      int				     =       torch.from_numpy(_a)

		tf_weights.pop(_a							,			_a)
		tf_weights.pop(name + "/RMSProp"							,			_a)
		tf_weights.pop(name + "/RMSProp_1"							,			_a)
		tf_weights.pop(name + "/ExponentialMovingAverage"							,			_a)

	logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}")
	return model

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       features.shape[-2:]
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       conv_layer.stride
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      int				     =       conv_layer.kernel_size

	if in_height % stride_height == 0:
		SCREAMING_SNAKE_CASE   :      str				     =       max(kernel_height - stride_height							,			0)
	else:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(kernel_height - (in_height % stride_height)							,			0)

	if in_width % stride_width == 0:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       max(kernel_width - stride_width							,			0)
	else:
		SCREAMING_SNAKE_CASE   :      str				     =       max(kernel_width - (in_width % stride_width)							,			0)

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       pad_along_width // 2
	SCREAMING_SNAKE_CASE   :      int				     =       pad_along_width - pad_left
	SCREAMING_SNAKE_CASE   :      List[str]				     =       pad_along_height // 2
	SCREAMING_SNAKE_CASE   :      List[str]				     =       pad_along_height - pad_top

	SCREAMING_SNAKE_CASE   :      int				     =       (pad_left, pad_right, pad_top, pad_bottom)
	return nn.functional.pad(_a							,			_a							,			"constant"							,			0.0)






class _UpperCamelCase							(							nn.Module   ):
			'''simple docstring'''







			def __init__(			self							: Tuple      ,      a							: MobileNetVaConfig      ,      a							: int      ,      a							: int      ,      a							: int      ,      a							: Optional[int] = 1      ,      a							: Optional[int] = 1      ,      a							: bool = False      ,      a							: Optional[bool] = True      ,      a							: Optional[bool or str] = True      ,      )							->       None:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       config

				if in_channels % groups != 0:
					raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups."	)
				if out_channels % groups != 0:
					raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups."	)

				SCREAMING_SNAKE_CASE   :      Any				     =       0 if config.tf_padding else int((kernel_size - 1) / 2	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       nn.Convad(
				    in_channels=a      ,      out_channels=a      ,      kernel_size=a      ,      stride=a      ,      padding=a      ,      groups=a      ,      bias=a      ,      padding_mode="zeros"      ,      )

				if use_normalization:
					SCREAMING_SNAKE_CASE   :      str				     =       nn.BatchNormad(
					    num_features=a      ,      eps=config.layer_norm_eps      ,      momentum=0.9997      ,      affine=a      ,      track_running_stats=a      ,      )
				else:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       None

				if use_activation:
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Tuple				     =       ACTaFN[use_activation]
					elif isinstance(config.hidden_act      ,      a	):
						SCREAMING_SNAKE_CASE   :      int				     =       ACTaFN[config.hidden_act]
					else:
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       config.hidden_act
				else:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None






			def 							__UpperCamelCase (			self							: Dict      ,      a							: torch.Tensor	)							->       torch.Tensor:

				"""simple docstring"""

				if self.config.tf_padding:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       apply_tf_padding(a      ,      self.convolution	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.convolution(a	)
				if self.normalization is not None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.normalization(a	)
				if self.activation is not None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.activation(a	)
				return features






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







			lowerCamelCase__           =MobileNetVaConfig
			lowerCamelCase__           =load_tf_weights_in_mobilenet_va
			lowerCamelCase__           ='mobilenet_v1'
			lowerCamelCase__           ='pixel_values'
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Union[nn.Linear, nn.Convad]	)							->       None:

				"""simple docstring"""

				if isinstance(a      ,      (nn.Linear, nn.Convad)	):
					module.weight.data.normal_(mean=0.0      ,      std=self.config.initializer_range	)
					if module.bias is not None:
						module.bias.data.zero_()
				elif isinstance(a      ,      nn.BatchNormad	):
					module.bias.data.zero_()
					module.weight.data.fill_(1.0	)

a_						     =    r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

a_						     =    r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'							,	__A							,	)
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      a							: MobileNetVaConfig      ,      a							: bool = True	)							->       Optional[int]:

				"""simple docstring"""

				super().__init__(a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       config

				SCREAMING_SNAKE_CASE   :      str				     =       32
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(int(depth * config.depth_multiplier	)      ,      config.min_depth	)

				SCREAMING_SNAKE_CASE   :      int				     =       MobileNetVaConvLayer(
				    a      ,      in_channels=config.num_channels      ,      out_channels=a      ,      kernel_size=3      ,      stride=2      ,      )

				SCREAMING_SNAKE_CASE   :      List[str]				     =       [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

				SCREAMING_SNAKE_CASE   :      str				     =       nn.ModuleList()
				for i in range(13	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       out_channels

					if strides[i] == 2 or i == 0:
						depth *= 2
						SCREAMING_SNAKE_CASE   :      int				     =       max(int(depth * config.depth_multiplier	)      ,      config.min_depth	)

					self.layer.append(
					    MobileNetVaConvLayer(
					        a      ,      in_channels=a      ,      out_channels=a      ,      kernel_size=3      ,      stride=strides[i]      ,      groups=a      ,      )	)

					self.layer.append(
					    MobileNetVaConvLayer(
					        a      ,      in_channels=a      ,      out_channels=a      ,      kernel_size=1      ,      )	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       nn.AdaptiveAvgPoolad((1, 1)	) if add_pooling_layer else None

				# Initialize weights and apply final processing
				self.post_init()
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: str	)							->       Optional[Any]:

				"""simple docstring"""

				raise NotImplementedError






			@add_start_docstrings_to_model_forward(a	)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC      ,      output_type=a      ,      config_class=_CONFIG_FOR_DOC      ,      modality="vision"      ,      expected_output=_EXPECTED_OUTPUT_SHAPE      ,      )
			def 							__UpperCamelCase (			self							: Any      ,      a							: Optional[torch.Tensor] = None      ,      a							: Optional[bool] = None      ,      a							: Optional[bool] = None      ,      )							->       Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				SCREAMING_SNAKE_CASE   :      Dict				     =       return_dict if return_dict is not None else self.config.use_return_dict

				if pixel_values is None:
					raise ValueError("You have to specify pixel_values"	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.conv_stem(a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       () if output_hidden_states else None

				for i, layer_module in enumerate(self.layer	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       layer_module(a	)

					if output_hidden_states:
						SCREAMING_SNAKE_CASE   :      Dict				     =       all_hidden_states + (hidden_states,)

				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_states

				if self.pooler is not None:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.flatten(self.pooler(a	)      ,      start_dim=1	)
				else:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       None

				if not return_dict:
					return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None	)

				return BaseModelOutputWithPoolingAndNoAttention(
				    last_hidden_state=a      ,      pooler_output=a      ,      hidden_states=a      ,      )






@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '							,	__A							,	)
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Dict      ,      a							: MobileNetVaConfig	)							->       None:

				"""simple docstring"""

				super().__init__(a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       config.num_labels
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MobileNetVaModel(a	)

				SCREAMING_SNAKE_CASE   :      str				     =       self.mobilenet_va.layer[-1].convolution.out_channels

				# Classifier head
				SCREAMING_SNAKE_CASE   :      int				     =       nn.Dropout(config.classifier_dropout_prob      ,      inplace=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       nn.Linear(a      ,      config.num_labels	) if config.num_labels > 0 else nn.Identity()

				# Initialize weights and apply final processing
				self.post_init()






			@add_start_docstrings_to_model_forward(a	)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT      ,      output_type=a      ,      config_class=_CONFIG_FOR_DOC      ,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,      )
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[torch.Tensor] = None      ,      a							: Optional[bool] = None      ,      a							: Optional[torch.Tensor] = None      ,      a							: Optional[bool] = None      ,      )							->       Union[tuple, ImageClassifierOutputWithNoAttention]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       return_dict if return_dict is not None else self.config.use_return_dict

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.mobilenet_va(a      ,      output_hidden_states=a      ,      return_dict=a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       outputs.pooler_output if return_dict else outputs[1]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.classifier(self.dropout(a	)	)

				SCREAMING_SNAKE_CASE   :      int				     =       None
				if labels is not None:
					if self.config.problem_type is None:
						if self.num_labels == 1:
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "regression"
						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
							SCREAMING_SNAKE_CASE   :      int				     =       "single_label_classification"
						else:
							SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "multi_label_classification"

					if self.config.problem_type == "regression":
						SCREAMING_SNAKE_CASE   :      Any				     =       MSELoss()
						if self.num_labels == 1:
							SCREAMING_SNAKE_CASE   :      List[Any]				     =       loss_fct(logits.squeeze()      ,      labels.squeeze()	)
						else:
							SCREAMING_SNAKE_CASE   :      int				     =       loss_fct(a      ,      a	)
					elif self.config.problem_type == "single_label_classification":
						SCREAMING_SNAKE_CASE   :      Dict				     =       CrossEntropyLoss()
						SCREAMING_SNAKE_CASE   :      List[str]				     =       loss_fct(logits.view(-1      ,      self.num_labels	)      ,      labels.view(-1	)	)
					elif self.config.problem_type == "multi_label_classification":
						SCREAMING_SNAKE_CASE   :      int				     =       BCEWithLogitsLoss()
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       loss_fct(a      ,      a	)

				if not return_dict:
					SCREAMING_SNAKE_CASE   :      Dict				     =       (logits,) + outputs[2:]
					return ((loss,) + output) if loss is not None else output

				return ImageClassifierOutputWithNoAttention(
				    loss=a      ,      logits=a      ,      hidden_states=outputs.hidden_states      ,      )


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