
from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_A     =  logging.get_logger(__name__)

# General docstring
_A     =  "RegNetConfig"

# Base docstring
_A     =  "facebook/regnet-y-040"
_A     =  [1, 10_88, 7, 7]

# Image classification docstring
_A     =  "facebook/regnet-y-040"
_A     =  "tabby, tabby cat"

_A     =  [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = 3       ,       _UpperCamelCase = 1       ,       _UpperCamelCase = 1       ,       _UpperCamelCase = "relu"       ,       **_UpperCamelCase       ,       )       ->   Optional[Any]:
       super().__init__(**A_  )
       # The padding and conv has been verified in
       # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
       lowerCAmelCase_      			=			tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2  )
       lowerCAmelCase_      			=			tf.keras.layers.ConvaD(
           filters=A_       ,       kernel_size=A_       ,       strides=A_       ,       padding="VALID"       ,       groups=A_       ,       use_bias=A_       ,       name="convolution"       ,       )
       lowerCAmelCase_      			=			tf.keras.layers.BatchNormalization(epsilon=1e-5       ,       momentum=0.9       ,       name="normalization"  )
       lowerCAmelCase_      			=			ACTaFN[activation] if activation is not None else tf.identity




    def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
       lowerCAmelCase_      			=			self.convolution(self.padding(A_  )  )
       lowerCAmelCase_      			=			self.normalization(A_  )
       lowerCAmelCase_      			=			self.activation(A_  )
       return hidden_state





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   Tuple:
       super().__init__(**A_  )
       lowerCAmelCase_      			=			config.num_channels
       lowerCAmelCase_      			=			TFRegNetConvLayer(
           out_channels=config.embedding_size       ,       kernel_size=3       ,       stride=2       ,       activation=config.hidden_act       ,       name="embedder"       ,       )




    def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
       lowerCAmelCase_      			=			shape_list(A_  )[1]
       if tf.executing_eagerly() and num_channels != self.num_channels:
          raise ValueError(
              "Make sure that the channel dimension of the pixel values match with the one set in the configuration."  )

       # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
       # So change the input format from `NCHW` to `NHWC`.
       # shape = (batch_size, in_height, in_width, in_channels=num_channels)
       lowerCAmelCase_      			=			tf.transpose(A_       ,       perm=(0, 2, 3, 1)  )
       lowerCAmelCase_      			=			self.embedder(A_  )
       return hidden_state





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = 2       ,       **_UpperCamelCase  )       ->   Dict:
       super().__init__(**A_  )
       lowerCAmelCase_      			=			tf.keras.layers.ConvaD(
           filters=A_       ,       kernel_size=1       ,       strides=A_       ,       use_bias=A_       ,       name="convolution"  )
       lowerCAmelCase_      			=			tf.keras.layers.BatchNormalization(epsilon=1e-5       ,       momentum=0.9       ,       name="normalization"  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = False  )       ->   tf.Tensor:
       return self.normalization(self.convolution(A_  )       ,       training=A_  )





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   Any:
       super().__init__(**A_  )
       lowerCAmelCase_      			=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_       ,       name="pooler"  )
       lowerCAmelCase_      			=			[
           tf.keras.layers.ConvaD(filters=A_       ,       kernel_size=1       ,       activation="relu"       ,       name="attention.0"  ),
           tf.keras.layers.ConvaD(filters=A_       ,       kernel_size=1       ,       activation="sigmoid"       ,       name="attention.2"  ),
       ]




    def   __a		(      self       ,       _UpperCamelCase  )       ->   List[str]:
       lowerCAmelCase_      			=			self.pooler(A_  )
       for layer_module in self.attention:
          lowerCAmelCase_      			=			layer_module(A_  )
       lowerCAmelCase_      			=			hidden_state * pooled
       return hidden_state





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = 1       ,       **_UpperCamelCase  )       ->   Dict:
       super().__init__(**A_  )
       lowerCAmelCase_      			=			in_channels != out_channels or stride != 1
       lowerCAmelCase_      			=			max(1       ,       out_channels // config.groups_width  )
       lowerCAmelCase_      			=			(
           TFRegNetShortCut(A_       ,       stride=A_       ,       name="shortcut"  )
           if should_apply_shortcut
           else tf.keras.layers.Activation("linear"       ,       name="shortcut"  )
       )
       # `self.layers` instead of `self.layer` because that is a reserved argument.
       lowerCAmelCase_      			=			[
           TFRegNetConvLayer(A_       ,       kernel_size=1       ,       activation=config.hidden_act       ,       name="layer.0"  ),
           TFRegNetConvLayer(
               A_       ,       stride=A_       ,       groups=A_       ,       activation=config.hidden_act       ,       name="layer.1"  ),
           TFRegNetConvLayer(A_       ,       kernel_size=1       ,       activation=A_       ,       name="layer.2"  ),
       ]
       lowerCAmelCase_      			=			ACTaFN[config.hidden_act]




    def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
       lowerCAmelCase_      			=			hidden_state
       for layer_module in self.layers:
          lowerCAmelCase_      			=			layer_module(A_  )
       lowerCAmelCase_      			=			self.shortcut(A_  )
       hidden_state += residual
       lowerCAmelCase_      			=			self.activation(A_  )
       return hidden_state





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = 1       ,       **_UpperCamelCase  )       ->   Optional[int]:
       super().__init__(**A_  )
       lowerCAmelCase_      			=			in_channels != out_channels or stride != 1
       lowerCAmelCase_      			=			max(1       ,       out_channels // config.groups_width  )
       lowerCAmelCase_      			=			(
           TFRegNetShortCut(A_       ,       stride=A_       ,       name="shortcut"  )
           if should_apply_shortcut
           else tf.keras.layers.Activation("linear"       ,       name="shortcut"  )
       )
       lowerCAmelCase_      			=			[
           TFRegNetConvLayer(A_       ,       kernel_size=1       ,       activation=config.hidden_act       ,       name="layer.0"  ),
           TFRegNetConvLayer(
               A_       ,       stride=A_       ,       groups=A_       ,       activation=config.hidden_act       ,       name="layer.1"  ),
           TFRegNetSELayer(A_       ,       reduced_channels=int(round(in_channels / 4  )  )       ,       name="layer.2"  ),
           TFRegNetConvLayer(A_       ,       kernel_size=1       ,       activation=A_       ,       name="layer.3"  ),
       ]
       lowerCAmelCase_      			=			ACTaFN[config.hidden_act]




    def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
       lowerCAmelCase_      			=			hidden_state
       for layer_module in self.layers:
          lowerCAmelCase_      			=			layer_module(A_  )
       lowerCAmelCase_      			=			self.shortcut(A_  )
       hidden_state += residual
       lowerCAmelCase_      			=			self.activation(A_  )
       return hidden_state





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = 2       ,       _UpperCamelCase = 2       ,       **_UpperCamelCase  )       ->   Any:
       super().__init__(**A_  )

       lowerCAmelCase_      			=			TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
       lowerCAmelCase_      			=			[
           # downsampling is done in the first layer with stride of 2
           layer(A_       ,       A_       ,       A_       ,       stride=A_       ,       name="layers.0"  ),
           *[layer(A_       ,       A_       ,       A_       ,       name=f"""layers.{i+1}"""  ) for i in range(depth - 1  )],
       ]




    def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
       for layer_module in self.layers:
          lowerCAmelCase_      			=			layer_module(A_  )
       return hidden_state





class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):




    def __init__(      self       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   Dict:
       super().__init__(**A_  )
       lowerCAmelCase_      			=			[]
       # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
       self.stages.append(
           TFRegNetStage(
               A_       ,       config.embedding_size       ,       config.hidden_sizes[0]       ,       stride=2 if config.downsample_in_first_stage else 1       ,       depth=config.depths[0]       ,       name="stages.0"       ,       )  )
       lowerCAmelCase_      			=			zip(config.hidden_sizes       ,       config.hidden_sizes[1:]  )
       for i, ((in_channels, out_channels), depth) in enumerate(zip(A_       ,       config.depths[1:]  )  ):
          self.stages.append(TFRegNetStage(A_       ,       A_       ,       A_       ,       depth=A_       ,       name=f"""stages.{i+1}"""  )  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = False       ,       _UpperCamelCase = True  )       ->   TFBaseModelOutputWithNoAttention:
       lowerCAmelCase_      			=			() if output_hidden_states else None

       for stage_module in self.stages:
          if output_hidden_states:
             lowerCAmelCase_      			=			hidden_states + (hidden_state,)

          lowerCAmelCase_      			=			stage_module(A_  )

       if output_hidden_states:
          lowerCAmelCase_      			=			hidden_states + (hidden_state,)

       if not return_dict:
          return tuple(v for v in [hidden_state, hidden_states] if v is not None  )

       return TFBaseModelOutputWithNoAttention(last_hidden_state=A_       ,       hidden_states=A_  )





@keras_serializable
class 			_lowerCAmelCase      (			tf.keras.layers.Layer ):
    _lowercase    	=RegNetConfig




    def __init__(      self       ,       _UpperCamelCase       ,       **_UpperCamelCase  )       ->   Tuple:
       super().__init__(**A_  )
       lowerCAmelCase_      			=			config
       lowerCAmelCase_      			=			TFRegNetEmbeddings(A_       ,       name="embedder"  )
       lowerCAmelCase_      			=			TFRegNetEncoder(A_       ,       name="encoder"  )
       lowerCAmelCase_      			=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_       ,       name="pooler"  )




    @unpack_inputs
    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False       ,       )       ->   TFBaseModelOutputWithPoolingAndNoAttention:
       lowerCAmelCase_      			=			(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCAmelCase_      			=			return_dict if return_dict is not None else self.config.use_return_dict

       lowerCAmelCase_      			=			self.embedder(A_       ,       training=A_  )

       lowerCAmelCase_      			=			self.encoder(
           A_       ,       output_hidden_states=A_       ,       return_dict=A_       ,       training=A_  )

       lowerCAmelCase_      			=			encoder_outputs[0]
       lowerCAmelCase_      			=			self.pooler(A_  )

       # Change to NCHW output format have uniformity in the modules
       lowerCAmelCase_      			=			tf.transpose(A_       ,       perm=(0, 3, 1, 2)  )
       lowerCAmelCase_      			=			tf.transpose(A_       ,       perm=(0, 3, 1, 2)  )

       # Change the other hidden state outputs to NCHW as well
       if output_hidden_states:
          lowerCAmelCase_      			=			tuple([tf.transpose(A_       ,       perm=(0, 3, 1, 2)  ) for h in encoder_outputs[1]]  )

       if not return_dict:
          return (last_hidden_state, pooled_output) + encoder_outputs[1:]

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=A_       ,       pooler_output=A_       ,       hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states       ,       )





class 			_lowerCAmelCase      (			_SCREAMING_SNAKE_CASE ):
    _lowercase    	=RegNetConfig
    _lowercase    	="regnet"
    _lowercase    	="pixel_values"




    @property
    def   __a		(      self  )       ->   int:
       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)       ,       dtype=tf.floataa  )}


_A     =  R"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

_A     =  R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__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 RegNet model outputting raw features without any specific head on top.'''      ,   _SCREAMING_SNAKE_CASE      ,   )
class 			_lowerCAmelCase      (			_SCREAMING_SNAKE_CASE ):




    def __init__(      self       ,       _UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Any:
       super().__init__(A_       ,       *A_       ,       **A_  )
       lowerCAmelCase_      			=			TFRegNetMainLayer(A_       ,       name="regnet"  )




    @unpack_inputs
    @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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase=False       ,       )       ->   Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
       lowerCAmelCase_      			=			(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCAmelCase_      			=			return_dict if return_dict is not None else self.config.use_return_dict

       lowerCAmelCase_      			=			self.regnet(
           pixel_values=A_       ,       output_hidden_states=A_       ,       return_dict=A_       ,       training=A_       ,       )
       if not return_dict:
          return (outputs[0],) + outputs[1:]

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=outputs.last_hidden_state       ,       pooler_output=outputs.pooler_output       ,       hidden_states=outputs.hidden_states       ,       )





@add_start_docstrings(
    '''\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '''      ,   _SCREAMING_SNAKE_CASE      ,   )
class 			_lowerCAmelCase      (			_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE ):




    def __init__(      self       ,       _UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
       super().__init__(A_       ,       *A_       ,       **A_  )
       lowerCAmelCase_      			=			config.num_labels
       lowerCAmelCase_      			=			TFRegNetMainLayer(A_       ,       name="regnet"  )
       # classification head
       lowerCAmelCase_      			=			[
           tf.keras.layers.Flatten(),
           tf.keras.layers.Dense(config.num_labels       ,       name="classifier.1"  ) if config.num_labels > 0 else tf.identity,
       ]




    @unpack_inputs
    @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   __a		(      self       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase=False       ,       )       ->   Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
       lowerCAmelCase_      			=			(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCAmelCase_      			=			return_dict if return_dict is not None else self.config.use_return_dict

       lowerCAmelCase_      			=			self.regnet(
           A_       ,       output_hidden_states=A_       ,       return_dict=A_       ,       training=A_  )

       lowerCAmelCase_      			=			outputs.pooler_output if return_dict else outputs[1]

       lowerCAmelCase_      			=			self.classifier[0](A_  )
       lowerCAmelCase_      			=			self.classifier[1](A_  )

       lowerCAmelCase_      			=			None if labels is None else self.hf_compute_loss(labels=A_       ,       logits=A_  )

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

       return TFSequenceClassifierOutput(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     =  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



