



'''simple docstring'''




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


lowerCamelCase							:str				    =					re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
lowerCamelCase							:Optional[Any]				    =					1_0
lowerCamelCase							:Dict				    =					2_5_6



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





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



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





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







class 	_lowerCAmelCase							:


	def __init__(self       ,      *,
	    lowercase = 0.85       ,      ):
				A_	:						List[str]	=     duplication_jaccard_threshold
				A_	:						int	=     NUM_PERM
				A_	:						Any	=     MinHashLSH(threshold=self._duplication_jaccard_threshold       ,      num_perm=self._num_perm							)

				A_	:						str	=     defaultdict(lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase							):
				A_	:						int	=     self._index.query(lowercase							)
				if code_key in self._index.keys:
							print(F'Duplicate key {code_key}'							)
							return

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


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


	def     _a	(self       ,      lowercase							):
				A_	:						Optional[Any]	=     self.get_duplicate_clusters()
				with open(lowercase       ,      """w"""							) as f:
							json.dump(lowercase       ,      lowercase							)






def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_,    A_	:						Tuple	=     element
			A_	:						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 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash						,      ThreadedIterator(lowerCamelCase__						,      max_queue_size=1_00_00    )						,      chunksize=1_00						,      ):
									if data is not None:
												yield data



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     DuplicationIndex(duplication_jaccard_threshold=lowerCamelCase__    )

			for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCamelCase__    )    )						,      max_queue_size=1_00    )    ):
						di.add(lowerCamelCase__						,      lowerCamelCase__    )

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



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     get_tokens(lowerCamelCase__    )
			A_	:						List[Any]	=     get_tokens(lowerCamelCase__    )
			return len(tokensa & tokensa    ) / len(tokensa | tokensa    )


lowerCamelCase							:Any				    =					None



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						str	=     []
			for elementa in cluster:
						A_	:						Tuple	=     _shared_dataset[elementa["""base_index"""]]["""content"""]
						for elementa in extremes:
									A_	:						Any	=     _shared_dataset[elementa["""base_index"""]]["""content"""]
									if jaccard_similarity(lowerCamelCase__						,      lowerCamelCase__    ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									A_	:						List[Any]	=     1
									extremes.append(lowerCamelCase__    )
			return extremes



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			global _shared_dataset
			A_	:						Tuple	=     dataset
			A_	:						Dict	=     []
			A_	:						int	=     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 	a			( lowerCamelCase__						,      lowerCamelCase__ = 0.85    ):
			'''simple docstring'''





			A_	:						int	=     make_duplicate_clusters(lowerCamelCase__						,      lowerCamelCase__    )
			A_	:						Optional[int]	=     {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
			A_	:						Dict	=     {}
			A_	:						Any	=     find_extremes(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
			for extremes in extremes_clusters:
						for element in extremes:
									A_	:						Any	=     element
			A_	:						Tuple	=     duplicate_indices - set(extreme_dict.keys()    )
			A_	:						Optional[Any]	=     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:
									A_	:						Tuple	=     element["""base_index"""] in extreme_dict
									if element["is_extreme"]:
												A_	:						Any	=     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




'''simple docstring'''




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=3       ,      lowercase=True       ,      lowercase=True       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=224       ,      lowercase=1000       ,      lowercase=[3, 3, 6, 4]       ,      lowercase=[48, 56, 112, 220]       ,      ):
				A_	:						Dict	=     parent
				A_	:						List[Any]	=     batch_size
				A_	:						Dict	=     num_channels
				A_	:						Optional[Any]	=     is_training
				A_	:						List[str]	=     use_labels
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[int]	=     attention_probs_dropout_prob
				A_	:						Tuple	=     num_labels
				A_	:						List[str]	=     image_size
				A_	:						str	=     layer_depths
				A_	:						Optional[int]	=     embed_dims


	def     _a	(self							):
				A_	:						Union[str, Any]	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				A_	:						int	=     None
				if self.use_labels:
							A_	:						Tuple	=     ids_tensor([self.batch_size]       ,      self.num_labels							)

				A_	:						int	=     self.get_config()

				return config, pixel_values, labels


	def     _a	(self							):
				return SwiftFormerConfig(
				    depths=self.layer_depths       ,      embed_dims=self.embed_dims       ,      mlp_ratio=4       ,      downsamples=[True, True, True, True]       ,      hidden_act="""gelu"""       ,      num_labels=self.num_labels       ,      down_patch_size=3       ,      down_stride=2       ,      down_pad=1       ,      drop_rate=0.0       ,      drop_path_rate=0.0       ,      use_layer_scale=lowercase       ,      layer_scale_init_value=1E-5       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     SwiftFormerModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Union[str, Any]	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.embed_dims[-1], 7, 7)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Any	=     self.num_labels
				A_	:						Any	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Optional[int]	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)

				A_	:						int	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_	:						int	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
				A_	:						Dict	=     model(lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)


	def     _a	(self							):
				((A_),    (A_),    (A_))	:						int	=     self.prepare_config_and_inputs()
				A_	:						Any	=     {"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__SCREAMING_SNAKE_CASE   :		int					=					False
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		List[str]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False


	def     _a	(self							):
				A_	:						Optional[int]	=     SwiftFormerModelTester(self							)
				A_	:						Any	=     ConfigTester(
				    self       ,      config_class=lowercase       ,      has_text_modality=lowercase       ,      hidden_size=37       ,      num_attention_heads=12       ,      num_hidden_layers=12       ,      )


	def     _a	(self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						Optional[Any]	=     model_class(lowercase							)
							A_	:						Optional[int]	=     model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase       ,      nn.Linear							)							)


	def     _a	(self							):
				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     model_class(lowercase							)
							A_	:						Optional[int]	=     inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_	:						int	=     [*signature.parameters.keys()]

							A_	:						Tuple	=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]       ,      lowercase							)


	def     _a	(self							):
				A_	:						str	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def     _a	(self							):
				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						Optional[Any]	=     SwiftFormerModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)


	@unittest.skip(reason="""SwiftFormer does not output attentions"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				def check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							):
							A_	:						str	=     model_class(lowercase							)
							model.to(lowercase							)
							model.eval()

							with torch.no_grad():
										A_	:						Optional[int]	=     model(**self._prepare_for_class(lowercase       ,      lowercase							)							)

							A_	:						Any	=     outputs.hidden_states

							A_	:						Any	=     8
							self.assertEqual(len(lowercase							)       ,      lowercase							)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(lowercase							)							):
										self.assertEqual(
										    hidden_states[i].shape       ,      torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ]							)       ,      )

				A_,    A_	:						str	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     True
							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A_	:						str	=     True

							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)


	def     _a	(self							):
				def _config_zero_init(lowercase							):
							A_	:						Optional[Any]	=     copy.deepcopy(lowercase							)
							for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowercase       ,      lowercase       ,      1E-10							)
										if isinstance(getattr(lowercase       ,      lowercase       ,      lowercase							)       ,      lowercase							):
													A_	:						Any	=     _config_zero_init(getattr(lowercase       ,      lowercase							)							)
													setattr(lowercase       ,      lowercase       ,      lowercase							)
							return configs_no_init

				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

				A_	:						Any	=     _config_zero_init(lowercase							)
				for model_class in self.all_model_classes:
							A_	:						List[str]	=     model_class(config=lowercase							)
							for name, param in model.named_parameters():
										if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1E9) / 1E9).round().item()       ,      [0.0, 1.0]       ,      msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,      )


	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
	def     _a	(self							):
				pass






def 	a			( ):
			'''simple docstring'''





			A_	:						List[str]	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image







@require_torch
@require_vision
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@cached_property
	def     _a	(self							):
				return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None


	@slow
	def     _a	(self							):
				A_	:						Any	=     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(lowercase							)

				A_	:						Dict	=     self.default_image_processor
				A_	:						Dict	=     prepare_img()
				A_	:						int	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_	:						int	=     model(**lowercase							)

				# verify the logits
				A_	:						int	=     torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape       ,      lowercase							)

				A_	:						List[str]	=     torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]							).to(lowercase							)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,      lowercase       ,      atol=1E-4							)							)