





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


UpperCAmelCase_  							=				re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
UpperCAmelCase_  							=				10
UpperCAmelCase_  							=				256




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]   )   ->			Optional[MinHash]:
    if len(__UpperCAmelCase   ) < MIN_NUM_TOKENS:
        return None
    UpperCamelCase__							:       Optional[Any]      =   MinHash(num_perm=__UpperCAmelCase   )
    for token in set(__UpperCAmelCase   ):
        min_hash.update(token.encode()   )
    return min_hash




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			Set[str]:
    return {t for t in NON_ALPHA.split(__UpperCAmelCase   ) if len(t.strip()   ) > 0}






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	*,
       __magic_name__ = 0.85,	)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   duplication_jaccard_threshold
       UpperCamelCase__							:       int      =   NUM_PERM
       UpperCamelCase__							:       Dict      =   MinHashLSH(threshold=self._duplication_jaccard_threshold,	num_perm=self._num_perm							)

       UpperCamelCase__							:       str      =   defaultdict(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   None:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self._index.query(__magic_name__							)
       if code_key in self._index.keys:
           print(f"Duplicate key {code_key}"							)
           return

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




   def    UpperCamelCase__						(					self							)				->   List[List[Dict]]:






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




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   None:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.get_duplicate_clusters()
       with open(__magic_name__,	'''w'''							) as f:
           json.dump(__magic_name__,	__magic_name__							)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]   )   ->			Tuple:
    UpperCamelCase__     ,UpperCamelCase__							:       Any      =   element
    UpperCamelCase__							:       List[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_					(						__UpperCAmelCase:    Type[Dataset]   )   ->			List[Any]:
    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash	,     ThreadedIterator(__UpperCAmelCase	,     max_queue_size=1_0000   )	,     chunksize=100	,     ):
            if data is not None:
                yield data




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Type[Dataset]	,     __UpperCAmelCase:    float   )   ->			Any:
    UpperCamelCase__							:       Any      =   DuplicationIndex(duplication_jaccard_threshold=__UpperCAmelCase   )

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

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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str   )   ->			float:
    UpperCamelCase__							:       Optional[Any]      =   get_tokens(__UpperCAmelCase   )
    UpperCamelCase__							:       str      =   get_tokens(__UpperCAmelCase   )
    return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


UpperCAmelCase_  							=				None




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Dict   )   ->			Any:
    UpperCamelCase__							:       str      =   []
    for elementa in cluster:
        UpperCamelCase__							:       List[Any]      =   _shared_dataset[elementa['''base_index''']]['''content''']
        for elementa in extremes:
            UpperCamelCase__							:       List[Any]      =   _shared_dataset[elementa['''base_index''']]['''content''']
            if jaccard_similarity(__UpperCAmelCase	,     __UpperCAmelCase   ) >= jaccard_threshold:
                elementa["copies"] += 1
                break
        else:
            UpperCamelCase__							:       Dict      =   1
            extremes.append(__UpperCAmelCase   )
    return extremes




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[Any]   )   ->			Dict:
    global _shared_dataset
    UpperCamelCase__							:       str      =   dataset
    UpperCamelCase__							:       Any      =   []
    UpperCamelCase__							:       Optional[int]      =   partial(_find_cluster_extremes_shared	,     jaccard_threshold=__UpperCAmelCase   )
    with mp.Pool() as pool:
        for extremes in tqdm(
            pool.imap_unordered(
                __UpperCAmelCase	,     __UpperCAmelCase	,     )	,     total=len(__UpperCAmelCase   )	,     ):
            extremes_list.append(__UpperCAmelCase   )
    return extremes_list





def 	lowerCAmelCase_					(						__UpperCAmelCase:    Type[Dataset]	,     __UpperCAmelCase:    float = 0.85   )   ->			Tuple[Type[Dataset], List[List[Dict]]]:
    UpperCamelCase__							:       List[str]      =   make_duplicate_clusters(__UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       Any      =   {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
    UpperCamelCase__							:       Union[str, Any]      =   {}
    UpperCamelCase__							:       int      =   find_extremes(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    for extremes in extremes_clusters:
        for element in extremes:
            UpperCamelCase__							:       Union[str, Any]      =   element
    UpperCamelCase__							:       Optional[Any]      =   duplicate_indices - set(extreme_dict.keys()   )
    UpperCamelCase__							:       Optional[Any]      =   dataset.filter(lambda __UpperCAmelCase	,     __UpperCAmelCase   : idx not in remove_indices	,     with_indices=__UpperCAmelCase   )

    # update duplicate_clusters
    for cluster in duplicate_clusters:
        for element in cluster:
            UpperCamelCase__							:       Tuple      =   element['''base_index'''] in extreme_dict
            if element["is_extreme"]:
                UpperCamelCase__							:       str      =   extreme_dict[element['''base_index''']]['''copies''']

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

    return ds_filter, duplicate_clusters








import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import CvtForImageClassification, CvtModel
  from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self.config_class(**self.inputs_dict							)
       self.parent.assertTrue(hasattr(__magic_name__,	'''embed_dim'''							)							)
       self.parent.assertTrue(hasattr(__magic_name__,	'''num_heads'''							)							)






class 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=13,	__magic_name__=64,	__magic_name__=3,	__magic_name__=[16, 48, 96],	__magic_name__=[1, 3, 6],	__magic_name__=[1, 2, 10],	__magic_name__=[7, 3, 3],	__magic_name__=[4, 2, 2],	__magic_name__=[2, 1, 1],	__magic_name__=[2, 2, 2],	__magic_name__=[False, False, True],	__magic_name__=[0.0, 0.0, 0.0],	__magic_name__=0.02,	__magic_name__=1E-12,	__magic_name__=True,	__magic_name__=True,	__magic_name__=2,	)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   parent
       UpperCamelCase__							:       Optional[Any]      =   batch_size
       UpperCamelCase__							:       Dict      =   image_size
       UpperCamelCase__							:       Optional[Any]      =   patch_sizes
       UpperCamelCase__							:       List[str]      =   patch_stride
       UpperCamelCase__							:       Tuple      =   patch_padding
       UpperCamelCase__							:       Dict      =   is_training
       UpperCamelCase__							:       Optional[int]      =   use_labels
       UpperCamelCase__							:       Union[str, Any]      =   num_labels
       UpperCamelCase__							:       List[Any]      =   num_channels
       UpperCamelCase__							:       Optional[Any]      =   embed_dim
       UpperCamelCase__							:       List[str]      =   num_heads
       UpperCamelCase__							:       Any      =   stride_kv
       UpperCamelCase__							:       Any      =   depth
       UpperCamelCase__							:       Tuple      =   cls_token
       UpperCamelCase__							:       List[Any]      =   attention_drop_rate
       UpperCamelCase__							:       Any      =   initializer_range
       UpperCamelCase__							:       str      =   layer_norm_eps




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

       UpperCamelCase__							:       str      =   self.get_config()
       return config, pixel_values, labels




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       return CvtConfig(
           image_size=self.image_size,	num_labels=self.num_labels,	num_channels=self.num_channels,	embed_dim=self.embed_dim,	num_heads=self.num_heads,	patch_sizes=self.patch_sizes,	patch_padding=self.patch_padding,	patch_stride=self.patch_stride,	stride_kv=self.stride_kv,	depth=self.depth,	cls_token=self.cls_token,	attention_drop_rate=self.attention_drop_rate,	initializer_range=self.initializer_range,	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   CvtModel(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:       Optional[int]      =   model(__magic_name__							)
       UpperCamelCase__							:       List[str]      =   (self.image_size, self.image_size)
       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   image_size[0], image_size[1]
       for i in range(len(self.depth							)							):
           UpperCamelCase__							:       Optional[int]      =   floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
           UpperCamelCase__							:       int      =   floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
       self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.embed_dim[-1], height, width)							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.num_labels
       UpperCamelCase__							:       str      =   CvtForImageClassification(__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:       Any      =   model(__magic_name__,	labels=__magic_name__							)
       self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)							)




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self.prepare_config_and_inputs()
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   config_and_inputs
       UpperCamelCase__							:       List[Any]      =   {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	(CvtModel, CvtForImageClassification) if is_torch_available() else ()
   a		:							Any     	=	(
       {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
       if is_torch_available()
       else {}
   )

   a		:							List[str]     	=	False
   a		:							int     	=	False
   a		:							Tuple     	=	False
   a		:							int     	=	False
   a		:							Tuple     	=	False




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   CvtModelTester(self							)
       UpperCamelCase__							:       List[Any]      =   ConfigTester(self,	config_class=__magic_name__,	has_text_modality=__magic_name__,	hidden_size=37							)




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       return




   @unittest.skip(reason='''Cvt does not output attentions'''							)
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       pass




   @unittest.skip(reason='''Cvt does not use inputs_embeds'''							)
   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       pass




   @unittest.skip(reason='''Cvt does not support input and output embeddings'''							)
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__							:       Tuple      =   ['''pixel_values''']
           self.assertListEqual(arg_names[:1],	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       def check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       str      =   model_class(__magic_name__							)
           model.to(__magic_name__							)
           model.eval()

           with torch.no_grad():
               UpperCamelCase__							:       List[Any]      =   model(**self._prepare_for_class(__magic_name__,	__magic_name__							)							)

           UpperCamelCase__							:       str      =   outputs.hidden_states

           UpperCamelCase__							:       Dict      =   len(self.model_tester.depth							)
           self.assertEqual(len(__magic_name__							),	__magic_name__							)

           # verify the first hidden states (first block)
           self.assertListEqual(
               list(hidden_states[0].shape[-3:]							),	[
                   self.model_tester.embed_dim[0],
                   self.model_tester.image_size // 4,
                   self.model_tester.image_size // 4,
               ],	)

       UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:       Optional[int]      =   True
           check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							)

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

           check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__magic_name__							)




   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       pass




   @slow
   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__							:       Union[str, Any]      =   CvtModel.from_pretrained(__magic_name__							)
           self.assertIsNotNone(__magic_name__							)




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Tuple      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
    return image






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

   '''simple docstring'''


   @cached_property
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)




   @slow
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   self.default_image_processor
       UpperCamelCase__							:       Any      =   prepare_img()
       UpperCamelCase__							:       List[str]      =   image_processor(images=__magic_name__,	return_tensors='''pt'''							).to(__magic_name__							)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__							:       Tuple      =   model(**__magic_name__							)

       # verify the logits
       UpperCamelCase__							:       Tuple      =   torch.Size((1, 1000)							)
       self.assertEqual(outputs.logits.shape,	__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   torch.tensor([0.9285, 0.9015, -0.3150]							).to(__magic_name__							)

       self.assertTrue(torch.allclose(outputs.logits[0, :3],	__magic_name__,	atol=1E-4							)							)

