

'''simple docstring'''




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTransfoXLForSequenceClassification,
          TFTransfoXLLMHeadModel,
          TFTransfoXLModel,
      )






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[str]			,				a__		:   int			,				):
      __magic_name__						       =    parent
      __magic_name__						       =    13
      __magic_name__						       =    7
      __magic_name__						       =    30
      __magic_name__						       =    self.seq_length + self.mem_len
      __magic_name__						       =    15
      __magic_name__						       =    True
      __magic_name__						       =    True
      __magic_name__						       =    99
      __magic_name__						       =    [10, 50, 80]
      __magic_name__						       =    32
      __magic_name__						       =    32
      __magic_name__						       =    4
      __magic_name__						       =    8
      __magic_name__						       =    128
      __magic_name__						       =    2
      __magic_name__						       =    2
      __magic_name__						       =    None
      __magic_name__						       =    1
      __magic_name__						       =    0
      __magic_name__						       =    3
      __magic_name__						       =    self.vocab_size - 1
      __magic_name__						       =    0.01



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )
      __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )

      __magic_name__						       =    None
      if self.use_labels:
           __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )

      __magic_name__						       =    TransfoXLConfig(
          vocab_size=self.vocab_size			,				mem_len=self.mem_len			,				clamp_len=self.clamp_len			,				cutoffs=self.cutoffs			,				d_model=self.hidden_size			,				d_embed=self.d_embed			,				n_head=self.num_attention_heads			,				d_head=self.d_head			,				d_inner=self.d_inner			,				div_val=self.div_val			,				n_layer=self.num_hidden_layers			,				eos_token_id=self.eos_token_id			,				pad_token_id=self.vocab_size - 1			,				init_range=self.init_range			,				num_labels=self.num_labels			,				)

      return (config, input_ids_a, input_ids_a, lm_labels)



 def 			snake_case__  (   self		:   str      ):
      random.seed(self.seed      )
      tf.random.set_seed(self.seed      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Tuple			,				a__		:   Any			,				a__		:   Union[str, Any]			,				a__		:   int      ):
      __magic_name__						       =    TFTransfoXLModel(a__      )

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      __magic_name__						       =    {'''input_ids''': input_ids_a, '''mems''': mems_a}

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape			,				(self.batch_size, self.seq_length, self.hidden_size)      )
      self.parent.assertEqual(hidden_states_a.shape			,				(self.batch_size, self.seq_length, self.hidden_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   int			,				a__		:   List[str]			,				a__		:   List[str]			,				a__		:   Dict      ):
      __magic_name__						       =    TFTransfoXLLMHeadModel(a__      )

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      __magic_name__						       =    {'''input_ids''': input_ids_a, '''labels''': lm_labels}
      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      __magic_name__       ,  __magic_name__						       =    model([input_ids_a, mems_a]      ).to_tuple()

      __magic_name__						       =    {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

      __magic_name__       ,  __magic_name__						       =    model(a__      ).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape			,				(self.batch_size, self.seq_length, self.vocab_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)

      self.parent.assertEqual(lm_logits_a.shape			,				(self.batch_size, self.seq_length, self.vocab_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]			,				[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,				)



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   str			,				a__		:   Optional[int]			,				a__		:   Dict			,				a__		:   List[Any]      ):
      __magic_name__						       =    TFTransfoXLForSequenceClassification(a__      )
      __magic_name__						       =    model(a__      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    self.prepare_config_and_inputs()
      ((__magic_name__)       ,  (__magic_name__)       ,  (__magic_name__)       ,  (__magic_name__))						       =    config_and_inputs
      __magic_name__						       =    {'''input_ids''': input_ids_a}
      return config, inputs_dict






@require_tf
class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					() if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE					:Any      	=					(
     {
         """feature-extraction""": TFTransfoXLModel,
         """text-classification""": TFTransfoXLForSequenceClassification,
         """text-generation""": TFTransfoXLLMHeadModel,
         """zero-shot""": TFTransfoXLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
 __SCREAMING_SNAKE_CASE					:List[str]      	=					False
 __SCREAMING_SNAKE_CASE					:str      	=					False
 __SCREAMING_SNAKE_CASE					:str      	=					False
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					False



 def 			snake_case__  (   self		:   int			,				a__		:   int			,				a__		:   List[str]			,				a__		:   Any			,				a__		:   Optional[Any]			,				a__		:   int      ):
      if pipeline_test_casse_name == "TextGenerationPipelineTests":
           # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
           # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
           # tokenizer.
           return True

      return False



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    TFTransfoXLModelTester(self      )
      __magic_name__						       =    ConfigTester(self			,				config_class=a__			,				d_embed=37      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      self.config_tester.run_common_tests()



 def 			snake_case__  (   self		:   int      ):
      self.model_tester.set_seed()
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      self.model_tester.set_seed()
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*a__      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__						       =    [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
           __magic_name__						       =    model_class(a__      )
           assert isinstance(model.get_input_embeddings()			,				tf.keras.layers.Layer      )
           if model_class in list_other_models_with_output_ebd:
                __magic_name__						       =    model.get_output_embeddings()
                assert isinstance(a__			,				tf.keras.layers.Layer      )
                __magic_name__						       =    model.get_bias()
                assert name is None
           else:
                __magic_name__						       =    model.get_output_embeddings()
                assert x is None
                __magic_name__						       =    model.get_bias()
                assert name is None



 def 			snake_case__  (   self		:   Dict      ):
      # TODO JP: Make TransfoXL XLA compliant
      pass



 @slow
 def 			snake_case__  (   self		:   List[Any]      ):
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    TFTransfoXLModel.from_pretrained(a__      )
           self.assertIsNotNone(a__      )



 @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
 def 			snake_case__  (   self		:   Any      ):
      pass






@require_tf
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @unittest.skip('''Skip test until #12651 is resolved.'''      )
 @slow
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
      # fmt: off
      __magic_name__						       =    tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]			,				dtype=tf.intaa      )  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      __magic_name__						       =    [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      __magic_name__						       =    model.generate(a__			,				max_length=200			,				do_sample=a__      )
      self.assertListEqual(output_ids[0].numpy().tolist()			,				a__      )









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




def 						UpperCamelCase	(			a			)				->     Optional[MinHash]:






     '''simple docstring'''
     if len(a			) < MIN_NUM_TOKENS:
          return None
     __magic_name__						       =    MinHash(num_perm=a			)
     for token in set(a			):
          min_hash.update(token.encode()			)
     return min_hash




def 						UpperCamelCase	(			a			)				->     Set[str]:






     '''simple docstring'''
     return {t for t in NON_ALPHA.split(a			) if len(t.strip()			) > 0}






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Any			,				*,
     a__		:   float = 0.85			,				):
      __magic_name__						       =    duplication_jaccard_threshold
      __magic_name__						       =    NUM_PERM
      __magic_name__						       =    MinHashLSH(threshold=self._duplication_jaccard_threshold			,				num_perm=self._num_perm      )

      __magic_name__						       =    defaultdict(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple			,				a__		:   MinHash      ):
      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    []
      for base, duplicates in self._duplicate_clusters.items():
           __magic_name__						       =    [base] + list(a__      )
           # reformat the cluster to be a list of dict
           __magic_name__						       =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(a__      )
      return duplicate_clusters



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple      ):
      __magic_name__						       =    self.get_duplicate_clusters()
      with open(a__			,				'''w'''      ) as f:
           json.dump(a__			,				a__      )




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    element
     __magic_name__						       =    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 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash		,						ThreadedIterator(a		,						max_queue_size=1_0000			)		,						chunksize=100		,						):
               if data is not None:
                    yield data




def 						UpperCamelCase	(			a		,						a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     float:






     '''simple docstring'''
     __magic_name__						       =    get_tokens(a			)
     __magic_name__						       =    get_tokens(a			)
     return len(tokensa & tokensa			) / len(tokensa | tokensa			)


_lowerCAmelCase         =						None




def 						UpperCamelCase	(			a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    []
     for elementa in cluster:
          __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
          for elementa in extremes:
               __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
               if jaccard_similarity(a		,						a			) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               __magic_name__						       =    1
               extremes.append(a			)
     return extremes




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     global _shared_dataset
     __magic_name__						       =    dataset
     __magic_name__						       =    []
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a = 0.85			)				->     Tuple[Type[Dataset], List[List[Dict]]]:






     '''simple docstring'''
     __magic_name__						       =    make_duplicate_clusters(a		,						a			)
     __magic_name__						       =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     __magic_name__						       =    {}
     __magic_name__						       =    find_extremes(a		,						a		,						a			)
     for extremes in extremes_clusters:
          for element in extremes:
               __magic_name__						       =    element
     __magic_name__						       =    duplicate_indices - set(extreme_dict.keys()			)
     __magic_name__						       =    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:
               __magic_name__						       =    element['''base_index'''] in extreme_dict
               if element["is_extreme"]:
                    __magic_name__						       =    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






