
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger


class 				lowercase					(		lowercase__   ):



		def __init__( self				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     params

				UpperCamelCase		=     np.array(lowercase_       )
				UpperCamelCase		=     np.array([len(lowercase_       ) for t in data]       )

				self.check()
				self.remove_long_sequences()
				self.remove_empty_sequences()
				self.remove_unknown_sequences()
				self.check()
				self.print_statistics()



		def __getitem__( self				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				return (self.token_ids[index], self.lengths[index])



		def __len__( self       )  ->					Optional[int]:
				"""simple docstring"""
				return len(self.lengths       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				assert len(self.token_ids       ) == len(self.lengths       )
				assert all(self.lengths[i] == len(self.token_ids[i]       ) for i in range(len(self.lengths       )       )       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.params.max_model_input_size
				UpperCamelCase		=     self.lengths > max_len
				logger.info(F'''Splitting {sum(lowercase_       )} too long sequences.'''       )

				def divide_chunks(A_				,      A_       ):
						return [l[i : i + n] for i in range(0				,      len(lowercase_       )				,      lowercase_       )]

				UpperCamelCase		=     []
				UpperCamelCase		=     []
				if self.params.mlm:
						UpperCamelCase		=     self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
				else:
						UpperCamelCase		=     self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

				for seq_, len_ in zip(self.token_ids				,      self.lengths       ):
						assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
						if len_ <= max_len:
								new_tok_ids.append(seq_       )
								new_lengths.append(len_       )
						else:
								UpperCamelCase		=     []
								for sub_s in divide_chunks(seq_				,      max_len - 2       ):
										if sub_s[0] != cls_id:
												UpperCamelCase		=     np.insert(lowercase_				,      0				,      lowercase_       )
										if sub_s[-1] != sep_id:
												UpperCamelCase		=     np.insert(lowercase_				,      len(lowercase_       )				,      lowercase_       )
										assert len(lowercase_       ) <= max_len
										assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
										sub_seqs.append(lowercase_       )

								new_tok_ids.extend(lowercase_       )
								new_lengths.extend([len(lowercase_       ) for l in sub_seqs]       )

				UpperCamelCase		=     np.array(lowercase_       )
				UpperCamelCase		=     np.array(lowercase_       )



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     len(self       )
				UpperCamelCase		=     self.lengths > 11
				UpperCamelCase		=     self.token_ids[indices]
				UpperCamelCase		=     self.lengths[indices]
				UpperCamelCase		=     len(self       )
				logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				if "unk_token" not in self.params.special_tok_ids:
						return
				else:
						UpperCamelCase		=     self.params.special_tok_ids["unk_token"]
				UpperCamelCase		=     len(self       )
				UpperCamelCase		=     np.array([np.count_nonzero(a == unk_token_id       ) for a in self.token_ids]       )
				UpperCamelCase		=     (unk_occs / self.lengths) < 0.5
				UpperCamelCase		=     self.token_ids[indices]
				UpperCamelCase		=     self.lengths[indices]
				UpperCamelCase		=     len(self       )
				logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				if not self.params.is_master:
						return
				logger.info(F'''{len(self       )} sequences'''       )
				# data_len = sum(self.lengths)
				# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
				# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

				# unk_idx = self.params.special_tok_ids['unk_token']
				# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
				# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     [t[0] for t in batch]
				UpperCamelCase		=     [t[1] for t in batch]
				assert len(lowercase_       ) == len(lowercase_       )

				# Max for paddings
				UpperCamelCase		=     max(lowercase_       )

				# Pad token ids
				if self.params.mlm:
						UpperCamelCase		=     self.params.special_tok_ids["pad_token"]
				else:
						UpperCamelCase		=     self.params.special_tok_ids["unk_token"]
				UpperCamelCase		=     [list(t.astype(lowercase_       )       ) + [pad_idx] * (max_seq_len_ - len(lowercase_       )) for t in token_ids]
				assert len(tk_       ) == len(lowercase_       )
				assert all(len(lowercase_       ) == max_seq_len_ for t in tk_       )

				UpperCamelCase		=     torch.tensor(tk_       )  # (bs, max_seq_len_)
				UpperCamelCase		=     torch.tensor(lowercase_       )  # (bs)
				return tk_t, lg_t


"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    {
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   UpperCAmelCase_					:					Optional[int]			     =    Dataset.from_dict(__lowerCamelCase  )
   return dataset


class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    get_dataset()
     UpperCAmelCase_					:					Any			     =    make_duplicate_clusters(lowercase_				,   0.85  )
     self.assertEqual(len(duplicate_clusters[0]  )				,   2  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    get_dataset()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    deduplicate_dataset(lowercase_  )
     self.assertEqual(len(lowercase_  )				,   2  )
     print(lowercase_  )
     self.assertEqual(duplicate_clusters[0][0]["copies"]				,   2  )
     self.assertEqual(duplicate_clusters[0][0]["is_extreme"]				,   lowercase_  )


