




'''simple docstring'''

import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
						from test_module.custom_tokenization_fast import CustomTokenizerFast



class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		mock.Mock()
						__snake_case			=		500
						__snake_case			=		{}
						__snake_case			=		HTTPError
						__snake_case			=		{}

						# Download this model to make sure it's in the cache.
						__snake_case			=		BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"							)

						# Under the mock environment we get a 500 error when trying to reach the tokenizer.
						with mock.patch("requests.Session.request"      ,		return_value=a_							) as mock_head:
									__snake_case			=		BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"							)
									# This check we did call the fake head request
									mock_head.assert_called()





			@require_tokenizers
			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		mock.Mock()
						__snake_case			=		500
						__snake_case			=		{}
						__snake_case			=		HTTPError
						__snake_case			=		{}

						# Download this model to make sure it's in the cache.
						__snake_case			=		GPTaTokenizerFast.from_pretrained("gpt2"							)

						# Under the mock environment we get a 500 error when trying to reach the tokenizer.
						with mock.patch("requests.Session.request"      ,		return_value=a_							) as mock_head:
									__snake_case			=		GPTaTokenizerFast.from_pretrained("gpt2"							)
									# This check we did call the fake head request
									mock_head.assert_called()





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						try:
									__snake_case			=		tempfile.mktemp()
									with open(a_      ,		"wb"							) as f:
												http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"      ,		a_							)

									__snake_case			=		AlbertTokenizer.from_pretrained(a_							)
						finally:
									os.remove(a_							)

						# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
						# the current folder and have the right name.
						if os.path.isfile("tokenizer.json"							):
									# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
									return
						try:
									with open("tokenizer.json"      ,		"wb"							) as f:
												http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"      ,		a_							)
									__snake_case			=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
									# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
									self.assertEqual(tokenizer.vocab_size      ,		1_000							)
									# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

						finally:
									os.remove("tokenizer.json"							)






			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"							)







@is_staging_test
class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]





			@classmethod
			def 							A (      cls						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		TOKEN
						HfFolder.save_token(a_							)





			@classmethod
			def 							A (      cls						:    List[Any]							):



						"""simple docstring"""




						try:
									delete_repo(token=cls._token      ,		repo_id="test-tokenizer"							)
						except HTTPError:
									pass

						try:
									delete_repo(token=cls._token      ,		repo_id="valid_org/test-tokenizer-org"							)
						except HTTPError:
									pass

						try:
									delete_repo(token=cls._token      ,		repo_id="test-dynamic-tokenizer"							)
						except HTTPError:
									pass





			def 							A (      self						:    int							):



						"""simple docstring"""




						with tempfile.TemporaryDirectory() as tmp_dir:
									__snake_case			=		os.path.join(a_      ,		"vocab.txt"							)
									with open(a_      ,		"w"      ,		encoding="utf-8"							) as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]							)							)
									__snake_case			=		BertTokenizer(a_							)

						tokenizer.push_to_hub("test-tokenizer"      ,		use_auth_token=self._token							)
						__snake_case			=		BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''							)
						self.assertDictEqual(new_tokenizer.vocab      ,		tokenizer.vocab							)

						# Reset repo
						delete_repo(token=self._token      ,		repo_id="test-tokenizer"							)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(a_      ,		repo_id="test-tokenizer"      ,		push_to_hub=a_      ,		use_auth_token=self._token							)

						__snake_case			=		BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''							)
						self.assertDictEqual(new_tokenizer.vocab      ,		tokenizer.vocab							)





			def 							A (      self						:    int							):



						"""simple docstring"""




						with tempfile.TemporaryDirectory() as tmp_dir:
									__snake_case			=		os.path.join(a_      ,		"vocab.txt"							)
									with open(a_      ,		"w"      ,		encoding="utf-8"							) as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]							)							)
									__snake_case			=		BertTokenizer(a_							)

						tokenizer.push_to_hub("valid_org/test-tokenizer-org"      ,		use_auth_token=self._token							)
						__snake_case			=		BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"							)
						self.assertDictEqual(new_tokenizer.vocab      ,		tokenizer.vocab							)

						# Reset repo
						delete_repo(token=self._token      ,		repo_id="valid_org/test-tokenizer-org"							)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(
									    a_      ,		repo_id="valid_org/test-tokenizer-org"      ,		push_to_hub=a_      ,		use_auth_token=self._token							)

						__snake_case			=		BertTokenizer.from_pretrained("valid_org/test-tokenizer-org"							)
						self.assertDictEqual(new_tokenizer.vocab      ,		tokenizer.vocab							)






			@require_tokenizers
			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						CustomTokenizer.register_for_auto_class()
						with tempfile.TemporaryDirectory() as tmp_dir:
									__snake_case			=		os.path.join(a_      ,		"vocab.txt"							)
									with open(a_      ,		"w"      ,		encoding="utf-8"							) as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]							)							)
									__snake_case			=		CustomTokenizer(a_							)

						# No fast custom tokenizer
						tokenizer.push_to_hub("test-dynamic-tokenizer"      ,		use_auth_token=self._token							)

						__snake_case			=		AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer'''      ,		trust_remote_code=a_							)
						# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__      ,		"CustomTokenizer"							)

						# Fast and slow custom tokenizer
						CustomTokenizerFast.register_for_auto_class()
						with tempfile.TemporaryDirectory() as tmp_dir:
									__snake_case			=		os.path.join(a_      ,		"vocab.txt"							)
									with open(a_      ,		"w"      ,		encoding="utf-8"							) as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]							)							)

									__snake_case			=		BertTokenizerFast.from_pretrained(a_							)
									bert_tokenizer.save_pretrained(a_							)
									__snake_case			=		CustomTokenizerFast.from_pretrained(a_							)

						tokenizer.push_to_hub("test-dynamic-tokenizer"      ,		use_auth_token=self._token							)

						__snake_case			=		AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer'''      ,		trust_remote_code=a_							)
						# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__      ,		"CustomTokenizerFast"							)
						__snake_case			=		AutoTokenizer.from_pretrained(
						    f'''{USER}/test-dynamic-tokenizer'''      ,		use_fast=a_      ,		trust_remote_code=a_							)
						# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__      ,		"CustomTokenizer"							)







class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		Trie()
						trie.add("Hello 友達"							)
						self.assertEqual(trie.data      ,		{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}							)
						trie.add("Hello"							)
						trie.data
						self.assertEqual(trie.data      ,		{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		Trie()
						self.assertEqual(trie.split("[CLS] This is a extra_id_100"							)      ,		["[CLS] This is a extra_id_100"]							)
						trie.add("[CLS]"							)
						trie.add("extra_id_1"							)
						trie.add("extra_id_100"							)
						self.assertEqual(trie.split("[CLS] This is a extra_id_100"							)      ,		["[CLS]", " This is a ", "extra_id_100"]							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		Trie()
						trie.add("A"							)
						self.assertEqual(trie.split("ABC"							)      ,		["A", "BC"]							)
						self.assertEqual(trie.split("BCA"							)      ,		["BC", "A"]							)





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		Trie()
						trie.add("TOKEN]"							)
						trie.add("[SPECIAL_TOKEN]"							)
						self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"							)      ,		["This is something ", "[SPECIAL_TOKEN]"]							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		Trie()
						trie.add("A"							)
						trie.add("P"							)
						trie.add("[SPECIAL_TOKEN]"							)
						self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"							)      ,		["This is something ", "[SPECIAL_TOKEN]"]							)





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		Trie()
						trie.add("AB"							)
						trie.add("B"							)
						trie.add("C"							)
						self.assertEqual(trie.split("ABC"							)      ,		["AB", "C"]							)





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		Trie()
						trie.add("ABC"							)
						trie.add("B"							)
						trie.add("CD"							)
						self.assertEqual(trie.split("ABCD"							)      ,		["ABC", "D"]							)






			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		Trie()
						__snake_case			=		trie.cut_text("ABC"      ,		[0, 0, 2, 1, 2, 3]							)
						self.assertEqual(a_      ,		["AB", "C"]							)









def 	_UpperCamelCase					(    )    ->list[list[int]]:
   return [list(range(1_0_0_0 - i	,    -1_0_0_0 - i	,    -1 ) ) for i in range(1_0_0_0 )]


__a	 =      generate_large_matrix()
__a	 =      (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->None:
   assert all(row == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for row in grid )
   assert all(list(lowerCAmelCase_ ) == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for col in zip(*lowerCAmelCase_ ) )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(lowerCAmelCase_ ) - 1

   # Edge cases such as no values or all numbers are negative.
   if not array or array[0] < 0:
      return 0

   while right + 1 > left:
      UpperCAmelCase      					=  (left + right) // 2
      UpperCAmelCase      					=  array[mid]

      # Num must be negative and the index must be greater than or equal to 0.
      if num < 0 and array[mid - 1] >= 0:
         return mid

      if num >= 0:
         UpperCAmelCase      					=  mid + 1
      else:
         UpperCAmelCase      					=  mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
   return len(lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(grid[0] )

   for i in range(len(lowerCAmelCase_ ) ):
      UpperCAmelCase      					=  find_negative_index(grid[i][:bound] )
      total += bound
   return (len(lowerCAmelCase_ ) * len(grid[0] )) - total
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   return len([number for row in grid for number in row if number < 0] )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   for row in grid:
      for i, number in enumerate(lowerCAmelCase_ ):
         if number < 0:
            total += len(lowerCAmelCase_ ) - i
            break
   return total
def 	_UpperCamelCase					(    )    ->None:
   from timeit import timeit

   print("""Running benchmarks""" )
   UpperCAmelCase      					=  (
       """from __main__ import count_negatives_binary_search, """
       """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
   )
   for func in (
       "count_negatives_binary_search",  # took 0.7727 seconds
       "count_negatives_brute_force_with_break",  # took 4.6505 seconds
       "count_negatives_brute_force",  # took 12.8160 seconds
   ):
      UpperCAmelCase      					=  timeit(F"""{func}(grid=grid)"""	,    setup=lowerCAmelCase_	,    number=5_0_0 )
      print(F"""{func}() took {time:0.4f} seconds""" )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()



