





"""simple docstring"""





import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 				_lowerCamelCase					(				lowercase_     ,	unittest.TestCase       ):
						UpperCAmelCase_       =	CLIPTokenizer
						UpperCAmelCase_       =	CLIPTokenizerFast
						UpperCAmelCase_       =	True
						UpperCAmelCase_       =	{}
						UpperCAmelCase_       =	False


						def 			snake_case_      (self						)       ->      List[str]:
								super().setUp()

								# fmt: off
								UpperCamelCase      =		["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
								# fmt: on
								UpperCamelCase      =		dict(zip(__UpperCamelCase      ,							range(len(__UpperCamelCase						)						)						)						)
								UpperCamelCase      =		["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
								UpperCamelCase      =		{"unk_token": "<unk>"}

								UpperCamelCase      =		os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["vocab_file"]						)
								UpperCamelCase      =		os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["merges_file"]						)
								with open(self.vocab_file      ,							"w"      ,							encoding="utf-8"						) as fp:
										fp.write(json.dumps(__UpperCamelCase						) + "\n"						)
								with open(self.merges_file      ,							"w"      ,							encoding="utf-8"						) as fp:
										fp.write("\n".join(__UpperCamelCase						)						)


						def 			snake_case_      (self      ,							**__a						)       ->      Dict:
								kwargs.update(self.special_tokens_map						)
								return CLIPTokenizer.from_pretrained(self.tmpdirname      ,							**__UpperCamelCase						)


						def 			snake_case_      (self      ,							**__a						)       ->      List[str]:
								kwargs.update(self.special_tokens_map						)
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,							**__UpperCamelCase						)


						def 			snake_case_      (self      ,							__a						)       ->      Optional[Any]:
								UpperCamelCase      =		"lower newer"
								UpperCamelCase      =		"lower newer"
								return input_text, output_text


						def 			snake_case_      (self						)       ->      List[str]:
								UpperCamelCase      =		CLIPTokenizer(self.vocab_file      ,							self.merges_file      ,							**self.special_tokens_map						)
								UpperCamelCase      =		"lower newer"
								UpperCamelCase      =		["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
								UpperCamelCase      =		tokenizer.tokenize(__UpperCamelCase						)
								self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase						)

								UpperCamelCase      =		tokens + [tokenizer.unk_token]
								UpperCamelCase      =		[10, 2, 16, 9, 3, 2, 16, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase						)      ,							__UpperCamelCase						)


						@require_ftfy
						def 			snake_case_      (self						)       ->      List[str]:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
												UpperCamelCase      =		self.tokenizer_class.from_pretrained(__UpperCamelCase      ,							**__UpperCamelCase						)
												UpperCamelCase      =		self.rust_tokenizer_class.from_pretrained(__UpperCamelCase      ,							**__UpperCamelCase						)

												UpperCamelCase      =		"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
												UpperCamelCase      =		tokenizer_s.tokenize(__UpperCamelCase						)
												UpperCamelCase      =		tokenizer_r.tokenize(__UpperCamelCase						)

												self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase						)

												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
												# with Tilde) encoded in 2 different ways
												UpperCamelCase      =		"xa\u0303y" + " " + "x\xe3y"
												UpperCamelCase      =		tokenizer_s.tokenize(__UpperCamelCase						)
												UpperCamelCase      =		tokenizer_r.tokenize(__UpperCamelCase						)

												self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase						)

												# Test that the tokenization is identical on unicode of space type
												UpperCamelCase      =		[
												    "\u0009",  # (horizontal tab, '\t')
												    "\u000B",  # (vertical tab)
												    "\u000C",  # (form feed)
												    "\u0020",  # (space, ' ')
												    "\u200E",  # (left-to-right mark):w
												    "\u200F",  # (right-to-left mark)
												]
												for unicode_seq in spaces_unicodes:
														UpperCamelCase      =		tokenizer_s.tokenize(__UpperCamelCase						)
														UpperCamelCase      =		tokenizer_r.tokenize(__UpperCamelCase						)

														self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase						)

												# Test that the tokenization is identical on unicode of line break type
												UpperCamelCase      =		[
												    "\u000A",  # (line feed, '\n')
												    "\r\n",  # (carriage return and line feed, '\r\n')
												    "\u000D",  # (carriage return, '\r')
												    "\r",  # (carriage return, '\r')
												    "\u000D",  # (carriage return, '\r')
												    "\u2028",  # (line separator)
												    "\u2029",  # (paragraph separator)
												    # "\u0085", # (next line)
												]

												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
												# space (and thus into an empty list).

												for unicode_seq in line_break_unicodes:
														UpperCamelCase      =		tokenizer_s.tokenize(__UpperCamelCase						)
														UpperCamelCase      =		tokenizer_r.tokenize(__UpperCamelCase						)

														self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase						)


						def 			snake_case_      (self						)       ->      Optional[Any]:
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
												UpperCamelCase      =		"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
												UpperCamelCase      =		F"{text_of_1_token} {text_of_1_token}"

												UpperCamelCase      =		self.rust_tokenizer_class.from_pretrained(
												    __UpperCamelCase      ,							use_fast=__UpperCamelCase      ,							)
												UpperCamelCase      =		tokenizer_r(__UpperCamelCase      ,							return_offsets_mapping=__UpperCamelCase      ,							add_special_tokens=__UpperCamelCase						)
												self.assertEqual(encoding.offset_mapping[0]      ,							(0, len(__UpperCamelCase						))						)
												self.assertEqual(
												    encoding.offset_mapping[1]      ,							(len(__UpperCamelCase						) + 1, len(__UpperCamelCase						) + 1 + len(__UpperCamelCase						))      ,							)

												UpperCamelCase      =		F" {text}"

												UpperCamelCase      =		self.rust_tokenizer_class.from_pretrained(
												    __UpperCamelCase      ,							use_fast=__UpperCamelCase      ,							)
												UpperCamelCase      =		tokenizer_r(__UpperCamelCase      ,							return_offsets_mapping=__UpperCamelCase      ,							add_special_tokens=__UpperCamelCase						)
												self.assertEqual(encoding.offset_mapping[0]      ,							(1, 1 + len(__UpperCamelCase						))						)
												self.assertEqual(
												    encoding.offset_mapping[1]      ,							(1 + len(__UpperCamelCase						) + 1, 1 + len(__UpperCamelCase						) + 1 + len(__UpperCamelCase						))      ,							)


						def 			snake_case_      (self						)       ->      List[Any]:
								# Test related to the breaking change introduced in transformers v4.17.0
								# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
								with self.assertRaises(__UpperCamelCase						) as context:
										self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"						)

								self.assertTrue(
								    context.exception.args[0].startswith(
								        "The `backend_tokenizer` provided does not match the expected format."						)						)


						@require_ftfy
						def 			snake_case_      (self						)       ->      Union[str, Any]:
								super().test_tokenization_python_rust_equals()




						def 			snake_case_      (self						)       ->      int:
								# CLIP always lower cases letters
								pass













"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


