






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						tempfile.mkdtemp()

            __snake_case			:Tuple          =						[
                """[UNK]""",
                """[CLS]""",
                """[SEP]""",
                """[PAD]""",
                """[MASK]""",
                """的""",
                """价""",
                """格""",
                """是""",
                """15""",
                """便""",
                """alex""",
                """##andra""",
                """，""",
                """。""",
                """-""",
                """t""",
                """shirt""",
            ]
            __snake_case			:List[Any]          =						os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["""vocab_file"""]    )
            with open(self.vocab_file			,						"""w"""			,						encoding="""utf-8"""    ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

            __snake_case			:Any          =						{
                """do_resize""": True,
                """size""": {"""height""": 2_24, """width""": 2_24},
                """do_center_crop""": True,
                """crop_size""": {"""height""": 18, """width""": 18},
                """do_normalize""": True,
                """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
                """do_convert_rgb""": True,
            }
            __snake_case			:Dict          =						os.path.join(self.tmpdirname			,						a__    )
            with open(self.image_processor_file			,						"""w"""			,						encoding="""utf-8"""    ) as fp:
                 json.dump(a__			,						a__    )



       def 					__lowercase						(							self			,						**a__    ) ->      List[str]:






            '''simple docstring'''





            return BertTokenizer.from_pretrained(self.tmpdirname			,						**a__    )



       def 					__lowercase						(							self			,						**a__    ) ->      str:






            '''simple docstring'''





            return BertTokenizerFast.from_pretrained(self.tmpdirname			,						**a__    )



       def 					__lowercase						(							self			,						**a__    ) ->      Dict:






            '''simple docstring'''





            return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname			,						**a__    )



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






            '''simple docstring'''





            shutil.rmtree(self.tmpdirname    )



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






            '''simple docstring'''





            __snake_case			:Dict          =						[np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta    )]

            __snake_case			:List[Any]          =						[Image.fromarray(np.moveaxis(a__			,						0			,						-1    )    ) for x in image_inputs]

            return image_inputs



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.get_tokenizer()
            __snake_case			:str          =						self.get_rust_tokenizer()
            __snake_case			:Optional[int]          =						self.get_image_processor()

            __snake_case			:List[Any]          =						ChineseCLIPProcessor(tokenizer=a__			,						image_processor=a__    )
            processor_slow.save_pretrained(self.tmpdirname    )
            __snake_case			:Any          =						ChineseCLIPProcessor.from_pretrained(self.tmpdirname			,						use_fast=a__    )

            __snake_case			:List[Any]          =						ChineseCLIPProcessor(tokenizer=a__			,						image_processor=a__    )
            processor_fast.save_pretrained(self.tmpdirname    )
            __snake_case			:Any          =						ChineseCLIPProcessor.from_pretrained(self.tmpdirname    )

            self.assertEqual(processor_slow.tokenizer.get_vocab()			,						tokenizer_slow.get_vocab()    )
            self.assertEqual(processor_fast.tokenizer.get_vocab()			,						tokenizer_fast.get_vocab()    )
            self.assertEqual(tokenizer_slow.get_vocab()			,						tokenizer_fast.get_vocab()    )
            self.assertIsInstance(processor_slow.tokenizer			,						a__    )
            self.assertIsInstance(processor_fast.tokenizer			,						a__    )

            self.assertEqual(processor_slow.image_processor.to_json_string()			,						image_processor.to_json_string()    )
            self.assertEqual(processor_fast.image_processor.to_json_string()			,						image_processor.to_json_string()    )
            self.assertIsInstance(processor_slow.image_processor			,						a__    )
            self.assertIsInstance(processor_fast.image_processor			,						a__    )



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






            '''simple docstring'''





            __snake_case			:str          =						ChineseCLIPProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()    )
            processor.save_pretrained(self.tmpdirname    )

            __snake_case			:Tuple          =						self.get_tokenizer(cls_token="""(CLS)"""			,						sep_token="""(SEP)"""    )
            __snake_case			:Optional[int]          =						self.get_image_processor(do_normalize=a__    )

            __snake_case			:Dict          =						ChineseCLIPProcessor.from_pretrained(
                self.tmpdirname			,						cls_token="""(CLS)"""			,						sep_token="""(SEP)"""			,						do_normalize=a__    )

            self.assertEqual(processor.tokenizer.get_vocab()			,						tokenizer_add_kwargs.get_vocab()    )
            self.assertIsInstance(processor.tokenizer			,						a__    )

            self.assertEqual(processor.image_processor.to_json_string()			,						image_processor_add_kwargs.to_json_string()    )
            self.assertIsInstance(processor.image_processor			,						a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Any          =						self.get_image_processor()
            __snake_case			:Union[str, Any]          =						self.get_tokenizer()

            __snake_case			:Optional[Any]          =						ChineseCLIPProcessor(tokenizer=a__			,						image_processor=a__    )

            __snake_case			:Union[str, Any]          =						self.prepare_image_inputs()

            __snake_case			:List[str]          =						image_processor(a__			,						return_tensors="""np"""    )
            __snake_case			:Union[str, Any]          =						processor(images=a__			,						return_tensors="""np"""    )

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()			,						input_processor[key].sum()			,						delta=1e-2    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Any          =						self.get_image_processor()
            __snake_case			:str          =						self.get_tokenizer()

            __snake_case			:Optional[int]          =						ChineseCLIPProcessor(tokenizer=a__			,						image_processor=a__    )

            __snake_case			:Optional[Any]          =						"""Alexandra，T-shirt的价格是15便士。"""

            __snake_case			:List[Any]          =						processor(text=a__    )

            __snake_case			:List[Any]          =						tokenizer(a__    )

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]			,						encoded_processor[key]    )



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:int          =						self.get_image_processor()
            __snake_case			:Optional[int]          =						self.get_tokenizer()

            __snake_case			:str          =						ChineseCLIPProcessor(tokenizer=a__			,						image_processor=a__    )

            __snake_case			:Optional[int]          =						"""Alexandra，T-shirt的价格是15便士。"""
            __snake_case			:Tuple          =						self.prepare_image_inputs()

            __snake_case			:Any          =						processor(text=a__			,						images=a__    )

            self.assertListEqual(list(inputs.keys()    )			,						["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]    )

            # test if it raises when no input is passed
            with pytest.raises(a__    ):
                 processor()



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:str          =						self.get_image_processor()
            __snake_case			:Optional[Any]          =						self.get_tokenizer()

            __snake_case			:Any          =						ChineseCLIPProcessor(tokenizer=a__			,						image_processor=a__    )

            __snake_case			:int          =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __snake_case			:List[Any]          =						processor.batch_decode(a__    )
            __snake_case			:str          =						tokenizer.batch_decode(a__    )

            self.assertListEqual(a__			,						a__    )



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






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.get_image_processor()
            __snake_case			:str          =						self.get_tokenizer()

            __snake_case			:Optional[int]          =						ChineseCLIPProcessor(tokenizer=a__			,						image_processor=a__    )

            __snake_case			:Tuple          =						"""Alexandra，T-shirt的价格是15便士。"""
            __snake_case			:str          =						self.prepare_image_inputs()

            __snake_case			:Any          =						processor(text=a__			,						images=a__    )

            self.assertListEqual(list(inputs.keys()    )			,						processor.model_input_names    )










from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:list[list[Edge]]          =						[[] for _ in range(a__    )]
            __snake_case			:List[str]          =						size



       def __getitem__(							self			,						a__    ) ->      Iterator[Edge]:






            '''simple docstring'''





            return iter(self._graph[vertex]    )



       @property
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            return self._size



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[Any]:






            '''simple docstring'''





            if weight not in (0, 1):
                 raise ValueError("""Edge weight must be either 0 or 1."""    )

            if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("""Vertex indexes must be in [0; size)."""    )

            self._graph[from_vertex].append(Edge(a__			,						a__    )    )



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int | None:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						deque([start_vertex]    )
            __snake_case			:list[int | None]          =						[None] * self.size
            __snake_case			:Tuple          =						0

            while queue:
                 __snake_case			:List[Any]          =						queue.popleft()
                 __snake_case			:Optional[Any]          =						distances[current_vertex]
                 if current_distance is None:
                      continue

                 for edge in self[current_vertex]:
                      __snake_case			:Optional[Any]          =						current_distance + edge.weight
                      __snake_case			:Dict          =						distances[edge.destination_vertex]
                      if (
                          isinstance(a__			,						a__    )
                          and new_distance >= dest_vertex_distance
                      ):
                           continue
                      __snake_case			:Dict          =						new_distance
                      if edge.weight == 0:
                           queue.appendleft(edge.destination_vertex    )
                      else:
                           queue.append(edge.destination_vertex    )

            if distances[finish_vertex] is None:
                 raise ValueError("""No path from start_vertex to finish_vertex."""    )

            return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()


