
"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
       from PIL import Image

       from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=tempfile.mkdtemp()

     # fmt: off
     a					=["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
     # fmt: on
     a					=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]						)						)

     a					={
         """do_resize""": True,
         """size""": {"""height""": 18, """width""": 18},
         """do_normalize""": True,
         """image_mean""": [0.5, 0.5, 0.5],
         """image_std""": [0.5, 0.5, 0.5],
     }
     a					=os.path.join(self.tmpdirname			,   snake_case_						)
     with open(self.image_processor_file			,   '''w'''			,   encoding='''utf-8'''						) as fp:
         json.dump(snake_case_			,   snake_case_						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					List[Any]:
     return BertTokenizer.from_pretrained(self.tmpdirname			,   **snake_case_						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Dict:
     return ViTImageProcessor.from_pretrained(self.tmpdirname			,   **snake_case_						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     shutil.rmtree(self.tmpdirname						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=[np.random.randint(255			,   size=(3, 30, 400)			,   dtype=np.uinta						)]

     a					=[Image.fromarray(np.moveaxis(snake_case_			,   0			,   -1						)						) for x in image_inputs]

     return image_inputs


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=self.get_tokenizer()
     a					=self.get_image_processor()

     a					=VisionTextDualEncoderProcessor(tokenizer=snake_case_			,   image_processor=snake_case_						)

     processor.save_pretrained(self.tmpdirname						)
     a					=VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

     self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer.get_vocab()						)
     self.assertIsInstance(processor.tokenizer			,   (BertTokenizer, BertTokenizerFast)						)

     self.assertEqual(processor.image_processor.to_json_string()			,   image_processor.to_json_string()						)
     self.assertIsInstance(processor.image_processor			,   snake_case_						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()			,   image_processor=self.get_image_processor()						)
     processor.save_pretrained(self.tmpdirname						)

     a					=self.get_tokenizer(bos_token='''(BOS)'''			,   eos_token='''(EOS)'''						)
     a					=self.get_image_processor(do_normalize=snake_case_			,   padding_value=1.0						)

     a					=VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname			,   bos_token='''(BOS)'''			,   eos_token='''(EOS)'''			,   do_normalize=snake_case_			,   padding_value=1.0						)

     self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer_add_kwargs.get_vocab()						)
     self.assertIsInstance(processor.tokenizer			,   (BertTokenizer, BertTokenizerFast)						)

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.get_image_processor()
     a					=self.get_tokenizer()

     a					=VisionTextDualEncoderProcessor(tokenizer=snake_case_			,   image_processor=snake_case_						)

     a					=self.prepare_image_inputs()

     a					=image_processor(snake_case_			,   return_tensors='''np'''						)
     a					=processor(images=snake_case_			,   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 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.get_image_processor()
     a					=self.get_tokenizer()

     a					=VisionTextDualEncoderProcessor(tokenizer=snake_case_			,   image_processor=snake_case_						)

     a					="""lower newer"""

     a					=processor(text=snake_case_						)

     a					=tokenizer(snake_case_						)

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=self.get_image_processor()
     a					=self.get_tokenizer()

     a					=VisionTextDualEncoderProcessor(tokenizer=snake_case_			,   image_processor=snake_case_						)

     a					="""lower newer"""
     a					=self.prepare_image_inputs()

     a					=processor(text=snake_case_			,   images=snake_case_						)

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

     # test if it raises when no input is passed
     with self.assertRaises(snake_case_						):
         processor()


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.get_image_processor()
     a					=self.get_tokenizer()

     a					=VisionTextDualEncoderProcessor(tokenizer=snake_case_			,   image_processor=snake_case_						)

     a					=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     a					=processor.batch_decode(snake_case_						)
     a					=tokenizer.batch_decode(snake_case_						)

     self.assertListEqual(snake_case_			,   snake_case_						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=self.get_image_processor()
     a					=self.get_tokenizer()

     a					=VisionTextDualEncoderProcessor(tokenizer=snake_case_			,   image_processor=snake_case_						)

     a					="""lower newer"""
     a					=self.prepare_image_inputs()

     a					=processor(text=snake_case_			,   images=snake_case_						)

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



"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_       :       List[Any]      						=  TypeVar("""T""")






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self			,   __A						) ->					None:
     a					=data
     a					=self
     a					=0






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # map from node name to the node object
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # create a new set with x as its member
     a					=DisjointSetTreeNode(__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					DisjointSetTreeNode[T]:
     # find the set x belongs to (with path-compression)
     a					=self.map[data]
     if elem_ref != elem_ref.parent:
         a					=self.find_set(elem_ref.parent.data						)
     return elem_ref.parent


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # helper function for union operation
     if nodea.rank > nodea.rank:
         a					=nodea
     else:
         a					=nodea
         if nodea.rank == nodea.rank:
             nodea.rank += 1





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					None:
     # merge 2 disjoint sets
     self.link(self.find_set(__A						)			,   self.find_set(__A						)						)






class 				__A    (	Generic[T]      ):






 """simple docstring"""




 def __init__(		self						) ->					None:
     # connections: map from the node to the neighbouring nodes (with weights)
     a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					None:
     # add a node ONLY if its not present in the graph
     if node not in self.connections:
         a					={}


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					None:
     # add an edge with the given weight
     self.add_node(__A						)
     self.add_node(__A						)
     a					=weight
     a					=weight





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					GraphUndirectedWeighted[T]:
     a					=[]
     a					=set()
     for start in self.connections:
         for end in self.connections[start]:
             if (start, end) not in seen:
                 seen.add((end, start)						)
                 edges.append((start, end, self.connections[start][end])						)
     edges.sort(key=lambda __A						: x[2]						)

     # creating the disjoint set
     a					=DisjointSetTree[T]()
     for node in self.connections:
         disjoint_set.make_set(__A						)

     # MST generation
     a					=0
     a					=0
     a					=GraphUndirectedWeighted[T]()
     while num_edges < len(self.connections						) - 1:
         a		,				a		,				a					=edges[index]
         index += 1
         a					=disjoint_set.find_set(__A						)
         a					=disjoint_set.find_set(__A						)
         if parent_u != parent_v:
             num_edges += 1
             graph.add_edge(__A			,   __A			,   __A						)
             disjoint_set.union(__A			,   __A						)
     return graph