
"""simple docstring"""


from __future__ import annotations

from typing import Generic, TypeVar

A_    							=					TypeVar("""T""")




class  __lowerCamelCase  ( Generic[T]			):




				def __init__(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  data
							lowerCamelCase_					=  self
							lowerCamelCase_					=  0




class  __lowerCamelCase  ( Generic[T]			):




				def __init__(				self   ):
							# map from node name to the node object
							lowerCamelCase_					=  {}




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							# create a new set with x as its member
							lowerCamelCase_					=  DisjointSetTreeNode(UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							# find the set x belongs to (with path-compression)
							lowerCamelCase_					=  self.map[data]
							if elem_ref != elem_ref.parent:
										lowerCamelCase_					=  self.find_set(elem_ref.parent.data   )
							return elem_ref.parent




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							# helper function for union operation
							if nodea.rank > nodea.rank:
										lowerCamelCase_					=  nodea
							else:
										lowerCamelCase_					=  nodea
										if nodea.rank == nodea.rank:
													nodea.rank += 1





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							# merge 2 disjoint sets
							self.link(self.find_set(UpperCAmelCase   )							,						self.find_set(UpperCAmelCase   )   )




class  __lowerCamelCase  ( Generic[T]			):




				def __init__(				self   ):
							# connections: map from the node to the neighbouring nodes (with weights)
							lowerCamelCase_					=  {}




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							# add a node ONLY if its not present in the graph
							if node not in self.connections:
										lowerCamelCase_					=  {}




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							# add an edge with the given weight
							self.add_node(UpperCAmelCase   )
							self.add_node(UpperCAmelCase   )
							lowerCamelCase_					=  weight
							lowerCamelCase_					=  weight





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  []
							lowerCamelCase_					=  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 UpperCAmelCase   : x[2]   )

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

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





"""simple docstring"""


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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  tempfile.mkdtemp()

							lowerCamelCase_					=  [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							lowerCamelCase_					=  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]   )   )

							lowerCamelCase_					=  {
							    '''do_resize''': True,
							    '''size''': 20,
							    '''do_center_crop''': True,
							    '''crop_size''': 18,
							    '''do_normalize''': True,
							    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							}
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						UpperCAmelCase   )
							with open(self.image_processor_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										json.dump(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizer.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							shutil.rmtree(self.tmpdirname   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta   )]
							lowerCamelCase_					=  [Image.fromarray(np.moveaxis(UpperCAmelCase							,						0							,						-1   )   ) for x in image_inputs]
							return image_inputs




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()
							lowerCamelCase_					=  self.get_rust_tokenizer()
							lowerCamelCase_					=  self.get_image_processor()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_slow.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname							,						use_fast=UpperCAmelCase   )

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_fast.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.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							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.tokenizer							,						UpperCAmelCase   )

							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							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AlignProcessor(tokenizer=self.get_tokenizer()							,						image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

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

							lowerCamelCase_					=  AlignProcessor.from_pretrained(
							    self.tmpdirname							,						bos_token='''(BOS)'''							,						eos_token='''(EOS)'''							,						do_normalize=UpperCAmelCase							,						padding_value=1.0   )

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

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  image_processor(UpperCAmelCase							,						return_tensors='''np'''   )
							lowerCamelCase_					=  processor(images=UpperCAmelCase							,						return_tensors='''np'''   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''

							lowerCamelCase_					=  processor(text=UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding='''max_length'''							,						max_length=64   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

							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(UpperCAmelCase   ):
										processor()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

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

							lowerCamelCase_					=  processor.batch_decode(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.batch_decode(UpperCAmelCase   )

							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

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



