





"""simple docstring"""




import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )
@require_vision
class 							lowerCamelCase		( unittest.TestCase				):







   def       a_							(							self ):
       UpperCamelCase				:   str          =							tempfile.mkdtemp()

       # fmt: off
       UpperCamelCase				:   Tuple          =							["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
       # fmt: on
       UpperCamelCase				:   Union[str, Any]          =							os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES["""vocab_file"""] )

       with open(self.vocab_file    ,      """w"""    ,      encoding="""utf-8""" ) as fp:
           fp.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

       UpperCamelCase				:   Tuple          =							{
           """image_mean""": FLAVA_IMAGE_MEAN,
           """image_std""": FLAVA_IMAGE_STD,
           """do_normalize""": True,
           """do_resize""": True,
           """size""": 224,
           """do_center_crop""": True,
           """crop_size""": 224,
           """input_size_patches""": 14,
           """total_mask_patches""": 75,
           """mask_group_max_patches""": None,
           """mask_group_min_patches""": 16,
           """mask_group_min_aspect_ratio""": 0.3,
           """mask_group_max_aspect_ratio""": None,
           """codebook_do_resize""": True,
           """codebook_size""": 112,
           """codebook_do_center_crop""": True,
           """codebook_crop_size""": 112,
           """codebook_do_map_pixels""": True,
           """codebook_do_normalize""": True,
           """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
           """codebook_image_std""": FLAVA_CODEBOOK_STD,
       }

       UpperCamelCase				:   Any          =							os.path.join(self.tmpdirname    ,      SCREAMING_SNAKE_CASE_ )
       with open(self.image_processor_file    ,      """w"""    ,      encoding="""utf-8""" ) as fp:
           json.dump(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      **SCREAMING_SNAKE_CASE_ ):
       return BertTokenizer.from_pretrained(self.tmpdirname    ,      **SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      **SCREAMING_SNAKE_CASE_ ):
       return BertTokenizerFast.from_pretrained(self.tmpdirname    ,      **SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      **SCREAMING_SNAKE_CASE_ ):
       return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,      **SCREAMING_SNAKE_CASE_ )







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







   def       a_							(							self ):
       UpperCamelCase				:   int          =							[np.random.randint(255    ,      size=(3, 30, 400)    ,      dtype=np.uinta )]

       UpperCamelCase				:   Tuple          =							[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_    ,      0    ,      -1 ) ) for x in image_inputs]

       return image_inputs







   def       a_							(							self ):
       UpperCamelCase				:   str          =							self.get_tokenizer()
       UpperCamelCase				:   Optional[Any]          =							self.get_rust_tokenizer()
       UpperCamelCase				:   Union[str, Any]          =							self.get_image_processor()

       UpperCamelCase				:   Dict          =							FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,      image_processor=SCREAMING_SNAKE_CASE_ )
       processor_slow.save_pretrained(self.tmpdirname )
       UpperCamelCase				:   List[str]          =							FlavaProcessor.from_pretrained(self.tmpdirname    ,      use_fast=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   int          =							FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,      image_processor=SCREAMING_SNAKE_CASE_ )
       processor_fast.save_pretrained(self.tmpdirname )
       UpperCamelCase				:   Union[str, Any]          =							FlavaProcessor.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    ,      SCREAMING_SNAKE_CASE_ )
       self.assertIsInstance(processor_fast.tokenizer    ,      SCREAMING_SNAKE_CASE_ )

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







   def       a_							(							self ):
       UpperCamelCase				:   int          =							FlavaProcessor(tokenizer=self.get_tokenizer()    ,      image_processor=self.get_image_processor() )
       processor.save_pretrained(self.tmpdirname )

       UpperCamelCase				:   str          =							self.get_tokenizer(bos_token="""(BOS)"""    ,      eos_token="""(EOS)""" )
       UpperCamelCase				:   int          =							self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_    ,      padding_value=1.0 )

       UpperCamelCase				:   Union[str, Any]          =							FlavaProcessor.from_pretrained(
           self.tmpdirname    ,      bos_token="""(BOS)"""    ,      eos_token="""(EOS)"""    ,      do_normalize=SCREAMING_SNAKE_CASE_    ,      padding_value=1.0 )

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

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







   def       a_							(							self ):
       UpperCamelCase				:   List[str]          =							self.get_image_processor()
       UpperCamelCase				:   List[str]          =							self.get_tokenizer()

       UpperCamelCase				:   List[Any]          =							FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,      image_processor=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   List[Any]          =							self.prepare_image_inputs()

       UpperCamelCase				:   Tuple          =							image_processor(SCREAMING_SNAKE_CASE_    ,      return_tensors="""np""" )
       UpperCamelCase				:   Tuple          =							processor(images=SCREAMING_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 )

       # With rest of the args
       random.seed(1234 )
       UpperCamelCase				:   Optional[int]          =							image_processor(
           SCREAMING_SNAKE_CASE_    ,      return_image_mask=SCREAMING_SNAKE_CASE_    ,      return_codebook_pixels=SCREAMING_SNAKE_CASE_    ,      return_tensors="""np""" )
       random.seed(1234 )
       UpperCamelCase				:   List[Any]          =							processor(
           images=SCREAMING_SNAKE_CASE_    ,      return_image_mask=SCREAMING_SNAKE_CASE_    ,      return_codebook_pixels=SCREAMING_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       a_							(							self ):
       UpperCamelCase				:   List[Any]          =							self.get_image_processor()
       UpperCamelCase				:   List[str]          =							self.get_tokenizer()

       UpperCamelCase				:   Union[str, Any]          =							FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,      image_processor=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   str          =							"""lower newer"""

       UpperCamelCase				:   Tuple          =							processor(text=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   List[str]          =							tokenizer(SCREAMING_SNAKE_CASE_ )

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







   def       a_							(							self ):
       UpperCamelCase				:   str          =							self.get_image_processor()
       UpperCamelCase				:   List[Any]          =							self.get_tokenizer()

       UpperCamelCase				:   str          =							FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,      image_processor=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   Tuple          =							"""lower newer"""
       UpperCamelCase				:   Union[str, Any]          =							self.prepare_image_inputs()

       UpperCamelCase				:   int          =							processor(text=SCREAMING_SNAKE_CASE_    ,      images=SCREAMING_SNAKE_CASE_ )

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

       # add extra args
       UpperCamelCase				:   Any          =							processor(text=SCREAMING_SNAKE_CASE_    ,      images=SCREAMING_SNAKE_CASE_    ,      return_codebook_pixels=SCREAMING_SNAKE_CASE_    ,      return_image_mask=SCREAMING_SNAKE_CASE_ )

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

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







   def       a_							(							self ):
       UpperCamelCase				:   Tuple          =							self.get_image_processor()
       UpperCamelCase				:   List[Any]          =							self.get_tokenizer()

       UpperCamelCase				:   Tuple          =							FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,      image_processor=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   Optional[Any]          =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase				:   List[Any]          =							processor.batch_decode(SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   List[str]          =							tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )

       self.assertListEqual(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )







   def       a_							(							self ):
       UpperCamelCase				:   Optional[Any]          =							self.get_image_processor()
       UpperCamelCase				:   int          =							self.get_tokenizer()

       UpperCamelCase				:   Optional[Any]          =							FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,      image_processor=SCREAMING_SNAKE_CASE_ )

       UpperCamelCase				:   Tuple          =							"""lower newer"""
       UpperCamelCase				:   Optional[Any]          =							self.prepare_image_inputs()

       UpperCamelCase				:   Any          =							processor(text=SCREAMING_SNAKE_CASE_    ,      images=SCREAMING_SNAKE_CASE_ )

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










"""simple docstring"""




import math
import sys
class 							lowerCamelCase		:







   def __init__(							self ):
       UpperCamelCase				:   Tuple          =							0
       UpperCamelCase				:   Optional[int]          =							[]
       UpperCamelCase				:   Optional[int]          =							{}  # To store the pos of node in array







   def       a_							(							self ):
       return self.cur_size == 0







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   List[str]          =							self.left(SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   Optional[int]          =							self.right(SCREAMING_SNAKE_CASE_ )
       if lc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
           UpperCamelCase				:   str          =							lc
       else:
           UpperCamelCase				:   Union[str, Any]          =							idx
       if rc < self.cur_size and self.array(SCREAMING_SNAKE_CASE_ )[0] < self.array(SCREAMING_SNAKE_CASE_ )[0]:
           UpperCamelCase				:   Optional[Any]          =							rc
       if smallest != idx:
           self.swap(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
           self.min_heapify(SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # Inserts a node into the Priority Queue
       UpperCamelCase				:   List[Any]          =							self.cur_size
       self.cur_size += 1
       self.array.append((sys.maxsize, tup[1]) )
       self.decrease_key((sys.maxsize, tup[1])    ,      tup[0] )







   def       a_							(							self ):
       # Removes and returns the min element at top of priority queue
       UpperCamelCase				:   str          =							self.array[0][1]
       UpperCamelCase				:   Optional[int]          =							self.array[self.cur_size - 1]
       self.cur_size -= 1
       self.min_heapify(1 )
       del self.pos[min_node]
       return min_node







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of left child
       return 2 * i + 1







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of right child
       return 2 * i + 2







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # returns the index of parent
       return math.floor(i / 2 )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       # swaps array elements at indices i and j
       # update the pos{}
       UpperCamelCase				:   Dict          =							j
       UpperCamelCase				:   List[str]          =							i
       UpperCamelCase				:   Optional[int]          =							self.array[i]
       UpperCamelCase				:   Union[str, Any]          =							self.array[j]
       UpperCamelCase				:   Optional[int]          =							temp







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Tuple          =							self.pos[tup[1]]
       # assuming the new_d is atmost old_d
       UpperCamelCase				:   Union[str, Any]          =							(new_d, tup[1])
       while idx > 0 and self.array[self.par(SCREAMING_SNAKE_CASE_ )][0] > self.array[idx][0]:
           self.swap(SCREAMING_SNAKE_CASE_    ,      self.par(SCREAMING_SNAKE_CASE_ ) )
           UpperCamelCase				:   Union[str, Any]          =							self.par(SCREAMING_SNAKE_CASE_ )







class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Any          =							{}  # To store graph: u -> (v,w)
       UpperCamelCase				:   int          =							num  # Number of nodes in graph
       # To store the distance from source vertex
       UpperCamelCase				:   Any          =							[0] * self.num_nodes
       UpperCamelCase				:   Union[str, Any]          =							[-1] * self.num_nodes  # To store the path







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       #  Edge going from node u to v and v to u with weight w
       # u (w)-> v, v (w) -> u
       # Check if u already in graph
       if u in self.adjList:
           self.adjList[u].append((v, w) )
       else:
           UpperCamelCase				:   Optional[int]          =							[(v, w)]

       # Assuming undirected graph
       if v in self.adjList:
           self.adjList[v].append((u, w) )
       else:
           UpperCamelCase				:   List[Any]          =							[(u, w)]







   def       a_							(							self ):
       # u -> v(w)
       for u in self.adjList:
           print(SCREAMING_SNAKE_CASE_    ,      """->"""    ,      """ -> """.join(str(f'{v}({w})' ) for v, w in self.adjList[u] ) )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       # Flush old junk values in par[]
       UpperCamelCase				:   Tuple          =							[-1] * self.num_nodes
       # src is the source node
       UpperCamelCase				:   Dict          =							0
       UpperCamelCase				:   int          =							PriorityQueue()
       q.insert((0, src) )  # (dist from src, node)
       for u in self.adjList:
           if u != src:
               UpperCamelCase				:   Any          =							sys.maxsize  # Infinity
               UpperCamelCase				:   int          =							-1

       while not q.is_empty():
           UpperCamelCase				:   int          =							q.extract_min()  # Returns node with the min dist from source
           # Update the distance of all the neighbours of u and
           # if their prev dist was INFINITY then push them in Q
           for v, w in self.adjList[u]:
               UpperCamelCase				:   List[Any]          =							self.dist[u] + w
               if self.dist[v] > new_dist:
                   if self.dist[v] == sys.maxsize:
                       q.insert((new_dist, v) )
                   else:
                       q.decrease_key((self.dist[v], v)    ,      SCREAMING_SNAKE_CASE_ )
                   UpperCamelCase				:   Any          =							new_dist
                   UpperCamelCase				:   Tuple          =							u

        # Show the shortest distances from src
       self.show_distances(SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       print(f'Distance from node: {src}' )
       for u in range(self.num_nodes ):
           print(f'Node {u} has distance: {self.dist[u]}' )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       # To show the shortest path from src to dest
       # WARNING: Use it *after* calling dijkstra
       UpperCamelCase				:   Tuple          =							[]
       UpperCamelCase				:   Optional[Any]          =							0
       UpperCamelCase				:   int          =							dest
       # Backtracking from dest to src
       while self.par[temp] != -1:
           path.append(SCREAMING_SNAKE_CASE_ )
           if temp != src:
               for v, w in self.adjList[temp]:
                   if v == self.par[temp]:
                       cost += w
                       break
           UpperCamelCase				:   int          =							self.par[temp]
       path.append(SCREAMING_SNAKE_CASE_ )
       path.reverse()

       print(f'----Path to reach {dest} from {src}----' )
       for u in path:
           print(f'{u}'    ,      end=""" """ )
           if u != dest:
               print("""-> """    ,      end="""""" )

       print("""\nTotal cost of path: """    ,      SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
  __A :		int      							=    Graph(9)
  graph.add_edge(0, 1, 4)
  graph.add_edge(0, 7, 8)
  graph.add_edge(1, 2, 8)
  graph.add_edge(1, 7, 11)
  graph.add_edge(2, 3, 7)
  graph.add_edge(2, 8, 2)
  graph.add_edge(2, 5, 4)
  graph.add_edge(3, 4, 9)
  graph.add_edge(3, 5, 14)
  graph.add_edge(4, 5, 10)
  graph.add_edge(5, 6, 2)
  graph.add_edge(6, 7, 1)
  graph.add_edge(6, 8, 6)
  graph.add_edge(7, 8, 7)
  graph.show_graph()
  graph.dijkstra(0)
  graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21




