




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    ):




    """simple docstring"""

    def    a      (       self     :     Any   )      ->       Union[str, Any]:
         lowerCAmelCase__		= tempfile.mkdtemp()

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

         lowerCAmelCase__		= {
             "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,
         }

         lowerCAmelCase__		= 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     :     Any   ,	**SCREAMING_SNAKE_CASE__     :     str   )      ->       Dict:
         return BertTokenizer.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   ,	**SCREAMING_SNAKE_CASE__     :     Dict   )      ->       Union[str, Any]:
         return BertTokenizerFast.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Union[str, Any]   ,	**SCREAMING_SNAKE_CASE__     :     Any   )      ->       Any:
         return FlavaImageProcessor.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Dict   )      ->       str:
         shutil.rmtree(self.tmpdirname   )


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

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

         return image_inputs


    def    a      (       self     :     Optional[Any]   )      ->       Tuple:
         lowerCAmelCase__		= self.get_tokenizer()
         lowerCAmelCase__		= self.get_rust_tokenizer()
         lowerCAmelCase__		= self.get_image_processor()

         lowerCAmelCase__		= FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,	image_processor=SCREAMING_SNAKE_CASE__   )
         processor_slow.save_pretrained(self.tmpdirname   )
         lowerCAmelCase__		= FlavaProcessor.from_pretrained(self.tmpdirname   ,	use_fast=SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,	image_processor=SCREAMING_SNAKE_CASE__   )
         processor_fast.save_pretrained(self.tmpdirname   )
         lowerCAmelCase__		= 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     :     Dict   )      ->       str:
         lowerCAmelCase__		= FlavaProcessor(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=SCREAMING_SNAKE_CASE__   ,	padding_value=1.0   )

         lowerCAmelCase__		= 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     :     str   )      ->       int:
         lowerCAmelCase__		= self.get_image_processor()
         lowerCAmelCase__		= self.get_tokenizer()

         lowerCAmelCase__		= FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,	image_processor=SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= self.prepare_image_inputs()

         lowerCAmelCase__		= image_processor(SCREAMING_SNAKE_CASE__   ,	return_tensors="np"   )
         lowerCAmelCase__		= 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(1_234   )
         lowerCAmelCase__		= image_processor(
             SCREAMING_SNAKE_CASE__   ,	return_image_mask=SCREAMING_SNAKE_CASE__   ,	return_codebook_pixels=SCREAMING_SNAKE_CASE__   ,	return_tensors="np"   )
         random.seed(1_234   )
         lowerCAmelCase__		= 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     :     List[str]   )      ->       List[str]:
         lowerCAmelCase__		= self.get_image_processor()
         lowerCAmelCase__		= self.get_tokenizer()

         lowerCAmelCase__		= FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,	image_processor=SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= "lower newer"

         lowerCAmelCase__		= processor(text=SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   )

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


    def    a      (       self     :     List[Any]   )      ->       List[str]:
         lowerCAmelCase__		= self.get_image_processor()
         lowerCAmelCase__		= self.get_tokenizer()

         lowerCAmelCase__		= FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,	image_processor=SCREAMING_SNAKE_CASE__   )

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

         lowerCAmelCase__		= 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
         lowerCAmelCase__		= 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     :     str   )      ->       Dict:
         lowerCAmelCase__		= self.get_image_processor()
         lowerCAmelCase__		= self.get_tokenizer()

         lowerCAmelCase__		= FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,	image_processor=SCREAMING_SNAKE_CASE__   )

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

         lowerCAmelCase__		= processor.batch_decode(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= tokenizer.batch_decode(SCREAMING_SNAKE_CASE__   )

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )


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

         lowerCAmelCase__		= FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__   ,	image_processor=SCREAMING_SNAKE_CASE__   )

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

         lowerCAmelCase__		= processor(text=SCREAMING_SNAKE_CASE__   ,	images=SCREAMING_SNAKE_CASE__   )

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






from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase								=     TypeVar('T')





class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       List[Any]:
         lowerCAmelCase__		= data  # Assign data
         lowerCAmelCase__		= None  # Initialize next as null
         lowerCAmelCase__		= None  # Initialize prev as null






class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     Any   )      ->       None:
         lowerCAmelCase__		= None


    def    a      (       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       None:
         if self.head is None:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
         else:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
              lowerCAmelCase__		= new_node
              lowerCAmelCase__		= self.head
              lowerCAmelCase__		= None
              lowerCAmelCase__		= new_node


    def    a      (       self     :     int   )      ->       T | None:
         if self.head is None:
              return None
         else:
              assert self.head is not None
              lowerCAmelCase__		= self.head.data
              lowerCAmelCase__		= self.head.next
              if self.head is not None:
                   lowerCAmelCase__		= None
              return temp


    def    a      (       self     :     int   )      ->       T | None:
         return self.head.data if self.head is not None else None


    def __len__(       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= self.head
         lowerCAmelCase__		= 0
         while temp is not None:
              count += 1
              lowerCAmelCase__		= temp.next
         return count


    def    a      (       self     :     Tuple   )      ->       bool:
         return self.head is None


    def    a      (       self     :     Dict   )      ->       None:
         print("stack elements are:"   )
         lowerCAmelCase__		= self.head
         while temp is not None:
              print(temp.data   ,	end="->"   )
              lowerCAmelCase__		= temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     UpperCamelCase								=     Stack()

     # Insert 4 at the beginning. So stack becomes 4->None
     print('Stack operations using Doubly LinkedList')
     stack.push(4)

     # Insert 5 at the beginning. So stack becomes 4->5->None
     stack.push(5)

     # Insert 6 at the beginning. So stack becomes 4->5->6->None
     stack.push(6)

     # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
     stack.push(7)

     # Print the stack
     stack.print_stack()

     # Print the top element
     print('\nTop element is ', stack.top())

     # Print the stack size
     print('Size of the stack is ', len(stack))

     # pop the top element
     stack.pop()

     # pop the top element
     stack.pop()

     # two elements have now been popped off
     stack.print_stack()

     # Print True if the stack is empty else False
     print('\nstack is empty:', stack.is_empty())
