


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 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     tempfile.mkdtemp()

       _UpperCAmelCase		:							List[Any]							=     [
           """[UNK]""",
           """[CLS]""",
           """[SEP]""",
           """[PAD]""",
           """[MASK]""",
           """want""",
           """##want""",
           """##ed""",
           """wa""",
           """un""",
           """runn""",
           """##ing""",
           """,""",
           """low""",
           """lowest""",
       ]
       _UpperCAmelCase		:							Optional[int]							=     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]    )    )

       _UpperCAmelCase		:							Optional[int]							=     {
           """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],
       }
       _UpperCAmelCase		:							Dict							=     os.path.join(self.tmpdirname							,					A_    )
       with open(self.image_processor_file							,					"""w"""							,					encoding="""utf-8"""    ) as fp:
         json.dump(A_							,					A_    )


     def        __snake_case(  self							,					**A_    ):
       return BertTokenizer.from_pretrained(self.tmpdirname							,					**A_    )


     def        __snake_case(  self							,					**A_    ):
       return BertTokenizerFast.from_pretrained(self.tmpdirname							,					**A_    )


     def        __snake_case(  self							,					**A_    ):
       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname							,					**A_    )


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


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     [np.random.randint(2_55							,					size=(3, 30, 4_00)							,					dtype=np.uinta    )]
       _UpperCAmelCase		:							Optional[int]							=     [Image.fromarray(np.moveaxis(A_							,					0							,					-1    )    ) for x in image_inputs]
       return image_inputs


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     self.get_tokenizer()
       _UpperCAmelCase		:							Tuple							=     self.get_rust_tokenizer()
       _UpperCAmelCase		:							str							=     self.get_image_processor()

       _UpperCAmelCase		:							Any							=     AlignProcessor(tokenizer=A_							,					image_processor=A_    )
       processor_slow.save_pretrained(self.tmpdirname    )
       _UpperCAmelCase		:							Optional[Any]							=     AlignProcessor.from_pretrained(self.tmpdirname							,					use_fast=A_    )

       _UpperCAmelCase		:							Union[str, Any]							=     AlignProcessor(tokenizer=A_							,					image_processor=A_    )
       processor_fast.save_pretrained(self.tmpdirname    )
       _UpperCAmelCase		:							List[Any]							=     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							,					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        __snake_case(  self    ):
       _UpperCAmelCase		:							int							=     AlignProcessor(tokenizer=self.get_tokenizer()							,					image_processor=self.get_image_processor()    )
       processor.save_pretrained(self.tmpdirname    )

       _UpperCAmelCase		:							Union[str, Any]							=     self.get_tokenizer(bos_token="""(BOS)"""							,					eos_token="""(EOS)"""    )
       _UpperCAmelCase		:							Union[str, Any]							=     self.get_image_processor(do_normalize=A_							,					padding_value=1.0    )

       _UpperCAmelCase		:							Union[str, Any]							=     AlignProcessor.from_pretrained(
           self.tmpdirname							,					bos_token="""(BOS)"""							,					eos_token="""(EOS)"""							,					do_normalize=A_							,					padding_value=1.0    )

       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        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.get_image_processor()
       _UpperCAmelCase		:							List[str]							=     self.get_tokenizer()

       _UpperCAmelCase		:							Optional[Any]							=     AlignProcessor(tokenizer=A_							,					image_processor=A_    )

       _UpperCAmelCase		:							Any							=     self.prepare_image_inputs()

       _UpperCAmelCase		:							List[str]							=     image_processor(A_							,					return_tensors="""np"""    )
       _UpperCAmelCase		:							List[str]							=     processor(images=A_							,					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        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     self.get_image_processor()
       _UpperCAmelCase		:							List[Any]							=     self.get_tokenizer()

       _UpperCAmelCase		:							int							=     AlignProcessor(tokenizer=A_							,					image_processor=A_    )

       _UpperCAmelCase		:							Optional[int]							=     """lower newer"""

       _UpperCAmelCase		:							Optional[Any]							=     processor(text=A_    )

       _UpperCAmelCase		:							Any							=     tokenizer(A_							,					padding="""max_length"""							,					max_length=64    )

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


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Dict							=     self.get_image_processor()
       _UpperCAmelCase		:							List[str]							=     self.get_tokenizer()

       _UpperCAmelCase		:							List[Any]							=     AlignProcessor(tokenizer=A_							,					image_processor=A_    )

       _UpperCAmelCase		:							Optional[int]							=     """lower newer"""
       _UpperCAmelCase		:							int							=     self.prepare_image_inputs()

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

       _UpperCAmelCase		:							Union[str, Any]							=     AlignProcessor(tokenizer=A_							,					image_processor=A_    )

       _UpperCAmelCase		:							str							=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _UpperCAmelCase		:							Optional[Any]							=     processor.batch_decode(A_    )
       _UpperCAmelCase		:							Optional[Any]							=     tokenizer.batch_decode(A_    )

       self.assertListEqual(A_							,					A_    )




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

       _UpperCAmelCase		:							int							=     AlignProcessor(tokenizer=A_							,					image_processor=A_    )

       _UpperCAmelCase		:							Optional[Any]							=     """lower newer"""
       _UpperCAmelCase		:							Optional[int]							=     self.prepare_image_inputs()

       _UpperCAmelCase		:							int							=     processor(text=A_							,					images=A_    )

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









from __future__ import annotations




def    a__    (		snake_case__					:							list[int]     ):
  if len(snake_case__     ) == 0:
    return array

  _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     min(snake_case__     ), max(snake_case__     )

  # Compute the variables
  _UpperCAmelCase		:							Tuple							=     _max - _min + 1
  _UpperCAmelCase,_UpperCAmelCase		:							Union[str, Any]							=     [0] * holes_range, [0] * holes_range

  # Make the sorting.
  for i in array:
    _UpperCAmelCase		:							Optional[int]							=     i - _min
    _UpperCAmelCase		:							Any							=     i
    holes_repeat[index] += 1

  # Makes the array back by replacing the numbers.
  _UpperCAmelCase		:							List[Any]							=     0
  for i in range(snake_case__     ):
    while holes_repeat[i] > 0:
      _UpperCAmelCase		:							Optional[Any]							=     holes[i]
      index += 1
      holes_repeat[i] -= 1

    # Returns the sorted array.
  return array


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 SCREAMING_SNAKE_CASE__					:     Dict       =     input('Enter numbers separated by comma:\n')
 SCREAMING_SNAKE_CASE__					:     Optional[int]       =     [int(x) for x in user_input.split(',')]
 print(pigeon_sort(unsorted))





