'''simple docstring'''
import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


lowercase__  :      Union[str, Any]        =					False
try:
      lowercase__  :      str        =					_is_package_available("google.colab")
except ModuleNotFoundError:
      pass







@input.register
class lowerCamelCase							:




  '''simple docstring'''



  def __init__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       str = None  ,							UpperCAmelCase__			:       list = []  )				->Optional[int]:
        UpperCAmelCase_            =  0
        UpperCAmelCase_            =  choices
        UpperCAmelCase_            =  prompt
        if sys.platform == "win32":
              UpperCAmelCase_            =  "*"
        else:
              UpperCAmelCase_            =  "➔ "






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       str = ""  )				->Optional[Any]:



        if sys.platform != "win32":
              writeColor(self.choices[index]  ,							32  ,							__lowerCamelCase  )
        else:
              forceWrite(self.choices[index]  ,							__lowerCamelCase  )






  def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       int  )				->List[Any]:



        if index == self.position:
              forceWrite(f""" {self.arrow_char} """  )
              self.write_choice(__lowerCamelCase  )
        else:
              forceWrite(f"""    {self.choices[index]}"""  )
        reset_cursor()






  def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       Direction  ,							UpperCAmelCase__			:       int = 1  )				->Optional[int]:
        UpperCAmelCase_            =  self.position
        if direction == Direction.DOWN:
              if self.position + 1 >= len(self.choices  ):
                    return
              self.position += num_spaces
        else:
              if self.position - 1 < 0:
                    return
              self.position -= num_spaces
        clear_line()
        self.print_choice(__lowerCamelCase  )
        move_cursor(__lowerCamelCase  ,							direction.name  )
        self.print_choice(self.position  )






  @input.mark(KEYMAP['''up''']  )
  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[str]:
        self.move_direction(Direction.UP  )






  @input.mark(KEYMAP['''down''']  )
  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->List[str]:
        self.move_direction(Direction.DOWN  )






  @input.mark(KEYMAP['''newline''']  )
  def  lowerCAmelCase__ (      self			:       str  )				->Optional[int]:
        move_cursor(len(self.choices  ) - self.position  ,							'''DOWN'''  )
        return self.position






  @input.mark(KEYMAP['''interrupt''']  )
  def  lowerCAmelCase__ (      self			:       Tuple  )				->int:
        move_cursor(len(self.choices  ) - self.position  ,							'''DOWN'''  )
        raise KeyboardInterrupt






  @input.mark_multiple(*[KEYMAP[str(__lowerCamelCase  )] for number in range(10  )]  )
  def  lowerCAmelCase__ (      self			:       str  )				->Union[str, Any]:
        UpperCAmelCase_            =  int(chr(self.current_selection  )  )
        UpperCAmelCase_            =  index - self.position
        if index == self.position:
              return
        if index < len(self.choices  ):
              if self.position > index:
                    self.move_direction(Direction.UP  ,							-movement  )
              elif self.position < index:
                    self.move_direction(Direction.DOWN  ,							__lowerCamelCase  )
              else:
                    return
        else:
              return






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       int = 0  )				->Optional[Any]:



        if self.prompt:
              linebreak()
              forceWrite(self.prompt  ,							'''\n'''  )
              if in_colab:
                    forceWrite('''Please input a choice index (starting from 0), and press enter'''  ,							'''\n'''  )
              else:
                    forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter'''  ,							'''\n'''  )
        UpperCAmelCase_            =  default_choice
        for i in range(len(self.choices  )  ):
              self.print_choice(__lowerCamelCase  )
              forceWrite('''\n'''  )
        move_cursor(len(self.choices  ) - self.position  ,							'''UP'''  )
        with cursor.hide():
              while True:
                    if in_colab:
                          try:
                                UpperCAmelCase_            =  int(builtins.input()  )
                          except ValueError:
                                UpperCAmelCase_            =  default_choice
                    else:
                          UpperCAmelCase_            =  self.handle_input()
                    if choice is not None:
                          reset_cursor()
                          for _ in range(len(self.choices  ) + 1  ):
                                move_cursor(1  ,							'''UP'''  )
                                clear_line()
                          self.write_choice(__lowerCamelCase  ,							'''\n'''  )
                          return choice


'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor







class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def __init__(      self			:       int  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[str, Any]=7  ,							UpperCAmelCase__			:       Optional[int]=30  ,							UpperCAmelCase__			:       Union[str, Any]=400  ,							UpperCAmelCase__			:       List[Any]=3  ,							)				->Dict:
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size if size is not None else {'''shortest_edge''': 288}
        UpperCAmelCase_            =  size_divisor
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  do_center_crop
        UpperCAmelCase_            =  image_mean
        UpperCAmelCase_            =  image_std
        UpperCAmelCase_            =  do_pad
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  min_resolution
        UpperCAmelCase_            =  max_resolution






  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Dict=False  )				->Any:
        if not batched:
              UpperCAmelCase_            =  self.size['''shortest_edge''']
              UpperCAmelCase_            =  image_inputs[0]
              if isinstance(UpperCAmelCase__  ,							Image.Image  ):
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.size
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.shape[1], image.shape[2]
              UpperCAmelCase_            =  size / min(UpperCAmelCase__  ,							UpperCAmelCase__  )
              if h < w:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  size, scale * w
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  scale * h, size

              UpperCAmelCase_            =  int((1333 / 800) * size  )
              if max(UpperCAmelCase__  ,							UpperCAmelCase__  ) > max_size:
                    UpperCAmelCase_            =  max_size / max(UpperCAmelCase__  ,							UpperCAmelCase__  )
                    UpperCAmelCase_            =  newh * scale
                    UpperCAmelCase_            =  neww * scale

              UpperCAmelCase_      ,		UpperCAmelCase_            =  int(newh + 0.5  ), int(neww + 0.5  )
              UpperCAmelCase_      ,		UpperCAmelCase_            =  (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

        else:
              UpperCAmelCase_            =  []
              for image in image_inputs:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  self.get_expected_values([image]  )
                    expected_values.append((expected_height, expected_width)  )
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[0]  )[0]
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[1]  )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	BridgeTowerImageProcessor if is_vision_available() else None






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->str:
        UpperCAmelCase_            =  BridgeTowerImageProcessingTester(self  )






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->Dict:
        return self.image_processor_tester.prepare_image_processor_dict()






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->int:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''image_mean'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''image_std'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_normalize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_resize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''size'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''size_divisor'''  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Union[str, Any]:
        pass






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Union[str, Any]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							Image.Image  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							numpify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							np.ndarray  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							torchify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							torch.Tensor  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)





