
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


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




@input.register
class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Tuple     ,				UpperCamelCase__     :					str = None     ,				UpperCamelCase__     :					list = []       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			0
    __magic_name__								=			choices
    __magic_name__								=			prompt
    if sys.platform == "win32":
      __magic_name__								=			"""*"""
    else:
      __magic_name__								=			"""➔ """
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					str = ""       )     ->      str:







    """simple docstring"""






    if sys.platform != "win32":
      writeColor(self.choices[index]     ,				32     ,				snake_case__       )
    else:
      forceWrite(self.choices[index]     ,				snake_case__       )
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					int       )     ->      List[Any]:







    """simple docstring"""






    if index == self.position:
      forceWrite(F''' {self.arrow_char} '''       )
      self.write_choice(snake_case__       )
    else:
      forceWrite(F'''    {self.choices[index]}'''       )
    reset_cursor()
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Direction     ,				UpperCamelCase__     :					int = 1       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			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(snake_case__       )
    move_cursor(snake_case__     ,				direction.name       )
    self.print_choice(self.position       )
  @input.mark(KEYMAP["""up"""]       )
  def 				_lowercase				(					self     :					int       )     ->      List[Any]:







    """simple docstring"""






    self.move_direction(Direction.UP       )
  @input.mark(KEYMAP["""down"""]       )
  def 				_lowercase				(					self     :					str       )     ->      Tuple:







    """simple docstring"""






    self.move_direction(Direction.DOWN       )
  @input.mark(KEYMAP["""newline"""]       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      int:







    """simple docstring"""






    move_cursor(len(self.choices       ) - self.position     ,				"""DOWN"""       )
    return self.position
  @input.mark(KEYMAP["""interrupt"""]       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      Dict:







    """simple docstring"""






    move_cursor(len(self.choices       ) - self.position     ,				"""DOWN"""       )
    raise KeyboardInterrupt
  @input.mark_multiple(*[KEYMAP[str(snake_case__       )] for number in range(10       )]       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			int(chr(self.current_selection       )       )
    __magic_name__								=			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     ,				snake_case__       )
      else:
        return
    else:
      return





  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					int = 0       )     ->      Any:







    """simple docstring"""






    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"""       )
    __magic_name__								=			default_choice
    for i in range(len(self.choices       )       ):
      self.print_choice(snake_case__       )
      forceWrite("""\n"""       )
    move_cursor(len(self.choices       ) - self.position     ,				"""UP"""       )
    with cursor.hide():
      while True:
        if in_colab:
          try:
            __magic_name__								=			int(builtins.input()       )
          except ValueError:
            __magic_name__								=			default_choice
        else:
          __magic_name__								=			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(snake_case__     ,				"""\n"""       )
          return choice


"""simple docstring"""




import unittest

import numpy as np
import requests

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

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			__lowerCAmelCase     :				int    				=    False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class    a_   (  unittest.TestCase      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=7	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  int=18	,    snake_case__    :  Dict=30	,    snake_case__    :  Optional[Any]=400	,    snake_case__    :  Tuple=None	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Any=True	,    snake_case__    :  Dict=None	,    ):
							lowerCAmelCase__   = size if size is not None else {"""height""": 20, """width""": 20}
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = min_resolution
							lowerCAmelCase__   = max_resolution
							lowerCAmelCase__   = size
							lowerCAmelCase__   = do_normalize
							lowerCAmelCase__   = do_convert_rgb
							lowerCAmelCase__   = [512, 1024, 2048, 4096]
							lowerCAmelCase__   = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
							lowerCAmelCase__   = Image.open(requests.get(snake_case__	,    stream=snake_case__  ).raw  ).convert("""RGB"""  )
							return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.image_processor_tester.prepare_dummy_image()

							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							lowerCAmelCase__   = 2048

							lowerCAmelCase__   = image_processor(snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,    torch.tensor(0.0606  )	,    atol=1E-3	,    rtol=1E-3  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							lowerCAmelCase__   = True

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(snake_case__  ):
											lowerCAmelCase__   = image_processor(
											    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches

									lowerCAmelCase__   = """Hello"""

									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    numpify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    np.ndarray  )

							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__	,    torchify=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    torch.Tensor  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self	,    num_channels=4  )
							lowerCAmelCase__   = 3




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=snake_case__  )
							for image in image_inputs:
									self.assertIsInstance(snake_case__	,    Image.Image  )

							# Test not batched input
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




