



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_		:     int        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''pixel_values''']

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       bool = True							,   __lowerCAmelCase			:       Optional[Dict[str, int]] = None							,   __lowerCAmelCase			:       PILImageResampling = PILImageResampling.BICUBIC							,   __lowerCAmelCase			:       bool = True							,   __lowerCAmelCase			:       bool = True							,   __lowerCAmelCase			:       Union[int, float] = 1 / 255							,   __lowerCAmelCase			:       Dict[str, int] = None							,   __lowerCAmelCase			:       bool = True							,   __lowerCAmelCase			:       Optional[Union[float, List[float]]] = None							,   __lowerCAmelCase			:       Optional[Union[float, List[float]]] = None							,   **__lowerCAmelCase			:       Dict							,   )       ->				None:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase							)
    a						=		size if size is not None else {"height": 224, "width": 224}
    a						=		get_size_dict(__lowerCAmelCase							)
    a						=		crop_size if crop_size is not None else {"height": 224, "width": 224}
    a						=		get_size_dict(__lowerCAmelCase							,   default_to_square=__lowerCAmelCase							,   param_name="crop_size"							)

    a						=		do_resize
    a						=		do_rescale
    a						=		do_normalize
    a						=		do_center_crop
    a						=		crop_size
    a						=		size
    a						=		resample
    a						=		rescale_factor
    a						=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
    a						=		image_std if image_std is not None else IMAGENET_DEFAULT_STD

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       np.ndarray							,   __lowerCAmelCase			:       Dict[str, int]							,   __lowerCAmelCase			:       PILImageResampling = PILImageResampling.BILINEAR							,   __lowerCAmelCase			:       Optional[Union[str, ChannelDimension]] = None							,   **__lowerCAmelCase			:       List[str]							,   )       ->				np.ndarray:


    """simple docstring"""






    a						=		get_size_dict(__lowerCAmelCase							)

    if "shortest_edge" in size:
     a						=		get_resize_output_image_size(__lowerCAmelCase							,   size=size["shortest_edge"]							,   default_to_square=__lowerCAmelCase							)
     # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
    elif "height" in size and "width" in size:
     a						=		(size["height"], size["width"])
    else:
     raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""							)
    return resize(__lowerCAmelCase							,   size=__lowerCAmelCase							,   resample=__lowerCAmelCase							,   data_format=__lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       np.ndarray							,   __lowerCAmelCase			:       Dict[str, int]							,   __lowerCAmelCase			:       Optional[Union[str, ChannelDimension]] = None							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				np.ndarray:


    """simple docstring"""






    a						=		get_size_dict(__lowerCAmelCase							)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""							)
    return center_crop(__lowerCAmelCase							,   size=(size["height"], size["width"])							,   data_format=__lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       np.ndarray							,   __lowerCAmelCase			:       float							,   __lowerCAmelCase			:       Optional[Union[str, ChannelDimension]] = None							,   **__lowerCAmelCase			:       int							)       ->				np.ndarray:


    """simple docstring"""






    return rescale(__lowerCAmelCase							,   scale=__lowerCAmelCase							,   data_format=__lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       np.ndarray							,   __lowerCAmelCase			:       Union[float, List[float]]							,   __lowerCAmelCase			:       Union[float, List[float]]							,   __lowerCAmelCase			:       Optional[Union[str, ChannelDimension]] = None							,   **__lowerCAmelCase			:       Dict							,   )       ->				np.ndarray:


    """simple docstring"""






    return normalize(__lowerCAmelCase							,   mean=__lowerCAmelCase							,   std=__lowerCAmelCase							,   data_format=__lowerCAmelCase							,   **__lowerCAmelCase							)




   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       ImageInput							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Dict[str, int] = None							,   __lowerCAmelCase			:       PILImageResampling = None							,   __lowerCAmelCase			:       bool = None							,   __lowerCAmelCase			:       int = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Optional[float] = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Optional[Union[float, List[float]]] = None							,   __lowerCAmelCase			:       Optional[Union[float, List[float]]] = None							,   __lowerCAmelCase			:       Optional[Union[str, TensorType]] = None							,   __lowerCAmelCase			:       Union[str, ChannelDimension] = ChannelDimension.FIRST							,   **__lowerCAmelCase			:       Tuple							,   )       ->				BatchFeature:


    """simple docstring"""






    a						=		do_resize if do_resize is not None else self.do_resize
    a						=		do_rescale if do_rescale is not None else self.do_rescale
    a						=		do_normalize if do_normalize is not None else self.do_normalize
    a						=		do_center_crop if do_center_crop is not None else self.do_center_crop
    a						=		crop_size if crop_size is not None else self.crop_size
    a						=		get_size_dict(__lowerCAmelCase							,   param_name="crop_size"							,   default_to_square=__lowerCAmelCase							)
    a						=		resample if resample is not None else self.resample
    a						=		rescale_factor if rescale_factor is not None else self.rescale_factor
    a						=		image_mean if image_mean is not None else self.image_mean
    a						=		image_std if image_std is not None else self.image_std

    a						=		size if size is not None else self.size
    a						=		get_size_dict(__lowerCAmelCase							)

    if not is_batched(__lowerCAmelCase							):
     a						=		[images]

    if not valid_images(__lowerCAmelCase							):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray."							)

    if do_resize and size is None:
     raise ValueError("Size must be specified if do_resize is True."							)

    if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True."							)

    if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True."							)

    # All transformations expect numpy arrays.
    a						=		[to_numpy_array(__lowerCAmelCase							) for image in images]

    if do_resize:
     a						=		[self.resize(image=__lowerCAmelCase							,   size=__lowerCAmelCase							,   resample=__lowerCAmelCase							) for image in images]

    if do_center_crop:
     a						=		[self.center_crop(image=__lowerCAmelCase							,   size=__lowerCAmelCase							) for image in images]

    if do_rescale:
     a						=		[self.rescale(image=__lowerCAmelCase							,   scale=__lowerCAmelCase							) for image in images]

    if do_normalize:
     a						=		[self.normalize(image=__lowerCAmelCase							,   mean=__lowerCAmelCase							,   std=__lowerCAmelCase							) for image in images]

    a						=		[to_channel_dimension_format(__lowerCAmelCase							,   __lowerCAmelCase							) for image in images]

    a						=		{"pixel_values": images}
    return BatchFeature(data=__lowerCAmelCase							,   tensor_type=__lowerCAmelCase							)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class 		_lowercase		(       unittest.TestCase	):

   def 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    a						=		[[1, 2, 4], [1, 2, 3, 4]]
    a						=		DisjunctiveConstraint(__lowerCAmelCase							)
    self.assertTrue(isinstance(dc.token_ids							,   __lowerCAmelCase							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint([torch.LongTensor([1, 2, 4]							), torch.LongTensor([1, 2, 3, 4, 5]							)]							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		[[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(__lowerCAmelCase							)  # fails here

   def 		A							(      self			:       int							)       ->				Any:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(3							)
    a						=		stepped is True and completed is True and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]							)




   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(4							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2, 4]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]							)

    dc.reset()

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 3							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 2							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.remaining() == 0							)
    self.assertTrue(dc.current_seq == [1, 2, 5]							)

