



import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
       import torch

if is_vision_available():
       import PIL


a__   :					Any 						=      logging.get_logger(__name__)

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__						):
      def constraint_to_multiple_of(A__    ,	A__    ,	A__=0    ,	A__=None						):
            lowercase__  =	round(val / multiple						) * multiple

            if max_val is not None and x > max_val:
                  lowercase__  =	math.floor(val / multiple						) * multiple

            if x < min_val:
                  lowercase__  =	math.ceil(val / multiple						) * multiple

            return x

      lowercase__  =	(output_size, output_size) if isinstance(A__    ,	A__						) else output_size

      lowercase__,						lowercase__  =	get_image_size(A__						)
      lowercase__,						lowercase__  =	output_size

      # determine new height and width
      lowercase__  =	output_height / input_height
      lowercase__  =	output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width						) < abs(1 - scale_height						):
                  # fit width
                  lowercase__  =	scale_width
            else:
                  # fit height
                  lowercase__  =	scale_height

      lowercase__  =	constraint_to_multiple_of(scale_height * input_height    ,	multiple=A__						)
      lowercase__  =	constraint_to_multiple_of(scale_width * input_width    ,	multiple=A__						)

      return (new_height, new_width)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     ["pixel_values"]


   def __init__(    self	:      Optional[Any]      ,					lowerCAmelCase	:      bool = True      ,					lowerCAmelCase	:      Dict[str, int] = None      ,					lowerCAmelCase	:      PILImageResampling = PILImageResampling.BILINEAR      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      int = 1      ,					lowerCAmelCase	:      bool = True      ,					lowerCAmelCase	:      Union[int, float] = 1 / 2_55      ,					lowerCAmelCase	:      bool = True      ,					lowerCAmelCase	:      Optional[Union[float, List[float]]] = None      ,					lowerCAmelCase	:      Optional[Union[float, List[float]]] = None      ,					**lowerCAmelCase	:      str      ,					)						->					None:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)
         lowercase__  =	size if size is not None else {'height': 3_84, 'width': 3_84}
         lowercase__  =	get_size_dict(lowerCAmelCase)
         lowercase__  =	do_resize
         lowercase__  =	size
         lowercase__  =	keep_aspect_ratio
         lowercase__  =	ensure_multiple_of
         lowercase__  =	resample
         lowercase__  =	do_rescale
         lowercase__  =	rescale_factor
         lowercase__  =	do_normalize
         lowercase__  =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase__  =	image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      np.ndarray      ,					lowerCAmelCase	:      Dict[str, int]      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      int = 1      ,					lowerCAmelCase	:      PILImageResampling = PILImageResampling.BICUBIC      ,					lowerCAmelCase	:      Optional[Union[str, ChannelDimension]] = None      ,					**lowerCAmelCase	:      Tuple      ,					)						->					np.ndarray:




         """simple docstring"""

         lowercase__  =	get_size_dict(lowerCAmelCase)
         if "height" not in size or "width" not in size:
               raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''')
         lowercase__  =	get_resize_output_image_size(
             lowerCAmelCase      ,					output_size=(size['height'], size['width'])      ,					keep_aspect_ratio=lowerCAmelCase      ,					multiple=lowerCAmelCase      ,					)
         return resize(lowerCAmelCase      ,					size=lowerCAmelCase      ,					resample=lowerCAmelCase      ,					data_format=lowerCAmelCase      ,					**lowerCAmelCase)


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




         """simple docstring"""

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


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




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      ImageInput      ,					lowerCAmelCase	:      bool = None      ,					lowerCAmelCase	:      int = None      ,					lowerCAmelCase	:      bool = None      ,					lowerCAmelCase	:      int = None      ,					lowerCAmelCase	:      PILImageResampling = None      ,					lowerCAmelCase	:      bool = None      ,					lowerCAmelCase	:      float = None      ,					lowerCAmelCase	:      bool = None      ,					lowerCAmelCase	:      Optional[Union[float, List[float]]] = None      ,					lowerCAmelCase	:      Optional[Union[float, List[float]]] = None      ,					lowerCAmelCase	:      Optional[Union[str, TensorType]] = None      ,					lowerCAmelCase	:      ChannelDimension = ChannelDimension.FIRST      ,					**lowerCAmelCase	:      int      ,					)						->					PIL.Image.Image:




         """simple docstring"""

         lowercase__  =	do_resize if do_resize is not None else self.do_resize
         lowercase__  =	size if size is not None else self.size
         lowercase__  =	get_size_dict(lowerCAmelCase)
         lowercase__  =	keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         lowercase__  =	ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         lowercase__  =	resample if resample is not None else self.resample
         lowercase__  =	do_rescale if do_rescale is not None else self.do_rescale
         lowercase__  =	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__  =	do_normalize if do_normalize is not None else self.do_normalize
         lowercase__  =	image_mean if image_mean is not None else self.image_mean
         lowercase__  =	image_std if image_std is not None else self.image_std

         lowercase__  =	make_list_of_images(lowerCAmelCase)

         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 or resample is None:
               raise ValueError('Size and resample must be specified if do_resize is True.')

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

         if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('Image mean and std must be specified if do_normalize is True.')

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

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

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

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

         lowercase__  =	[to_channel_dimension_format(lowerCAmelCase      ,					lowerCAmelCase) for image in images]

         lowercase__  =	{'pixel_values': images}
         return BatchFeature(data=lowerCAmelCase      ,					tensor_type=lowerCAmelCase)



   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      List[Tuple] = None)						->					str:




         """simple docstring"""

         lowercase__  =	outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
               if len(lowerCAmelCase) != len(lowerCAmelCase):
                     raise ValueError(
                         'Make sure that you pass in as many target sizes as the batch dimension of the logits')

               if is_torch_tensor(lowerCAmelCase):
                     lowercase__  =	target_sizes.numpy()

               lowercase__  =	[]

               for idx in range(len(lowerCAmelCase)):
                     lowercase__  =	torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0)      ,					size=target_sizes[idx]      ,					mode='bilinear'      ,					align_corners=lowerCAmelCase)
                     lowercase__  =	resized_logits[0].argmax(dim=0)
                     semantic_segmentation.append(lowerCAmelCase)
         else:
               lowercase__  =	logits.argmax(dim=1)
               lowercase__  =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

         return semantic_segmentation





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	int												=     CTRLTokenizer
   A     :	Tuple												=     False
   A     :	Any												=     False


   def       UpperCAmelCase		(    self	:      int)						->					Any:




         """simple docstring"""

         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowercase__  =	['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
         lowercase__  =	dict(zip(lowerCAmelCase      ,					range(len(lowerCAmelCase))))
         lowercase__  =	['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
         lowercase__  =	{'unk_token': '<unk>'}

         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['vocab_file'])
         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['merges_file'])
         with open(self.vocab_file      ,					'w'      ,					encoding='utf-8') as fp:
               fp.write(json.dumps(lowerCAmelCase) + '\n')
         with open(self.merges_file      ,					'w'      ,					encoding='utf-8') as fp:
               fp.write('\n'.join(lowerCAmelCase))


   def       UpperCAmelCase		(    self	:      int      ,					**lowerCAmelCase	:      str)						->					Dict:




         """simple docstring"""

         kwargs.update(self.special_tokens_map)
         return CTRLTokenizer.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt react readapt apt'
         return input_text, output_text



   def       UpperCAmelCase		(    self	:      Optional[int])						->					str:




         """simple docstring"""

         lowercase__  =	CTRLTokenizer(self.vocab_file      ,					self.merges_file      ,					**self.special_tokens_map)
         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)

         lowercase__  =	tokens + [tokenizer.unk_token]

         lowercase__  =	[0, 1, 2, 4, 5, 1, 0, 3, 6]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)
