






from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCAmelCase :			int              =	logging.get_logger(__name__)


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Any     					=    ["pixel_values"]



		def __init__(			self    ,				A_ = True    ,				A_ = None    ,				A_ = PILImageResampling.BICUBIC    ,				A_ = True    ,				A_ = None    ,				A_ = True    ,				A_ = 1 / 255    ,				A_ = True    ,				A_ = IMAGENET_DEFAULT_MEAN    ,				A_ = IMAGENET_DEFAULT_STD    ,				**A_    ,				)  -> None:
				"""simple docstring"""
				super().__init__(**A_	)
				UpperCamelCase          =					size if size is not None else {'shortest_edge': 224}
				UpperCamelCase          =					get_size_dict(A_    ,				default_to_square=A_	)
				UpperCamelCase          =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
				UpperCamelCase          =					get_size_dict(A_    ,				param_name='crop_size'	)

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = PILImageResampling.BICUBIC    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase          =					get_size_dict(A_    ,				default_to_square=A_	)
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
						UpperCamelCase          =					int((256 / 224) * size['shortest_edge']	)
						UpperCamelCase          =					get_resize_output_image_size(A_    ,				size=A_    ,				default_to_square=A_	)
						UpperCamelCase          =					{'height': output_size[0], 'width': output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
						raise ValueError(
						    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''	)
				return resize(
				    A_    ,				size=(size_dict['height'], size_dict['width'])    ,				resample=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase          =					get_size_dict(A_	)
				if "height" not in size or "width" not in size:
						raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
				return center_crop(A_    ,				size=(size['height'], size['width'])    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				return rescale(A_    ,				scale=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_ = None    ,				**A_    ,				)  -> np.ndarray:
				"""simple docstring"""
				return normalize(A_    ,				mean=A_    ,				std=A_    ,				data_format=A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = ChannelDimension.FIRST    ,				**A_    ,				)  -> BatchFeature:
				"""simple docstring"""
				UpperCamelCase          =					do_resize if do_resize is not None else self.do_resize
				UpperCamelCase          =					resample if resample is not None else self.resample
				UpperCamelCase          =					do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCamelCase          =					do_rescale if do_rescale is not None else self.do_rescale
				UpperCamelCase          =					rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCamelCase          =					do_normalize if do_normalize is not None else self.do_normalize
				UpperCamelCase          =					image_mean if image_mean is not None else self.image_mean
				UpperCamelCase          =					image_std if image_std is not None else self.image_std

				UpperCamelCase          =					size if size is not None else self.size
				UpperCamelCase          =					get_size_dict(A_    ,				default_to_square=A_	)
				UpperCamelCase          =					crop_size if crop_size is not None else self.crop_size
				UpperCamelCase          =					get_size_dict(A_    ,				param_name='crop_size'	)

				UpperCamelCase          =					make_list_of_images(A_	)

				if not valid_images(A_	):
						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.'	)

				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.
				UpperCamelCase          =					[to_numpy_array(A_	) for image in images]

				if do_resize:
						UpperCamelCase          =					[self.resize(A_    ,				A_    ,				A_	) for image in images]

				if do_center_crop:
						UpperCamelCase          =					[self.center_crop(A_    ,				A_	) for image in images]

				if do_rescale:
						UpperCamelCase          =					[self.rescale(A_    ,				A_	) for image in images]

				if do_normalize:
						UpperCamelCase          =					[self.normalize(A_    ,				A_    ,				A_	) for image in images]

				UpperCamelCase          =					[to_channel_dimension_format(A_    ,				A_	) for image in images]

				UpperCamelCase          =					{'pixel_values': images}
				return BatchFeature(data=A_    ,				tensor_type=A_	)


import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    CTRLTokenizer
		__lowercase	:	Any     					=    False
		__lowercase	:	Union[str, Any]     					=    False



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()

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

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



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map	)
				return CTRLTokenizer.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt react readapt apt'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					CTRLTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map	)
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
				UpperCamelCase          =					tokenizer.tokenize(A_	)
				self.assertListEqual(A_    ,				A_	)

				UpperCamelCase          =					tokens + [tokenizer.unk_token]

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