import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin




class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = CTRLTokenizer
   lowerCAmelCase   :     Optional[int]	      = False
   lowerCAmelCase   :     Tuple	      = False



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    super().setUp()

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

    _UpperCamelCase:  Optional[int]    						=			os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES['''vocab_file''']				)
    _UpperCamelCase:  List[str]    						=			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(_lowercase				) + '''\n'''				)
    with open(self.merges_file		,	'''w'''		,	encoding='''utf-8'''				) as fp:
     fp.write('''\n'''.join(_lowercase				)				)



   def 	lowerCAmelCase    (						self       :	List[Any]		,	**_lowercase       :	List[str]				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			'''adapt react readapt apt'''
    _UpperCamelCase:  Optional[int]    						=			'''adapt react readapt apt'''
    return input_text, output_text



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			CTRLTokenizer(self.vocab_file		,	self.merges_file		,	**self.special_tokens_map				)
    _UpperCamelCase:  List[Any]    						=			'''adapt react readapt apt'''
    _UpperCamelCase:  int    						=			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
    _UpperCamelCase:  List[str]    						=			tokenizer.tokenize(_lowercase				)
    self.assertListEqual(_lowercase		,	_lowercase				)

    _UpperCamelCase:  int    						=			tokens + [tokenizer.unk_token]

    _UpperCamelCase:  int    						=			[0, 1, 2, 4, 5, 1, 0, 3, 6]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase				)		,	_lowercase				)
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_           =				logging.get_logger(__name__)




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Union[str, Any]		)     ->					Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			b.T
 _UpperCamelCase:  Optional[Any]    						=			np.sum(np.square(lowercase		)	,       axis=1		)
 _UpperCamelCase:  List[Any]    						=			np.sum(np.square(lowercase		)	,       axis=0		)
 _UpperCamelCase:  Tuple    						=			np.matmul(lowercase	,       lowercase		)
 _UpperCamelCase:  Tuple    						=			aa[:, None] - 2 * ab + ba[None, :]
 return d




def    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				List[Any]		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			x.reshape(-1	,       3		)
 _UpperCamelCase:  Dict    						=			squared_euclidean_distance(lowercase	,       lowercase		)
 return np.argmin(lowercase	,       axis=1		)





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = ['''pixel_values''']



   def __init__(						self       :	List[str]		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	bool = True		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	bool = True		,	_lowercase       :	bool = True		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  Tuple    						=			size if size is not None else {'''height''': 256, '''width''': 256}
    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Dict    						=			np.array(_lowercase				) if clusters is not None else None
    _UpperCamelCase:  Optional[Any]    						=			do_resize
    _UpperCamelCase:  Union[str, Any]    						=			size
    _UpperCamelCase:  Optional[int]    						=			resample
    _UpperCamelCase:  Any    						=			do_normalize
    _UpperCamelCase:  int    						=			do_color_quantize



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Dict[str, int]		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""				)
    return resize(
        _lowercase		,	size=(size['''height'''], size['''width'''])		,	resample=_lowercase		,	data_format=_lowercase		,	**_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			rescale(image=_lowercase		,	scale=1 / 127.5		,	data_format=_lowercase				)
    _UpperCamelCase:  List[Any]    						=			image - 1
    return image



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	ImageInput		,	_lowercase       :	bool = None		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = None		,	_lowercase       :	bool = None		,	_lowercase       :	Optional[bool] = None		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	Optional[Union[str, TensorType]] = None		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST		,	**_lowercase       :	List[str]		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			do_resize if do_resize is not None else self.do_resize
    _UpperCamelCase:  Optional[Any]    						=			size if size is not None else self.size
    _UpperCamelCase:  Optional[Any]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			resample if resample is not None else self.resample
    _UpperCamelCase:  Union[str, Any]    						=			do_normalize if do_normalize is not None else self.do_normalize
    _UpperCamelCase:  Any    						=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    _UpperCamelCase:  Dict    						=			clusters if clusters is not None else self.clusters
    _UpperCamelCase:  Optional[Any]    						=			np.array(_lowercase				)

    _UpperCamelCase:  List[str]    						=			make_list_of_images(_lowercase				)

    if not valid_images(_lowercase				):
     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_color_quantize and clusters is None:
     raise ValueError('''Clusters must be specified if do_color_quantize is True.'''				)

    # All transformations expect numpy arrays.
    _UpperCamelCase:  Optional[int]    						=			[to_numpy_array(_lowercase				) for image in images]

    if do_resize:
     _UpperCamelCase:  int    						=			[self.resize(image=_lowercase		,	size=_lowercase		,	resample=_lowercase				) for image in images]

    if do_normalize:
     _UpperCamelCase:  Any    						=			[self.normalize(image=_lowercase				) for image in images]

    if do_color_quantize:
     _UpperCamelCase:  Tuple    						=			[to_channel_dimension_format(_lowercase		,	ChannelDimension.LAST				) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     _UpperCamelCase:  Tuple    						=			np.array(_lowercase				)
     _UpperCamelCase:  List[Any]    						=			color_quantize(_lowercase		,	_lowercase				).reshape(images.shape[:-1]				)

     # flatten to (batch_size, height*width)
     _UpperCamelCase:  Any    						=			images.shape[0]
     _UpperCamelCase:  Optional[int]    						=			images.reshape(_lowercase		,	-1				)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     _UpperCamelCase:  Tuple    						=			list(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			[to_channel_dimension_format(_lowercase		,	_lowercase				) for image in images]

    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': images}
    return BatchFeature(data=_lowercase		,	tensor_type=_lowercase				)