


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


snake_case_						:			Dict													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =b.T
				_SCREAMING_SNAKE_CASE			  =np.sum(np.square(_lowercase)	,axis=1)
				_SCREAMING_SNAKE_CASE			  =np.sum(np.square(_lowercase)	,axis=0)
				_SCREAMING_SNAKE_CASE			  =np.matmul(_lowercase	,_lowercase)
				_SCREAMING_SNAKE_CASE			  =aa[:, None] - 2 * ab + ba[None, :]
				return d







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =x.reshape(-1	,3)
				_SCREAMING_SNAKE_CASE			  =squared_euclidean_distance(_lowercase	,_lowercase)
				return np.argmin(_lowercase	,axis=1)

class 						A__							(		a__  ):
						UpperCAmelCase             =						["""pixel_values"""]




						def __init__(       self					:      str				,    _a					:      str = None				,    _a					:      Optional[Any] = True				,    _a					:      int = None				,    _a					:      Tuple = PILImageResampling.BILINEAR				,    _a					:      List[str] = True				,    _a					:      Tuple = True				,    **_a					:      Dict				,    )							->     None:


										"""simple docstring"""





										super().__init__(**lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =size if size is not None else {"height": 256, "width": 256}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =np.array(lowerCAmelCase__  ) if clusters is not None else None
										_SCREAMING_SNAKE_CASE			  =do_resize
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =resample
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =do_color_quantize




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      List[str]				,    _a					:      List[str]				,    _a					:      List[str] = PILImageResampling.BILINEAR				,    _a					:      Dict = None				,    **_a					:      Tuple				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(lowerCAmelCase__  )
										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(
										    lowerCAmelCase__				,    size=(size['''height'''], size['''width'''])				,    resample=lowerCAmelCase__				,    data_format=lowerCAmelCase__				,    **lowerCAmelCase__  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      int				,    _a					:      Any = None				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =rescale(image=lowerCAmelCase__				,    scale=1 / 1_27.5				,    data_format=lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =image - 1
										return image




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      List[Any]				,    _a					:      List[Any] = None				,    _a					:      Any = None				,    _a					:      Optional[Any] = None				,    _a					:      List[str] = None				,    _a					:      List[Any] = None				,    _a					:      Optional[int] = None				,    _a					:      str = None				,    _a					:      Dict = ChannelDimension.FIRST				,    **_a					:      List[str]				,    )							->     PIL.Image.Image:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE			  =size if size is not None else self.size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(lowerCAmelCase__  )
										_SCREAMING_SNAKE_CASE			  =resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE			  =do_normalize if do_normalize is not None else self.do_normalize
										_SCREAMING_SNAKE_CASE			  =do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										_SCREAMING_SNAKE_CASE			  =clusters if clusters is not None else self.clusters
										_SCREAMING_SNAKE_CASE			  =np.array(lowerCAmelCase__  )

										_SCREAMING_SNAKE_CASE			  =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_color_quantize and clusters is None:
														raise ValueError('''Clusters must be specified if do_color_quantize is True.'''  )

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

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

										if do_normalize:
														_SCREAMING_SNAKE_CASE			  =[self.normalize(image=lowerCAmelCase__  ) for image in images]

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

														# flatten to (batch_size, height*width)
														_SCREAMING_SNAKE_CASE			  =images.shape[0]
														_SCREAMING_SNAKE_CASE			  =images.reshape(lowerCAmelCase__				,    -1  )

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

										_SCREAMING_SNAKE_CASE			  ={"input_ids": images}
										return BatchFeature(data=lowerCAmelCase__				,    tensor_type=lowerCAmelCase__  )



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LxmertTokenizer
						UpperCAmelCase             =						LxmertTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										if not self.test_rust_tokenizer:
														return

										_SCREAMING_SNAKE_CASE			  =self.get_tokenizer()
										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()

										_SCREAMING_SNAKE_CASE			  ='''I was born in 92000, and this is falsé.'''

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.tokenize(_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a				,    add_special_tokens=_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a  )
										self.assertListEqual(_a				,    _a  )