



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						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Dict =       LxmertTokenizer
							A				:List[Any] =       LxmertTokenizerFast
							A				:List[str] =       True
							A				:Union[str, Any] =       True



							def 		_A	(							self				):







											"""simple docstring"""



											super().setUp()

											a__	:							Tuple      						= [
											    "[UNK]",
											    "[CLS]",
											    "[SEP]",
											    "want",
											    "##want",
											    "##ed",
											    "wa",
											    "un",
											    "runn",
											    "##ing",
											    ",",
											    "low",
											    "lowest",
											]
											a__	:							Optional[int]      						= 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 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							str      						= "UNwant\u00E9d,running"
											a__	:							int      						= "unwanted, running"
											return input_text, output_text



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.tokenizer_class(self.vocab_file				)

											a__	:							int      						= tokenizer.tokenize("UNwant\u00E9d,running"				)
											self.assertListEqual(__UpperCAmelCase ,					["un", "##want", "##ed", ",", "runn", "##ing"]				)
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				) ,					[7, 4, 5, 10, 8, 9]				)




							def 		_A	(							self				):







											"""simple docstring"""



											if not self.test_rust_tokenizer:
															return

											a__	:							Optional[Any]      						= self.get_tokenizer()
											a__	:							Optional[int]      						= self.get_rust_tokenizer()

											a__	:							Optional[Any]      						= "I was born in 92000, and this is falsé."

											a__	:							List[str]      						= tokenizer.tokenize(__UpperCAmelCase				)
											a__	:							str      						= rust_tokenizer.tokenize(__UpperCAmelCase				)
											self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase				)

											a__	:							Any      						= tokenizer.encode(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase				)
											a__	:							Any      						= rust_tokenizer.encode(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase				)
											self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase				)

											a__	:							Tuple      						= self.get_rust_tokenizer()
											a__	:							Optional[Any]      						= tokenizer.encode(__UpperCAmelCase				)
											a__	:							Any      						= rust_tokenizer.encode(__UpperCAmelCase				)
											self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase				)







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[List[ImageInput]]:
				if isinstance(__UpperCamelCase	,				(list, tuple) ) and isinstance(videos[0]	,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(__UpperCamelCase	,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(__UpperCamelCase ):
								return [[videos]]

				raise ValueError(F'Could not make batched video from {videos}' )






class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:List[str] =       ["pixel_values"]



							def __init__(							self ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = 1 / 255 ,					__UpperCAmelCase = True ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											super().__init__(**__UpperCAmelCase				)
											a__	:							int      						= size if size is not None else {"shortest_edge": 256}
											a__	:							str      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= crop_size if crop_size is not None else {"height": 224, "width": 224}
											a__	:							int      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

											a__	:							str      						= do_resize
											a__	:							Union[str, Any]      						= size
											a__	:							Any      						= do_center_crop
											a__	:							Optional[int]      						= crop_size
											a__	:							Optional[int]      						= resample
											a__	:							Optional[Any]      						= do_rescale
											a__	:							Union[str, Any]      						= rescale_factor
											a__	:							Dict      						= offset
											a__	:							List[str]      						= do_normalize
											a__	:							Dict      						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__	:							Tuple      						= image_std if image_std is not None else IMAGENET_STANDARD_STD



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											if "shortest_edge" in size:
															a__	:							Optional[Any]      						= get_resize_output_image_size(__UpperCAmelCase ,					size["shortest_edge"] ,					default_to_square=__UpperCAmelCase				)
											elif "height" in size and "width" in size:
															a__	:							Union[str, Any]      						= (size["height"], size["width"])
											else:
															raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'				)
											return resize(__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= get_size_dict(__UpperCAmelCase				)
											if "height" not in size or "width" not in size:
															raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'				)
											return center_crop(__UpperCAmelCase ,					size=(size["height"], size["width"]) ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= image.astype(np.floataa				)
											if offset:
															a__	:							Dict      						= image - (scale / 2)
											return rescale(__UpperCAmelCase ,					scale=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



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



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					):







											"""simple docstring"""



											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_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."				)

											if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."				)

											# All transformations expect numpy arrays.
											a__	:							int      						= to_numpy_array(__UpperCAmelCase				)

											if do_resize:
															a__	:							List[Any]      						= self.resize(image=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase				)

											if do_center_crop:
															a__	:							str      						= self.center_crop(__UpperCAmelCase ,					size=__UpperCAmelCase				)

											if do_rescale:
															a__	:							Optional[Any]      						= self.rescale(image=__UpperCAmelCase ,					scale=__UpperCAmelCase ,					offset=__UpperCAmelCase				)

											if do_normalize:
															a__	:							Union[str, Any]      						= self.normalize(image=__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase				)

											a__	:							Optional[int]      						= to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase				)
											return image




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							List[Any]      						= do_resize if do_resize is not None else self.do_resize
											a__	:							Dict      						= resample if resample is not None else self.resample
											a__	:							Tuple      						= do_center_crop if do_center_crop is not None else self.do_center_crop
											a__	:							Optional[Any]      						= do_rescale if do_rescale is not None else self.do_rescale
											a__	:							Optional[int]      						= rescale_factor if rescale_factor is not None else self.rescale_factor
											a__	:							List[Any]      						= offset if offset is not None else self.offset
											a__	:							Optional[Any]      						= do_normalize if do_normalize is not None else self.do_normalize
											a__	:							int      						= image_mean if image_mean is not None else self.image_mean
											a__	:							List[Any]      						= image_std if image_std is not None else self.image_std

											a__	:							int      						= size if size is not None else self.size
											a__	:							Tuple      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							List[Any]      						= crop_size if crop_size is not None else self.crop_size
											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

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

											a__	:							Union[str, Any]      						= make_batched(__UpperCAmelCase				)

											a__	:							Optional[Any]      						= [
											    [
											        self._preprocess_image(
											            image=__UpperCAmelCase ,					do_resize=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					do_center_crop=__UpperCAmelCase ,					crop_size=__UpperCAmelCase ,					do_rescale=__UpperCAmelCase ,					rescale_factor=__UpperCAmelCase ,					offset=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					image_mean=__UpperCAmelCase ,					image_std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					)
											        for img in video
											    ]
											    for video in videos
											]

											a__	:							Any      						= {"pixel_values": videos}
											return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase				)


