



'''simple docstring'''





import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE     				=						"▁"

SCREAMING_SNAKE_CASE     				=						get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    str									=  BigBirdTokenizer
					lowerCAmelCase_				:    Union[str, Any]									=  BigBirdTokenizerFast
					lowerCAmelCase_				:    Optional[int]									=  True
					lowerCAmelCase_				:    Union[str, Any]									=  True



					def    A__		(			self     ):
							super().setUp()

							UpperCAmelCase_						=				self.tokenizer_class(lowerCAmelCase    ,	keep_accents=lowerCAmelCase     )
							tokenizer.save_pretrained(self.tmpdirname     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				"<s>"
							UpperCAmelCase_						=				1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase     )    ,	lowerCAmelCase     )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase     )    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				list(self.get_tokenizer().get_vocab().keys()     )

							self.assertEqual(vocab_keys[0]    ,	"<unk>"     )
							self.assertEqual(vocab_keys[1]    ,	"<s>"     )
							self.assertEqual(vocab_keys[-1]    ,	"[MASK]"     )
							self.assertEqual(len(lowerCAmelCase     )    ,	1004     )



					def    A__		(			self     ):
							self.assertEqual(self.get_tokenizer().vocab_size    ,	1000     )



					def    A__		(			self     ):
							if not self.test_rust_tokenizer:
									return

							UpperCAmelCase_						=				self.get_tokenizer()
							UpperCAmelCase_						=				self.get_rust_tokenizer()

							UpperCAmelCase_						=				"I was born in 92000, and this is falsé."

							UpperCAmelCase_						=				tokenizer.tokenize(lowerCAmelCase     )
							UpperCAmelCase_						=				rust_tokenizer.tokenize(lowerCAmelCase     )
							self.assertListEqual(lowerCAmelCase    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				tokenizer.encode(lowerCAmelCase    ,	add_special_tokens=lowerCAmelCase     )
							UpperCAmelCase_						=				rust_tokenizer.encode(lowerCAmelCase    ,	add_special_tokens=lowerCAmelCase     )
							self.assertListEqual(lowerCAmelCase    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				self.get_rust_tokenizer()
							UpperCAmelCase_						=				tokenizer.encode(lowerCAmelCase     )
							UpperCAmelCase_						=				rust_tokenizer.encode(lowerCAmelCase     )
							self.assertListEqual(lowerCAmelCase    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				BigBirdTokenizer(lowerCAmelCase    ,	keep_accents=lowerCAmelCase     )

							UpperCAmelCase_						=				tokenizer.tokenize("This is a test"     )
							self.assertListEqual(lowerCAmelCase    ,	["▁This", "▁is", "▁a", "▁t", "est"]     )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCAmelCase     )    ,	[285, 46, 10, 170, 382]    ,	)

							UpperCAmelCase_						=				tokenizer.tokenize("I was born in 92000, and this is falsé."     )
							self.assertListEqual(
							    lowerCAmelCase    ,	[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "9",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "é",
							        ".",
							    ]    ,	)
							UpperCAmelCase_						=				tokenizer.convert_tokens_to_ids(lowerCAmelCase     )
							self.assertListEqual(
							    lowerCAmelCase    ,	[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]    ,	)

							UpperCAmelCase_						=				tokenizer.convert_ids_to_tokens(lowerCAmelCase     )
							self.assertListEqual(
							    lowerCAmelCase    ,	[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "<unk>",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "<unk>",
							        ".",
							    ]    ,	)



					@cached_property
					def    A__		(			self     ):
							return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"     )



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				"Hello World!"
							UpperCAmelCase_						=				[65, 1_8536, 2260, 101, 66]

							self.assertListEqual(lowerCAmelCase    ,	self.big_tokenizer.encode(lowerCAmelCase     )     )



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				(
							    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
							    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
							)
							# fmt: off
							UpperCAmelCase_						=				[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66]  # noqa: E231
							# fmt: on
							self.assertListEqual(lowerCAmelCase    ,	self.big_tokenizer.encode(lowerCAmelCase     )     )



					@require_torch
					@slow
					def    A__		(			self     ):
							import torch

							from transformers import BigBirdConfig, BigBirdModel

							# Build sequence
							UpperCAmelCase_						=				list(self.big_tokenizer.get_vocab().keys()     )[:10]
							UpperCAmelCase_						=				" ".join(lowerCAmelCase     )
							UpperCAmelCase_						=				self.big_tokenizer.encode_plus(lowerCAmelCase    ,	return_tensors="pt"    ,	return_token_type_ids=lowerCAmelCase     )
							UpperCAmelCase_						=				self.big_tokenizer.batch_encode_plus(
							    [sequence + " " + sequence]    ,	return_tensors="pt"    ,	return_token_type_ids=lowerCAmelCase     )

							UpperCAmelCase_						=				BigBirdConfig(attention_type="original_full"     )
							UpperCAmelCase_						=				BigBirdModel(lowerCAmelCase     )

							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

							with torch.no_grad():
									model(**lowerCAmelCase     )
									model(**lowerCAmelCase     )



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"     )
							UpperCAmelCase_						=				tokenizer.decode(tokenizer("Paris is the [MASK]."     ).input_ids     )

							self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"     )



					@slow
					def    A__		(			self     ):
							# fmt: off
							UpperCAmelCase_						=				{"input_ids": [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=lowerCAmelCase    ,	model_name="google/bigbird-roberta-base"    ,	revision="215c99f1600e06f83acce68422f2035b2b5c3510"    ,	)






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 (
    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 TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







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

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

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

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



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[Any]									=  ['pixel_values']



					def __init__(			self    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	lowerCAmelCase = 1 / 255    ,	lowerCAmelCase = True    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							super().__init__(**lowerCAmelCase     )
							UpperCAmelCase_						=				size if size is not None else {"shortest_edge": 224}
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							UpperCAmelCase_						=				crop_size if crop_size is not None else {"height": 224, "width": 224}
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	param_name="crop_size"     )

							UpperCAmelCase_						=				do_resize
							UpperCAmelCase_						=				size
							UpperCAmelCase_						=				do_center_crop
							UpperCAmelCase_						=				crop_size
							UpperCAmelCase_						=				resample
							UpperCAmelCase_						=				do_rescale
							UpperCAmelCase_						=				rescale_factor
							UpperCAmelCase_						=				do_normalize
							UpperCAmelCase_						=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCAmelCase_						=				image_std if image_std is not None else IMAGENET_STANDARD_STD



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = PILImageResampling.BILINEAR    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							if "shortest_edge" in size:
									UpperCAmelCase_						=				get_resize_output_image_size(lowerCAmelCase    ,	size["shortest_edge"]    ,	default_to_square=lowerCAmelCase     )
							elif "height" in size and "width" in size:
									UpperCAmelCase_						=				(size["height"], size["width"])
							else:
									raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''     )
							return resize(lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase     )
							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(lowerCAmelCase    ,	size=(size["height"], size["width"])    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return rescale(lowerCAmelCase    ,	scale=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							return normalize(lowerCAmelCase    ,	mean=lowerCAmelCase    ,	std=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	):
							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."     )

							# All transformations expect numpy arrays.
							UpperCAmelCase_						=				to_numpy_array(lowerCAmelCase     )

							if do_resize:
									UpperCAmelCase_						=				self.resize(image=lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase     )

							if do_center_crop:
									UpperCAmelCase_						=				self.center_crop(lowerCAmelCase    ,	size=lowerCAmelCase     )

							if do_rescale:
									UpperCAmelCase_						=				self.rescale(image=lowerCAmelCase    ,	scale=lowerCAmelCase     )

							if do_normalize:
									UpperCAmelCase_						=				self.normalize(image=lowerCAmelCase    ,	mean=lowerCAmelCase    ,	std=lowerCAmelCase     )

							UpperCAmelCase_						=				to_channel_dimension_format(lowerCAmelCase    ,	lowerCAmelCase     )
							return image



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = ChannelDimension.FIRST    ,	**lowerCAmelCase    ,	):
							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(lowerCAmelCase    ,	default_to_square=lowerCAmelCase     )
							UpperCAmelCase_						=				crop_size if crop_size is not None else self.crop_size
							UpperCAmelCase_						=				get_size_dict(lowerCAmelCase    ,	param_name="crop_size"     )

							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."     )

							UpperCAmelCase_						=				make_batched(lowerCAmelCase     )

							UpperCAmelCase_						=				[
							    [
							        self._preprocess_image(
							            image=lowerCAmelCase    ,	do_resize=lowerCAmelCase    ,	size=lowerCAmelCase    ,	resample=lowerCAmelCase    ,	do_center_crop=lowerCAmelCase    ,	crop_size=lowerCAmelCase    ,	do_rescale=lowerCAmelCase    ,	rescale_factor=lowerCAmelCase    ,	do_normalize=lowerCAmelCase    ,	image_mean=lowerCAmelCase    ,	image_std=lowerCAmelCase    ,	data_format=lowerCAmelCase    ,	)
							        for img in video
							    ]
							    for video in videos
							]

							UpperCAmelCase_						=				{"pixel_values": videos}
							return BatchFeature(data=lowerCAmelCase    ,	tensor_type=lowerCAmelCase     )

