
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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case :		Any							      =  logging.get_logger(__name__)


if is_vision_available():
				import PIL





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    ['pixel_values']







			def __init__(				self						,   _a = True						,   _a = None						,   _a = PILImageResampling.BICUBIC						,   _a = True						,   _a = None						,   _a = True						,   _a = 1 / 255						,   _a = True						,   _a = None						,   _a = None						,   _a = True						,   **_a						,   ):
				super().__init__(**_a		)
				__magic_name__		:		int	   =     size if size is not None else {"shortest_edge": 224}
				__magic_name__		:		str	   =     get_size_dict(_a						,   default_to_square=_a		)
				__magic_name__		:		str	   =     crop_size if crop_size is not None else {"height": 224, "width": 224}
				__magic_name__		:		Optional[Any]	   =     get_size_dict(_a						,   default_to_square=_a						,   param_name="crop_size"		)

				__magic_name__		:		Dict	   =     do_resize
				__magic_name__		:		Any	   =     size
				__magic_name__		:		Any	   =     resample
				__magic_name__		:		Optional[Any]	   =     do_center_crop
				__magic_name__		:		Any	   =     crop_size
				__magic_name__		:		int	   =     do_rescale
				__magic_name__		:		Union[str, Any]	   =     rescale_factor
				__magic_name__		:		int	   =     do_normalize
				__magic_name__		:		Optional[Any]	   =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				__magic_name__		:		str	   =     image_std if image_std is not None else OPENAI_CLIP_STD
				__magic_name__		:		Union[str, Any]	   =     do_convert_rgb







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a = PILImageResampling.BICUBIC						,   _a = None						,   **_a						,   ):
				__magic_name__		:		int	   =     get_size_dict(_a						,   default_to_square=_a		)
				if "shortest_edge" not in size:
					raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
				__magic_name__		:		str	   =     get_resize_output_image_size(_a						,   size=size["shortest_edge"]						,   default_to_square=_a		)
				return resize(_a						,   size=_a						,   resample=_a						,   data_format=_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a = None						,   **_a						,   ):
				__magic_name__		:		Optional[int]	   =     get_size_dict(_a		)
				if "height" not in size or "width" not in size:
					raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
				return center_crop(_a						,   size=(size["height"], size["width"])						,   data_format=_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a = None						,   **_a						,   ):
				return rescale(_a						,   scale=_a						,   data_format=_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a = None						,   **_a						,   ):
				return normalize(_a						,   mean=_a						,   std=_a						,   data_format=_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				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 = None						,   _a = ChannelDimension.FIRST						,   **_a						,   ):
				__magic_name__		:		int	   =     do_resize if do_resize is not None else self.do_resize
				__magic_name__		:		str	   =     size if size is not None else self.size
				__magic_name__		:		List[str]	   =     get_size_dict(_a						,   param_name="size"						,   default_to_square=_a		)
				__magic_name__		:		List[Any]	   =     resample if resample is not None else self.resample
				__magic_name__		:		Tuple	   =     do_center_crop if do_center_crop is not None else self.do_center_crop
				__magic_name__		:		Optional[Any]	   =     crop_size if crop_size is not None else self.crop_size
				__magic_name__		:		Any	   =     get_size_dict(_a						,   param_name="crop_size"						,   default_to_square=_a		)
				__magic_name__		:		Optional[Any]	   =     do_rescale if do_rescale is not None else self.do_rescale
				__magic_name__		:		str	   =     rescale_factor if rescale_factor is not None else self.rescale_factor
				__magic_name__		:		Optional[Any]	   =     do_normalize if do_normalize is not None else self.do_normalize
				__magic_name__		:		Union[str, Any]	   =     image_mean if image_mean is not None else self.image_mean
				__magic_name__		:		Optional[Any]	   =     image_std if image_std is not None else self.image_std
				__magic_name__		:		Optional[Any]	   =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				__magic_name__		:		Optional[Any]	   =     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."		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
					__magic_name__		:		Union[str, Any]	   =     [convert_to_rgb(_a		) for image in images]

				# All transformations expect numpy arrays.
				__magic_name__		:		Any	   =     [to_numpy_array(_a		) for image in images]

				if do_resize:
					__magic_name__		:		List[Any]	   =     [self.resize(image=_a						,   size=_a						,   resample=_a		) for image in images]

				if do_center_crop:
					__magic_name__		:		Dict	   =     [self.center_crop(image=_a						,   size=_a		) for image in images]

				if do_rescale:
					__magic_name__		:		int	   =     [self.rescale(image=_a						,   scale=_a		) for image in images]

				if do_normalize:
					__magic_name__		:		Dict	   =     [self.normalize(image=_a						,   mean=_a						,   std=_a		) for image in images]

				__magic_name__		:		int	   =     [to_channel_dimension_format(_a						,   _a		) for image in images]

				__magic_name__		:		List[str]	   =     {"pixel_values": images}
				return BatchFeature(data=_a						,   tensor_type=_a		)


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


snake_case :		str							      =  "▁"

snake_case :		List[Any]							      =  get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    BigBirdTokenizer
			UpperCamelCase__          =    BigBirdTokenizerFast
			UpperCamelCase__          =    True
			UpperCamelCase__          =    True







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

				__magic_name__		:		Optional[Any]	   =     self.tokenizer_class(_a						,   keep_accents=_a		)
				tokenizer.save_pretrained(self.tmpdirname		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     "<s>"
				__magic_name__		:		Dict	   =     1

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     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(_a		)						,   1_004		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self.assertEqual(self.get_tokenizer().vocab_size						,   1_000		)







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

				__magic_name__		:		Dict	   =     self.get_tokenizer()
				__magic_name__		:		str	   =     self.get_rust_tokenizer()

				__magic_name__		:		Any	   =     "I was born in 92000, and this is falsé."

				__magic_name__		:		Dict	   =     tokenizer.tokenize(_a		)
				__magic_name__		:		Any	   =     rust_tokenizer.tokenize(_a		)
				self.assertListEqual(_a						,   _a		)

				__magic_name__		:		List[Any]	   =     tokenizer.encode(_a						,   add_special_tokens=_a		)
				__magic_name__		:		List[str]	   =     rust_tokenizer.encode(_a						,   add_special_tokens=_a		)
				self.assertListEqual(_a						,   _a		)

				__magic_name__		:		str	   =     self.get_rust_tokenizer()
				__magic_name__		:		Dict	   =     tokenizer.encode(_a		)
				__magic_name__		:		Optional[int]	   =     rust_tokenizer.encode(_a		)
				self.assertListEqual(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     BigBirdTokenizer(_a						,   keep_accents=_a		)

				__magic_name__		:		str	   =     tokenizer.tokenize("This is a test"		)
				self.assertListEqual(_a						,   ["▁This", "▁is", "▁a", "▁t", "est"]		)

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

				__magic_name__		:		Dict	   =     tokenizer.tokenize("I was born in 92000, and this is falsé."		)
				self.assertListEqual(
				    _a						,   [
				        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",
				        ".",
				    ]						,   )
				__magic_name__		:		Union[str, Any]	   =     tokenizer.convert_tokens_to_ids(_a		)
				self.assertListEqual(
				    _a						,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,   )

				__magic_name__		:		int	   =     tokenizer.convert_ids_to_tokens(_a		)
				self.assertListEqual(
				    _a						,   [
				        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 			SCREAMING_SNAKE_CASE    (				self		):
				return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     "Hello World!"
				__magic_name__		:		Dict	   =     [65, 18_536, 2_260, 101, 66]

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







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     (
				    "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
				__magic_name__		:		List[str]	   =     [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
				# fmt: on
				self.assertListEqual(_a						,   self.big_tokenizer.encode(_a		)		)







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

				from transformers import BigBirdConfig, BigBirdModel

				# Build sequence
				__magic_name__		:		Optional[Any]	   =     list(self.big_tokenizer.get_vocab().keys()		)[:10]
				__magic_name__		:		List[Any]	   =     " ".join(_a		)
				__magic_name__		:		Any	   =     self.big_tokenizer.encode_plus(_a						,   return_tensors="pt"						,   return_token_type_ids=_a		)
				__magic_name__		:		Union[str, Any]	   =     self.big_tokenizer.batch_encode_plus(
				    [sequence + " " + sequence]						,   return_tensors="pt"						,   return_token_type_ids=_a		)

				__magic_name__		:		List[str]	   =     BigBirdConfig(attention_type="original_full"		)
				__magic_name__		:		Optional[int]	   =     BigBirdModel(_a		)

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

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







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

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







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				# fmt: off
				__magic_name__		:		Optional[Any]	   =     {"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 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, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 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=_a						,   model_name="google/bigbird-roberta-base"						,   revision="215c99f1600e06f83acce68422f2035b2b5c3510"						,   )
