
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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


UpperCamelCase_						:     Dict	       =							get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class   __lowercase						(						__snake_case   , unittest.TestCase       ):
	_A       =   ReformerTokenizer
	_A       =   ReformerTokenizerFast
	_A       =   True
	_A       =   False
	_A       =   True






	def       _a(self				: Optional[int]    ) ->			Union[str, Any]:
							super().setUp()

							_lowercase       :	int           =   ReformerTokenizer(snake_case      ,	keep_accents=snake_case    )
							tokenizer.save_pretrained(self.tmpdirname    )






	def       _a(self				: str    ) ->			str:
							_lowercase       :	str           =   "<s>"
							_lowercase       :	str           =   1

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






	def       _a(self				: str    ) ->			Any:
							_lowercase       :	Union[str, Any]           =   list(self.get_tokenizer().get_vocab().keys()    )

							self.assertEqual(vocab_keys[0]      ,	"<unk>"    )
							self.assertEqual(vocab_keys[1]      ,	"<s>"    )
							self.assertEqual(vocab_keys[-1]      ,	"j"    )
							self.assertEqual(len(snake_case    )      ,	1000    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							self.assertEqual(self.get_tokenizer().vocab_size      ,	1000    )






	def       _a(self				: Union[str, Any]    ) ->			Optional[Any]:
							if not self.test_rust_tokenizer:
													return

							_lowercase       :	Union[str, Any]           =   self.get_tokenizer()
							_lowercase       :	Union[str, Any]           =   self.get_rust_tokenizer()

							_lowercase       :	int           =   "I was born in 92000, and this is falsé."

							_lowercase       :	Optional[int]           =   tokenizer.tokenize(snake_case    )
							_lowercase       :	str           =   rust_tokenizer.tokenize(snake_case    )
							self.assertListEqual(snake_case      ,	snake_case    )

							_lowercase       :	Optional[Any]           =   tokenizer.encode(snake_case      ,	add_special_tokens=snake_case    )
							_lowercase       :	Optional[Any]           =   rust_tokenizer.encode(snake_case      ,	add_special_tokens=snake_case    )
							self.assertListEqual(snake_case      ,	snake_case    )

							_lowercase       :	Dict           =   self.get_rust_tokenizer()
							_lowercase       :	Tuple           =   tokenizer.encode(snake_case    )
							_lowercase       :	Optional[int]           =   rust_tokenizer.encode(snake_case    )
							self.assertListEqual(snake_case      ,	snake_case    )






	def       _a(self				: Any      ,	snake_case				: List[Any]=15    ) ->			int:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
																			_lowercase       :	Union[str, Any]           =   self.rust_tokenizer_class.from_pretrained(snake_case      ,	**snake_case    )

																			# Simple input
																			_lowercase       :	Optional[int]           =   "This is a simple input"
																			_lowercase       :	Union[str, Any]           =   ["This is a simple input 1", "This is a simple input 2"]
																			_lowercase       :	Dict           =   ("This is a simple input", "This is a pair")
																			_lowercase       :	Any           =   [
																			    ("This is a simple input 1", "This is a simple input 2"),
																			    ("This is a simple pair 1", "This is a simple pair 2"),
																			]

																			# Simple input tests
																			self.assertRaises(snake_case      ,	tokenizer_r.encode      ,	snake_case      ,	max_length=snake_case      ,	padding="max_length"    )

																			# Simple input
																			self.assertRaises(snake_case      ,	tokenizer_r.encode_plus      ,	snake_case      ,	max_length=snake_case      ,	padding="max_length"    )

																			# Simple input
																			self.assertRaises(
																			    snake_case      ,	tokenizer_r.batch_encode_plus      ,	snake_case      ,	max_length=snake_case      ,	padding="max_length"      ,	)

																			# Pair input
																			self.assertRaises(snake_case      ,	tokenizer_r.encode      ,	snake_case      ,	max_length=snake_case      ,	padding="max_length"    )

																			# Pair input
																			self.assertRaises(snake_case      ,	tokenizer_r.encode_plus      ,	snake_case      ,	max_length=snake_case      ,	padding="max_length"    )

																			# Pair input
																			self.assertRaises(
																			    snake_case      ,	tokenizer_r.batch_encode_plus      ,	snake_case      ,	max_length=snake_case      ,	padding="max_length"      ,	)






	def       _a(self				: Tuple    ) ->			List[str]:
							pass






	def       _a(self				: List[Any]    ) ->			Optional[Any]:
							_lowercase       :	Optional[int]           =   ReformerTokenizer(snake_case      ,	keep_accents=snake_case    )

							_lowercase       :	Union[str, Any]           =   tokenizer.tokenize("This is a test"    )
							self.assertListEqual(snake_case      ,	["▁This", "▁is", "▁a", "▁t", "est"]    )

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

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

							_lowercase       :	int           =   tokenizer.convert_ids_to_tokens(snake_case    )
							self.assertListEqual(
							    snake_case      ,	[
							        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				: Union[str, Any]    ) ->			List[Any]:
							return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"    )






	@slow
	def       _a(self				: int    ) ->			Dict:
							_lowercase       :	str           =   "Hello World!"
							_lowercase       :	List[Any]           =   [126, 32, 262, 152, 38, 72, 287]

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






	@slow
	def       _a(self				: Tuple    ) ->			Any:
							_lowercase       :	str           =   (
							    "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"
							)
							_lowercase       :	Tuple           =   [
							    108,
							    265,
							    24,
							    111,
							    4,
							    258,
							    156,
							    35,
							    28,
							    275,
							    3,
							    259,
							    297,
							    260,
							    84,
							    4,
							    35,
							    110,
							    44,
							    8,
							    259,
							    91,
							    268,
							    21,
							    11,
							    209,
							    274,
							    109,
							    266,
							    277,
							    117,
							    86,
							    93,
							    315,
							    258,
							    278,
							    258,
							    277,
							    258,
							    0,
							    258,
							    288,
							    258,
							    319,
							    258,
							    0,
							    258,
							    0,
							    258,
							    0,
							    258,
							    0,
							    258,
							    287,
							    258,
							    315,
							    258,
							    289,
							    258,
							    278,
							    99,
							    269,
							    266,
							    262,
							    8,
							    259,
							    241,
							    4,
							    217,
							    230,
							    268,
							    266,
							    55,
							    168,
							    106,
							    75,
							    193,
							    266,
							    223,
							    27,
							    49,
							    26,
							    282,
							    25,
							    264,
							    299,
							    19,
							    26,
							    0,
							    258,
							    277,
							    117,
							    86,
							    93,
							    176,
							    183,
							    270,
							    11,
							    262,
							    42,
							    61,
							    265,
							]

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






	@require_torch
	@slow
	def       _a(self				: Any    ) ->			Union[str, Any]:
							import torch

							from transformers import ReformerConfig, ReformerModel

							# Build sequence
							_lowercase       :	Tuple           =   list(self.big_tokenizer.get_vocab().keys()    )[:10]
							_lowercase       :	List[str]           =   " ".join(snake_case    )
							_lowercase       :	Optional[Any]           =   self.big_tokenizer.encode_plus(snake_case      ,	return_tensors="pt"    )
							_lowercase       :	List[Any]           =   self.big_tokenizer.batch_encode_plus([sequence, sequence]      ,	return_tensors="pt"    )

							_lowercase       :	str           =   ReformerConfig()
							# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
							_lowercase       :	Optional[int]           =   encoded_sequence["input_ids"].shape
							_lowercase       :	List[Any]           =   ReformerModel(snake_case    )

							# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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






	@slow
	def       _a(self				: Any    ) ->			Union[str, Any]:
							# fmt: off
							_lowercase       :	List[str]           =   {"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "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]]}  # noqa: E501
							# fmt: on

							# This tokenizer does not know some characters like ")".
							# That is the reason why we use very simple texts here.
							# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
							_lowercase       :	str           =   [
							    "This is a very simple sentence.",
							    "The quick brown fox jumps over the lazy dog.",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case      ,	model_name="google/reformer-crime-and-punishment"      ,	revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"      ,	padding=snake_case      ,	sequences=snake_case      ,	)






from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_						:     Any	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: Dict      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,	**snake_case				: Optional[int]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	str           =   size if size is not None else {"shortest_edge": 224}
							_lowercase       :	List[str]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Optional[int]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	List[Any]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Optional[int]           =   do_resize
							_lowercase       :	List[str]           =   size
							_lowercase       :	Tuple           =   resample
							_lowercase       :	Union[str, Any]           =   do_center_crop
							_lowercase       :	List[str]           =   crop_size
							_lowercase       :	Tuple           =   do_rescale
							_lowercase       :	Union[str, Any]           =   rescale_factor
							_lowercase       :	str           =   do_normalize
							_lowercase       :	List[Any]           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_lowercase       :	List[Any]           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: str      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
													_lowercase       :	List[str]           =   int((256 / 224) * size["shortest_edge"]    )
													_lowercase       :	Dict           =   get_resize_output_image_size(snake_case      ,	size=snake_case      ,	default_to_square=snake_case    )
													_lowercase       :	int           =   {"height": output_size[0], "width": output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
													raise ValueError(
													    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""    )
							return resize(
							    snake_case      ,	size=(size_dict["height"], size_dict["width"])      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Any      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case    )
							if "height" not in size or "width" not in size:
													raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""    )
							return center_crop(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: np.ndarray      ,	snake_case				: Union[int, float]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[str]      ,	) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Optional[int]      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: ImageInput      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[float] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[TensorType] = None      ,	snake_case				: ChannelDimension = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			BatchFeature:
							_lowercase       :	Optional[Any]           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	Any           =   resample if resample is not None else self.resample
							_lowercase       :	Optional[Any]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	Union[str, Any]           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	Optional[Any]           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	str           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Dict           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	Dict           =   image_std if image_std is not None else self.image_std

							_lowercase       :	Optional[int]           =   size if size is not None else self.size
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Dict           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Dict           =   make_list_of_images(snake_case    )

							if not valid_images(snake_case    ):
													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."    )

							# All transformations expect numpy arrays.
							_lowercase       :	Dict           =   [to_numpy_array(snake_case    ) for image in images]

							if do_resize:
													_lowercase       :	List[Any]           =   [self.resize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							if do_center_crop:
													_lowercase       :	Union[str, Any]           =   [self.center_crop(snake_case      ,	snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	List[str]           =   [self.rescale(snake_case      ,	snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	str           =   [self.normalize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Any           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	str           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )




