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


__lowerCamelCase					:	str       =  get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							ReformerTokenizer
					A	       =							ReformerTokenizerFast
					A	       =							True
					A	       =							False
					A	       =							True

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							super().setUp()

							SCREAMING_SNAKE_CASE_  :		int				      =    ReformerTokenizer(_A,keep_accents=_A    )
							tokenizer.save_pretrained(self.tmpdirname    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    "<s>"
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    1

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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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(_A    ),1000    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							self.assertEqual(self.get_tokenizer().vocab_size,1000    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							if not self.test_rust_tokenizer:
									return

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

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

							SCREAMING_SNAKE_CASE_  :		Dict				      =    tokenizer.tokenize(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    rust_tokenizer.tokenize(_A    )
							self.assertListEqual(_A,_A    )

							SCREAMING_SNAKE_CASE_  :		int				      =    tokenizer.encode(_A,add_special_tokens=_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    rust_tokenizer.encode(_A,add_special_tokens=_A    )
							self.assertListEqual(_A,_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tokenizer.encode(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    rust_tokenizer.encode(_A    )
							self.assertListEqual(_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str]=15    ):







							"""simple docstring"""






							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
											SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.rust_tokenizer_class.from_pretrained(_A,**_A    )

											# Simple input
											SCREAMING_SNAKE_CASE_  :		int				      =    "This is a simple input"
											SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ["This is a simple input 1", "This is a simple input 2"]
											SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ("This is a simple input", "This is a pair")
											SCREAMING_SNAKE_CASE_  :		List[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(_A,tokenizer_r.encode,_A,max_length=_A,padding="max_length"    )

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

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

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

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

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

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							pass

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    ReformerTokenizer(_A,keep_accents=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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],)

							SCREAMING_SNAKE_CASE_  :		str				      =    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",
							        ".",
							    ],)
							SCREAMING_SNAKE_CASE_  :		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],)

							SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"    )

					@slow
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    "Hello World!"
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [126, 32, 262, 152, 38, 72, 287]

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

					@slow
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    (
							    "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"
							)
							SCREAMING_SNAKE_CASE_  :		str				      =    [
							    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(_A,self.big_tokenizer.encode(_A    )    )

					@require_torch
					@slow
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							import torch

							from transformers import ReformerConfig, ReformerModel

							# Build sequence
							SCREAMING_SNAKE_CASE_  :		Any				      =    list(self.big_tokenizer.get_vocab().keys()    )[:10]
							SCREAMING_SNAKE_CASE_  :		str				      =    " ".join(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    self.big_tokenizer.encode_plus(_A,return_tensors="pt"    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.big_tokenizer.batch_encode_plus([sequence, sequence],return_tensors="pt"    )

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

							# 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(**_A    )
									model(**_A    )






					@slow
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"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
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [
							    "This is a very simple sentence.",
							    "The quick brown fox jumps over the lazy dog.",
							]

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








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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    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


a		:       Union[str, Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Any							       =	['pixel_values']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = 0.9		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			Union[int, float] = 1 / 255		,      __lowercase					:			bool = True		,      __lowercase					:			bool = True		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      **__lowercase					:			Any		,      )							->      None:
        super().__init__(**__lowercase					)
        __UpperCAmelCase							:	Tuple           =  size if size is not None else {"""shortest_edge""": 224}
        __UpperCAmelCase							:	Union[str, Any]           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        __UpperCAmelCase							:	Union[str, Any]           =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:	Any           =  get_size_dict(__lowercase		,      param_name="""crop_size"""					)

        __UpperCAmelCase							:	Dict           =  do_resize
        __UpperCAmelCase							:	Dict           =  size
        __UpperCAmelCase							:	Tuple           =  crop_pct
        __UpperCAmelCase							:	List[Any]           =  resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop
        __UpperCAmelCase							:	List[Any]           =  crop_size
        __UpperCAmelCase							:	Any           =  do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor
        __UpperCAmelCase							:	int           =  do_normalize
        __UpperCAmelCase							:	List[Any]           =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __UpperCAmelCase							:	List[str]           =  image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[float] = None		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Optional[int]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Tuple           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
          raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""					)
        if crop_pct is not None:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	Union[str, Any]           =  int(size["""shortest_edge"""] / crop_pct					)
          elif "height" in size and "width" in size:
            if size["height"] == size["width"]:
              __UpperCAmelCase							:	Tuple           =  int(size["""height"""] / crop_pct					)
            else:
              __UpperCAmelCase							:	str           =  (int(size["""height"""] / crop_pct					), int(size["""width"""] / crop_pct					))
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

          __UpperCAmelCase							:	str           =  get_resize_output_image_size(__lowercase		,      size=__lowercase		,      default_to_square=__lowercase					)
        else:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	List[str]           =  get_resize_output_image_size(__lowercase		,      size=size["""shortest_edge"""]		,      default_to_square=__lowercase					)
          elif "height" in size and "width" in size:
            __UpperCAmelCase							:	int           =  (size["""height"""], size["""width"""])
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

        return resize(__lowercase		,      size=__lowercase		,      resample=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Union[str, Any]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Optional[Any]           =  get_size_dict(__lowercase					)
        if "height" not in size or "width" not in size:
          raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""					)
        return center_crop(__lowercase		,      size=(size["""height"""], size["""width"""])		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[int, float]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			int		,      )							->      int:
        return rescale(__lowercase		,      scale=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			List[Any]		,      )							->      np.ndarray:
        return normalize(__lowercase		,      mean=__lowercase		,      std=__lowercase		,      data_format=__lowercase		,      **__lowercase					)




      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			ImageInput		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = None		,      __lowercase					:			PILImageResampling = None		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			bool = None		,      __lowercase					:			float = None		,      __lowercase					:			bool = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[str, TensorType]] = None		,      __lowercase					:			ChannelDimension = ChannelDimension.FIRST		,      **__lowercase					:			List[str]		,      )							->      PIL.Image.Image:
        __UpperCAmelCase							:	Any           =  do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase							:	Optional[int]           =  crop_pct if crop_pct is not None else self.crop_pct
        __UpperCAmelCase							:	Optional[Any]           =  resample if resample is not None else self.resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase							:	Dict           =  do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase							:	Union[str, Any]           =  do_normalize if do_normalize is not None else self.do_normalize
        __UpperCAmelCase							:	Tuple           =  image_mean if image_mean is not None else self.image_mean
        __UpperCAmelCase							:	Any           =  image_std if image_std is not None else self.image_std

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

        __UpperCAmelCase							:	Dict           =  make_list_of_images(__lowercase					)

        if not valid_images(__lowercase					):
          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_center_crop and crop_pct is None:
          raise ValueError("""Crop_pct 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							:	str           =  [to_numpy_array(__lowercase					) for image in images]

        if do_resize:
          __UpperCAmelCase							:	str           =  [self.resize(image=__lowercase		,      size=__lowercase		,      crop_pct=__lowercase		,      resample=__lowercase					) for image in images]

        if do_center_crop:
          __UpperCAmelCase							:	Any           =  [self.center_crop(image=__lowercase		,      size=__lowercase					) for image in images]

        if do_rescale:
          __UpperCAmelCase							:	List[str]           =  [self.rescale(image=__lowercase		,      scale=__lowercase					) for image in images]

        if do_normalize:
          __UpperCAmelCase							:	str           =  [self.normalize(image=__lowercase		,      mean=__lowercase		,      std=__lowercase					) for image in images]

        __UpperCAmelCase							:	List[str]           =  [to_channel_dimension_format(__lowercase		,      __lowercase					) for image in images]

        __UpperCAmelCase							:	Any           =  {"""pixel_values""": images}
        return BatchFeature(data=__lowercase		,      tensor_type=__lowercase					)


