'''simple docstring'''

from typing import Dict, List, Optional, Tuple, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
       import torch


lowerCAmelCase_ : Dict						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Tuple   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Optional[Dict[str, int]] = None   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Union[int, float] = 1 / 255   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					None:
         super().__init__(**__lowerCAmelCase)
         lowercase_     =      size if size is not None else {"shortest_edge": 256}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="crop_size")
         lowercase_     =      do_resize
         lowercase_     =      size
         lowercase_     =      resample
         lowercase_     =      do_center_crop
         lowercase_     =      crop_size
         lowercase_     =      do_rescale
         lowercase_     =      rescale_factor
         lowercase_     =      do_normalize
         lowercase_     =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase_     =      image_std if image_std is not None else IMAGENET_STANDARD_STD


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	PILImageResampling = PILImageResampling.BICUBIC   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					np.ndarray:
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         if "shortest_edge" not in size:
               raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
         lowercase_     =      get_resize_output_image_size(__lowerCAmelCase   ,		size=size["shortest_edge"]   ,		default_to_square=__lowerCAmelCase)
         return resize(__lowerCAmelCase   ,		size=__lowerCAmelCase   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict[str, int]   ,		__lowerCAmelCase				:	Optional[Union[str, ChannelDimension]] = None   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					np.ndarray:
         lowercase_     =      get_size_dict(__lowerCAmelCase)
         if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}')
         return center_crop(__lowerCAmelCase   ,		size=(size["height"], size["width"])   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


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


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


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	ImageInput   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	PILImageResampling = None   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	Dict[str, int] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[float] = None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		__lowerCAmelCase				:	Optional[Union[float, List[float]]] = None   ,		__lowerCAmelCase				:	Optional[Union[str, TensorType]] = None   ,		__lowerCAmelCase				:	Union[str, ChannelDimension] = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	Tuple   ,		)      ->					List[Any]:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      size if size is not None else self.size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		default_to_square=__lowerCAmelCase)
         lowercase_     =      resample if resample is not None else self.resample
         lowercase_     =      do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase_     =      crop_size if crop_size is not None else self.crop_size
         lowercase_     =      get_size_dict(__lowerCAmelCase   ,		param_name="crop_size")
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase_     =      do_normalize if do_normalize is not None else self.do_normalize
         lowercase_     =      image_mean if image_mean is not None else self.image_mean
         lowercase_     =      image_std if image_std is not None else self.image_std

         lowercase_     =      make_list_of_images(__lowerCAmelCase)

         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.")

         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_     =      [to_numpy_array(__lowerCAmelCase) for image in images]

         if do_resize:
               lowercase_     =      [self.resize(image=__lowerCAmelCase   ,		size=__lowerCAmelCase   ,		resample=__lowerCAmelCase) for image in images]

         if do_center_crop:
               lowercase_     =      [self.center_crop(image=__lowerCAmelCase   ,		size=__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase) for image in images]

         if do_normalize:
               lowercase_     =      [self.normalize(image=__lowerCAmelCase   ,		mean=__lowerCAmelCase   ,		std=__lowerCAmelCase) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	List[Tuple] = None)      ->					Any:
         lowercase_     =      outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
               if len(__lowerCAmelCase) != len(__lowerCAmelCase):
                     raise ValueError(
                         "Make sure that you pass in as many target sizes as the batch dimension of the logits")

               if is_torch_tensor(__lowerCAmelCase):
                     lowercase_     =      target_sizes.numpy()

               lowercase_     =      []

               for idx in range(len(__lowerCAmelCase)):
                     lowercase_     =      torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0)   ,		size=target_sizes[idx]   ,		mode="bilinear"   ,		align_corners=__lowerCAmelCase)
                     lowercase_     =      resized_logits[0].argmax(dim=0)
                     semantic_segmentation.append(__lowerCAmelCase)
         else:
               lowercase_     =      logits.argmax(dim=1)
               lowercase_     =      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

         return semantic_segmentation







'''simple docstring'''

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





@require_sentencepiece
@require_tokenizers
class 							lowercase						(						__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=ReformerTokenizer
   lowerCamelCase_    					=ReformerTokenizerFast
   lowerCamelCase_    					=True
   lowerCamelCase_    					=False
   lowerCamelCase_    					=True


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[Any]:
         super().setUp()

         lowercase_     =      ReformerTokenizer(__lowerCAmelCase   ,		keep_accents=__lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


   def __UpperCAmelCase       (   self				:	int)      ->					int:
         lowercase_     =      "<s>"
         lowercase_     =      1

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


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      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(__lowerCAmelCase)   ,		1000)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size   ,		1000)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					List[Any]:
         if not self.test_rust_tokenizer:
               return

         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_rust_tokenizer()

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

         lowercase_     =      tokenizer.tokenize(__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.tokenize(__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      tokenizer.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      self.get_rust_tokenizer()
         lowercase_     =      tokenizer.encode(__lowerCAmelCase)
         lowercase_     =      rust_tokenizer.encode(__lowerCAmelCase)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[str]=15)      ->					Tuple:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                     lowercase_     =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase   ,		**__lowerCAmelCase)

                     # Simple input
                     lowercase_     =      "This is a simple input"
                     lowercase_     =      ["This is a simple input 1", "This is a simple input 2"]
                     lowercase_     =      ("This is a simple input", "This is a pair")
                     lowercase_     =      [
                         ("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(__lowerCAmelCase   ,		tokenizer_r.encode   ,		__lowerCAmelCase   ,		max_length=__lowerCAmelCase   ,		padding="max_length")

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

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

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

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

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


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Tuple:
         pass


   def __UpperCAmelCase       (   self				:	Any)      ->					Optional[Any]:
         lowercase_     =      ReformerTokenizer(__lowerCAmelCase   ,		keep_accents=__lowerCAmelCase)

         lowercase_     =      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]   ,		)

         lowercase_     =      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",
                 ".",
             ]   ,		)
         lowercase_     =      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]   ,		)

         lowercase_     =      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 __UpperCAmelCase       (   self				:	List[str])      ->					Any:
         return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")


   @slow
   def __UpperCAmelCase       (   self				:	Tuple)      ->					str:
         lowercase_     =      "Hello World!"
         lowercase_     =      [126, 32, 262, 152, 38, 72, 287]

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


   @slow
   def __UpperCAmelCase       (   self				:	Any)      ->					Dict:
         lowercase_     =      (
             "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_     =      [
             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(__lowerCAmelCase   ,		self.big_tokenizer.encode(__lowerCAmelCase))


   @require_torch
   @slow
   def __UpperCAmelCase       (   self				:	Any)      ->					Tuple:
         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         lowercase_     =      list(self.big_tokenizer.get_vocab().keys())[:10]
         lowercase_     =      " ".join(__lowerCAmelCase)
         lowercase_     =      self.big_tokenizer.encode_plus(__lowerCAmelCase   ,		return_tensors="pt")
         lowercase_     =      self.big_tokenizer.batch_encode_plus([sequence, sequence]   ,		return_tensors="pt")

         lowercase_     =      ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         lowercase_     =      encoded_sequence["input_ids"].shape
         lowercase_     =      ReformerModel(__lowerCAmelCase)

         # 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(**__lowerCAmelCase)
               model(**__lowerCAmelCase)


   @slow
   def __UpperCAmelCase       (   self				:	List[Any])      ->					Tuple:
         # fmt: off
         lowercase_     =      {"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_     =      [
             "This is a very simple sentence.",
             "The quick brown fox jumps over the lazy dog.",
         ]

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






