


'''simple docstring'''


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				LongformerTokenizer
     lowerCamelCase__       						=				True
     lowerCamelCase__       						=				LongformerTokenizerFast
     lowerCamelCase__       						=				True


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          __A	:       List[str]      =							[
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          __A	:       Union[str, Any]      =							dict(zip(lowerCamelCase			,     range(len(lowerCamelCase		)		)		)		)
          __A	:       List[Any]      =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          __A	:       Union[str, Any]      =							{"unk_token": "<unk>"}

          __A	:       List[Any]      =							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["vocab_file"]		)
          __A	:       Any      =							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["merges_file"]		)
          with open(self.vocab_file			,     "w"			,     encoding="utf-8"		) as fp:
               fp.write(json.dumps(lowerCamelCase		) + "\n"		)
          with open(self.merges_file			,     "w"			,     encoding="utf-8"		) as fp:
               fp.write("\n".join(lowerCamelCase		)		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          kwargs.update(self.special_tokens_map		)
          return self.tokenizer_class.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          kwargs.update(self.special_tokens_map		)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							"lower newer"
          __A	:       str      =							"lower newer"
          return input_text, output_text


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.tokenizer_class(self.vocab_file			,     self.merges_file			,     **self.special_tokens_map		)
          __A	:       List[Any]      =							"lower newer"
          __A	:       int      =							["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
          __A	:       Tuple      =							tokenizer.tokenize(lowerCamelCase		)  # , add_prefix_space=True)
          self.assertListEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Tuple      =							tokens + [tokenizer.unk_token]
          __A	:       int      =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		)			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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

          self.assertListEqual(tokenizer.encode("Hello world!"			,     add_special_tokens=lowerCamelCase		)			,     [0, 3_1414, 232, 328, 2]		)
          self.assertListEqual(
              tokenizer.encode("Hello world! cécé herlolip 418"			,     add_special_tokens=lowerCamelCase		)			,     [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]			,     )


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"		)

          __A	:       int      =							tokenizer.encode("sequence builders"			,     add_special_tokens=lowerCamelCase		)
          __A	:       int      =							tokenizer.encode("multi-sequence build"			,     add_special_tokens=lowerCamelCase		)

          __A	:       Tuple      =							tokenizer.encode(
              "sequence builders"			,     add_special_tokens=lowerCamelCase			,     add_prefix_space=lowerCamelCase		)
          __A	:       int      =							tokenizer.encode(
              "sequence builders"			,     "multi-sequence build"			,     add_special_tokens=lowerCamelCase			,     add_prefix_space=lowerCamelCase		)

          __A	:       Optional[Any]      =							tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
          __A	:       List[str]      =							tokenizer.build_inputs_with_special_tokens(lowerCamelCase			,     lowerCamelCase		)

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.get_tokenizer()

          __A	:       List[Any]      =							"Encode this sequence."
          __A	:       Dict      =							tokenizer.byte_encoder[" ".encode("utf-8"		)[0]]

          # Testing encoder arguments
          __A	:       Optional[Any]      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase			,     add_prefix_space=lowerCamelCase		)
          __A	:       Union[str, Any]      =							tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
          self.assertNotEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Union[str, Any]      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase			,     add_prefix_space=lowerCamelCase		)
          __A	:       Any      =							tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
          self.assertEqual(lowerCamelCase			,     lowerCamelCase		)

          tokenizer.add_special_tokens({"bos_token": "<s>"}		)
          __A	:       int      =							tokenizer.encode(lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
          __A	:       Tuple      =							tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
          self.assertNotEqual(lowerCamelCase			,     lowerCamelCase		)

          # Testing spaces after special tokens
          __A	:       List[Any]      =							"<mask>"
          tokenizer.add_special_tokens(
              {"mask_token": AddedToken(lowerCamelCase			,     lstrip=lowerCamelCase			,     rstrip=lowerCamelCase		)}		)  # mask token has a left space
          __A	:       Any      =							tokenizer.convert_tokens_to_ids(lowerCamelCase		)

          __A	:       Any      =							"Encode <mask> sequence"
          __A	:       int      =							"Encode <mask>sequence"

          __A	:       Union[str, Any]      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Union[str, Any]      =							encoded.index(lowerCamelCase		)
          __A	:       int      =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
          self.assertEqual(lowerCamelCase			,     lowerCamelCase		)

          __A	:       Tuple      =							tokenizer.encode(lowerCamelCase		)
          __A	:       Optional[int]      =							encoded.index(lowerCamelCase		)
          __A	:       Optional[int]      =							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
          self.assertNotEqual(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    __A	:       List[str]      =							self.rust_tokenizer_class.from_pretrained(lowerCamelCase			,     **lowerCamelCase		)
                    __A	:       str      =							self.tokenizer_class.from_pretrained(lowerCamelCase			,     **lowerCamelCase		)
                    __A	:       Any      =							"A, <mask> AllenNLP sentence."
                    __A	:       Tuple      =							tokenizer_r.encode_plus(lowerCamelCase			,     add_special_tokens=lowerCamelCase			,     return_token_type_ids=lowerCamelCase		)
                    __A	:       List[str]      =							tokenizer_p.encode_plus(lowerCamelCase			,     add_special_tokens=lowerCamelCase			,     return_token_type_ids=lowerCamelCase		)

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r["token_type_ids"]		)			,     sum(tokens_p["token_type_ids"]		)		)

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r["attention_mask"]		) / len(tokens_r["attention_mask"]		)			,     sum(tokens_p["attention_mask"]		) / len(tokens_p["attention_mask"]		)			,     )

                    __A	:       List[str]      =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
                    __A	:       Optional[Any]      =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]		)

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p["input_ids"]			,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
                    self.assertSequenceEqual(tokens_r["input_ids"]			,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

                    self.assertSequenceEqual(
                        lowerCamelCase			,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)
                    self.assertSequenceEqual(
                        lowerCamelCase			,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for trim_offsets, add_prefix_space in itertools.product([True, False]			,     repeat=2		):
               __A	:       Union[str, Any]      =							self.rust_tokenizer_class.from_pretrained(
                   self.tmpdirname			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)

               __A	:       str      =							json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
               __A	:       List[str]      =							json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

               self.assertEqual(pre_tokenizer_state["add_prefix_space"]			,     lowerCamelCase		)

               self.assertEqual(post_processor_state["add_prefix_space"]			,     lowerCamelCase		)
               self.assertEqual(post_processor_state["trim_offsets"]			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    __A	:       List[Any]      =							"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                    __A	:       Tuple      =							f"{text_of_1_token} {text_of_1_token}"

                    __A	:       int      =							self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)
                    __A	:       int      =							tokenizer_r(lowerCamelCase			,     return_offsets_mapping=lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
                    self.assertEqual(encoding.offset_mapping[0]			,     (0, len(lowerCamelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]			,     (len(lowerCamelCase		) + 1, len(lowerCamelCase		) + 1 + len(lowerCamelCase		))			,     )

                    __A	:       List[Any]      =							self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)
                    __A	:       str      =							tokenizer_r(lowerCamelCase			,     return_offsets_mapping=lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
                    self.assertEqual(encoding.offset_mapping[0]			,     (0, len(lowerCamelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]			,     (len(lowerCamelCase		) + 1, len(lowerCamelCase		) + 1 + len(lowerCamelCase		))			,     )

                    __A	:       int      =							self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)
                    __A	:       Any      =							tokenizer_r(lowerCamelCase			,     return_offsets_mapping=lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
                    self.assertEqual(encoding.offset_mapping[0]			,     (0, len(lowerCamelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]			,     (len(lowerCamelCase		), len(lowerCamelCase		) + 1 + len(lowerCamelCase		))			,     )

                    __A	:       int      =							self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)
                    __A	:       Optional[int]      =							tokenizer_r(lowerCamelCase			,     return_offsets_mapping=lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
                    self.assertEqual(encoding.offset_mapping[0]			,     (0, len(lowerCamelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]			,     (len(lowerCamelCase		), len(lowerCamelCase		) + 1 + len(lowerCamelCase		))			,     )

                    __A	:       Optional[int]      =							f" {text}"

                    # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                    # )
                    # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                    # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                    # self.assertEqual(
                    #     encoding.offset_mapping[1],
                    #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                    # )

                    __A	:       List[Any]      =							self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)
                    __A	:       Tuple      =							tokenizer_r(lowerCamelCase			,     return_offsets_mapping=lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
                    self.assertEqual(encoding.offset_mapping[0]			,     (1, 1 + len(lowerCamelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]			,     (1 + len(lowerCamelCase		) + 1, 1 + len(lowerCamelCase		) + 1 + len(lowerCamelCase		))			,     )

                    __A	:       Any      =							self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)
                    __A	:       Optional[Any]      =							tokenizer_r(lowerCamelCase			,     return_offsets_mapping=lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
                    self.assertEqual(encoding.offset_mapping[0]			,     (0, 1 + len(lowerCamelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]			,     (1 + len(lowerCamelCase		), 1 + len(lowerCamelCase		) + 1 + len(lowerCamelCase		))			,     )

                    __A	:       int      =							self.rust_tokenizer_class.from_pretrained(
                        lowerCamelCase			,     use_fast=lowerCamelCase			,     add_prefix_space=lowerCamelCase			,     trim_offsets=lowerCamelCase		)
                    __A	:       Dict      =							tokenizer_r(lowerCamelCase			,     return_offsets_mapping=lowerCamelCase			,     add_special_tokens=lowerCamelCase		)
                    self.assertEqual(encoding.offset_mapping[0]			,     (0, 1 + len(lowerCamelCase		))		)
                    self.assertEqual(
                        encoding.offset_mapping[1]			,     (1 + len(lowerCamelCase		), 1 + len(lowerCamelCase		) + 1 + len(lowerCamelCase		))			,     )





'''simple docstring'''


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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


_UpperCamelCase 							=  logging.get_logger(__name__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							b.T
     __A	:       Optional[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=1							)
     __A	:       List[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=0							)
     __A	:       List[Any]      =							np.matmul(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     __A	:       str      =							aa[:, None] - 2 * ab + ba[None, :]
     return d


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							x.reshape(-1       ,					3							)
     __A	:       Any      =							squared_euclidean_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     return np.argmin(SCREAMING_SNAKE_CASE       ,					axis=1							)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['pixel_values']


     def __init__(   self			,     lowerCamelCase = None			,     lowerCamelCase = True			,     lowerCamelCase = None			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = True			,     lowerCamelCase = True			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
          __A	:       int      =							get_size_dict(lowerCamelCase		)
          __A	:       str      =							np.array(lowerCamelCase		) if clusters is not None else None
          __A	:       List[Any]      =							do_resize
          __A	:       Dict      =							size
          __A	:       str      =							resample
          __A	:       Optional[Any]      =							do_normalize
          __A	:       Dict      =							do_color_quantize


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[Any]      =							get_size_dict(lowerCamelCase		)
          if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"		)
          return resize(
              lowerCamelCase			,     size=(size["height"], size["width"])			,     resample=lowerCamelCase			,     data_format=lowerCamelCase			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     ):


          '''simple docstring'''





          __A	:       List[str]      =							rescale(image=lowerCamelCase			,     scale=1 / 127.5			,     data_format=lowerCamelCase		)
          __A	:       List[Any]      =							image - 1
          return image


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = ChannelDimension.FIRST			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       List[Any]      =							do_resize if do_resize is not None else self.do_resize
          __A	:       int      =							size if size is not None else self.size
          __A	:       Tuple      =							get_size_dict(lowerCamelCase		)
          __A	:       Tuple      =							resample if resample is not None else self.resample
          __A	:       Union[str, Any]      =							do_normalize if do_normalize is not None else self.do_normalize
          __A	:       Tuple      =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
          __A	:       Any      =							clusters if clusters is not None else self.clusters
          __A	:       Dict      =							np.array(lowerCamelCase		)

          __A	:       Optional[int]      =							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 or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

          if do_color_quantize and clusters is None:
               raise ValueError("Clusters must be specified if do_color_quantize is True."		)

          # All transformations expect numpy arrays.
          __A	:       Optional[Any]      =							[to_numpy_array(lowerCamelCase		) for image in images]

          if do_resize:
               __A	:       Dict      =							[self.resize(image=lowerCamelCase			,     size=lowerCamelCase			,     resample=lowerCamelCase		) for image in images]

          if do_normalize:
               __A	:       List[Any]      =							[self.normalize(image=lowerCamelCase		) for image in images]

          if do_color_quantize:
               __A	:       Union[str, Any]      =							[to_channel_dimension_format(lowerCamelCase			,     ChannelDimension.LAST		) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               __A	:       int      =							np.array(lowerCamelCase		)
               __A	:       Any      =							color_quantize(lowerCamelCase			,     lowerCamelCase		).reshape(images.shape[:-1]		)

               # flatten to (batch_size, height*width)
               __A	:       Tuple      =							images.shape[0]
               __A	:       List[Any]      =							images.reshape(lowerCamelCase			,     -1		)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               __A	:       Optional[int]      =							list(lowerCamelCase		)
          else:
               __A	:       Tuple      =							[to_channel_dimension_format(lowerCamelCase			,     lowerCamelCase		) for image in images]

          __A	:       Optional[int]      =							{"input_ids": images}
          return BatchFeature(data=lowerCamelCase			,     tensor_type=lowerCamelCase		)

