
"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							tempfile.mkdtemp()

          # fmt: off
          lowercase    :				List[Any]       		=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
          # fmt: on
          lowercase    :				Any       		=							os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["vocab_file"]				)
          with open(self.vocab_file	,    "w"	,    encoding="utf-8"				) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)

          lowercase    :				Dict       		=							{
              "do_resize": True,
              "size": {"height": 18, "width": 18},
              "do_normalize": True,
              "image_mean": [0.5, 0.5, 0.5],
              "image_std": [0.5, 0.5, 0.5],
          }
          lowercase    :				List[Any]       		=							os.path.join(self.tmpdirname	,    _a				)
          with open(self.image_processor_file	,    "w"	,    encoding="utf-8"				) as fp:
                 json.dump(_a	,    _a				)
   def 			__magic_name__			(			self	,    **_a				):
          return BertTokenizer.from_pretrained(self.tmpdirname	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							[np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta				)]

          lowercase    :				Tuple       		=							[Image.fromarray(np.moveaxis(_a	,    0	,    -1				)				) for x in image_inputs]

          return image_inputs
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_tokenizer()
          lowercase    :				List[str]       		=							self.get_image_processor()

          lowercase    :				Dict       		=							VisionTextDualEncoderProcessor(tokenizer=_a	,    image_processor=_a				)

          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				List[Any]       		=							VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    (BertTokenizer, BertTokenizerFast)				)

          self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()				)
          self.assertIsInstance(processor.image_processor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()	,    image_processor=self.get_image_processor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				List[Any]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[int]       		=							self.get_image_processor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				str       		=							VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    (BertTokenizer, BertTokenizerFast)				)

          self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.image_processor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_image_processor()
          lowercase    :				str       		=							self.get_tokenizer()

          lowercase    :				List[Any]       		=							VisionTextDualEncoderProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Any       		=							self.prepare_image_inputs()

          lowercase    :				Tuple       		=							image_processor(_a	,    return_tensors="np"				)
          lowercase    :				Optional[Any]       		=							processor(images=_a	,    return_tensors="np"				)

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1E-2				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							self.get_image_processor()
          lowercase    :				Tuple       		=							self.get_tokenizer()

          lowercase    :				Optional[int]       		=							VisionTextDualEncoderProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Tuple       		=							"lower newer"

          lowercase    :				Optional[Any]       		=							processor(text=_a				)

          lowercase    :				Any       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_image_processor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				Optional[Any]       		=							VisionTextDualEncoderProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				str       		=							"lower newer"
          lowercase    :				Optional[Any]       		=							self.prepare_image_inputs()

          lowercase    :				Dict       		=							processor(text=_a	,    images=_a				)

          self.assertListEqual(list(inputs.keys()				)	,    ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]				)

          # test if it raises when no input is passed
          with self.assertRaises(_a				):
                 processor()
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.get_image_processor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				Optional[Any]       		=							VisionTextDualEncoderProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Dict       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				int       		=							processor.batch_decode(_a				)
          lowercase    :				int       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.get_image_processor()
          lowercase    :				Optional[Any]       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							VisionTextDualEncoderProcessor(tokenizer=_a	,    image_processor=_a				)

          lowercase    :				Optional[int]       		=							"lower newer"
          lowercase    :				Optional[int]       		=							self.prepare_image_inputs()

          lowercase    :				str       		=							processor(text=_a	,    images=_a				)

          self.assertListEqual(list(inputs.keys()				)	,    processor.model_input_names				)




"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   _A			:       Union[str, Any]   		=       None

_A			:       str   		=       logging.get_logger(__name__)
_A			:       Tuple   		=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A			:       Optional[Any]   		=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_A			:       Optional[Any]   		=       {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_A			:       List[str]   		=       """▁"""

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """token_type_ids"""]
   __lowerCAmelCase            =				FNetTokenizer
   def __init__(			self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=True	,    _a="<unk>"	,    _a="[SEP]"	,    _a="<pad>"	,    _a="[CLS]"	,    _a="[MASK]"	,    **_a	,    ):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          lowercase    :				int       		=							(
              AddedToken(_a	,    lstrip=_a	,    rstrip=_a	,    normalized=_a				)
              if isinstance(_a	,    _a				)
              else mask_token
          )

          super().__init__(
              _a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    **_a	,    )

          lowercase    :				Dict       		=							do_lower_case
          lowercase    :				Union[str, Any]       		=							remove_space
          lowercase    :				Any       		=							keep_accents
          lowercase    :				List[Any]       		=							vocab_file
          lowercase    :				Union[str, Any]       		=							False if not self.vocab_file else True
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[Any]       		=							[self.sep_token_id]
          lowercase    :				str       		=							[self.cls_token_id]
          if token_ids_a is None:
                 return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Any       		=							[self.sep_token_id]
          lowercase    :				Dict       		=							[self.cls_token_id]

          if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				Optional[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
                 copyfile(self.vocab_file	,    _a				)

          return (out_vocab_file,)


