

import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


_snake_case         =  '▁'

_snake_case         =  get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class        a__  (							lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							BigBirdTokenizer
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							BigBirdTokenizerFast
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							True
   _SCREAMING_SNAKE_CASE   :					int     	=							True


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      super().setUp()

      _lowercase      :				int      					=       self.tokenizer_class(_UpperCamelCase      ,    keep_accents=_UpperCamelCase  )
      tokenizer.save_pretrained(self.tmpdirname  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       "<s>"
      _lowercase      :				List[Any]      					=       1

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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       list(self.get_tokenizer().get_vocab().keys()  )

      self.assertEqual(vocab_keys[0]      ,    "<unk>"  )
      self.assertEqual(vocab_keys[1]      ,    "<s>"  )
      self.assertEqual(vocab_keys[-1]      ,    "[MASK]"  )
      self.assertEqual(len(_UpperCamelCase  )      ,    1004  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if not self.test_rust_tokenizer:
         return

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

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

      _lowercase      :				Any      					=       tokenizer.tokenize(_UpperCamelCase  )
      _lowercase      :				List[str]      					=       rust_tokenizer.tokenize(_UpperCamelCase  )
      self.assertListEqual(_UpperCamelCase      ,    _UpperCamelCase  )

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

      _lowercase      :				Optional[Any]      					=       self.get_rust_tokenizer()
      _lowercase      :				Any      					=       tokenizer.encode(_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       rust_tokenizer.encode(_UpperCamelCase  )
      self.assertListEqual(_UpperCamelCase      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       BigBirdTokenizer(_UpperCamelCase      ,    keep_accents=_UpperCamelCase  )

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

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

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

      _lowercase      :				str      					=       tokenizer.convert_ids_to_tokens(_UpperCamelCase  )
      self.assertListEqual(
          _UpperCamelCase      ,    [
              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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"  )


   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       "Hello World!"
      _lowercase      :				str      					=       [65, 18536, 2260, 101, 66]

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


   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       (
          "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"
      )
      # fmt: off
      _lowercase      :				List[str]      					=       [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
      # fmt: on
      self.assertListEqual(_UpperCamelCase      ,    self.big_tokenizer.encode(_UpperCamelCase  )  )


   @require_torch
   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      import torch

      from transformers import BigBirdConfig, BigBirdModel

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

      _lowercase      :				List[str]      					=       BigBirdConfig(attention_type="original_full"  )
      _lowercase      :				Optional[int]      					=       BigBirdModel(_UpperCamelCase  )

      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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


   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"  )
      _lowercase      :				Optional[Any]      					=       tokenizer.decode(tokenizer("Paris is the [MASK]."  ).input_ids  )

      self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"  )






   @slow
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       {"input_ids": [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_UpperCamelCase      ,    model_name="google/bigbird-roberta-base"      ,    revision="215c99f1600e06f83acce68422f2035b2b5c3510"      ,    )









'''simple docstring'''
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					str     	=							'OwlViTImageProcessor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							('CLIPTokenizer', 'CLIPTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,    _UpperCamelCase      ,    )
         _lowercase      :				Optional[int]      					=       kwargs.pop("feature_extractor"  )

      _lowercase      :				str      					=       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."  )
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."  )

      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase="max_length"      ,    _UpperCamelCase="np"      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if text is None and query_images is None and images is None:
         raise ValueError(
             "You have to specify at least one text or query image or image. All three cannot be none."  )

      if text is not None:
         if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) or (isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and not isinstance(text[0]      ,    _UpperCamelCase  )):
            _lowercase      :				int      					=       [self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )]

         elif isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and isinstance(text[0]      ,    _UpperCamelCase  ):
            _lowercase      :				str      					=       []

            # Maximum number of queries across batch
            _lowercase      :				str      					=       max([len(_UpperCamelCase  ) for t in text]  )

            # Pad all batch samples to max number of text queries
            for t in text:
               if len(_UpperCamelCase  ) != max_num_queries:
                  _lowercase      :				List[Any]      					=       t + [" "] * (max_num_queries - len(_UpperCamelCase  ))

               _lowercase      :				Tuple      					=       self.tokenizer(_UpperCamelCase      ,    padding=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )
               encodings.append(_UpperCamelCase  )
         else:
            raise TypeError("Input text should be a string, a list of strings or a nested list of strings"  )

         if return_tensors == "np":
            _lowercase      :				List[Any]      					=       np.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				Optional[Any]      					=       np.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "jax" and is_flax_available():
            import jax.numpy as jnp

            _lowercase      :				Union[str, Any]      					=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				int      					=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         elif return_tensors == "pt" and is_torch_available():
            import torch

            _lowercase      :				int      					=       torch.cat([encoding["input_ids"] for encoding in encodings]      ,    dim=0  )
            _lowercase      :				Dict      					=       torch.cat([encoding["attention_mask"] for encoding in encodings]      ,    dim=0  )

         elif return_tensors == "tf" and is_tf_available():
            import tensorflow as tf

            _lowercase      :				Optional[int]      					=       tf.stack([encoding["input_ids"] for encoding in encodings]      ,    axis=0  )
            _lowercase      :				List[str]      					=       tf.stack([encoding["attention_mask"] for encoding in encodings]      ,    axis=0  )

         else:
            raise ValueError("Target return tensor type could not be returned"  )

         _lowercase      :				Optional[int]      					=       BatchEncoding()
         _lowercase      :				List[Any]      					=       input_ids
         _lowercase      :				Dict      					=       attention_mask

      if query_images is not None:
         _lowercase      :				int      					=       BatchEncoding()
         _lowercase      :				Any      					=       self.image_processor(
             _UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  ).pixel_values
         _lowercase      :				Any      					=       query_pixel_values

      if images is not None:
         _lowercase      :				str      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is not None and images is not None:
         _lowercase      :				List[Any]      					=       image_features.pixel_values
         return encoding
      elif query_images is not None and images is not None:
         _lowercase      :				Optional[Any]      					=       image_features.pixel_values
         return encoding
      elif text is not None or query_images is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**_UpperCamelCase  )      ,    tensor_type=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_object_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor_class






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor






