





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
 _UpperCAmelCase       : Optional[int]			     =       """pt"""
elif is_tf_available():
 _UpperCAmelCase       : str			     =       """tf"""
else:
 _UpperCAmelCase       : Tuple			     =       """jax"""

class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	PerceiverTokenizer
 UpperCAmelCase__									=	False
 def   A_			(   self						:   Optional[int]			)    ->			Any:
     super().setUp()
     lowerCamelCase__					:   Optional[int]      				=	PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname			)
 @cached_property
 def   A_			(   self						:   int			)    ->			Optional[int]:
     return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'			)
 def   A_			(   self						:   Optional[int]    ,  **UpperCAmelCase						:   int			)    ->			PerceiverTokenizer:
     return self.tokenizer_class.from_pretrained(self.tmpdirname    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Any=False    ,  UpperCAmelCase						:   List[str]=20    ,  UpperCAmelCase						:   List[str]=5			)    ->			Tuple[str, list]:
     # XXX The default common tokenizer tests assume that every ID is decodable on its own.
     # This assumption is invalid for Perceiver because single bytes might not be
     # valid utf-8 (byte 128 for instance).
     # Here we're overriding the smallest possible method to provide
     # a clean sequence without making the same assumption.

     lowerCamelCase__					:   Optional[int]      				=	[]
     for i in range(len(UpperCAmelCase			)			):
         try:
             lowerCamelCase__					:   Any      				=	tokenizer.decode([i]    ,  clean_up_tokenization_spaces=UpperCAmelCase			)
         except UnicodeDecodeError:
             pass
         toks.append((i, tok)			)

     lowerCamelCase__					:   int      				=	list(filter(lambda UpperCAmelCase			: re.match(R'^[ a-zA-Z]+$'    ,  t[1]			)    ,  UpperCAmelCase			)			)
     lowerCamelCase__					:   Tuple      				=	list(filter(lambda UpperCAmelCase			: [t[0]] == tokenizer.encode(t[1]    ,  add_special_tokens=UpperCAmelCase			)    ,  UpperCAmelCase			)			)
     if max_length is not None and len(UpperCAmelCase			) > max_length:
         lowerCamelCase__					:   Optional[int]      				=	toks[:max_length]
     if min_length is not None and len(UpperCAmelCase			) < min_length and len(UpperCAmelCase			) > 0:
         while len(UpperCAmelCase			) < min_length:
             lowerCamelCase__					:   Optional[Any]      				=	toks + toks
        # toks_str = [t[1] for t in toks]
     lowerCamelCase__					:   Any      				=	[t[0] for t in toks]

     # Ensure consistency
     lowerCamelCase__					:   List[str]      				=	tokenizer.decode(UpperCAmelCase    ,  clean_up_tokenization_spaces=UpperCAmelCase			)
     if " " not in output_txt and len(UpperCAmelCase			) > 1:
         lowerCamelCase__					:   Union[str, Any]      				=	(
             tokenizer.decode([toks_ids[0]]    ,  clean_up_tokenization_spaces=UpperCAmelCase			)
             + ' '
             + tokenizer.decode(toks_ids[1:]    ,  clean_up_tokenization_spaces=UpperCAmelCase			)
         )
     if with_prefix_space:
         lowerCamelCase__					:   Union[str, Any]      				=	' ' + output_txt
     lowerCamelCase__					:   Union[str, Any]      				=	tokenizer.encode(UpperCAmelCase    ,  add_special_tokens=UpperCAmelCase			)
     return output_txt, output_ids
 def   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	self.perceiver_tokenizer
     lowerCamelCase__					:   Tuple      				=	'Unicode €.'
     lowerCamelCase__					:   List[Any]      				=	tokenizer(UpperCAmelCase			)
     lowerCamelCase__					:   Dict      				=	[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['input_ids']    ,  UpperCAmelCase			)

     # decoding
     lowerCamelCase__					:   Any      				=	tokenizer.decode(UpperCAmelCase			)
     self.assertEqual(UpperCAmelCase    ,  '[CLS]Unicode €.[SEP]'			)

     lowerCamelCase__					:   Dict      				=	tokenizer('e è é ê ë'			)
     lowerCamelCase__					:   Union[str, Any]      				=	[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['input_ids']    ,  UpperCAmelCase			)
     # decoding
     lowerCamelCase__					:   Any      				=	tokenizer.decode(UpperCAmelCase			)
     self.assertEqual(UpperCAmelCase    ,  '[CLS]e è é ê ë[SEP]'			)

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'			)			)    ,  '[CLS]e è é ê ë[SEP]'			)
 def   A_			(   self						:   List[str]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	self.perceiver_tokenizer
     lowerCamelCase__					:   List[str]      				=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
     # fmt: off
     lowerCamelCase__					:   Tuple      				=	[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
     # fmt: on
     lowerCamelCase__					:   Optional[int]      				=	tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase			)
     self.assertIsInstance(UpperCAmelCase    ,  UpperCAmelCase			)

     if FRAMEWORK != "jax":
         lowerCamelCase__					:   List[str]      				=	list(batch.input_ids.numpy()[0]			)
     else:
         lowerCamelCase__					:   List[Any]      				=	list(batch.input_ids.tolist()[0]			)

     self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)

     self.assertEqual((2, 38)    ,  batch.input_ids.shape			)
     self.assertEqual((2, 38)    ,  batch.attention_mask.shape			)
 def   A_			(   self						:   List[str]			)    ->			str:
     lowerCamelCase__					:   Tuple      				=	self.perceiver_tokenizer
     lowerCamelCase__					:   int      				=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
     lowerCamelCase__					:   Dict      				=	tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase			)
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('input_ids'    ,  UpperCAmelCase			)
     self.assertIn('attention_mask'    ,  UpperCAmelCase			)
     self.assertNotIn('decoder_input_ids'    ,  UpperCAmelCase			)
     self.assertNotIn('decoder_attention_mask'    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[int]      				=	self.perceiver_tokenizer
     lowerCamelCase__					:   int      				=	[
         'Summary of the text.',
         'Another summary.',
     ]
     lowerCamelCase__					:   Optional[int]      				=	tokenizer(
         text_target=UpperCAmelCase    ,  max_length=32    ,  padding='max_length'    ,  truncation=UpperCAmelCase    ,  return_tensors=UpperCAmelCase			)
     self.assertEqual(32    ,  targets['input_ids'].shape[1]			)
 def   A_			(   self						:   str			)    ->			List[Any]:
     # safety check on max_len default value so we are sure the test works
     lowerCamelCase__					:   str      				=	self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
             self.assertNotEqual(tokenizer.model_max_length    ,  42			)

        # Now let's start the test
     lowerCamelCase__					:   str      				=	self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
             # Isolate this from the other tests because we save additional tokens/etc
             lowerCamelCase__					:   List[str]      				=	tempfile.mkdtemp()

             lowerCamelCase__					:   Any      				=	' He is very happy, UNwant\u00E9d,running'
             lowerCamelCase__					:   Optional[int]      				=	tokenizer.encode(UpperCAmelCase    ,  add_special_tokens=UpperCAmelCase			)
             tokenizer.save_pretrained(UpperCAmelCase			)

             lowerCamelCase__					:   List[str]      				=	tokenizer.__class__.from_pretrained(UpperCAmelCase			)
             lowerCamelCase__					:   Optional[Any]      				=	after_tokenizer.encode(UpperCAmelCase    ,  add_special_tokens=UpperCAmelCase			)
             self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)

             shutil.rmtree(UpperCAmelCase			)

     lowerCamelCase__					:   Union[str, Any]      				=	self.get_tokenizers(model_max_length=42			)
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
             # Isolate this from the other tests because we save additional tokens/etc
             lowerCamelCase__					:   Any      				=	tempfile.mkdtemp()

             lowerCamelCase__					:   Optional[Any]      				=	' He is very happy, UNwant\u00E9d,running'
             tokenizer.add_tokens(['bim', 'bambam']			)
             lowerCamelCase__					:   Any      				=	tokenizer.additional_special_tokens
             additional_special_tokens.append('new_additional_special_token'			)
             tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}			)
             lowerCamelCase__					:   Optional[int]      				=	tokenizer.encode(UpperCAmelCase    ,  add_special_tokens=UpperCAmelCase			)
             tokenizer.save_pretrained(UpperCAmelCase			)

             lowerCamelCase__					:   Optional[int]      				=	tokenizer.__class__.from_pretrained(UpperCAmelCase			)
             lowerCamelCase__					:   List[Any]      				=	after_tokenizer.encode(UpperCAmelCase    ,  add_special_tokens=UpperCAmelCase			)
             self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)
             self.assertIn('new_additional_special_token'    ,  after_tokenizer.additional_special_tokens			)
             self.assertEqual(after_tokenizer.model_max_length    ,  42			)

             lowerCamelCase__					:   Union[str, Any]      				=	tokenizer.__class__.from_pretrained(UpperCAmelCase    ,  model_max_length=43			)
             self.assertEqual(tokenizer.model_max_length    ,  43			)

             shutil.rmtree(UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			List[str]:
     lowerCamelCase__					:   Optional[Any]      				=	[]
     if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())			)

     if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())			)

     for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer_utils.save_pretrained(UpperCAmelCase			)

             with open(os.path.join(UpperCAmelCase    ,  'special_tokens_map.json'			)    ,  encoding='utf-8'			) as json_file:
                 lowerCamelCase__					:   str      				=	json.load(UpperCAmelCase			)

             with open(os.path.join(UpperCAmelCase    ,  'tokenizer_config.json'			)    ,  encoding='utf-8'			) as json_file:
                 lowerCamelCase__					:   int      				=	json.load(UpperCAmelCase			)

             lowerCamelCase__					:   Dict      				=	[F"""<extra_id_{i}>""" for i in range(125			)]

             lowerCamelCase__					:   Optional[int]      				=	added_tokens_extra_ids + [
                 'an_additional_special_token'
             ]
             lowerCamelCase__					:   List[Any]      				=	added_tokens_extra_ids + [
                 'an_additional_special_token'
             ]

             with open(os.path.join(UpperCAmelCase    ,  'special_tokens_map.json'			)    ,  'w'    ,  encoding='utf-8'			) as outfile:
                 json.dump(UpperCAmelCase    ,  UpperCAmelCase			)
             with open(os.path.join(UpperCAmelCase    ,  'tokenizer_config.json'			)    ,  'w'    ,  encoding='utf-8'			) as outfile:
                 json.dump(UpperCAmelCase    ,  UpperCAmelCase			)

             # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
             # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
             # "special_tokens_map.json" files
             lowerCamelCase__					:   Any      				=	tokenizer_class.from_pretrained(
                 UpperCAmelCase    ,  )
             self.assertIn(
                 'an_additional_special_token'    ,  tokenizer_without_change_in_init.additional_special_tokens			)
             self.assertEqual(
                 ['an_additional_special_token']    ,  tokenizer_without_change_in_init.convert_ids_to_tokens(
                     tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']			)			)    ,  )

             # Now we test that we can change the value of additional_special_tokens in the from_pretrained
             lowerCamelCase__					:   Tuple      				=	added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'    ,  lstrip=UpperCAmelCase			)]
             lowerCamelCase__					:   Any      				=	tokenizer_class.from_pretrained(
                 UpperCAmelCase    ,  additional_special_tokens=UpperCAmelCase    ,  )

             self.assertIn('a_new_additional_special_token'    ,  tokenizer.additional_special_tokens			)
             self.assertEqual(
                 ['a_new_additional_special_token']    ,  tokenizer.convert_ids_to_tokens(
                     tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']			)			)    ,  )
 def   A_			(   self						:   str			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178]			)    ,  '�'			)
 def   A_			(   self						:   str			)    ->			str:
     pass
 def   A_			(   self						:   Optional[Any]			)    ->			Union[str, Any]:
     pass
 def   A_			(   self						:   List[str]			)    ->			Optional[Any]:
     pass
 def   A_			(   self						:   Dict			)    ->			Any:
     pass







 def   A_			(   self						:   Tuple			)    ->			int:
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     lowerCamelCase__					:   Tuple      				=	self.get_tokenizers(fast=UpperCAmelCase    ,  do_lower_case=UpperCAmelCase			)
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
             lowerCamelCase__					:   str      				=	['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
             lowerCamelCase__					:   Optional[int]      				=	tokenizer.convert_tokens_to_string(UpperCAmelCase			)

             self.assertIsInstance(UpperCAmelCase    ,  UpperCAmelCase			)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_UpperCAmelCase       : Optional[Any]			     =       {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : int			     =       ["""CLIPTokenizerFast"""]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : Optional[int]			     =       ["""CLIPFeatureExtractor"""]
    _UpperCAmelCase       : Union[str, Any]			     =       ["""CLIPImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : int			     =       [
        """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CLIPModel""",
        """CLIPPreTrainedModel""",
        """CLIPTextModel""",
        """CLIPTextModelWithProjection""",
        """CLIPVisionModel""",
        """CLIPVisionModelWithProjection""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : Tuple			     =       [
        """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCLIPModel""",
        """TFCLIPPreTrainedModel""",
        """TFCLIPTextModel""",
        """TFCLIPVisionModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : Optional[int]			     =       [
        """FlaxCLIPModel""",
        """FlaxCLIPPreTrainedModel""",
        """FlaxCLIPTextModel""",
        """FlaxCLIPTextPreTrainedModel""",
        """FlaxCLIPVisionModel""",
        """FlaxCLIPVisionPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

    _UpperCAmelCase       : Optional[Any]			     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





