

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_big_bird import BigBirdTokenizer
else:
     lowercase_       		=  None

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

lowercase_       		=  {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_       		=  {
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}


lowercase_       		=  "▁"


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							VOCAB_FILES_NAMES
    lowerCamelCase			=							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase			=							BigBirdTokenizer
    lowerCamelCase			=							['input_ids', 'attention_mask']
    lowerCamelCase			=							[]


    def __init__(      self :       List[Any],lowercase_ :       Union[str, Any]=None,lowercase_ :       List[str]=None,lowercase_ :       int="<unk>",lowercase_ :       Optional[Any]="<s>",lowercase_ :       Optional[Any]="</s>",lowercase_ :       Optional[int]="<pad>",lowercase_ :       int="[SEP]",lowercase_ :       int="[MASK]",lowercase_ :       Dict="[CLS]",**lowercase_ :       Optional[Any],)->    Any:





        '''simple docstring'''







        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else bos_token
        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else eos_token
        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else unk_token
        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else pad_token
        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else cls_token
        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else mask_token

        super().__init__(
            lowercase_,tokenizer_file=lowercase_,bos_token=lowercase_,eos_token=lowercase_,unk_token=lowercase_,sep_token=lowercase_,pad_token=lowercase_,cls_token=lowercase_,mask_token=lowercase_,**lowercase_,)

        A__          =   vocab_file
        A__          =   False if not self.vocab_file else True


    def 						snake_case__     (      self :       int,lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None		)->    List[int]:





        '''simple docstring'''







        A__          =   [self.sep_token_id]
        A__          =   [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 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None,lowercase_ :       bool = False		)->    List[int]:





        '''simple docstring'''







        if already_has_special_tokens:
            if token_ids_a is not None:
                raise ValueError(
                    'You should not supply a second sequence if the provided sequence of '
                    'ids is already formatted with special tokens for the model.'		)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None		)->    List[int]:





        '''simple docstring'''







        A__          =   [self.sep_token_id]
        A__          =   [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 						snake_case__     (      self :       int,lowercase_ :       str,lowercase_ :       Optional[str] = None		)->    Tuple[str]:





        '''simple docstring'''







        if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'		)

        if not os.path.isdir(lowercase_		):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
            return
        A__          =   os.path.join(
            lowercase_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

        return (out_vocab_file,)




from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       Any,lowercase_ :       Union[str, Any]=1_3,lowercase_ :       Tuple=3_0,lowercase_ :       List[Any]=2,lowercase_ :       Optional[int]=3,lowercase_ :       Union[str, Any]=True,lowercase_ :       Tuple=True,lowercase_ :       Any=3_2,lowercase_ :       List[str]=2,lowercase_ :       Optional[int]=4,lowercase_ :       Union[str, Any]=3_7,lowercase_ :       Tuple="gelu",lowercase_ :       str=0.1,lowercase_ :       Tuple=0.1,lowercase_ :       Union[str, Any]=1_0,lowercase_ :       int=0.02,lowercase_ :       List[Any]=3,lowercase_ :       Any=None,)->    Dict:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   patch_size
        A__          =   num_channels
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   type_sequence_label_size
        A__          =   initializer_range
        A__          =   scope

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        A__          =   (image_size // patch_size) ** 2
        A__          =   num_patches + 1


    def 						snake_case__     (      self :       int		)->    List[str]:





        '''simple docstring'''







        A__          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        A__          =   None
        if self.use_labels:
            A__          =   ids_tensor([self.batch_size],self.type_sequence_label_size		)

        A__          =   self.get_config()

        return config, pixel_values, labels


    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        return ViTConfig(
            image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,is_decoder=lowercase_,initializer_range=self.initializer_range,)


    def 						snake_case__     (      self :       List[str],lowercase_ :       int,lowercase_ :       Union[str, Any],lowercase_ :       Tuple		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   TFViTModel(config=lowercase_		)
        A__          =   model(lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        A__          =   (image_size // self.patch_size) ** 2 + 1
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   self.type_sequence_label_size
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   model(lowercase_,labels=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        A__          =   1
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__      ,						A__          =   config_and_inputs
        A__          =   {'pixel_values': pixel_values}
        return config, inputs_dict




@require_tf
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       int		)->    List[Any]:





        '''simple docstring'''







        A__          =   TFViTModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_,hidden_size=3_7		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        pass


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       str		)->    Dict:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            self.assertIsInstance(model.get_input_embeddings(),(tf.keras.layers.Layer)		)
            A__          =   model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowercase_,tf.keras.layers.Layer		)		)


    def 						snake_case__     (      self :       int		)->    List[str]:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            A__          =   inspect.signature(model.call		)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A__          =   [*signature.parameters.keys()]

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


    @slow
    def 						snake_case__     (      self :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   TFViTModel.from_pretrained('google/vit-base-patch16-224'		)
        self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      str:





    '''simple docstring'''






    A__          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    return image


@require_tf
@require_vision
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       List[Any]		)->    str:





        '''simple docstring'''







        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'		) if is_vision_available() else None


    @slow
    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='tf'		)

        # forward pass
        A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   tf.TensorShape((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   tf.constant([-0.2_744, 0.8_215, -0.0_836]		)

        tf.debugging.assert_near(outputs.logits[0, :3],lowercase_,atol=1E-4		)

