"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""image_processor""", """tokenizer"""]
      a__				=		"""LayoutLMv3ImageProcessor"""
      a__				=		("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      **lowercase)     ->      Any:





        '''simple docstring'''





        a__:					Tuple      							=     None
        if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'   ,      lowercase   ,      )
          a__:					Union[str, Any]      							=     kwargs.pop('feature_extractor')

        a__:					Optional[Any]      							=     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__(lowercase   ,      lowercase)



      def __call__(						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = True   ,      lowercase = False   ,      lowercase = None   ,      lowercase = None   ,      lowercase = 0   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = False   ,      lowercase = False   ,      lowercase = False   ,      lowercase = False   ,      lowercase = True   ,      lowercase = None   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        if self.image_processor.apply_ocr and (boxes is not None):
          raise ValueError(
              'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.')

        if self.image_processor.apply_ocr and (word_labels is not None):
          raise ValueError(
              'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')

        # first, apply the image processor
        a__:					Optional[int]      							=     self.image_processor(images=lowercase   ,      return_tensors=lowercase)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
          if isinstance(lowercase   ,      lowercase):
            a__:					List[Any]      							=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
          a__:					Any      							=     features['words']

        a__:					Dict      							=     self.tokenizer(
            text=text if text is not None else features['words']   ,      text_pair=text_pair if text_pair is not None else None   ,      boxes=boxes if boxes is not None else features['boxes']   ,      word_labels=lowercase   ,      add_special_tokens=lowercase   ,      padding=lowercase   ,      truncation=lowercase   ,      max_length=lowercase   ,      stride=lowercase   ,      pad_to_multiple_of=lowercase   ,      return_token_type_ids=lowercase   ,      return_attention_mask=lowercase   ,      return_overflowing_tokens=lowercase   ,      return_special_tokens_mask=lowercase   ,      return_offsets_mapping=lowercase   ,      return_length=lowercase   ,      verbose=lowercase   ,      return_tensors=lowercase   ,      **lowercase   ,      )

        # add pixel values
        a__:					List[str]      							=     features.pop('pixel_values')
        if return_overflowing_tokens is True:
          a__:					str      							=     self.get_overflowing_images(lowercase   ,      encoded_inputs['overflow_to_sample_mapping'])
        a__:					Optional[Any]      							=     images

        return encoded_inputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[int]      							=     []
        for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx])

        if len(lowercase) != len(lowercase):
          raise ValueError(
              'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
              f' {len(lowercase)} and {len(lowercase)}')

        return images_with_overflow



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      str:





        '''simple docstring'''





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



      @property
      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        return ["input_ids", "bbox", "attention_mask", "pixel_values"]



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





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



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





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



"""simple docstring"""
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=12   ,      lowercase=7   ,      lowercase=True   ,      lowercase=True   ,      lowercase=True   ,      lowercase=99   ,      lowercase=32   ,      lowercase=32   ,      lowercase=2   ,      lowercase=4   ,      lowercase=37   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=5_12   ,      lowercase=0.02   ,      lowercase=0   ,      lowercase=None   ,      )     ->      Optional[int]:





        '''simple docstring'''





        a__:					List[Any]      							=     parent
        a__:					Any      							=     batch_size
        a__:					int      							=     seq_length
        a__:					List[str]      							=     is_training
        a__:					Any      							=     use_input_mask
        a__:					Optional[Any]      							=     use_labels
        a__:					List[Any]      							=     vocab_size
        a__:					Optional[Any]      							=     hidden_size
        a__:					Any      							=     projection_dim
        a__:					List[str]      							=     num_hidden_layers
        a__:					Dict      							=     num_attention_heads
        a__:					int      							=     intermediate_size
        a__:					Tuple      							=     dropout
        a__:					Union[str, Any]      							=     attention_dropout
        a__:					str      							=     max_position_embeddings
        a__:					List[str]      							=     initializer_range
        a__:					Optional[int]      							=     scope
        a__:					List[Any]      							=     bos_token_id



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					str      							=     ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size)

        a__:					str      							=     None
        if self.use_input_mask:
          a__:					List[str]      							=     random_attention_mask([self.batch_size, self.seq_length])

        if input_mask is not None:
          a__:					str      							=     input_mask.numpy()
          a__							,   a__:					Any      							=     input_mask.shape
          a__:					Tuple      							=     np.random.randint(1   ,      seq_length - 1   ,      size=(batch_size,))
          for batch_idx, start_index in enumerate(lowercase):
            a__:					Tuple      							=     1
            a__:					Any      							=     0

        a__:					Any      							=     self.get_config()

        return config, input_ids, tf.convert_to_tensor(lowercase)



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





        '''simple docstring'''





        return BlipTextConfig(
            vocab_size=self.vocab_size   ,      hidden_size=self.hidden_size   ,      projection_dim=self.projection_dim   ,      num_hidden_layers=self.num_hidden_layers   ,      num_attention_heads=self.num_attention_heads   ,      intermediate_size=self.intermediate_size   ,      dropout=self.dropout   ,      attention_dropout=self.attention_dropout   ,      max_position_embeddings=self.max_position_embeddings   ,      initializer_range=self.initializer_range   ,      bos_token_id=self.bos_token_id   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Any      							=     TFBlipTextModel(config=lowercase)
        a__:					Union[str, Any]      							=     model(lowercase   ,      attention_mask=lowercase   ,      training=lowercase)
        a__:					List[Any]      							=     model(lowercase   ,      training=lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape   ,      (self.batch_size, self.hidden_size))



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Tuple      							=     self.prepare_config_and_inputs()
        a__							,   a__							,   a__:					Optional[Any]      							=     config_and_inputs
        a__:					Dict      							=     {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		(TFBlipTextModel,) if is_tf_available() else ()
      a__				=		False
      a__				=		False
      a__				=		False



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     BlipTextModelTester(self)
        a__:					int      							=     ConfigTester(self   ,      config_class=lowercase   ,      hidden_size=37)



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



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





        '''simple docstring'''





        a__:					List[str]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        pass



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        pass



      @unittest.skip(reason='Blip does not use inputs_embeds')
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        pass



      @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING')
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        pass



      @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING')
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        pass



      @slow
      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					Tuple      							=     TFBlipTextModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase=True)     ->      str:





        '''simple docstring'''





        super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase)


