"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

lowercase__ 	=     logging.get_logger(__name__)




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		"""vision-encoder-decoder"""
      a__				=		True



      def __init__(						self   ,      **lowercase)     ->      Optional[int]:





        '''simple docstring'''





        super().__init__(**lowercase)
        if "encoder" not in kwargs or "decoder" not in kwargs:
          raise ValueError(
              f'A configuraton of type {self.model_type} cannot be instantiated because '
              f'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}')

        a__:					List[Any]      							=     kwargs.pop('encoder')
        a__:					int      							=     encoder_config.pop('model_type')
        a__:					List[Any]      							=     kwargs.pop('decoder')
        a__:					str      							=     decoder_config.pop('model_type')

        a__:					Tuple      							=     AutoConfig.for_model(lowercase   ,      **lowercase)
        a__:					Optional[Any]      							=     AutoConfig.for_model(lowercase   ,      **lowercase)
        a__:					str      							=     True



      @classmethod
      def   lowerCamelCase_       (						cls   ,      lowercase   ,      lowercase   ,      **lowercase)     ->      PretrainedConfig:





        '''simple docstring'''





        logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
        a__:					int      							=     True
        a__:					Tuple      							=     True

        return cls(encoder=encoder_config.to_dict()   ,      decoder=decoder_config.to_dict()   ,      **lowercase)



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





        '''simple docstring'''





        a__:					List[str]      							=     copy.deepcopy(self.__dict__)
        a__:					int      							=     self.encoder.to_dict()
        a__:					List[Any]      							=     self.decoder.to_dict()
        a__:					List[Any]      							=     self.__class__.model_type
        return output







class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		version.parse("""1.11"""    )



      @property
      def   lowerCamelCase_       (						self)     ->      Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ])



      @property
      def   lowerCamelCase_       (						self)     ->      float:





        '''simple docstring'''





        return 1e-4



      @property
      def   lowerCamelCase_       (						self)     ->      Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}})







class        __snake_case	(					__lowerCAmelCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        a__:					Tuple      							=     OrderedDict()
        a__:					List[str]      							=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        a__:					Any      							=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        a__:					Any      							=     {0: 'batch', 1: 'encoder_sequence'}

        return common_inputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = -1   ,      lowercase = -1   ,      lowercase = False   ,      lowercase = None   ,      )     ->      Mapping[str, Any]:





        '''simple docstring'''





        import torch

        a__:					Union[str, Any]      							=     OrderedDict()

        a__:					str      							=     super().generate_dummy_inputs(
            lowercase   ,      batch_size=lowercase   ,      seq_length=lowercase   ,      is_pair=lowercase   ,      framework=lowercase)

        a__							,   a__:					Optional[int]      							=     dummy_input['input_ids'].shape
        a__:					List[str]      							=     (batch, encoder_sequence, self._config.encoder_hidden_size)
        a__:					Tuple      							=     dummy_input.pop('input_ids')
        a__:					Union[str, Any]      							=     dummy_input.pop('attention_mask')
        a__:					List[Any]      							=     torch.zeros(lowercase)

        return common_inputs







class        __snake_case	(					__lowerCAmelCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      None:





        '''simple docstring'''





        pass



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      OnnxConfig:





        '''simple docstring'''





        return VisionEncoderDecoderEncoderOnnxConfig(lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = "default")     ->      OnnxConfig:





        '''simple docstring'''





        a__:					Any      							=     encoder_config.hidden_size
        return VisionEncoderDecoderDecoderOnnxConfig(lowercase   ,      lowercase)



"""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)


