'''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        __A      :



    def __init__(self    :    Dict   ,			__a    :    List[Any]   ,			__a    :    str=12   ,			__a    :    Optional[Any]=7   ,			__a    :    Optional[int]=True   ,			__a    :    Union[str, Any]=True   ,			__a    :    Optional[int]=True   ,			__a    :    Dict=99   ,			__a    :    Union[str, Any]=32   ,			__a    :    Dict=32   ,			__a    :    Dict=2   ,			__a    :    Tuple=4   ,			__a    :    Optional[int]=37   ,			__a    :    Tuple=0.1   ,			__a    :    Union[str, Any]=0.1   ,			__a    :    Optional[Any]=512   ,			__a    :    Tuple=0.02   ,			__a    :    Union[str, Any]=0   ,			__a    :    Optional[int]=None   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   seq_length
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_input_mask
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   projection_dim
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   dropout
         UpperCAmelCase_           =   attention_dropout
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   scope
         UpperCAmelCase_           =   bos_token_id



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size )

         UpperCAmelCase_           =   None
         if self.use_input_mask:
              UpperCAmelCase_           =   random_attention_mask([self.batch_size, self.seq_length] )

         if input_mask is not None:
              UpperCAmelCase_           =   input_mask.numpy()
              UpperCAmelCase_    , UpperCAmelCase_           =   input_mask.shape
              UpperCAmelCase_           =   np.random.randint(1   ,			seq_length - 1   ,			size=(batch_size,) )
              for batch_idx, start_index in enumerate(_UpperCamelCase ):
                   UpperCAmelCase_           =   1
                   UpperCAmelCase_           =   0

         UpperCAmelCase_           =   self.get_config()

         return config, input_ids, tf.convert_to_tensor(_UpperCamelCase )



    def 					_lowercase						(self    :    str ):
         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 					_lowercase						(self    :    List[Any]   ,			__a    :    List[Any]   ,			__a    :    Union[str, Any]   ,			__a    :    Dict ):
         UpperCAmelCase_           =   TFBlipTextModel(config=_UpperCamelCase )
         UpperCAmelCase_           =   model(_UpperCamelCase   ,			attention_mask=_UpperCamelCase   ,			training=_UpperCamelCase )
         UpperCAmelCase_           =   model(_UpperCamelCase   ,			training=_UpperCamelCase )
         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 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   config_and_inputs
         UpperCAmelCase_           =   {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict




@require_tf
class        __A      ( __A		,	unittest.TestCase	):
    a__       :						List[Any]          =  (TFBlipTextModel,) if is_tf_available() else ()
    a__       :						Any          =  False
    a__       :						Optional[Any]          =  False
    a__       :						str          =  False



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   BlipTextModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=_UpperCamelCase   ,			hidden_size=37 )



    def 					_lowercase						(self    :    int ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_UpperCamelCase )



    def 					_lowercase						(self    :    Any ):
         pass



    def 					_lowercase						(self    :    int ):
         pass



    @unittest.skip(reason="Blip does not use inputs_embeds" )
    def 					_lowercase						(self    :    Optional[int] ):
         pass



    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
    def 					_lowercase						(self    :    Tuple ):
         pass



    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
    def 					_lowercase						(self    :    int ):
         pass



    @slow
    def 					_lowercase						(self    :    str ):
         for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_           =   TFBlipTextModel.from_pretrained(_UpperCamelCase )
              self.assertIsNotNone(_UpperCamelCase )






    def 					_lowercase						(self    :    List[str]   ,			__a    :    Tuple=True ):
         super().test_pt_tf_model_equivalence(allow_missing_keys=_UpperCamelCase )







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_       	=	{'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''DPTFeatureExtractor''']
					lowerCAmelCase_       	=	['''DPTImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPTForDepthEstimation''',
					    '''DPTForSemanticSegmentation''',
					    '''DPTModel''',
					    '''DPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_dpt import DPTFeatureExtractor
										from .image_processing_dpt import DPTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpt import (
										    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPTForDepthEstimation,
										    DPTForSemanticSegmentation,
										    DPTModel,
										    DPTPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)