






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 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   int		,     __A:   Dict=12		,     __A:   Tuple=7		,     __A:   Union[str, Any]=True		,     __A:   List[str]=True		,     __A:   List[str]=True		,     __A:   Tuple=99		,     __A:   Dict=32		,     __A:   Tuple=32		,     __A:   List[Any]=2		,     __A:   Optional[int]=4		,     __A:   str=37		,     __A:   Any=0.1		,     __A:   List[Any]=0.1		,     __A:   Dict=5_12		,     __A:   Tuple=0.02		,     __A:   List[Any]=0		,     __A:   Optional[Any]=None		,     )    ->				List[Any]:
												_A								= parent
												_A								= batch_size
												_A								= seq_length
												_A								= is_training
												_A								= use_input_mask
												_A								= use_labels
												_A								= vocab_size
												_A								= hidden_size
												_A								= projection_dim
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= intermediate_size
												_A								= dropout
												_A								= attention_dropout
												_A								= max_position_embeddings
												_A								= initializer_range
												_A								= scope
												_A								= bos_token_id

							def       __A (  self:   Optional[int]   )    ->				str:
												_A								= ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size   )

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

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

												_A								= self.get_config()

												return config, input_ids, tf.convert_to_tensor(UpperCamelCase_   )

							def       __A (  self:   Optional[int]   )    ->				List[Any]:
												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       __A (  self:   Union[str, Any]		,     __A:   Dict		,     __A:   List[str]		,     __A:   List[Any]   )    ->				Union[str, Any]:
												_A								= TFBlipTextModel(config=UpperCamelCase_   )
												_A								= model(UpperCamelCase_		,     attention_mask=UpperCamelCase_		,     training=UpperCamelCase_   )
												_A								= 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       __A (  self:   Optional[Any]   )    ->				Dict:
												_A								= self.prepare_config_and_inputs()
												_A								= config_and_inputs
												_A								= {"input_ids": input_ids, "attention_mask": input_mask}
												return config, inputs_dict





@require_tf
class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					(TFBlipTextModel,) if is_tf_available() else ()
							A_						  =					False
							A_						  =					False
							A_						  =					False

							def       __A (  self:   Optional[int]   )    ->				Dict:
												_A								= BlipTextModelTester(self   )
												_A								= ConfigTester(self		,     config_class=UpperCamelCase_		,     hidden_size=37   )

							def       __A (  self:   List[str]   )    ->				str:
												self.config_tester.run_common_tests()

							def       __A (  self:   List[str]   )    ->				Optional[Any]:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase_   )

							def       __A (  self:   Dict   )    ->				Union[str, Any]:
												pass

							def       __A (  self:   List[Any]   )    ->				List[Any]:
												pass

							@unittest.skip(reason='''Blip does not use inputs_embeds'''   )
							def       __A (  self:   Tuple   )    ->				Dict:
												pass

							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''   )
							def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
												pass

							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''   )
							def       __A (  self:   Tuple   )    ->				List[str]:
												pass

							@slow
							def       __A (  self:   List[str]   )    ->				List[str]:
												for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_A								= TFBlipTextModel.from_pretrained(UpperCamelCase_   )
																	self.assertIsNotNone(UpperCamelCase_   )



							def       __A (  self:   Tuple		,     __A:   int=True   )    ->				Optional[Any]:
												super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase_   )











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



