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

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

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

						SCREAMING_SNAKE_CASE__						=					self.get_config()

						return config, input_ids, tf.convert_to_tensor(a__				)
	def 	A_							(     self				:  Dict				):
						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				:  Dict			,  UpperCAmelCase_				:  Optional[int]			,  UpperCAmelCase_				:  Tuple			,  UpperCAmelCase_				:  List[str]				):
						SCREAMING_SNAKE_CASE__						=					TFBlipTextModel(config=a__				)
						SCREAMING_SNAKE_CASE__						=					model(a__			,  attention_mask=a__			,  training=a__				)
						SCREAMING_SNAKE_CASE__						=					model(a__			,  training=a__				)
						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				:  List[Any]				):
						SCREAMING_SNAKE_CASE__						=					self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__						=					config_and_inputs
						SCREAMING_SNAKE_CASE__						=					{"input_ids": input_ids, "attention_mask": input_mask}
						return config, inputs_dict





@require_tf
class 		lowercase__    (		UpperCamelCase_	,			unittest.TestCase    ):
	A__       :  List[str]										=(TFBlipTextModel,) if is_tf_available() else ()
	A__       :  Union[str, Any]										=False
	A__       :  Any										=False
	A__       :  Optional[int]										=False
	def 	A_							(     self				:  Any				):
						SCREAMING_SNAKE_CASE__						=					BlipTextModelTester(self				)
						SCREAMING_SNAKE_CASE__						=					ConfigTester(self			,  config_class=a__			,  hidden_size=37				)
	def 	A_							(     self				:  str				):
						self.config_tester.run_common_tests()
	def 	A_							(     self				:  Union[str, Any]				):
						SCREAMING_SNAKE_CASE__						=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a__				)
	def 	A_							(     self				:  Union[str, Any]				):
						pass
	def 	A_							(     self				:  Union[str, Any]				):
						pass
	@unittest.skip(reason='Blip does not use inputs_embeds'				)
	def 	A_							(     self				:  Optional[Any]				):
						pass
	@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
	def 	A_							(     self				:  Union[str, Any]				):
						pass
	@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
	def 	A_							(     self				:  List[str]				):
						pass
	@slow
	def 	A_							(     self				:  List[str]				):

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





	def 	A_							(     self				:  int			,  UpperCAmelCase_				:  Optional[int]=True				):
						super().test_pt_tf_model_equivalence(allow_missing_keys=a__				)







'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 if height >= 1:
  move_tower(height - 1      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
  move_disk(lowerCamelCase_      ,			lowerCamelCase_			)
  move_tower(height - 1      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 print("moving disk from"      ,			lowerCamelCase_      ,			"to"      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	int(input("Height of hanoi: "			).strip()			)
 move_tower(lowerCamelCase_      ,			"A"      ,			"B"      ,			"C"			)


if __name__ == "__main__":
     main()





