

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 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=1_2			,				_a=7			,				_a=True			,				_a=True			,				_a=True			,				_a=9_9			,				_a=3_2			,				_a=3_2			,				_a=2			,				_a=4			,				_a=3_7			,				_a=0.1			,				_a=0.1			,				_a=5_1_2			,				_a=0.02			,				_a=0			,				_a=None			,				)       ->       Optional[int]:
												_a      :       Any   =						parent
												_a      :       Dict   =						batch_size
												_a      :       Any   =						seq_length
												_a      :       Optional[int]   =						is_training
												_a      :       Any   =						use_input_mask
												_a      :       Dict   =						use_labels
												_a      :       Optional[int]   =						vocab_size
												_a      :       List[str]   =						hidden_size
												_a      :       List[Any]   =						projection_dim
												_a      :       Optional[int]   =						num_hidden_layers
												_a      :       Dict   =						num_attention_heads
												_a      :       Optional[Any]   =						intermediate_size
												_a      :       Optional[Any]   =						dropout
												_a      :       Optional[Any]   =						attention_dropout
												_a      :       Union[str, Any]   =						max_position_embeddings
												_a      :       List[Any]   =						initializer_range
												_a      :       Optional[int]   =						scope
												_a      :       List[str]   =						bos_token_id


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       str   =						ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

												_a      :       Dict   =						None
												if self.use_input_mask:
																	_a      :       Any   =						random_attention_mask([self.batch_size, self.seq_length]			)

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

												_a      :       Optional[int]   =						self.get_config()

												return config, input_ids, tf.convert_to_tensor(__lowercase			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												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			,				_a			,				_a			,				_a			)       ->       List[Any]:
												_a      :       Tuple   =						TFBlipTextModel(config=__lowercase			)
												_a      :       Optional[int]   =						model(__lowercase			,				attention_mask=__lowercase			,				training=__lowercase			)
												_a      :       int   =						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 							__lowercase							(     self			)       ->       str:
												_a      :       int   =						self.prepare_config_and_inputs()
												_a ,   _a ,   _a      :       int   =						config_and_inputs
												_a      :       Dict   =						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict



@require_tf
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       (TFBlipTextModel,) if is_tf_available() else ()
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		Tuple             =       False


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						BlipTextModelTester(self			)
												_a      :       Optional[Any]   =						ConfigTester(self			,				config_class=__lowercase			,				hidden_size=3_7			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												self.config_tester.run_common_tests()


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__lowercase			)


							def 							__lowercase							(     self			)       ->       str:
												pass


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass


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


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


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


							@slow
							def 							__lowercase							(     self			)       ->       Tuple:







												for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Dict   =						TFBlipTextModel.from_pretrained(__lowercase			)
																	self.assertIsNotNone(__lowercase			)




							def 							__lowercase							(     self			,				_a=True			)       ->       Optional[int]:
												super().test_pt_tf_model_equivalence(allow_missing_keys=__lowercase			)






'''simple docstring'''

# Function to print upper half of diamond (pyramid)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     for i in range(0,       lowercase__			):
          for _ in range(0,       n - i - 1			):  # printing spaces
               print(' ',       end=''			)
          for _ in range(0,       i + 1			):  # printing stars
               print('* ',       end=''			)
          print()

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     for i in range(lowercase__,       0,       -1			):
          for _ in range(lowercase__,       0,       -1			):  # printing stars
               print('* ',       end=''			)
          print()
          for _ in range(n - i + 1,       0,       -1			):  # printing spaces
               print(' ',       end=''			)

def 					__UpperCamelCase				(						lowercase__    :	Any			):






     '''simple docstring'''





     if n <= 0:
          print('       ...       ....        nothing printing :('			)
          return
     floyd(lowercase__			)  # upper half
     reverse_floyd(lowercase__			)  # lower half


if __name__ == "__main__":
       print(r'''| /\ | |- |  |-  |--| |\  /| |-''')
       print(r'''|/  \| |- |_ |_  |__| | \/ | |_''')
       UpperCAmelCase					 =	1
       while K:
              UpperCAmelCase					 =	int(input('''enter the number and , and see the magic : '''))
              print()
              pretty_print(user_number)
              UpperCAmelCase					 =	int(input('''press 0 to exit... and 1 to continue...'''))

       print('''Good Bye...''')


