"""simple docstring"""






import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
				import torch

				from transformers import BlipTextModel
				from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=1_2       ,     _SCREAMING_SNAKE_CASE=7       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=True       ,     _SCREAMING_SNAKE_CASE=9_9       ,     _SCREAMING_SNAKE_CASE=3_2       ,     _SCREAMING_SNAKE_CASE=3_2       ,     _SCREAMING_SNAKE_CASE=5       ,     _SCREAMING_SNAKE_CASE=4       ,     _SCREAMING_SNAKE_CASE=3_7       ,     _SCREAMING_SNAKE_CASE=0.1       ,     _SCREAMING_SNAKE_CASE=0.1       ,     _SCREAMING_SNAKE_CASE=5_1_2       ,     _SCREAMING_SNAKE_CASE=0.0_2       ,     _SCREAMING_SNAKE_CASE=0       ,     _SCREAMING_SNAKE_CASE=None       ,     )    ->	Optional[int]:
							a_							:			Union[str, Any]     		=					parent
							a_							:			Optional[int]     		=					batch_size
							a_							:			str     		=					seq_length
							a_							:			Tuple     		=					is_training
							a_							:			Any     		=					use_input_mask
							a_							:			Optional[Any]     		=					use_labels
							a_							:			Optional[Any]     		=					vocab_size
							a_							:			Tuple     		=					hidden_size
							a_							:			str     		=					projection_dim
							a_							:			Tuple     		=					num_hidden_layers
							a_							:			Optional[Any]     		=					num_attention_heads
							a_							:			Dict     		=					intermediate_size
							a_							:			List[str]     		=					dropout
							a_							:			Dict     		=					attention_dropout
							a_							:			Any     		=					max_position_embeddings
							a_							:			Optional[int]     		=					initializer_range
							a_							:			str     		=					scope
							a_							:			str     		=					bos_token_id







	def       A    (			self )    ->	Dict:
							a_							:			Optional[int]     		=					ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size )

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

							if input_mask is not None:
													a_		,					a_							:			Optional[Any]     		=					input_mask.shape
													a_							:			str     		=					np.random.randint(1       ,     seq_length - 1       ,     size=(batch_size,) )
													for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE ):
																			a_							:			int     		=					1
																			a_							:			Union[str, Any]     		=					0

							a_							:			Optional[Any]     		=					self.get_config()

							return config, input_ids, input_mask







	def       A    (			self )    ->	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       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	int:
							a_							:			Optional[Any]     		=					BlipTextModel(config=_SCREAMING_SNAKE_CASE )
							model.to(_SCREAMING_SNAKE_CASE )
							model.eval()
							with torch.no_grad():
													a_							:			Optional[Any]     		=					model(_SCREAMING_SNAKE_CASE       ,     attention_mask=_SCREAMING_SNAKE_CASE )
													a_							:			Tuple     		=					model(_SCREAMING_SNAKE_CASE )
							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]:
							a_							:			Any     		=					self.prepare_config_and_inputs()
							a_		,					a_		,					a_							:			Optional[int]     		=					config_and_inputs
							a_							:			str     		=					{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_torch
class   UpperCAmelCase__				(    __lowerCamelCase,      unittest.TestCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					Optional[int]						      =							(BlipTextModel,) if is_torch_available() else ()
	lowerCAmelCase__    :					List[str]						      =							False
	lowerCAmelCase__    :					Union[str, Any]						      =							False
	lowerCAmelCase__    :					Union[str, Any]						      =							False







	def       A    (			self )    ->	int:
							a_							:			List[str]     		=					BlipTextModelTester(self )
							a_							:			List[Any]     		=					ConfigTester(self       ,     config_class=_SCREAMING_SNAKE_CASE       ,     hidden_size=3_7 )







	def       A    (			self )    ->	str:
							self.config_tester.run_common_tests()







	def       A    (			self )    ->	Tuple:
							a_							:			str     		=					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	Any:
							pass







	def       A    (			self )    ->	Tuple:
							pass







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







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







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







	@slow
	def       A    (			self )    ->	Any:
							for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													a_							:			List[Any]     		=					BlipTextModel.from_pretrained(_SCREAMING_SNAKE_CASE )
													self.assertIsNotNone(_SCREAMING_SNAKE_CASE )







	def       A    (			self )    ->	Tuple:
							super().test_pt_tf_model_equivalence(allow_missing_keys=_SCREAMING_SNAKE_CASE )





"""simple docstring"""






import numpy as np

UpperCamelCase     					=     [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]
class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self )    ->	None:
							a_							:			List[Any]     		=					np.array(_SCREAMING_SNAKE_CASE )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	np.ndarray:
							a_		,					a_							:			List[str]     		=					np.where(letter == self.SQUARE )
							a_							:			Union[str, Any]     		=					np.concatenate([indexa + 1, indexa + 1] )
							return indexes







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							return self.SQUARE[indexa - 1, indexa - 1]







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			List[str]     		=					message.lower()
							a_							:			Union[str, Any]     		=					message.replace("j"       ,     "i" )

							a_							:			Tuple     		=					""
							for letter_index in range(len(_SCREAMING_SNAKE_CASE ) ):
													if message[letter_index] != " ":
																			a_							:			Dict     		=					self.letter_to_numbers(message[letter_index] )
																			a_							:			str     		=					encoded_message + str(numbers[0] ) + str(numbers[1] )
													elif message[letter_index] == " ":
																			a_							:			Any     		=					encoded_message + " "

							return encoded_message







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							a_							:			Dict     		=					message.replace(" "       ,     "  " )
							a_							:			str     		=					""
							for numbers_index in range(int(len(_SCREAMING_SNAKE_CASE ) / 2 ) ):
													if message[numbers_index * 2] != " ":
																			a_							:			str     		=					message[numbers_index * 2]
																			a_							:			str     		=					message[numbers_index * 2 + 1]

																			a_							:			Optional[Any]     		=					self.numbers_to_letter(int(_SCREAMING_SNAKE_CASE )       ,     int(_SCREAMING_SNAKE_CASE ) )
																			a_							:			Optional[Any]     		=					decoded_message + letter
													elif message[numbers_index * 2] == " ":
																			a_							:			str     		=					decoded_message + " "

							return decoded_message




