


"""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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=12	,					lowerCamelCase__=7	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=99	,					lowerCamelCase__=32	,					lowerCamelCase__=32	,					lowerCamelCase__=5	,					lowerCamelCase__=4	,					lowerCamelCase__=37	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=512	,					lowerCamelCase__=0.02	,					lowerCamelCase__=0	,					lowerCamelCase__=None	,					)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      parent
								snake_case__  :						str		=      batch_size
								snake_case__  :						Optional[int]		=      seq_length
								snake_case__  :						Optional[int]		=      is_training
								snake_case__  :						str		=      use_input_mask
								snake_case__  :						Any		=      use_labels
								snake_case__  :						Tuple		=      vocab_size
								snake_case__  :						Optional[Any]		=      hidden_size
								snake_case__  :						List[str]		=      projection_dim
								snake_case__  :						List[Any]		=      num_hidden_layers
								snake_case__  :						List[str]		=      num_attention_heads
								snake_case__  :						Optional[Any]		=      intermediate_size
								snake_case__  :						Any		=      dropout
								snake_case__  :						Optional[int]		=      attention_dropout
								snake_case__  :						List[Any]		=      max_position_embeddings
								snake_case__  :						Optional[Any]		=      initializer_range
								snake_case__  :						Tuple		=      scope
								snake_case__  :						int		=      bos_token_id




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size)

								snake_case__  :						List[str]		=      None
								if self.use_input_mask:
									snake_case__  :						List[Any]		=      random_attention_mask([self.batch_size, self.seq_length])

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

								snake_case__  :						str		=      self.get_config()

								return config, input_ids, input_mask




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      BlipTextModel(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								with torch.no_grad():
									snake_case__  :						Union[str, Any]		=      model(lowerCamelCase__	,					attention_mask=lowerCamelCase__)
									snake_case__  :						Optional[int]		=      model(lowerCamelCase__)
								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        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Any		=      self.prepare_config_and_inputs()
								snake_case__,					snake_case__,					snake_case__  :						Tuple		=      config_and_inputs
								snake_case__  :						Dict		=      {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Tuple				  =  (BlipTextModel,) if is_torch_available() else ()
							__magic_name__					:     Tuple				  =  False
							__magic_name__					:     Tuple				  =  False
							__magic_name__					:     int				  =  False




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      BlipTextModelTester(self)
								snake_case__  :						Any		=      ConfigTester(self	,					config_class=lowerCamelCase__	,					hidden_size=37)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								self.config_tester.run_common_tests()




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								pass




							@unittest.skip(reason="Blip does not use inputs_embeds")
							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								pass




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







								'''simple docstring'''




								pass




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







								'''simple docstring'''




								pass




							@slow
							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__  :						Optional[int]		=      BlipTextModel.from_pretrained(lowerCamelCase__)
									self.assertIsNotNone(lowerCamelCase__)







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__)












'''simple docstring'''

def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       ):
		__UpperCAmelCase	:							Optional[int]     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     len(_UpperCAmelCase  )

		__UpperCAmelCase	:							int     =     [
		    [0 for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]
		__UpperCAmelCase	:							Any     =     [
		    ["0" for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]

		for i in range(1,       len_source_seq + 1  ):
				__UpperCAmelCase	:							Optional[Any]     =     i * delete_cost
				__UpperCAmelCase	:							Optional[int]     =     F"D{source_seq[i - 1]:c}"

		for i in range(1,       len_destination_seq + 1  ):
				__UpperCAmelCase	:							Optional[int]     =     i * insert_cost
				__UpperCAmelCase	:							List[Any]     =     F"I{destination_seq[i - 1]:c}"

		for i in range(1,       len_source_seq + 1  ):
				for j in range(1,       len_destination_seq + 1  ):
						if source_seq[i - 1] == destination_seq[j - 1]:
								__UpperCAmelCase	:							List[str]     =     costs[i - 1][j - 1] + copy_cost
								__UpperCAmelCase	:							Tuple     =     F"C{source_seq[i - 1]:c}"
						else:
								__UpperCAmelCase	:							Optional[int]     =     costs[i - 1][j - 1] + replace_cost
								__UpperCAmelCase	:							Dict     =     F"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]  )

						if costs[i - 1][j] + delete_cost < costs[i][j]:
								__UpperCAmelCase	:							Union[str, Any]     =     costs[i - 1][j] + delete_cost
								__UpperCAmelCase	:							int     =     F"D{source_seq[i - 1]:c}"

						if costs[i][j - 1] + insert_cost < costs[i][j]:
								__UpperCAmelCase	:							List[str]     =     costs[i][j - 1] + insert_cost
								__UpperCAmelCase	:							Dict     =     F"I{destination_seq[j - 1]:c}"

		return costs, ops


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		if i == 0 and j == 0:
				return []
		else:
				if ops[i][j][0] == "C" or ops[i][j][0] == "R":
						__UpperCAmelCase	:							int     =     assemble_transformation(_UpperCAmelCase,       i - 1,       j - 1  )
						seq.append(ops[i][j]  )
						return seq
				elif ops[i][j][0] == "D":
						__UpperCAmelCase	:							List[Any]     =     assemble_transformation(_UpperCAmelCase,       i - 1,       _UpperCAmelCase  )
						seq.append(ops[i][j]  )
						return seq
				else:
						__UpperCAmelCase	:							Optional[Any]     =     assemble_transformation(_UpperCAmelCase,       _UpperCAmelCase,       j - 1  )
						seq.append(ops[i][j]  )
						return seq


if __name__ == "__main__":
			lowerCAmelCase__	,			lowerCAmelCase__      :   Optional[int]      	=			compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

			lowerCAmelCase__      :   Optional[Any]      	=			len(operations)
			lowerCAmelCase__      :   int      	=			len(operations[0])
			lowerCAmelCase__      :   Dict      	=			assemble_transformation(operations, m - 1, n - 1)

			lowerCAmelCase__      :   str      	=			list("Python")
			lowerCAmelCase__      :   Any      	=			0
			lowerCAmelCase__      :   Dict      	=			0

			with open("min_cost.txt", "w") as file:
						for op in sequence:
									print("".join(string))

									if op[0] == "C":
												file.write("%-16s" % "Copy %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost -= 1
									elif op[0] == "R":
												lowerCAmelCase__      :   Optional[int]      	=			op[2]

												file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
												file.write("\t\t" + "".join(string))
												file.write("\r\n")

												cost += 1
									elif op[0] == "D":
												string.pop(i)

												file.write("%-16s" % "Delete %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2
									else:
												string.insert(i, op[1])

												file.write("%-16s" % "Insert %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2

									i += 1

						print("".join(string))
						print("Cost: ", cost)

						file.write("\r\nMinimum cost: " + str(cost))




