




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ["image_processor", "tokenizer"]
 lowercase_							       =       "BlipImageProcessor"
 lowercase_							       =       ("BertTokenizer", "BertTokenizerFast")



 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     False
       super().__init__(_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.image_processor



 def __call__(				self    :						str							,							_lowerCAmelCase    :						ImageInput = None							,							_lowerCAmelCase    :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Union[bool, str, PaddingStrategy] = False							,							_lowerCAmelCase    :						Union[bool, str, TruncationStrategy] = None							,							_lowerCAmelCase    :						Optional[int] = None							,							_lowerCAmelCase    :						int = 0							,							_lowerCAmelCase    :						Optional[int] = None							,							_lowerCAmelCase    :						Optional[bool] = None							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Optional[Union[str, TensorType]] = None							,							**_lowerCAmelCase    :						Optional[int]							,							):
       if images is None and text is None:
             raise ValueError('You have to specify either images or text.'		)

       # Get only text
       if images is None:
             SCREAMING_SNAKE_CASE_      		=     self.tokenizer
             SCREAMING_SNAKE_CASE_      		=     self.tokenizer(
                 text=_lowerCAmelCase							,							add_special_tokens=_lowerCAmelCase							,							padding=_lowerCAmelCase							,							truncation=_lowerCAmelCase							,							max_length=_lowerCAmelCase							,							stride=_lowerCAmelCase							,							pad_to_multiple_of=_lowerCAmelCase							,							return_attention_mask=_lowerCAmelCase							,							return_overflowing_tokens=_lowerCAmelCase							,							return_special_tokens_mask=_lowerCAmelCase							,							return_offsets_mapping=_lowerCAmelCase							,							return_token_type_ids=_lowerCAmelCase							,							return_length=_lowerCAmelCase							,							verbose=_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase							,							)
             return text_encoding

       # add pixel_values
       SCREAMING_SNAKE_CASE_      		=     self.image_processor(_lowerCAmelCase							,							return_tensors=_lowerCAmelCase		)

       if text is not None:
             SCREAMING_SNAKE_CASE_      		=     self.tokenizer(
                 text=_lowerCAmelCase							,							add_special_tokens=_lowerCAmelCase							,							padding=_lowerCAmelCase							,							truncation=_lowerCAmelCase							,							max_length=_lowerCAmelCase							,							stride=_lowerCAmelCase							,							pad_to_multiple_of=_lowerCAmelCase							,							return_attention_mask=_lowerCAmelCase							,							return_overflowing_tokens=_lowerCAmelCase							,							return_special_tokens_mask=_lowerCAmelCase							,							return_offsets_mapping=_lowerCAmelCase							,							return_token_type_ids=_lowerCAmelCase							,							return_length=_lowerCAmelCase							,							verbose=_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase							,							)
       else:
             SCREAMING_SNAKE_CASE_      		=     None

       if text_encoding is not None:
             encoding_image_processor.update(_lowerCAmelCase		)

       return encoding_image_processor



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							*_lowerCAmelCase    :						Tuple							,							**_lowerCAmelCase    :						List[str]		):
       return self.tokenizer.batch_decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							*_lowerCAmelCase    :						Dict							,							**_lowerCAmelCase    :						Union[str, Any]		):
       return self.tokenizer.decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)




 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE_      		=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
"""simple docstring"""



from collections import defaultdict
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			total  # total no of tasks (N)

										# DP table will have a dimension of (2^M)*N
										# initially all values are set to -1
										A__       :     Optional[int]								=			[
										    [-1 for i in range(total + 1							)] for j in range(2 ** len(snake_case_							)							)
										]

										A__       :     Optional[int]								=			defaultdict(snake_case_							)  # stores the list of persons for each task

										# final_mask is used to check if all persons are included by setting all bits
										# to 1
										A__       :     Optional[int]								=			(1 << len(snake_case_							)) - 1





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if mask == self.final_mask:
																	return 1

										# if not everyone gets the task and no more tasks are available, return 0
										if task_no > self.total_tasks:
																	return 0

										# if case already considered
										if self.dp[mask][task_no] != -1:
																	return self.dp[mask][task_no]

										# Number of ways when we don't this task in the arrangement
										A__       :     Optional[int]								=			self.count_ways_until(snake_case_   ,      task_no + 1							)

										# now assign the tasks one by one to all possible persons and recursively
										# assign for the remaining tasks.
										if task_no in self.task:
																	for p in self.task[task_no]:
																								# if p is already given a task
																								if mask & (1 << p):
																															continue

																								# assign this task to p and change the mask value. And recursively
																								# assign tasks with the new mask value.
																								total_ways_util += self.count_ways_until(mask | (1 << p)   ,      task_no + 1							)

        # save the value.
										A__       :     List[str]								=			total_ways_util

										return self.dp[mask][task_no]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for i in range(len(snake_case_							)							):
																	for j in task_performed[i]:
																								self.task[j].append(snake_case_							)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
										return self.count_ways_until(0   ,      1							)


if __name__ == "__main__":
				_UpperCamelCase								=		5  # total no of tasks (the value of N)

				# the list of tasks that can be done by M persons.
				_UpperCamelCase								=		[[1, 3, 4], [1, 2, 5], [3, 4]]
				print(
				    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
				        task_performed
				    )
				)





