

import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
    '''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
    '''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       "owlvit_text_model"


							def __init__(     self			,				_a=4_9_4_0_8			,				_a=5_1_2			,				_a=2_0_4_8			,				_a=1_2			,				_a=8			,				_a=1_6			,				_a="quick_gelu"			,				_a=1e-5			,				_a=0.0			,				_a=0.02			,				_a=1.0			,				_a=0			,				_a=4_9_4_0_6			,				_a=4_9_4_0_7			,				**_a			,				)       ->       int:
												super().__init__(pad_token_id=_a			,				bos_token_id=_a			,				eos_token_id=_a			,				**_a			)

												_a      :       Tuple   =						vocab_size
												_a      :       Tuple   =						hidden_size
												_a      :       Optional[int]   =						intermediate_size
												_a      :       Any   =						num_hidden_layers
												_a      :       int   =						num_attention_heads
												_a      :       Tuple   =						max_position_embeddings
												_a      :       List[Any]   =						hidden_act
												_a      :       str   =						layer_norm_eps
												_a      :       Dict   =						attention_dropout
												_a      :       Any   =						initializer_range
												_a      :       Union[str, Any]   =						initializer_factor




							@classmethod
							def 							__lowercase							(     cls			,				_a			,				**_a			)       ->       "PretrainedConfig":
												cls._set_token_in_kwargs(_a			)

												_a ,   _a      :       Optional[int]   =						cls.get_config_dict(_a			,				**_a			)

												# get the text config dict if we are loading from OwlViTConfig
												if config_dict.get('''model_type'''			) == "owlvit":
																	_a      :       int   =						config_dict['''text_config''']

												if "model_type" in config_dict and hasattr(cls			,				'''model_type'''			) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
																	    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""			)

												return cls.from_dict(_a			,				**_a			)



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "owlvit_vision_model"


							def __init__(     self			,				_a=7_6_8			,				_a=3_0_7_2			,				_a=1_2			,				_a=1_2			,				_a=3			,				_a=7_6_8			,				_a=3_2			,				_a="quick_gelu"			,				_a=1e-5			,				_a=0.0			,				_a=0.02			,				_a=1.0			,				**_a			,				)       ->       Dict:
												super().__init__(**_a			)

												_a      :       Dict   =						hidden_size
												_a      :       Optional[Any]   =						intermediate_size
												_a      :       Optional[Any]   =						num_hidden_layers
												_a      :       int   =						num_attention_heads
												_a      :       int   =						num_channels
												_a      :       int   =						image_size
												_a      :       Dict   =						patch_size
												_a      :       List[str]   =						hidden_act
												_a      :       Tuple   =						layer_norm_eps
												_a      :       int   =						attention_dropout
												_a      :       Any   =						initializer_range
												_a      :       Dict   =						initializer_factor




							@classmethod
							def 							__lowercase							(     cls			,				_a			,				**_a			)       ->       "PretrainedConfig":
												cls._set_token_in_kwargs(_a			)

												_a ,   _a      :       Tuple   =						cls.get_config_dict(_a			,				**_a			)

												# get the vision config dict if we are loading from OwlViTConfig
												if config_dict.get('''model_type'''			) == "owlvit":
																	_a      :       str   =						config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls			,				'''model_type'''			) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
																	    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""			)

												return cls.from_dict(_a			,				**_a			)



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       "owlvit"
							UpperCAmelCase__			:		str             =       True


							def __init__(     self			,				_a=None			,				_a=None			,				_a=5_1_2			,				_a=2.6592			,				_a=True			,				**_a			,				)       ->       Any:
												super().__init__(**_a			)

												if text_config is None:
																	_a      :       Union[str, Any]   =						{}
																	logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''			)

												if vision_config is None:
																	_a      :       List[str]   =						{}
																	logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''			)

												_a      :       str   =						OwlViTTextConfig(**_a			)
												_a      :       Dict   =						OwlViTVisionConfig(**_a			)

												_a      :       int   =						projection_dim
												_a      :       Optional[int]   =						logit_scale_init_value
												_a      :       List[Any]   =						return_dict
												_a      :       Optional[Any]   =						1.0


							@classmethod
							def 							__lowercase							(     cls			,				_a			,				**_a			)       ->       "PretrainedConfig":
												cls._set_token_in_kwargs(_a			)

												_a ,   _a      :       Dict   =						cls.get_config_dict(_a			,				**_a			)

												if "model_type" in config_dict and hasattr(cls			,				'''model_type'''			) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
																	    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""			)

												return cls.from_dict(_a			,				**_a			)


							@classmethod
							def 							__lowercase							(     cls			,				_a			,				_a			,				**_a			)       ->       Dict:
												_a      :       int   =						{}
												_a      :       Optional[int]   =						text_config
												_a      :       Optional[Any]   =						vision_config

												return cls.from_dict(_a			,				**_a			)




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Any   =						copy.deepcopy(self.__dict__			)
												_a      :       Optional[int]   =						self.text_config.to_dict()
												_a      :       Optional[Any]   =						self.vision_config.to_dict()
												_a      :       Union[str, Any]   =						self.__class__.model_type
												return output



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
												    ]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ('''logits_per_image''', {0: '''batch'''}),
												        ('''logits_per_text''', {0: '''batch'''}),
												        ('''text_embeds''', {0: '''batch'''}),
												        ('''image_embeds''', {0: '''batch'''}),
												    ]			)


							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4


							def 							__lowercase							(     self			,				_a			,				_a = -1			,				_a = -1			,				_a = None			,				)       ->       Mapping[str, Any]:
												_a      :       Dict   =						super().generate_dummy_inputs(
												    processor.tokenizer			,				batch_size=_a			,				seq_length=_a			,				framework=_a			)
												_a      :       Optional[Any]   =						super().generate_dummy_inputs(
												    processor.image_processor			,				batch_size=_a			,				framework=_a			)
												return {**text_input_dict, **image_input_dict}




							@property
							def 							__lowercase							(     self			)       ->       int:
												return 1_4









"""simple docstring"""

class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       _A     =  None
       _A     =  graph

       self._normalize_graph(_UpperCAmelCase       ,				_UpperCAmelCase						)
       _A     =  len(_UpperCAmelCase						)
       _A     =  None

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Dict						):
       if sources is int:
             _A     =  [sources]
       if sinks is int:
             _A     =  [sinks]

       if len(_UpperCAmelCase						) == 0 or len(_UpperCAmelCase						) == 0:
             return

       _A     =  sources[0]
       _A     =  sinks[0]

       # make fake vertex if there are more
       # than one source or sink
       if len(_UpperCAmelCase						) > 1 or len(_UpperCAmelCase						) > 1:
             _A     =  0
             for i in sources:
                   max_input_flow += sum(self.graph[i]						)

             _A     =  len(self.graph						) + 1
             for room in self.graph:
                   room.insert(0       ,				0						)
             self.graph.insert(0       ,				[0] * size						)
             for i in sources:
                   _A     =  max_input_flow
             _A     =  0

             _A     =  len(self.graph						) + 1
             for room in self.graph:
                   room.append(0						)
             self.graph.append([0] * size						)
             for i in sinks:
                   _A     =  max_input_flow
             _A     =  size - 1

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if self.maximum_flow_algorithm is None:
             raise Exception('You need to set maximum flow algorithm before.'						)
       if self.source_index is None or self.sink_index is None:
             return 0

       self.maximum_flow_algorithm.execute()
       return self.maximum_flow_algorithm.getMaximumFlow()



 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  algorithm(self						)





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  flow_network
       _A     =  flow_network.verticesCount
       _A     =  flow_network.sourceIndex
       _A     =  flow_network.sinkIndex
       # it's just a reference, so you shouldn't change
       # it in your algorithms, use deep copy before doing that
       _A     =  flow_network.graph
       _A     =  False

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if not self.executed:
             self._algorithm()
             _A     =  True



 def      lowerCAmelCase_				(					self			:			int						):
       pass





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       super().__init__(_UpperCAmelCase						)
       # use this to save your result
       _A     =  -1



 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if not self.executed:
             raise Exception('You should execute algorithm before using its result!'						)

       return self.maximum_flow





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]						):
       super().__init__(_UpperCAmelCase						)

       _A     =  [[0] * self.verticies_count for i in range(self.verticies_count						)]

       _A     =  [0] * self.verticies_count
       _A     =  [0] * self.verticies_count

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  self.verticies_count

       # push some substance to graph
       for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]						):
             self.preflow[self.source_index][nextvertex_index] += bandwidth
             self.preflow[nextvertex_index][self.source_index] -= bandwidth
             self.excesses[nextvertex_index] += bandwidth

       # Relabel-to-front selection rule
       _A     =  [
           i
           for i in range(self.verticies_count						)
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       _A     =  0
       while i < len(_UpperCAmelCase						):
             _A     =  vertices_list[i]
             _A     =  self.heights[vertex_index]
             self.process_vertex(_UpperCAmelCase						)
             if self.heights[vertex_index] > previous_height:
                   # if it was relabeled, swap elements
                   # and start from 0 index
                   vertices_list.insert(0       ,				vertices_list.pop(_UpperCAmelCase						)						)
                   _A     =  0
             else:
                   i += 1

       _A     =  sum(self.preflow[self.source_index]						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       while self.excesses[vertex_index] > 0:
             for neighbour_index in range(self.verticies_count						):
                   # if it's neighbour and current vertex is higher
                   if (
                       self.graph[vertex_index][neighbour_index]
                       - self.preflow[vertex_index][neighbour_index]
                       > 0
                       and self.heights[vertex_index] > self.heights[neighbour_index]
                   ):
                         self.push(_UpperCAmelCase       ,				_UpperCAmelCase						)

             self.relabel(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Tuple						):
       _A     =  min(
           self.excesses[from_index]       ,				self.graph[from_index][to_index] - self.preflow[from_index][to_index]       ,				)
       self.preflow[from_index][to_index] += preflow_delta
       self.preflow[to_index][from_index] -= preflow_delta
       self.excesses[from_index] -= preflow_delta
       self.excesses[to_index] += preflow_delta



 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       for to_index in range(self.verticies_count						):
             if (
                 self.graph[vertex_index][to_index]
                 - self.preflow[vertex_index][to_index]
                 > 0
             ) and (min_height is None or self.heights[to_index] < min_height):
                   _A     =  self.heights[to_index]

       if min_height is not None:
             _A     =  min_height + 1


if __name__ == "__main__":
       a							      =     [0]
       a							      =     [3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       a							      =     [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       a							      =     FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       a							      =     flow_network.find_maximum_flow()

       print(F'''maximum flow is {maximum_flow}''')

