






"""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 	__lowerCamelCase       :





		'''simple docstring'''






		def __init__(     self							:			Union[str, Any]		,    a_							:			Dict		,    a_							:			Optional[int]=12		,    a_							:			Dict=7		,    a_							:			Union[str, Any]=True		,    a_							:			List[Any]=True		,    a_							:			Any=True		,    a_							:			int=99		,    a_							:			List[str]=32		,    a_							:			Tuple=32		,    a_							:			Optional[int]=5		,    a_							:			Tuple=4		,    a_							:			List[Any]=37		,    a_							:			Optional[Any]=0.1		,    a_							:			str=0.1		,    a_							:			List[Any]=5_12		,    a_							:			Optional[int]=0.02		,    a_							:			List[Any]=0		,    a_							:			Optional[Any]=None		,    ):
				lowerCAmelCase_     :							Tuple             =    parent
				lowerCAmelCase_     :							Optional[Any]             =    batch_size
				lowerCAmelCase_     :							List[Any]             =    seq_length
				lowerCAmelCase_     :							Optional[Any]             =    is_training
				lowerCAmelCase_     :							Optional[Any]             =    use_input_mask
				lowerCAmelCase_     :							int             =    use_labels
				lowerCAmelCase_     :							Optional[Any]             =    vocab_size
				lowerCAmelCase_     :							Dict             =    hidden_size
				lowerCAmelCase_     :							Union[str, Any]             =    projection_dim
				lowerCAmelCase_     :							Any             =    num_hidden_layers
				lowerCAmelCase_     :							Any             =    num_attention_heads
				lowerCAmelCase_     :							int             =    intermediate_size
				lowerCAmelCase_     :							Dict             =    dropout
				lowerCAmelCase_     :							List[str]             =    attention_dropout
				lowerCAmelCase_     :							Dict             =    max_position_embeddings
				lowerCAmelCase_     :							Union[str, Any]             =    initializer_range
				lowerCAmelCase_     :							int             =    scope
				lowerCAmelCase_     :							Optional[int]             =    bos_token_id





		def      lowerCamelCase (     self							:			Optional[int]			):
				lowerCAmelCase_     :							int             =    ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

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

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

				lowerCAmelCase_     :							Dict             =    self.get_config()

				return config, input_ids, input_mask





		def      lowerCamelCase (     self							:			Tuple			):
				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      lowerCamelCase (     self							:			str		,    a_							:			Tuple		,    a_							:			Tuple		,    a_							:			Tuple			):
				lowerCAmelCase_     :							List[Any]             =    BlipTextModel(config=a_			)
				model.to(a_			)
				model.eval()
				with torch.no_grad():
						lowerCAmelCase_     :							Optional[Any]             =    model(a_		,    attention_mask=a_			)
						lowerCAmelCase_     :							Any             =    model(a_			)
				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      lowerCamelCase (     self							:			List[Any]			):
				lowerCAmelCase_     :							Dict             =    self.prepare_config_and_inputs()
				lowerCAmelCase_     :							Union[str, Any]             =    config_and_inputs
				lowerCAmelCase_     :							Dict             =    {"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict







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





		'''simple docstring'''






		a_			:       Dict      =							(BlipTextModel,) if is_torch_available() else ()
		a_			:       Tuple      =							False
		a_			:       Optional[int]      =							False
		a_			:       List[Any]      =							False





		def      lowerCamelCase (     self							:			Any			):
				lowerCAmelCase_     :							str             =    BlipTextModelTester(self			)
				lowerCAmelCase_     :							Any             =    ConfigTester(self		,    config_class=a_		,    hidden_size=37			)





		def      lowerCamelCase (     self							:			Optional[Any]			):
				self.config_tester.run_common_tests()





		def      lowerCamelCase (     self							:			str			):
				lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a_			)





		def      lowerCamelCase (     self							:			Optional[Any]			):
				pass





		def      lowerCamelCase (     self							:			int			):
				pass





		@unittest.skip(reason="Blip does not use inputs_embeds"			)
		def      lowerCamelCase (     self							:			Union[str, Any]			):
				pass





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





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





		@slow
		def      lowerCamelCase (     self							:			Any			):
				for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowerCAmelCase_     :							str             =    BlipTextModel.from_pretrained(a_			)
						self.assertIsNotNone(a_			)





		def      lowerCamelCase (     self							:			Any			):
				super().test_pt_tf_model_equivalence(allow_missing_keys=a_			)








"""simple docstring"""

from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowercase__      =				{
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}

def 	__lowerCamelCase			(						__UpperCamelCase )		->					set[tuple[str, str]]:

		"""simple docstring"""







		lowerCAmelCase_     :							Union[str, Any]             =    {node: {node} for node in graph}

		lowerCAmelCase_     :							List[str]             =    {node: graph[node][:] for node in graph}

		while len(__UpperCamelCase ) > 2:
				# Choose a random edge.
				lowerCAmelCase_     :							int             =    random.choice(list(graph_copy.keys() ) )
				lowerCAmelCase_     :							Any             =    random.choice(graph_copy[u] )

				# Contract edge (u, v) to new node uv
				lowerCAmelCase_     :							Union[str, Any]             =    u + v
				lowerCAmelCase_     :							List[Any]             =    list(set(graph_copy[u] + graph_copy[v] ) )
				uv_neighbors.remove(__UpperCamelCase )
				uv_neighbors.remove(__UpperCamelCase )
				lowerCAmelCase_     :							str             =    uv_neighbors
				for neighbor in uv_neighbors:
						graph_copy[neighbor].append(__UpperCamelCase )

				lowerCAmelCase_     :							Optional[int]             =    set(contracted_nodes[u].union(contracted_nodes[v] ) )

				# Remove nodes u and v.
				del graph_copy[u]
				del graph_copy[v]
				for neighbor in uv_neighbors:
						if u in graph_copy[neighbor]:
								graph_copy[neighbor].remove(__UpperCamelCase )
						if v in graph_copy[neighbor]:
								graph_copy[neighbor].remove(__UpperCamelCase )

    # Find cutset.
		lowerCAmelCase_     :							Union[str, Any]             =    [contracted_nodes[node] for node in graph_copy]
		return {
		    (node, neighbor)
		    for node in groups[0]
		    for neighbor in graph[node]
		    if neighbor in groups[1]
		}


if __name__ == "__main__":
					print(partition_graph(TEST_GRAPH))
