code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import MarianConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
  
  
  if is_flax_available():
  					import os
  
  					# The slow tests are often failing with OOM error on GPU
  					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  					lowercase__      =				"""platform"""
  
  					import jax
  					import jax.numpy as jnp
  
  					from transformers import MarianTokenizer
  					from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , __UpperCamelCase=None   , )		->					Union[str, Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if attention_mask is None:
  				lowerCAmelCase_     :							Optional[int]             =    np.where(input_ids != config.pad_token_id   , 1   , 0 )
  		if decoder_attention_mask is None:
  				lowerCAmelCase_     :							Union[str, Any]             =    np.where(decoder_input_ids != config.pad_token_id   , 1   , 0 )
  		if head_mask is None:
  				lowerCAmelCase_     :							Union[str, Any]             =    np.ones((config.encoder_layers, config.encoder_attention_heads) )
  		if decoder_head_mask is None:
  				lowerCAmelCase_     :							Union[str, Any]             =    np.ones((config.decoder_layers, config.decoder_attention_heads) )
  		if cross_attn_head_mask is None:
  				lowerCAmelCase_     :							int             =    np.ones((config.decoder_layers, config.decoder_attention_heads) )
  		return {
  		    "input_ids": input_ids,
  		    "decoder_input_ids": decoder_input_ids,
  		    "attention_mask": attention_mask,
  		    "decoder_attention_mask": attention_mask,
  		}
  
  
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Optional[Any]		,    a_							:			str		,    a_							:			List[Any]=13		,    a_							:			Dict=7		,    a_							:			Union[str, Any]=True		,    a_							:			Any=False		,    a_							:			int=99		,    a_							:			str=16		,    a_							:			Optional[int]=2		,    a_							:			Optional[Any]=4		,    a_							:			Union[str, Any]=4		,    a_							:			Dict="gelu"		,    a_							:			Union[str, Any]=0.1		,    a_							:			Optional[int]=0.1		,    a_							:			Optional[int]=32		,    a_							:			str=2		,    a_							:			Dict=1		,    a_							:			Optional[int]=0		,    a_							:			Any=0.02		,    ):
  				lowerCAmelCase_     :							List[str]             =    parent
  				lowerCAmelCase_     :							Dict             =    batch_size
  				lowerCAmelCase_     :							Any             =    seq_length
  				lowerCAmelCase_     :							Dict             =    is_training
  				lowerCAmelCase_     :							Union[str, Any]             =    use_labels
  				lowerCAmelCase_     :							Dict             =    vocab_size
  				lowerCAmelCase_     :							Any             =    hidden_size
  				lowerCAmelCase_     :							int             =    num_hidden_layers
  				lowerCAmelCase_     :							List[Any]             =    num_attention_heads
  				lowerCAmelCase_     :							List[Any]             =    intermediate_size
  				lowerCAmelCase_     :							Optional[Any]             =    hidden_act
  				lowerCAmelCase_     :							str             =    hidden_dropout_prob
  				lowerCAmelCase_     :							Any             =    attention_probs_dropout_prob
  				lowerCAmelCase_     :							str             =    max_position_embeddings
  				lowerCAmelCase_     :							Optional[int]             =    eos_token_id
  				lowerCAmelCase_     :							int             =    pad_token_id
  				lowerCAmelCase_     :							List[str]             =    bos_token_id
  				lowerCAmelCase_     :							Union[str, Any]             =    initializer_range
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							List[str]             =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,    self.vocab_size			)		,    3		,    self.vocab_size			)
  				lowerCAmelCase_     :							Tuple             =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,    dtype=np.intaa			))		,    -1			)
  
  				lowerCAmelCase_     :							List[Any]             =    shift_tokens_right(a_		,    1		,    2			)
  
  				lowerCAmelCase_     :							Optional[int]             =    MarianConfig(
  				    vocab_size=self.vocab_size		,    d_model=self.hidden_size		,    encoder_layers=self.num_hidden_layers		,    decoder_layers=self.num_hidden_layers		,    encoder_attention_heads=self.num_attention_heads		,    decoder_attention_heads=self.num_attention_heads		,    encoder_ffn_dim=self.intermediate_size		,    decoder_ffn_dim=self.intermediate_size		,    dropout=self.hidden_dropout_prob		,    attention_dropout=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    eos_token_id=self.eos_token_id		,    bos_token_id=self.bos_token_id		,    pad_token_id=self.pad_token_id		,    initializer_range=self.initializer_range		,    use_cache=a_		,    )
  				lowerCAmelCase_     :							Tuple             =    prepare_marian_inputs_dict(a_		,    a_		,    a_			)
  				return config, inputs_dict
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Optional[int]             =    self.prepare_config_and_inputs()
  				return config, inputs_dict
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Optional[int]		,    a_							:			List[str]		,    a_							:			Union[str, Any]			):
  				lowerCAmelCase_     :							Optional[int]             =    20
  				lowerCAmelCase_     :							List[Any]             =    model_class_name(a_			)
  
  				lowerCAmelCase_     :							Optional[Any]             =    model.encode(inputs_dict["input_ids"]			)
  
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							int             =    (
  				    inputs_dict["decoder_input_ids"],
  				    inputs_dict["decoder_attention_mask"],
  				)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    model.init_cache(decoder_input_ids.shape[0]		,    a_		,    a_			)
  				lowerCAmelCase_     :							Tuple             =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,    dtype="i4"			)
  
  				lowerCAmelCase_     :							str             =    jnp.broadcast_to(
  				    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]		,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,    )
  				lowerCAmelCase_     :							Optional[Any]             =    model.decode(
  				    decoder_input_ids[:, :-1]		,    a_		,    decoder_attention_mask=a_		,    past_key_values=a_		,    decoder_position_ids=a_		,    )
  
  				lowerCAmelCase_     :							int             =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,    dtype="i4"			)
  				lowerCAmelCase_     :							Any             =    model.decode(
  				    decoder_input_ids[:, -1:]		,    a_		,    decoder_attention_mask=a_		,    past_key_values=outputs_cache.past_key_values		,    decoder_position_ids=a_		,    )
  
  				lowerCAmelCase_     :							int             =    model.decode(a_		,    a_			)
  
  				lowerCAmelCase_     :							Optional[Any]             =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
  				self.parent.assertTrue(diff < 1e-3		,    msg=f'''Max diff is {diff}'''			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]		,    a_							:			Union[str, Any]		,    a_							:			Optional[Any]		,    a_							:			List[Any]			):
  				lowerCAmelCase_     :							Union[str, Any]             =    20
  				lowerCAmelCase_     :							Dict             =    model_class_name(a_			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    model.encode(inputs_dict["input_ids"]			)
  
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Any             =    (
  				    inputs_dict["decoder_input_ids"],
  				    inputs_dict["decoder_attention_mask"],
  				)
  
  				lowerCAmelCase_     :							Tuple             =    jnp.concatenate(
  				    [
  				        decoder_attention_mask,
  				        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
  				    ]		,    axis=-1		,    )
  
  				lowerCAmelCase_     :							Any             =    model.init_cache(decoder_input_ids.shape[0]		,    a_		,    a_			)
  				lowerCAmelCase_     :							Tuple             =    jnp.broadcast_to(
  				    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]		,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,    )
  
  				lowerCAmelCase_     :							Optional[int]             =    model.decode(
  				    decoder_input_ids[:, :-1]		,    a_		,    decoder_attention_mask=a_		,    past_key_values=a_		,    decoder_position_ids=a_		,    )
  				lowerCAmelCase_     :							Optional[int]             =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,    dtype="i4"			)
  				lowerCAmelCase_     :							Optional[Any]             =    model.decode(
  				    decoder_input_ids[:, -1:]		,    a_		,    past_key_values=outputs_cache.past_key_values		,    decoder_attention_mask=a_		,    decoder_position_ids=a_		,    )
  
  				lowerCAmelCase_     :							Any             =    model.decode(a_		,    a_		,    decoder_attention_mask=a_			)
  
  				lowerCAmelCase_     :							Any             =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
  				self.parent.assertTrue(diff < 1e-3		,    msg=f'''Max diff is {diff}'''			)
  
  
  
  
  
  
  
  @require_flax
  class 	__lowerCamelCase       (				A__				,							unittest.TestCase				,							A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Any      =							True
  		a_			:       Union[str, Any]      =							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  		a_			:       List[str]      =							(FlaxMarianMTModel,) if is_flax_available() else ()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  				lowerCAmelCase_     :							List[Any]             =    FlaxMarianModelTester(self			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							List[Any]             =    self.model_tester.prepare_config_and_inputs()
  				for model_class in self.all_model_classes:
  						self.model_tester.check_use_cache_forward(a_		,    a_		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							str             =    self.model_tester.prepare_config_and_inputs()
  				for model_class in self.all_model_classes:
  						self.model_tester.check_use_cache_forward_with_attn_mask(a_		,    a_		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							List[Any]             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						with self.subTest(model_class.__name__			):
  								lowerCAmelCase_     :							Union[str, Any]             =    self._prepare_for_class(a_		,    a_			)
  								lowerCAmelCase_     :							Optional[Any]             =    model_class(a_			)
  
  								@jax.jit
  								def encode_jitted(a_							:			str		,    a_							:			List[Any]=None		,    **a_							:			Dict			):
  										return model.encode(input_ids=a_		,    attention_mask=a_			)
  
  								with self.subTest("JIT Enabled"			):
  										lowerCAmelCase_     :							Optional[int]             =    encode_jitted(**a_			).to_tuple()
  
  								with self.subTest("JIT Disabled"			):
  										with jax.disable_jit():
  												lowerCAmelCase_     :							Tuple             =    encode_jitted(**a_			).to_tuple()
  
  								self.assertEqual(len(a_			)		,    len(a_			)			)
  								for jitted_output, output in zip(a_		,    a_			):
  										self.assertEqual(jitted_output.shape		,    output.shape			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict			):
  				lowerCAmelCase_       ,			lowerCAmelCase_     :							Dict             =    self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						with self.subTest(model_class.__name__			):
  								lowerCAmelCase_     :							List[Any]             =    model_class(a_			)
  								lowerCAmelCase_     :							Dict             =    model.encode(inputs_dict["input_ids"]		,    inputs_dict["attention_mask"]			)
  
  								lowerCAmelCase_     :							Optional[int]             =    {
  								    "decoder_input_ids": inputs_dict["decoder_input_ids"],
  								    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
  								    "encoder_outputs": encoder_outputs,
  								}
  
  								@jax.jit
  								def decode_jitted(a_							:			Optional[int]		,    a_							:			str		,    a_							:			Any			):
  										return model.decode(
  										    decoder_input_ids=a_		,    decoder_attention_mask=a_		,    encoder_outputs=a_		,    )
  
  								with self.subTest("JIT Enabled"			):
  										lowerCAmelCase_     :							Union[str, Any]             =    decode_jitted(**a_			).to_tuple()
  
  								with self.subTest("JIT Disabled"			):
  										with jax.disable_jit():
  												lowerCAmelCase_     :							Tuple             =    decode_jitted(**a_			).to_tuple()
  
  								self.assertEqual(len(a_			)		,    len(a_			)			)
  								for jitted_output, output in zip(a_		,    a_			):
  										self.assertEqual(jitted_output.shape		,    output.shape			)
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			List[str]			):
  				for model_class_name in self.all_model_classes:
  						lowerCAmelCase_     :							List[Any]             =    model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"			)
  						# FlaxMarianForSequenceClassification expects eos token in input_ids
  						lowerCAmelCase_     :							List[Any]             =    np.ones((1, 1)			) * model.config.eos_token_id
  						lowerCAmelCase_     :							List[Any]             =    model(a_			)
  						self.assertIsNotNone(a_			)
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Optional[int]      =							None
  		a_			:       Optional[int]      =							None
  
  
  
  
  
  		@classmethod
  		def      lowerCamelCase (     cls							:			Tuple			):
  				lowerCAmelCase_     :							Optional[Any]             =    f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
  				return cls
  
  
  
  
  
  		@cached_property
  		def      lowerCamelCase (     self							:			Any			):
  				return MarianTokenizer.from_pretrained(self.model_name			)
  
  
  
  
  
  		@property
  		def      lowerCamelCase (     self							:			Any			):
  				return self.tokenizer.eos_token_id
  
  
  
  
  
  		@cached_property
  		def      lowerCamelCase (     self							:			Tuple			):
  				lowerCAmelCase_     :							FlaxMarianMTModel             =    FlaxMarianMTModel.from_pretrained(self.model_name			)
  				self.assertEqual(model.config.decoder_start_token_id		,    model.config.pad_token_id			)
  				return model
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    **a_							:			Tuple			):
  				lowerCAmelCase_     :							Any             =    self.translate_src_text(**a_			)
  				self.assertListEqual(self.expected_text		,    a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int		,    **a_							:			Dict			):
  				lowerCAmelCase_     :							int             =    self.tokenizer(self.src_text		,    padding=a_		,    return_tensors="np"		,    **a_			)
  				lowerCAmelCase_     :							List[str]             =    self.model.generate(
  				    model_inputs.input_ids		,    attention_mask=model_inputs.attention_mask		,    num_beams=2		,    max_length=1_28		,    ).sequences
  				lowerCAmelCase_     :							List[Any]             =    self.tokenizer.batch_decode(a_		,    skip_special_tokens=a_			)
  				return generated_words
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Optional[int]      =							"""en"""
  		a_			:       Union[str, Any]      =							"""fr"""
  		a_			:       Tuple      =							[
  		    """I am a small frog.""",
  		    """Now I can forget the 100 words of german that I know.""",
  		]
  		a_			:       str      =							[
  		    """Je suis une petite grenouille.""",
  		    """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
  		]
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Any      =							"""fr"""
  		a_			:       Optional[int]      =							"""en"""
  		a_			:       Tuple      =							[
  		    """Donnez moi le micro.""",
  		    """Tom et Mary étaient assis à une table.""",  # Accents
  		]
  		a_			:       Union[str, Any]      =							[
  		    """Give me the microphone.""",
  		    """Tom and Mary were sitting at a table.""",
  		]
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Tuple			):
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       List[Any]      =							"""mt"""
  		a_			:       Optional[Any]      =							"""en"""
  		a_			:       List[Any]      =							["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
  		a_			:       Dict      =							["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			List[str]			):
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Optional[int]      =							"""en"""
  		a_			:       int      =							"""de"""
  		a_			:       Dict      =							[
  		    """I am a small frog.""",
  		    """Now I can forget the 100 words of german that I know.""",
  		    """Tom asked his teacher for advice.""",
  		    """That's how I would do it.""",
  		    """Tom really admired Mary's courage.""",
  		    """Turn around and close your eyes.""",
  		]
  		a_			:       Any      =							[
  		    """Ich bin ein kleiner Frosch.""",
  		    """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
  		    """Tom bat seinen Lehrer um Rat.""",
  		    """So würde ich das machen.""",
  		    """Tom bewunderte Marias Mut wirklich.""",
  		    """Drehen Sie sich um und schließen Sie die Augen.""",
  		]
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			str			):
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       str      =							"""en"""
  		a_			:       int      =							"""zh"""
  		a_			:       List[Any]      =							["""My name is Wolfgang and I live in Berlin"""]
  		a_			:       int      =							["""我叫沃尔夫冈 我住在柏林"""]
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			str			):
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       List[Any]      =							"""ru"""
  		a_			:       Dict      =							"""fr"""
  		a_			:       List[Any]      =							["""Он показал мне рукопись своей новой пьесы."""]
  		a_			:       List[str]      =							["""Il m'a montré le manuscrit de sa nouvelle pièce."""]
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Dict			):
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       List[str]      =							"""en"""
  		a_			:       Optional[Any]      =							"""ROMANCE"""
  		a_			:       int      =							[
  		    """>>fr<< Don't spend so much time watching TV.""",
  		    """>>pt<< Your message has been sent.""",
  		    """>>es<< He's two years older than me.""",
  		]
  		a_			:       int      =							[
  		    """Ne passez pas autant de temps à regarder la télé.""",
  		    """A sua mensagem foi enviada.""",
  		    """Es dos años más viejo que yo.""",
  		]
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Tuple			):
  				self._assert_generated_batch_equal_expected()
  
style_context: 
  
  
  
  
  
  
  """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))
  