



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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
				__A   			='''platform'''

				import jax
				import jax.numpy as jnp

				from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    shift_tokens_right,
				)

def        a						(      _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int=None ,   _UpperCAmelCase							:					str=None ,   _UpperCAmelCase							:					Tuple=None ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Any=None ,   _UpperCAmelCase							:					Union[str, Any]=None ,   ):







		'''simple docstring'''





		if attention_mask is None:
				__UpperCAmelCase    :     int													=	np.where(input_ids != config.pad_token_id ,   1 ,   0						)
		if decoder_attention_mask is None:
				__UpperCAmelCase    :     Optional[int]													=	np.where(decoder_input_ids != config.pad_token_id ,   1 ,   0						)
		if head_mask is None:
				__UpperCAmelCase    :     List[str]													=	np.ones((config.encoder_layers, config.encoder_attention_heads)						)
		if decoder_head_mask is None:
				__UpperCAmelCase    :     List[str]													=	np.ones((config.decoder_layers, config.decoder_attention_heads)						)
		if cross_attn_head_mask is None:
				__UpperCAmelCase    :     Tuple													=	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 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Optional[int]       ,       a_							: str       ,       a_							: int=13       ,       a_							: Union[str, Any]=7       ,       a_							: List[Any]=True       ,       a_							: Optional[int]=False       ,       a_							: Tuple=99       ,       a_							: Optional[int]=16       ,       a_							: List[str]=2       ,       a_							: int=4       ,       a_							: Any=4       ,       a_							: Any="gelu"       ,       a_							: Union[str, Any]=0.1       ,       a_							: List[Any]=0.1       ,       a_							: Union[str, Any]=32       ,       a_							: List[str]=2       ,       a_							: Tuple=1       ,       a_							: Optional[int]=0       ,       a_							: List[Any]=0.0_2       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	parent
					__UpperCAmelCase    :     List[str]													=	batch_size
					__UpperCAmelCase    :     List[Any]													=	seq_length
					__UpperCAmelCase    :     Optional[Any]													=	is_training
					__UpperCAmelCase    :     int													=	use_labels
					__UpperCAmelCase    :     Optional[int]													=	vocab_size
					__UpperCAmelCase    :     Optional[Any]													=	hidden_size
					__UpperCAmelCase    :     Optional[Any]													=	num_hidden_layers
					__UpperCAmelCase    :     int													=	num_attention_heads
					__UpperCAmelCase    :     List[str]													=	intermediate_size
					__UpperCAmelCase    :     Union[str, Any]													=	hidden_act
					__UpperCAmelCase    :     Union[str, Any]													=	hidden_dropout_prob
					__UpperCAmelCase    :     List[str]													=	attention_probs_dropout_prob
					__UpperCAmelCase    :     List[Any]													=	max_position_embeddings
					__UpperCAmelCase    :     Tuple													=	eos_token_id
					__UpperCAmelCase    :     int													=	pad_token_id
					__UpperCAmelCase    :     Any													=	bos_token_id
					__UpperCAmelCase    :     Union[str, Any]													=	initializer_range




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size )       ,       3       ,       self.vocab_size )
					__UpperCAmelCase    :     Union[str, Any]													=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,       dtype=np.intaa ))       ,       -1 )

					__UpperCAmelCase    :     Union[str, Any]													=	shift_tokens_right(_A       ,       1       ,       2 )

					__UpperCAmelCase    :     Union[str, Any]													=	BlenderbotSmallConfig(
					    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       ,       )
					__UpperCAmelCase    :     List[Any]													=	prepare_blenderbot_inputs_dict(_A       ,       _A       ,       _A )
					return config, inputs_dict




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.prepare_config_and_inputs()
					return config, inputs_dict




			def  snake_case__     (   self							: List[Any]       ,       a_							: Tuple       ,       a_							: Tuple       ,       a_							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	20
					__UpperCAmelCase    :     Any													=	model_class_name(_A )

					__UpperCAmelCase    :     Optional[int]													=	model.encode(inputs_dict['''input_ids'''] )

					__UpperCAmelCase    :     Dict													=	(
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

					__UpperCAmelCase    :     int													=	model.init_cache(decoder_input_ids.shape[0]       ,       _A       ,       _A )
					__UpperCAmelCase    :     Union[str, Any]													=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,       dtype='''i4''' )

					__UpperCAmelCase    :     Union[str, Any]													=	jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,       )
					__UpperCAmelCase    :     Dict													=	model.decode(
					    decoder_input_ids[:, :-1]       ,       _A       ,       decoder_attention_mask=_A       ,       past_key_values=_A       ,       decoder_position_ids=_A       ,       )

					__UpperCAmelCase    :     Dict													=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,       dtype='''i4''' )
					__UpperCAmelCase    :     Union[str, Any]													=	model.decode(
					    decoder_input_ids[:, -1:]       ,       _A       ,       decoder_attention_mask=_A       ,       past_key_values=outputs_cache.past_key_values       ,       decoder_position_ids=_A       ,       )

					__UpperCAmelCase    :     Optional[int]													=	model.decode(_A       ,       _A )

					__UpperCAmelCase    :     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  snake_case__     (   self							: Union[str, Any]       ,       a_							: int       ,       a_							: Tuple       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	20
					__UpperCAmelCase    :     int													=	model_class_name(_A )

					__UpperCAmelCase    :     List[str]													=	model.encode(inputs_dict['''input_ids'''] )

					__UpperCAmelCase    :     str													=	(
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

					__UpperCAmelCase    :     Dict													=	jnp.concatenate(
					    [
					        decoder_attention_mask,
					        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
					    ]       ,       axis=-1       ,       )

					__UpperCAmelCase    :     Optional[int]													=	model.init_cache(decoder_input_ids.shape[0]       ,       _A       ,       _A )
					__UpperCAmelCase    :     Any													=	jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]       ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,       )

					__UpperCAmelCase    :     Dict													=	model.decode(
					    decoder_input_ids[:, :-1]       ,       _A       ,       decoder_attention_mask=_A       ,       past_key_values=_A       ,       decoder_position_ids=_A       ,       )
					__UpperCAmelCase    :     str													=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,       dtype='''i4''' )
					__UpperCAmelCase    :     int													=	model.decode(
					    decoder_input_ids[:, -1:]       ,       _A       ,       past_key_values=outputs_cache.past_key_values       ,       decoder_attention_mask=_A       ,       decoder_position_ids=_A       ,       )

					__UpperCAmelCase    :     Tuple													=	model.decode(_A       ,       _A       ,       decoder_attention_mask=_A )

					__UpperCAmelCase    :     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}' )

@require_flax
class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       99




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	np.array(
					    [
					        [71, 82, 18, 33, 46, 91, 2],
					        [68, 34, 26, 58, 30, 82, 2],
					        [5, 97, 17, 39, 94, 40, 2],
					        [76, 83, 94, 25, 70, 78, 2],
					        [87, 59, 41, 35, 48, 66, 2],
					        [55, 13, 16, 58, 5, 2, 1],  # note padding
					        [64, 27, 31, 51, 12, 75, 2],
					        [52, 64, 86, 17, 83, 39, 2],
					        [48, 61, 9, 24, 71, 82, 2],
					        [26, 1, 60, 48, 22, 13, 2],
					        [21, 5, 62, 28, 14, 76, 2],
					        [45, 98, 37, 86, 59, 48, 2],
					        [70, 70, 50, 9, 28, 0, 2],
					    ]       ,       dtype=np.intaa       ,       )

					__UpperCAmelCase    :     Any													=	input_ids.shape[0]
					__UpperCAmelCase    :     Dict													=	BlenderbotSmallConfig(
					    vocab_size=self.vocab_size       ,       d_model=24       ,       encoder_layers=2       ,       decoder_layers=2       ,       encoder_attention_heads=2       ,       decoder_attention_heads=2       ,       encoder_ffn_dim=32       ,       decoder_ffn_dim=32       ,       max_position_embeddings=48       ,       eos_token_id=2       ,       pad_token_id=1       ,       bos_token_id=0       ,       )
					return config, input_ids, batch_size




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self._get_config_and_data()
					__UpperCAmelCase    :     Dict													=	FlaxBlenderbotSmallForConditionalGeneration(_A )
					__UpperCAmelCase    :     Dict													=	lm_model(input_ids=_A )
					__UpperCAmelCase    :     List[Any]													=	(batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs['''logits'''].shape       ,       _A )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	BlenderbotSmallConfig(
					    vocab_size=self.vocab_size       ,       d_model=14       ,       encoder_layers=2       ,       decoder_layers=2       ,       encoder_attention_heads=2       ,       decoder_attention_heads=2       ,       encoder_ffn_dim=8       ,       decoder_ffn_dim=8       ,       max_position_embeddings=48       ,       )
					__UpperCAmelCase    :     Dict													=	FlaxBlenderbotSmallForConditionalGeneration(_A )
					__UpperCAmelCase    :     int													=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,       dtype=np.intaa )
					__UpperCAmelCase    :     Tuple													=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,       dtype=np.intaa )
					__UpperCAmelCase    :     Any													=	lm_model(input_ids=_A       ,       decoder_input_ids=_A )
					__UpperCAmelCase    :     Tuple													=	(*summary.shape, config.vocab_size)
					self.assertEqual(outputs['''logits'''].shape       ,       _A )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,       dtype=np.intaa )
					__UpperCAmelCase    :     Optional[int]													=	shift_tokens_right(_A       ,       1       ,       2 )
					__UpperCAmelCase    :     int													=	np.equal(_A       ,       1 ).astype(np.floataa ).sum()
					__UpperCAmelCase    :     Optional[int]													=	np.equal(_A       ,       1 ).astype(np.floataa ).sum()
					self.assertEqual(shifted.shape       ,       input_ids.shape )
					self.assertEqual(_A       ,       n_pad_before - 1 )
					self.assertTrue(np.equal(shifted[:, 0]       ,       2 ).all() )

@require_flax
class 		UpperCAmelCase__			(			A__						,unittest.TestCase						,A__   ):




			'''simple docstring'''






			UpperCamelCase									=       True
			UpperCamelCase									=       (
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			UpperCamelCase									=       (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	FlaxBlenderbotSmallModelTester(self )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	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  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	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  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__ ):
									__UpperCAmelCase    :     Optional[int]													=	self._prepare_for_class(_A       ,       _A )
									__UpperCAmelCase    :     List[str]													=	model_class(_A )

									@jax.jit
									def encode_jitted(a_							: Dict       ,       a_							: Optional[int]=None       ,       **a_							: int ):
											return model.encode(input_ids=_A       ,       attention_mask=_A )

									with self.subTest('''JIT Enabled''' ):
											__UpperCAmelCase    :     Tuple													=	encode_jitted(**_A ).to_tuple()

									with self.subTest('''JIT Disabled''' ):
											with jax.disable_jit():
													__UpperCAmelCase    :     Dict													=	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  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__ ):
									__UpperCAmelCase    :     str													=	model_class(_A )
									__UpperCAmelCase    :     int													=	model.encode(inputs_dict['''input_ids''']       ,       inputs_dict['''attention_mask'''] )

									__UpperCAmelCase    :     Dict													=	{
									    "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_							: str       ,       a_							: Any       ,       a_							: int ):
											return model.decode(
											    decoder_input_ids=_A       ,       decoder_attention_mask=_A       ,       encoder_outputs=_A       ,       )

									with self.subTest('''JIT Enabled''' ):
											__UpperCAmelCase    :     Tuple													=	decode_jitted(**_A ).to_tuple()

									with self.subTest('''JIT Disabled''' ):
											with jax.disable_jit():
													__UpperCAmelCase    :     Optional[Any]													=	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  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					for model_class_name in self.all_model_classes:
							__UpperCAmelCase    :     Dict													=	model_class_name.from_pretrained('''facebook/blenderbot_small-90M''' )
							# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
							__UpperCAmelCase    :     str													=	np.ones((1, 1) ) * model.config.eos_token_id
							__UpperCAmelCase    :     Any													=	model(_A )
							self.assertIsNotNone(_A )






from collections import defaultdict




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    1
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    True
		for v in tree[start]:
				if v not in visited:
						ret += dfs(lowerCAmelCase	)
		if ret % 2 == 0:
				cuts.append(lowerCAmelCase	)
		return ret




def       _snake_case	(   ):
		"""simple docstring"""







		dfs(1	)


if __name__ == "__main__":
		__lowerCamelCase   ,						__lowerCamelCase					:	Union[str, Any]       =  10, 9
		__lowerCamelCase					:	Optional[int]       =  defaultdict(list)
		__lowerCamelCase					:	dict[int, bool]       =  {}
		__lowerCamelCase					:	list[int]       =  []
		__lowerCamelCase					:	Optional[Any]       =  0
		__lowerCamelCase					:	Any       =  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
		for u, v in edges:
				tree[u].append(v)
				tree[v].append(u)
		even_tree()
		print(len(cuts) - 1)
