


"""simple docstring"""


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
   import os

   import jax.numpy as jnp
   from jax import jit

   from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
   from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

   _snake_case          =							"0.12"  # assumed parallelism: 8


if is_torch_available():
   import torch



def   snake_case						(				_a:     Optional[int]		,   _a:     List[Any]		,   _a:     List[Any]=None							)->		str:
      '''simple docstring'''


      if rng is None:
            lowerCamelCase__           =		random.Random()

      lowerCamelCase__           =		1
      for dim in shape:
            total_dims *= dim

      lowerCamelCase__           =		[]
      for _ in range(_a							):
            values.append(rng.randint(0		,   vocab_size - 1							)							)

      lowerCamelCase__           =		np.array(_a		,   dtype=jnp.intaa							).reshape(_a							)

      return output



def   snake_case						(				_a:     Optional[Any]		,   _a:     Tuple=None							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		ids_tensor(_a		,   vocab_size=2		,   rng=_a							)
      # make sure that at least one token is attended to for each batch
      lowerCamelCase__           =		1
      return attn_mask



@require_flax
class   _a      :
    a_    :				Optional[Any]             =    None
    a_    :				List[Any]             =    ()
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__           =		self.model_tester.prepare_config_and_inputs_for_common()

          # cut to half length & take max batch_size 3
          lowerCamelCase__           =		2
          lowerCamelCase__           =		inputs['input_ids'].shape[-1] // 2
          lowerCamelCase__           =		inputs['input_ids'][:max_batch_size, :sequence_length]

          lowerCamelCase__           =		jnp.ones_like(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		attention_mask[:max_batch_size, :sequence_length]

          # generate max 5 tokens
          lowerCamelCase__           =		input_ids.shape[-1] + 5
          if config.eos_token_id is not None and config.pad_token_id is None:
                # hack to allow generate for models such as GPT2 as is done in `generate()`
                lowerCamelCase__           =		config.eos_token_id
          return config, input_ids, attention_mask, max_length
    @is_pt_flax_cross_test
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		0

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                lowerCamelCase__           =		getattr(SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		pt_model_class(SCREAMING_SNAKE_CASE__				).eval()
                lowerCamelCase__           =		load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__							,     flax_model.params				)

                lowerCamelCase__           =		flax_model.generate(SCREAMING_SNAKE_CASE__				).sequences
                lowerCamelCase__           =		pt_model.generate(torch.tensor(SCREAMING_SNAKE_CASE__							,     dtype=torch.long				)				)

                if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                      lowerCamelCase__           =		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

                self.assertListEqual(pt_generation_outputs.numpy().tolist()							,     flax_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		True
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		2

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		2
          lowerCamelCase__           =		2

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[0]							,     input_ids.shape[0] * config.num_return_sequences				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		True
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		0.8
          lowerCamelCase__           =		10
          lowerCamelCase__           =		0.3
          lowerCamelCase__           =		1
          lowerCamelCase__           =		8
          lowerCamelCase__           =		9

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		1
          lowerCamelCase__           =		8
          lowerCamelCase__           =		9

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()
          lowerCamelCase__           =		max_length
          lowerCamelCase__           =		2
          lowerCamelCase__           =		1
          lowerCamelCase__           =		8
          lowerCamelCase__           =		9

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()

          # pad attention mask on the left
          lowerCamelCase__           =		attention_mask.at[(0, 0)].set(0				)

          lowerCamelCase__           =		False
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__							,     attention_mask=SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__							,     attention_mask=SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)
    def      _UpperCamelCase       ( self							:					Dict				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()

          # pad attention mask on the left
          lowerCamelCase__           =		attention_mask.at[(0, 0)].set(0				)

          lowerCamelCase__           =		True
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__							,     attention_mask=SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__							,     attention_mask=SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)


    def      _UpperCamelCase       ( self							:					Optional[Any]				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self._get_input_ids_and_config()

          # pad attention mask on the left
          lowerCamelCase__           =		attention_mask.at[(0, 0)].set(0				)

          lowerCamelCase__           =		2
          lowerCamelCase__           =		max_length

          for model_class in self.all_generative_model_classes:
                lowerCamelCase__           =		model_class(SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model.generate(SCREAMING_SNAKE_CASE__							,     attention_mask=SCREAMING_SNAKE_CASE__				).sequences
                self.assertEqual(generation_outputs.shape[-1]							,     SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		jit(model.generate				)
                lowerCamelCase__           =		jit_generate(SCREAMING_SNAKE_CASE__							,     attention_mask=SCREAMING_SNAKE_CASE__				).sequences

                self.assertListEqual(generation_outputs.tolist()							,     jit_generation_outputs.tolist()				)





@require_flax
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'				)
          lowerCamelCase__           =		FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'				)

          lowerCamelCase__           =		'Hello world'
          lowerCamelCase__           =		tokenizer(SCREAMING_SNAKE_CASE__							,     return_tensors='np'				).input_ids

          # typos are quickly detected (the correct argument is `do_sample`)
          with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__							,     'do_samples'				):
                model.generate(SCREAMING_SNAKE_CASE__							,     do_samples=SCREAMING_SNAKE_CASE__				)

          # arbitrary arguments that will not be used anywhere are also not accepted
          with self.assertRaisesRegex(SCREAMING_SNAKE_CASE__							,     'foo'				):
                lowerCamelCase__           =		{'foo': 'bar'}
                model.generate(SCREAMING_SNAKE_CASE__							,     **SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def   snake_case						(				_a:     Optional[Any]							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[-1] * len(_a							)

      def dfs(_a:     Any		,   _a:     Optional[int]							):
            lowerCamelCase__           =		True
            lowerCamelCase__           =		c
            for u in graph[v]:
                  if not visited[u]:
                        dfs(_a		,   1 - c							)

      for i in range(len(_a							)							):
            if not visited[i]:
                  dfs(_a		,   0							)

      for i in range(len(_a							)							):
            for j in graph[i]:
                  if color[i] == color[j]:
                        return False

      return True


# Adjacency list of graph
_snake_case          =							{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}






print(check_bipartite_dfs(graph))



