




"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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
					lowerCAmelCase							:    Optional[int]  							=							"""platform"""

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				)		->      Tuple:
			if attention_mask is None:
						lowerCamelCase       =      np.where(input_ids != config.pad_token_id ,				1 ,				0       )
			if decoder_attention_mask is None:
						lowerCamelCase       =      np.where(decoder_input_ids != config.pad_token_id ,				1 ,				0       )
			if head_mask is None:
						lowerCamelCase       =      np.ones((config.encoder_layers, config.encoder_attention_heads)       )
			if decoder_head_mask is None:
						lowerCamelCase       =      np.ones((config.decoder_layers, config.decoder_attention_heads)       )
			if cross_attn_head_mask is None:
						lowerCamelCase       =      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 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=13	,				_a=7	,				_a=True	,				_a=False	,				_a=99	,				_a=16	,				_a=2	,				_a=4	,				_a=4	,				_a="gelu"	,				_a=0.1	,				_a=0.1	,				_a=32	,				_a=2	,				_a=1	,				_a=0	,				_a=0.02	,				):





							"""simple docstring"""
							lowerCamelCase       =      parent
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      seq_length
							lowerCamelCase       =      is_training
							lowerCamelCase       =      use_labels
							lowerCamelCase       =      vocab_size
							lowerCamelCase       =      hidden_size
							lowerCamelCase       =      num_hidden_layers
							lowerCamelCase       =      num_attention_heads
							lowerCamelCase       =      intermediate_size
							lowerCamelCase       =      hidden_act
							lowerCamelCase       =      hidden_dropout_prob
							lowerCamelCase       =      attention_probs_dropout_prob
							lowerCamelCase       =      max_position_embeddings
							lowerCamelCase       =      eos_token_id
							lowerCamelCase       =      pad_token_id
							lowerCamelCase       =      bos_token_id
							lowerCamelCase       =      initializer_range





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,				self.vocab_size				)	,				3	,				self.vocab_size				)
							lowerCamelCase       =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,				dtype=np.intaa				))	,				-1				)

							lowerCamelCase       =      shift_tokens_right(lowerCAmelCase_	,				1	,				2				)

							lowerCamelCase       =      BlenderbotConfig(
							    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=lowerCAmelCase_	,				)
							lowerCamelCase       =      prepare_blenderbot_inputs_dict(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_				)
							return config, inputs_dict





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.prepare_config_and_inputs()
							return config, inputs_dict





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      20
							lowerCamelCase       =      model_class_name(lowerCAmelCase_				)

							lowerCamelCase       =      model.encode(inputs_dict["""input_ids"""]				)

							lowerCamelCase   ,     lowerCamelCase       =      (
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

							lowerCamelCase       =      model.init_cache(decoder_input_ids.shape[0]	,				lowerCAmelCase_	,				lowerCAmelCase_				)
							lowerCamelCase       =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,				dtype="""i4"""				)

							lowerCamelCase       =      jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]	,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,				)
							lowerCamelCase       =      model.decode(
							    decoder_input_ids[:, :-1]	,				lowerCAmelCase_	,				decoder_attention_mask=lowerCAmelCase_	,				past_key_values=lowerCAmelCase_	,				decoder_position_ids=lowerCAmelCase_	,				)

							lowerCamelCase       =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,				dtype="""i4"""				)
							lowerCamelCase       =      model.decode(
							    decoder_input_ids[:, -1:]	,				lowerCAmelCase_	,				decoder_attention_mask=lowerCAmelCase_	,				past_key_values=outputs_cache.past_key_values	,				decoder_position_ids=lowerCAmelCase_	,				)

							lowerCamelCase       =      model.decode(lowerCAmelCase_	,				lowerCAmelCase_				)

							lowerCamelCase       =      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	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      20
							lowerCamelCase       =      model_class_name(lowerCAmelCase_				)

							lowerCamelCase       =      model.encode(inputs_dict["""input_ids"""]				)

							lowerCamelCase   ,     lowerCamelCase       =      (
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

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

							lowerCamelCase       =      model.init_cache(decoder_input_ids.shape[0]	,				lowerCAmelCase_	,				lowerCAmelCase_				)
							lowerCamelCase       =      jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]	,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,				)

							lowerCamelCase       =      model.decode(
							    decoder_input_ids[:, :-1]	,				lowerCAmelCase_	,				decoder_attention_mask=lowerCAmelCase_	,				past_key_values=lowerCAmelCase_	,				decoder_position_ids=lowerCAmelCase_	,				)
							lowerCamelCase       =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,				dtype="""i4"""				)
							lowerCamelCase       =      model.decode(
							    decoder_input_ids[:, -1:]	,				lowerCAmelCase_	,				past_key_values=outputs_cache.past_key_values	,				decoder_attention_mask=lowerCAmelCase_	,				decoder_position_ids=lowerCAmelCase_	,				)

							lowerCamelCase       =      model.decode(lowerCAmelCase_	,				lowerCAmelCase_	,				decoder_attention_mask=lowerCAmelCase_				)

							lowerCamelCase       =      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 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		99





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      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	,				)

							lowerCamelCase       =      input_ids.shape[0]
							lowerCamelCase       =      BlenderbotConfig(
							    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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      self._get_config_and_data()
							lowerCamelCase       =      FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_				)
							lowerCamelCase       =      lm_model(input_ids=lowerCAmelCase_				)
							lowerCamelCase       =      (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape	,				lowerCAmelCase_				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      BlenderbotConfig(
							    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	,				)
							lowerCamelCase       =      FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_				)
							lowerCamelCase       =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,				dtype=np.intaa				)
							lowerCamelCase       =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,				dtype=np.intaa				)
							lowerCamelCase       =      lm_model(input_ids=lowerCAmelCase_	,				decoder_input_ids=lowerCAmelCase_				)
							lowerCamelCase       =      (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape	,				lowerCAmelCase_				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,				dtype=np.intaa				)
							lowerCamelCase       =      shift_tokens_right(lowerCAmelCase_	,				1	,				2				)
							lowerCamelCase       =      np.equal(lowerCAmelCase_	,				1				).astype(np.floataa				).sum()
							lowerCamelCase       =      np.equal(lowerCAmelCase_	,				1				).astype(np.floataa				).sum()
							self.assertEqual(shifted.shape	,				input_ids.shape				)
							self.assertEqual(lowerCAmelCase_	,				n_pad_before - 1				)
							self.assertTrue(np.equal(shifted[:, 0]	,				2				).all()				)






@require_flax
class 					__magic_name__							( __lowerCAmelCase		,      unittest.TestCase		,      __lowerCAmelCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		True
				__UpperCamelCase    						=		(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				__UpperCamelCase    						=		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      FlaxBlenderbotModelTester(self				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      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(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
													lowerCamelCase       =      self._prepare_for_class(lowerCAmelCase_	,				lowerCAmelCase_				)
													lowerCamelCase       =      model_class(lowerCAmelCase_				)

													@jax.jit
													def encode_jitted(_a	,				_a=None	,				**_a				):
																return model.encode(input_ids=lowerCAmelCase_	,				attention_mask=lowerCAmelCase_				)

													with self.subTest("""JIT Enabled"""				):
																lowerCamelCase       =      encode_jitted(**lowerCAmelCase_				).to_tuple()

													with self.subTest("""JIT Disabled"""				):
																with jax.disable_jit():
																			lowerCamelCase       =      encode_jitted(**lowerCAmelCase_				).to_tuple()

													self.assertEqual(len(lowerCAmelCase_				)	,				len(lowerCAmelCase_				)				)
													for jitted_output, output in zip(lowerCAmelCase_	,				lowerCAmelCase_				):
																self.assertEqual(jitted_output.shape	,				output.shape				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
													lowerCamelCase       =      model_class(lowerCAmelCase_				)
													lowerCamelCase       =      model.encode(inputs_dict["""input_ids"""]	,				inputs_dict["""attention_mask"""]				)

													lowerCamelCase       =      {
													    """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	,				_a	,				_a				):
																return model.decode(
																    decoder_input_ids=lowerCAmelCase_	,				decoder_attention_mask=lowerCAmelCase_	,				encoder_outputs=lowerCAmelCase_	,				)

													with self.subTest("""JIT Enabled"""				):
																lowerCamelCase       =      decode_jitted(**lowerCAmelCase_				).to_tuple()

													with self.subTest("""JIT Disabled"""				):
																with jax.disable_jit():
																			lowerCamelCase       =      decode_jitted(**lowerCAmelCase_				).to_tuple()

													self.assertEqual(len(lowerCAmelCase_				)	,				len(lowerCAmelCase_				)				)
													for jitted_output, output in zip(lowerCAmelCase_	,				lowerCAmelCase_				):
																self.assertEqual(jitted_output.shape	,				output.shape				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for model_class_name in self.all_model_classes:
										lowerCamelCase       =      model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""				)
										# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
										lowerCamelCase       =      np.ones((1, 1)				) * model.config.eos_token_id
										lowerCamelCase       =      model(lowerCAmelCase_				)
										self.assertIsNotNone(lowerCAmelCase_				)





				@unittest.skipUnless(jax_device != """cpu"""	,				"""3B test too slow on CPU."""				)
				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
							lowerCamelCase       =      {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

							lowerCamelCase       =      FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""	,				from_pt=lowerCAmelCase_				)
							lowerCamelCase       =      BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""				)

							lowerCamelCase       =      ["""Sam"""]
							lowerCamelCase       =      tokenizer(lowerCAmelCase_	,				return_tensors="""jax"""				)

							lowerCamelCase       =      model.generate(**lowerCAmelCase_	,				**lowerCAmelCase_				)
							lowerCamelCase       =      """Sam is a great name. It means \"sun\" in Gaelic."""

							lowerCamelCase       =      tokenizer.batch_decode(lowerCAmelCase_	,				**lowerCAmelCase_				)
							assert generated_txt[0].strip() == tgt_text









"""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 					a__				(       snake_case__       )		->      Dict:
			lowerCamelCase       =      [False] * len(snake_case__       )
			lowerCamelCase       =      [-1] * len(snake_case__       )

			def dfs(snake_case__ ,				snake_case__       ):
						lowerCamelCase       =      True
						lowerCamelCase       =      c
						for u in graph[v]:
									if not visited[u]:
												dfs(snake_case__ ,				1 - c       )

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

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

			return True


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


print(check_bipartite_dfs(graph))



