



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
			SCREAMING_SNAKE_CASE     				=						"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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     )		->							Union[str, Any]:
		if attention_mask is None:
				UpperCAmelCase_						=				np.where(input_ids != config.pad_token_id			,     1			,     0 )
		if decoder_attention_mask is None:
				UpperCAmelCase_						=				np.where(decoder_input_ids != config.pad_token_id			,     1			,     0 )
		if head_mask is None:
				UpperCAmelCase_						=				np.ones((config.encoder_layers, config.encoder_attention_heads) )
		if decoder_head_mask is None:
				UpperCAmelCase_						=				np.ones((config.decoder_layers, config.decoder_attention_heads) )
		if cross_attn_head_mask is None:
				UpperCAmelCase_						=				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    ,	lowerCAmelCase    ,	lowerCAmelCase=13    ,	lowerCAmelCase=7    ,	lowerCAmelCase=True    ,	lowerCAmelCase=False    ,	lowerCAmelCase=99    ,	lowerCAmelCase=16    ,	lowerCAmelCase=2    ,	lowerCAmelCase=4    ,	lowerCAmelCase=4    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=32    ,	lowerCAmelCase=2    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	lowerCAmelCase=0.02    ,	):
							UpperCAmelCase_						=				parent
							UpperCAmelCase_						=				batch_size
							UpperCAmelCase_						=				seq_length
							UpperCAmelCase_						=				is_training
							UpperCAmelCase_						=				use_labels
							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				intermediate_size
							UpperCAmelCase_						=				hidden_act
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				max_position_embeddings
							UpperCAmelCase_						=				eos_token_id
							UpperCAmelCase_						=				pad_token_id
							UpperCAmelCase_						=				bos_token_id
							UpperCAmelCase_						=				initializer_range



					def    A__		(			self     ):
							UpperCAmelCase_						=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,	self.vocab_size     )    ,	3    ,	self.vocab_size     )
							UpperCAmelCase_						=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,	dtype=np.intaa     ))    ,	-1     )

							UpperCAmelCase_						=				shift_tokens_right(lowerCAmelCase    ,	1    ,	2     )

							UpperCAmelCase_						=				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    ,	)
							UpperCAmelCase_						=				prepare_blenderbot_inputs_dict(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )
							return config, inputs_dict



					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.prepare_config_and_inputs()
							return config, inputs_dict



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				20
							UpperCAmelCase_						=				model_class_name(lowerCAmelCase     )

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

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

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

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

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

							UpperCAmelCase_						=				model.decode(lowerCAmelCase    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				20
							UpperCAmelCase_						=				model_class_name(lowerCAmelCase     )

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

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

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

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

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

							UpperCAmelCase_						=				model.decode(lowerCAmelCase    ,	lowerCAmelCase    ,	decoder_attention_mask=lowerCAmelCase     )

							UpperCAmelCase_						=				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      (						unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Dict									=  99



					def    A__		(			self     ):
							UpperCAmelCase_						=				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_						=				input_ids.shape[0]
							UpperCAmelCase_						=				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    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				self._get_config_and_data()
							UpperCAmelCase_						=				FlaxBlenderbotForConditionalGeneration(lowerCAmelCase     )
							UpperCAmelCase_						=				lm_model(input_ids=lowerCAmelCase     )
							UpperCAmelCase_						=				(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				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    ,	)
							UpperCAmelCase_						=				FlaxBlenderbotForConditionalGeneration(lowerCAmelCase     )
							UpperCAmelCase_						=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,	dtype=np.intaa     )
							UpperCAmelCase_						=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,	dtype=np.intaa     )
							UpperCAmelCase_						=				lm_model(input_ids=lowerCAmelCase    ,	decoder_input_ids=lowerCAmelCase     )
							UpperCAmelCase_						=				(*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,	dtype=np.intaa     )
							UpperCAmelCase_						=				shift_tokens_right(lowerCAmelCase    ,	1    ,	2     )
							UpperCAmelCase_						=				np.equal(lowerCAmelCase    ,	1     ).astype(np.floataa     ).sum()
							UpperCAmelCase_						=				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 	lowerCamelCase      (						lowercase__,     unittest.TestCase,     lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Dict									=  True
					lowerCAmelCase_				:    List[str]									=  (
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					lowerCAmelCase_				:    Optional[int]									=  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



					def    A__		(			self     ):
							UpperCAmelCase_						=				FlaxBlenderbotModelTester(self     )



					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				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    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				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    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__     ):
											UpperCAmelCase_						=				self._prepare_for_class(lowerCAmelCase    ,	lowerCAmelCase     )
											UpperCAmelCase_						=				model_class(lowerCAmelCase     )

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

											with self.subTest("JIT Enabled"     ):
													UpperCAmelCase_						=				encode_jitted(**lowerCAmelCase     ).to_tuple()

											with self.subTest("JIT Disabled"     ):
													with jax.disable_jit():
															UpperCAmelCase_						=				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    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

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

											UpperCAmelCase_						=				{
											    "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(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
													return model.decode(
													    decoder_input_ids=lowerCAmelCase    ,	decoder_attention_mask=lowerCAmelCase    ,	encoder_outputs=lowerCAmelCase    ,	)

											with self.subTest("JIT Enabled"     ):
													UpperCAmelCase_						=				decode_jitted(**lowerCAmelCase     ).to_tuple()

											with self.subTest("JIT Disabled"     ):
													with jax.disable_jit():
															UpperCAmelCase_						=				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    A__		(			self     ):
							for model_class_name in self.all_model_classes:
									UpperCAmelCase_						=				model_class_name.from_pretrained("facebook/blenderbot-400M-distill"     )
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									UpperCAmelCase_						=				np.ones((1, 1)     ) * model.config.eos_token_id
									UpperCAmelCase_						=				model(lowerCAmelCase     )
									self.assertIsNotNone(lowerCAmelCase     )



					@unittest.skipUnless(jax_device != "cpu"    ,	"3B test too slow on CPU."     )
					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
							UpperCAmelCase_						=				{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

							UpperCAmelCase_						=				["Sam"]
							UpperCAmelCase_						=				tokenizer(lowerCAmelCase    ,	return_tensors="jax"     )

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

							UpperCAmelCase_						=				tokenizer.batch_decode(lowerCAmelCase    ,	**lowerCAmelCase     )
							assert generated_txt[0].strip() == tgt_text






def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				1
		for i in range(1			,     num + 1 ):
				fact *= i
		return fact







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							int:
		UpperCAmelCase_						=				0
		while number > 0:
				UpperCAmelCase_						=				number % 10
				sum_of_digits += last_digit
				UpperCAmelCase_						=				number // 10  # Removing the last_digit from the given number
		return sum_of_digits







def snake_case__  (			__SCREAMING_SNAKE_CASE = 100 )		->							int:
		UpperCAmelCase_						=				factorial(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				split_and_add(__SCREAMING_SNAKE_CASE )
		return result


if __name__ == "__main__":
			print(solution(int(input("Enter the Number: ").strip())))

