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
		UpperCamelCase_ :     List[Any]          =  '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 				_snake_case     (       lowerCAmelCase							:       List[str]       ,			lowerCAmelCase							:       List[str]       ,			lowerCAmelCase							:       Tuple=None       ,			lowerCAmelCase							:       Union[str, Any]=None       ,			lowerCAmelCase							:       int=None       ,			lowerCAmelCase							:       Optional[Any]=None       ,			lowerCAmelCase							:       List[str]=None       ,			lowerCAmelCase							:       Any=None       ,			):
		"""simple docstring"""







		if attention_mask is None:
				SCREAMING_SNAKE_CASE_  :		Any    				=   np.where(input_ids != config.pad_token_id       ,			1       ,			0     )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   np.where(decoder_input_ids != config.pad_token_id       ,			1       ,			0     )
		if head_mask is None:
				SCREAMING_SNAKE_CASE_  :		Any    				=   np.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE_  :		Dict    				=   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE_  :		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 		a__						:

					def __init__(   self				:	Union[str, Any],_A				:	Tuple,_A				:	Any=13,_A				:	int=7,_A				:	Optional[Any]=True,_A				:	int=False,_A				:	List[str]=99,_A				:	Union[str, Any]=16,_A				:	Union[str, Any]=2,_A				:	Optional[int]=4,_A				:	str=4,_A				:	Dict="gelu",_A				:	int=0.1,_A				:	List[Any]=0.1,_A				:	Any=32,_A				:	Tuple=2,_A				:	List[str]=1,_A				:	List[str]=0,_A				:	Optional[Any]=0.02,):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		str    				=   shift_tokens_right(snake_case_,1,2   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   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=snake_case_,)
							SCREAMING_SNAKE_CASE_  :		int    				=   prepare_marian_inputs_dict(snake_case_,snake_case_,snake_case_   )
							return config, inputs_dict

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   self.prepare_config_and_inputs()
							return config, inputs_dict

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	str,_A				:	List[str],_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   20
							SCREAMING_SNAKE_CASE_  :		Any    				=   model_class_name(snake_case_   )

							SCREAMING_SNAKE_CASE_  :		str    				=   model.encode(inputs_dict["input_ids"]   )

							SCREAMING_SNAKE_CASE_  :		Any    				=   (
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   model.init_cache(decoder_input_ids.shape[0],snake_case_,snake_case_   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length),dtype="i4"   )

							SCREAMING_SNAKE_CASE_  :		int    				=   jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)
							SCREAMING_SNAKE_CASE_  :		str    				=   model.decode(
							    decoder_input_ids[:, :-1],snake_case_,decoder_attention_mask=snake_case_,past_key_values=snake_case_,decoder_position_ids=snake_case_,)

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

							SCREAMING_SNAKE_CASE_  :		int    				=   model.decode(snake_case_,snake_case_   )

							SCREAMING_SNAKE_CASE_  :		Dict    				=   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     __UpperCamelCase       (   self				:	Tuple,_A				:	Union[str, Any],_A				:	Optional[Any],_A				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   20
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model_class_name(snake_case_   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   model.encode(inputs_dict["input_ids"]   )

							SCREAMING_SNAKE_CASE_  :		Any    				=   (
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

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

							SCREAMING_SNAKE_CASE_  :		str    				=   model.init_cache(decoder_input_ids.shape[0],snake_case_,snake_case_   )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :],(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),)

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   model.decode(
							    decoder_input_ids[:, :-1],snake_case_,decoder_attention_mask=snake_case_,past_key_values=snake_case_,decoder_position_ids=snake_case_,)
							SCREAMING_SNAKE_CASE_  :		Dict    				=   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],dtype="i4"   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   model.decode(
							    decoder_input_ids[:, -1:],snake_case_,past_key_values=outputs_cache.past_key_values,decoder_attention_mask=snake_case_,decoder_position_ids=snake_case_,)

							SCREAMING_SNAKE_CASE_  :		str    				=   model.decode(snake_case_,snake_case_,decoder_attention_mask=snake_case_   )

							SCREAMING_SNAKE_CASE_  :		int    				=   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 		a__						( UpperCAmelCase__   ,		unittest.TestCase   ,		UpperCAmelCase__ ):
					A     	=							True
					A     	=							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
					A     	=							(FlaxMarianMTModel,) if is_flax_available() else ()

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   FlaxMarianModelTester(self   )

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(snake_case_,snake_case_,snake_case_   )

					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		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(snake_case_,snake_case_,snake_case_   )

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__   ):
											SCREAMING_SNAKE_CASE_  :		int    				=   self._prepare_for_class(snake_case_,snake_case_   )
											SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   model_class(snake_case_   )

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

											with self.subTest("JIT Enabled"   ):
													SCREAMING_SNAKE_CASE_  :		List[str]    				=   encode_jitted(**snake_case_   ).to_tuple()

											with self.subTest("JIT Disabled"   ):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE_  :		int    				=   encode_jitted(**snake_case_   ).to_tuple()

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

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__   ):
											SCREAMING_SNAKE_CASE_  :		List[Any]    				=   model_class(snake_case_   )
											SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   model.encode(inputs_dict["input_ids"],inputs_dict["attention_mask"]   )

											SCREAMING_SNAKE_CASE_  :		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				:	str,_A				:	Dict,_A				:	Any   ):
													return model.decode(
													    decoder_input_ids=snake_case_,decoder_attention_mask=snake_case_,encoder_outputs=snake_case_,)

											with self.subTest("JIT Enabled"   ):
													SCREAMING_SNAKE_CASE_  :		str    				=   decode_jitted(**snake_case_   ).to_tuple()

											with self.subTest("JIT Disabled"   ):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE_  :		List[str]    				=   decode_jitted(**snake_case_   ).to_tuple()

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






					@slow
					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"   )
									# FlaxMarianForSequenceClassification expects eos token in input_ids
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   np.ones((1, 1)   ) * model.config.eos_token_id
									SCREAMING_SNAKE_CASE_  :		str    				=   model(snake_case_   )
									self.assertIsNotNone(snake_case_   )







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( unittest.TestCase ):
					A     	=							None
					A     	=							None

					@classmethod
					def     __UpperCamelCase       (   cls				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
							return cls

					@cached_property
					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							return MarianTokenizer.from_pretrained(self.model_name   )

					@property
					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							return self.tokenizer.eos_token_id

					@cached_property
					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		FlaxMarianMTModel    				=   FlaxMarianMTModel.from_pretrained(self.model_name   )
							self.assertEqual(model.config.decoder_start_token_id,model.config.pad_token_id   )
							return model

					def     __UpperCamelCase       (   self				:	List[Any],**_A				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.translate_src_text(**snake_case_   )
							self.assertListEqual(self.expected_text,snake_case_   )






					def     __UpperCamelCase       (   self				:	Tuple,**_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.tokenizer(self.src_text,padding=snake_case_,return_tensors="np",**snake_case_   )
							SCREAMING_SNAKE_CASE_  :		int    				=   self.model.generate(
							    model_inputs.input_ids,attention_mask=model_inputs.attention_mask,num_beams=2,max_length=128,).sequences
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_   )
							return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( UpperCAmelCase__ ):
					A     	=							"""en"""
					A     	=							"""fr"""
					A     	=							[
					    """I am a small frog.""",
					    """Now I can forget the 100 words of german that I know.""",
					]
					A     	=							[
					    """Je suis une petite grenouille.""",
					    """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
					]

					@slow
					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( UpperCAmelCase__ ):
					A     	=							"""fr"""
					A     	=							"""en"""
					A     	=							[
					    """Donnez moi le micro.""",
					    """Tom et Mary étaient assis à une table.""",  # Accents
					]
					A     	=							[
					    """Give me the microphone.""",
					    """Tom and Mary were sitting at a table.""",
					]

					@slow
					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( UpperCAmelCase__ ):
					A     	=							"""mt"""
					A     	=							"""en"""
					A     	=							["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
					A     	=							["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

					@slow
					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( UpperCAmelCase__ ):
					A     	=							"""en"""
					A     	=							"""de"""
					A     	=							[
					    """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     	=							[
					    """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     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( UpperCAmelCase__ ):
					A     	=							"""en"""
					A     	=							"""zh"""
					A     	=							["""My name is Wolfgang and I live in Berlin"""]
					A     	=							["""我叫沃尔夫冈 我住在柏林"""]

					@slow
					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( UpperCAmelCase__ ):
					A     	=							"""ru"""
					A     	=							"""fr"""
					A     	=							["""Он показал мне рукопись своей новой пьесы."""]
					A     	=							["""Il m'a montré le manuscrit de sa nouvelle pièce."""]

					@slow
					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						( UpperCAmelCase__ ):
					A     	=							"""en"""
					A     	=							"""ROMANCE"""
					A     	=							[
					    """>>fr<< Don't spend so much time watching TV.""",
					    """>>pt<< Your message has been sent.""",
					    """>>es<< He's two years older than me.""",
					]
					A     	=							[
					    """Ne passez pas autant de temps à regarder la télé.""",
					    """A sua mensagem foi enviada.""",
					    """Es dos años más viejo que yo.""",
					]

					@slow
					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							self._assert_generated_batch_equal_expected()

from __future__ import annotations

import sys




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







		if n < 0:
				raise ValueError("Negative arguments are not supported"     )
		return _fib(lowerCAmelCase     )[0]




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







		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Dict    				=   _fib(n // 2     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		__lowerCamelCase :     str          =  int(sys.argv[1])
		print(f'''fibonacci({n}) is {fibonacci(n)}''')
