


'''simple docstring'''



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
	__lowercase			:		Dict =			'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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     int=None		,   _SCREAMING_SNAKE_CASE   :     int=None		,   _SCREAMING_SNAKE_CASE   :     Dict=None		,   _SCREAMING_SNAKE_CASE   :     int=None		,   _SCREAMING_SNAKE_CASE   :     Tuple=None		,   _SCREAMING_SNAKE_CASE   :     Dict=None		,   ):
					if attention_mask is None:
										__a    : str      					=						np.where(input_ids != config.pad_token_id		,   1		,   0     )
					if decoder_attention_mask is None:
										__a    : str      					=						np.where(decoder_input_ids != config.pad_token_id		,   1		,   0     )
					if head_mask is None:
										__a    : Optional[Any]      					=						np.ones((config.encoder_layers, config.encoder_attention_heads)     )
					if decoder_head_mask is None:
										__a    : List[str]      					=						np.ones((config.decoder_layers, config.decoder_attention_heads)     )
					if cross_attn_head_mask is None:
										__a    : Any      					=						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       :




	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'''





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




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





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

						__a    : List[Any]      					=						shift_tokens_right(__a		,       1		,       2							)

						__a    : List[str]      					=						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=__a		,       )
						__a    : Union[str, Any]      					=						prepare_marian_inputs_dict(__a		,       __a		,       __a							)
						return config, inputs_dict




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a					,       __a    : List[Any]      					=						self.prepare_config_and_inputs()
						return config, inputs_dict




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):

						'''simple docstring'''





						__a    : List[str]      					=						20
						__a    : Any      					=						model_class_name(__a							)

						__a    : Any      					=						model.encode(inputs_dict['input_ids']							)

						__a					,       __a    : Dict      					=						(
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						__a    : List[str]      					=						model.init_cache(decoder_input_ids.shape[0]		,       __a		,       __a							)
						__a    : Tuple      					=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,       dtype='i4'							)

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

						__a    : Dict      					=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,       dtype='i4'							)
						__a    : 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		,       )

						__a    : Tuple      					=						model.decode(__a		,       __a							)

						__a    : List[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    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):

						'''simple docstring'''





						__a    : str      					=						20
						__a    : Dict      					=						model_class_name(__a							)

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

						__a					,       __a    : Dict      					=						(
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

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

						__a    : Tuple      					=						model.init_cache(decoder_input_ids.shape[0]		,       __a		,       __a							)
						__a    : Dict      					=						jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]		,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,       )

						__a    : List[Any]      					=						model.decode(
						    decoder_input_ids[:, :-1]		,       __a		,       decoder_attention_mask=__a		,       past_key_values=__a		,       decoder_position_ids=__a		,       )
						__a    : Optional[int]      					=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,       dtype='i4'							)
						__a    : Tuple      					=						model.decode(
						    decoder_input_ids[:, -1:]		,       __a		,       past_key_values=outputs_cache.past_key_values		,       decoder_attention_mask=__a		,       decoder_position_ids=__a		,       )

						__a    : Tuple      					=						model.decode(__a		,       __a		,       decoder_attention_mask=__a							)

						__a    : List[str]      					=						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       (	lowerCAmelCase_	,   unittest.TestCase	,   lowerCAmelCase_      ):
	A_			    =					True
	A_			    =					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	A_			    =					(FlaxMarianMTModel,) if is_flax_available() else ()




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a    : Any      					=						FlaxMarianModelTester(self							)




	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a					,       __a    : Any      					=						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    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a					,       __a    : 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    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a					,       __a    : str      					=						self.model_tester.prepare_config_and_inputs_for_common()

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

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

																with self.subTest('JIT Enabled'							):
																					__a    : List[Any]      					=						encode_jitted(**__a							).to_tuple()

																with self.subTest('JIT Disabled'							):
																					with jax.disable_jit():
																										__a    : Any      					=						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    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a					,       __a    : Any      					=						self.model_tester.prepare_config_and_inputs_for_common()

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

																__a    : 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		,       __a		,       __a							):
																					return model.decode(
																					    decoder_input_ids=__a		,       decoder_attention_mask=__a		,       encoder_outputs=__a		,       )

																with self.subTest('JIT Enabled'							):
																					__a    : Optional[Any]      					=						decode_jitted(**__a							).to_tuple()

																with self.subTest('JIT Disabled'							):
																					with jax.disable_jit():
																										__a    : Tuple      					=						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    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						for model_class_name in self.all_model_classes:
											__a    : Any      					=						model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'							)
											# FlaxMarianForSequenceClassification expects eos token in input_ids
											__a    : Optional[Any]      					=						np.ones((1, 1)							) * model.config.eos_token_id
											__a    : int      					=						model(__a							)
											self.assertIsNotNone(__a							)





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	unittest.TestCase      ):
	A_			    =					None
	A_			    =					None




	@classmethod
	def    __UpperCAmelCase							(     cls							):

						'''simple docstring'''





						__a    : List[Any]      					=						f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
						return cls




	@cached_property
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						return MarianTokenizer.from_pretrained(self.model_name							)




	@property
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						return self.tokenizer.eos_token_id




	@cached_property
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						__a    : 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		,       **__a							):

						'''simple docstring'''





						__a    : int      					=						self.translate_src_text(**__a							)
						self.assertListEqual(self.expected_text		,       __a							)





	def    __UpperCAmelCase							(     self		,       **__a							):

						'''simple docstring'''





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





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	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							):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	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							):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	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							):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	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							):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	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							):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	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							):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase       (	lowerCAmelCase_      ):
	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							):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()







'''simple docstring'''



def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
					__a    : Dict      					=						exponent + 2
					__a    : List[Any]      					=						(1 + 5**0.5) / 2
					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
	print(f'''{solution() = }''')



