
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     :					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      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_=None	,				lowerCAmelCase_=None	,				lowerCAmelCase_=None	,				lowerCAmelCase_=None	,				lowerCAmelCase_=None	,				lowerCAmelCase_=None	,				)->	Tuple:
				if attention_mask is None:
								lowerCAmelCase_	: Union[str, Any]      			=			np.where(input_ids != config.pad_token_id	,				1	,				0	)
				if decoder_attention_mask is None:
								lowerCAmelCase_	: int      			=			np.where(decoder_input_ids != config.pad_token_id	,				1	,				0	)
				if head_mask is None:
								lowerCAmelCase_	: Dict      			=			np.ones((config.encoder_layers, config.encoder_attention_heads)	)
				if decoder_head_mask is None:
								lowerCAmelCase_	: Optional[int]      			=			np.ones((config.decoder_layers, config.decoder_attention_heads)	)
				if cross_attn_head_mask is None:
								lowerCAmelCase_	: Dict      			=			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 			snake_case__:







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase=1_3				,  __lowercase=7				,  __lowercase=True				,  __lowercase=False				,  __lowercase=9_9				,  __lowercase=1_6				,  __lowercase=2				,  __lowercase=4				,  __lowercase=4				,  __lowercase="gelu"				,  __lowercase=0.1				,  __lowercase=0.1				,  __lowercase=3_2				,  __lowercase=2				,  __lowercase=1				,  __lowercase=0				,  __lowercase=0.02				,  )   ->      Optional[int]:
										lowerCAmelCase_	: Any      			=			parent
										lowerCAmelCase_	: str      			=			batch_size
										lowerCAmelCase_	: int      			=			seq_length
										lowerCAmelCase_	: Tuple      			=			is_training
										lowerCAmelCase_	: Any      			=			use_labels
										lowerCAmelCase_	: Any      			=			vocab_size
										lowerCAmelCase_	: List[Any]      			=			hidden_size
										lowerCAmelCase_	: Union[str, Any]      			=			num_hidden_layers
										lowerCAmelCase_	: str      			=			num_attention_heads
										lowerCAmelCase_	: Union[str, Any]      			=			intermediate_size
										lowerCAmelCase_	: str      			=			hidden_act
										lowerCAmelCase_	: Tuple      			=			hidden_dropout_prob
										lowerCAmelCase_	: Tuple      			=			attention_probs_dropout_prob
										lowerCAmelCase_	: Optional[int]      			=			max_position_embeddings
										lowerCAmelCase_	: Union[str, Any]      			=			eos_token_id
										lowerCAmelCase_	: List[str]      			=			pad_token_id
										lowerCAmelCase_	: int      			=			bos_token_id
										lowerCAmelCase_	: Tuple      			=			initializer_range




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

										lowerCAmelCase_	: Dict      			=			shift_tokens_right(__lowercase				,  1				,  2							)

										lowerCAmelCase_	: 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=__lowercase				,  )
										lowerCAmelCase_	: int      			=			prepare_marian_inputs_dict(__lowercase				,  __lowercase				,  __lowercase							)
										return config, inputs_dict




						def   lowercase_	(			self							)   ->      Dict:
										lowerCAmelCase_	: List[str]      			=			self.prepare_config_and_inputs()
										return config, inputs_dict




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      List[str]:
										lowerCAmelCase_	: Optional[Any]      			=			2_0
										lowerCAmelCase_	: Union[str, Any]      			=			model_class_name(__lowercase							)

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

										lowerCAmelCase_	: Union[str, Any]      			=			(
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										lowerCAmelCase_	: Optional[Any]      			=			model.init_cache(decoder_input_ids.shape[0]				,  __lowercase				,  __lowercase							)
										lowerCAmelCase_	: Tuple      			=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,  dtype='''i4'''							)

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

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

										lowerCAmelCase_	: int      			=			model.decode(__lowercase				,  __lowercase							)

										lowerCAmelCase_	: 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}"""							)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      List[Any]:
										lowerCAmelCase_	: Optional[int]      			=			2_0
										lowerCAmelCase_	: Dict      			=			model_class_name(__lowercase							)

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

										lowerCAmelCase_	: List[str]      			=			(
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

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

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

										lowerCAmelCase_	: Union[str, Any]      			=			model.decode(
										    decoder_input_ids[:, :-1]				,  __lowercase				,  decoder_attention_mask=__lowercase				,  past_key_values=__lowercase				,  decoder_position_ids=__lowercase				,  )
										lowerCAmelCase_	: Union[str, Any]      			=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,  dtype='''i4'''							)
										lowerCAmelCase_	: Tuple      			=			model.decode(
										    decoder_input_ids[:, -1:]				,  __lowercase				,  past_key_values=outputs_cache.past_key_values				,  decoder_attention_mask=__lowercase				,  decoder_position_ids=__lowercase				,  )

										lowerCAmelCase_	: Dict      			=			model.decode(__lowercase				,  __lowercase				,  decoder_attention_mask=__lowercase							)

										lowerCAmelCase_	: Optional[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 			snake_case__(							UpperCAmelCase__,  unittest.TestCase,  UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					str   =							True
						SCREAMING_SNAKE_CASE__ :					str   =							(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						SCREAMING_SNAKE_CASE__ :					Any   =							(FlaxMarianMTModel,) if is_flax_available() else ()




						def   lowercase_	(			self							)   ->      Tuple:
										lowerCAmelCase_	: Union[str, Any]      			=			FlaxMarianModelTester(self							)




						def   lowercase_	(			self							)   ->      Tuple:
										lowerCAmelCase_	: Optional[int]      			=			self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
														self.model_tester.check_use_cache_forward(__lowercase				,  __lowercase				,  __lowercase							)




						def   lowercase_	(			self							)   ->      Optional[Any]:
										lowerCAmelCase_	: Optional[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(__lowercase				,  __lowercase				,  __lowercase							)




						def   lowercase_	(			self							)   ->      int:
										lowerCAmelCase_	: Union[str, Any]      			=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__							):
																		lowerCAmelCase_	: Dict      			=			self._prepare_for_class(__lowercase				,  __lowercase							)
																		lowerCAmelCase_	: Union[str, Any]      			=			model_class(__lowercase							)

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

																		with self.subTest('''JIT Enabled'''							):
																						lowerCAmelCase_	: Dict      			=			encode_jitted(**__lowercase							).to_tuple()

																		with self.subTest('''JIT Disabled'''							):
																						with jax.disable_jit():
																										lowerCAmelCase_	: Optional[int]      			=			encode_jitted(**__lowercase							).to_tuple()

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




						def   lowercase_	(			self							)   ->      Optional[int]:
										lowerCAmelCase_	: Tuple      			=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__							):
																		lowerCAmelCase_	: Any      			=			model_class(__lowercase							)
																		lowerCAmelCase_	: List[Any]      			=			model.encode(inputs_dict['''input_ids''']				,  inputs_dict['''attention_mask''']							)

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

																		with self.subTest('''JIT Enabled'''							):
																						lowerCAmelCase_	: List[Any]      			=			decode_jitted(**__lowercase							).to_tuple()

																		with self.subTest('''JIT Disabled'''							):
																						with jax.disable_jit():
																										lowerCAmelCase_	: List[str]      			=			decode_jitted(**__lowercase							).to_tuple()

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




						@slow
						def   lowercase_	(			self							)   ->      Optional[Any]:
										for model_class_name in self.all_model_classes:
														lowerCAmelCase_	: List[str]      			=			model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''							)
														# FlaxMarianForSequenceClassification expects eos token in input_ids
														lowerCAmelCase_	: str      			=			np.ones((1, 1)							) * model.config.eos_token_id
														lowerCAmelCase_	: Any      			=			model(__lowercase							)
														self.assertIsNotNone(__lowercase							)







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[Any]   =							None
						SCREAMING_SNAKE_CASE__ :					Any   =							None




						@classmethod
						def   lowercase_	(			cls							)   ->      None:
										lowerCAmelCase_	: Optional[Any]      			=			f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
										return cls




						@cached_property
						def   lowercase_	(			self							)   ->      Tuple:
										return MarianTokenizer.from_pretrained(self.model_name							)




						@property
						def   lowercase_	(			self							)   ->      int:
										return self.tokenizer.eos_token_id




						@cached_property
						def   lowercase_	(			self							)   ->      List[Any]:
										lowerCAmelCase_	: FlaxMarianMTModel      			=			FlaxMarianMTModel.from_pretrained(self.model_name							)
										self.assertEqual(model.config.decoder_start_token_id				,  model.config.pad_token_id							)
										return model




						def   lowercase_	(			self				,  **__lowercase							)   ->      int:
										lowerCAmelCase_	: Optional[Any]      			=			self.translate_src_text(**__lowercase							)
										self.assertListEqual(self.expected_text				,  __lowercase							)




						def   lowercase_	(			self				,  **__lowercase							)   ->      Optional[Any]:
										lowerCAmelCase_	: Dict      			=			self.tokenizer(self.src_text				,  padding=__lowercase				,  return_tensors='''np'''				,  **__lowercase							)
										lowerCAmelCase_	: Union[str, Any]      			=			self.model.generate(
										    model_inputs.input_ids				,  attention_mask=model_inputs.attention_mask				,  num_beams=2				,  max_length=1_2_8				,  ).sequences
										lowerCAmelCase_	: List[Any]      			=			self.tokenizer.batch_decode(__lowercase				,  skip_special_tokens=__lowercase							)
										return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Any   =							"""en"""
						SCREAMING_SNAKE_CASE__ :					Tuple   =							"""fr"""
						SCREAMING_SNAKE_CASE__ :					Dict   =							[
						    """I am a small frog.""",
						    """Now I can forget the 100 words of german that I know.""",
						]
						SCREAMING_SNAKE_CASE__ :					Optional[int]   =							[
						    """Je suis une petite grenouille.""",
						    """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
						]




						@slow
						def   lowercase_	(			self							)   ->      Any:
										self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					int   =							"""fr"""
						SCREAMING_SNAKE_CASE__ :					List[str]   =							"""en"""
						SCREAMING_SNAKE_CASE__ :					Union[str, Any]   =							[
						    """Donnez moi le micro.""",
						    """Tom et Mary étaient assis à une table.""",  # Accents
						]
						SCREAMING_SNAKE_CASE__ :					Tuple   =							[
						    """Give me the microphone.""",
						    """Tom and Mary were sitting at a table.""",
						]




						@slow
						def   lowercase_	(			self							)   ->      Optional[Any]:
										self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Union[str, Any]   =							"""mt"""
						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							"""en"""
						SCREAMING_SNAKE_CASE__ :					Union[str, Any]   =							["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
						SCREAMING_SNAKE_CASE__ :					Dict   =							["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]




						@slow
						def   lowercase_	(			self							)   ->      Optional[Any]:
										self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					str   =							"""en"""
						SCREAMING_SNAKE_CASE__ :					int   =							"""de"""
						SCREAMING_SNAKE_CASE__ :					Optional[Any]   =							[
						    """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.""",
						]
						SCREAMING_SNAKE_CASE__ :					Any   =							[
						    """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   lowercase_	(			self							)   ->      Dict:
										self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					List[str]   =							"""en"""
						SCREAMING_SNAKE_CASE__ :					List[Any]   =							"""zh"""
						SCREAMING_SNAKE_CASE__ :					List[str]   =							["""My name is Wolfgang and I live in Berlin"""]
						SCREAMING_SNAKE_CASE__ :					Dict   =							["""我叫沃尔夫冈 我住在柏林"""]




						@slow
						def   lowercase_	(			self							)   ->      Optional[Any]:
										self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					str   =							"""ru"""
						SCREAMING_SNAKE_CASE__ :					Any   =							"""fr"""
						SCREAMING_SNAKE_CASE__ :					Union[str, Any]   =							["""Он показал мне рукопись своей новой пьесы."""]
						SCREAMING_SNAKE_CASE__ :					List[str]   =							["""Il m'a montré le manuscrit de sa nouvelle pièce."""]




						@slow
						def   lowercase_	(			self							)   ->      int:
										self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




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




						@slow
						def   lowercase_	(			self							)   ->      Tuple:
										self._assert_generated_batch_equal_expected()



import math







class 			snake_case__:







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Tuple:
										lowerCAmelCase_	: List[str]      			=			x
										lowerCAmelCase_	: Dict      			=			y
										lowerCAmelCase_	: List[Any]      			=			z




						def __repr__(			self							)   ->      str:
										return f"""Point({self.x}, {self.y}, {self.z})"""






def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	float:
				return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2	)	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()