





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
				__A       :					Optional[int]     					=     "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 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__=None    ,    UpperCamelCase__=None    ,    UpperCamelCase__=None    ,    UpperCamelCase__=None    ,    UpperCamelCase__=None    ,    UpperCamelCase__=None    ,    )						->       List[str]:







							'''simple docstring'''

							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 			A_ :
	def __init__(					self							,      _A							,      _A=1_3							,      _A=7							,      _A=True							,      _A=False							,      _A=9_9							,      _A=1_6							,      _A=2							,      _A=4							,      _A=4							,      _A="gelu"							,      _A=0.1							,      _A=0.1							,      _A=3_2							,      _A=2							,      _A=1							,      _A=0							,      _A=0.02							,      ):






								'''simple docstring'''







								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   _lowercase       (					self	):






								'''simple docstring'''







								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(_A							,      1							,      2	)

								UpperCAmelCase     	=       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							,      )
								UpperCAmelCase     	=       prepare_marian_inputs_dict(_A							,      _A							,      _A	)
								return config, inputs_dict
	def   _lowercase       (					self	):






								'''simple docstring'''







								UpperCAmelCase	,    UpperCAmelCase     	=       self.prepare_config_and_inputs()
								return config, inputs_dict
	def   _lowercase       (					self							,      _A							,      _A							,      _A	):






								'''simple docstring'''







								UpperCAmelCase     	=       2_0
								UpperCAmelCase     	=       model_class_name(_A	)

								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]							,      _A							,      _A	)
								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]							,      _A							,      decoder_attention_mask=_A							,      past_key_values=_A							,      decoder_position_ids=_A							,      )

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

								UpperCAmelCase     	=       model.decode(_A							,      _A	)

								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   _lowercase       (					self							,      _A							,      _A							,      _A	):






								'''simple docstring'''







								UpperCAmelCase     	=       2_0
								UpperCAmelCase     	=       model_class_name(_A	)

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

								UpperCAmelCase     	=       model.decode(_A							,      _A							,      decoder_attention_mask=_A	)

								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 			A_ (a_  ,							unittest.TestCase  ,							a_    ):
	UpperCAmelCase__      =    True
	UpperCAmelCase__      =    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	UpperCAmelCase__      =    (FlaxMarianMTModel,) if is_flax_available() else ()
	def   _lowercase       (					self	):






								'''simple docstring'''







								UpperCAmelCase     	=       FlaxMarianModelTester(self	)
	def   _lowercase       (					self	):






								'''simple docstring'''







								UpperCAmelCase	,    UpperCAmelCase     	=       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   _lowercase       (					self	):






								'''simple docstring'''







								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(_A							,      _A							,      _A	)
	def   _lowercase       (					self	):






								'''simple docstring'''







								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(_A							,      _A	)
																						UpperCAmelCase     	=       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'''	):
																													UpperCAmelCase     	=       encode_jitted(**_A	).to_tuple()

																						with self.subTest('''JIT Disabled'''	):
																													with jax.disable_jit():
																																				UpperCAmelCase     	=       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   _lowercase       (					self	):






								'''simple docstring'''







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

																						with self.subTest('''JIT Enabled'''	):
																													UpperCAmelCase     	=       decode_jitted(**_A	).to_tuple()

																						with self.subTest('''JIT Disabled'''	):
																													with jax.disable_jit():
																																				UpperCAmelCase     	=       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   _lowercase       (					self	):






								'''simple docstring'''







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

@require_flax
@require_sentencepiece
@require_tokenizers
class 			A_ (unittest.TestCase    ):
	UpperCAmelCase__      =    None
	UpperCAmelCase__      =    None
	@classmethod
	def   _lowercase       (					cls	):






								'''simple docstring'''







								UpperCAmelCase     	=       F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls
	@cached_property
	def   _lowercase       (					self	):






								'''simple docstring'''







								return MarianTokenizer.from_pretrained(self.model_name	)
	@property
	def   _lowercase       (					self	):






								'''simple docstring'''







								return self.tokenizer.eos_token_id
	@cached_property
	def   _lowercase       (					self	):






								'''simple docstring'''







								UpperCAmelCase     	=       FlaxMarianMTModel.from_pretrained(self.model_name	)
								self.assertEqual(model.config.decoder_start_token_id							,      model.config.pad_token_id	)
								return model
	def   _lowercase       (					self							,      **_A	):






								'''simple docstring'''







								UpperCAmelCase     	=       self.translate_src_text(**_A	)
								self.assertListEqual(self.expected_text							,      _A	)







	def   _lowercase       (					self							,      **_A	):






								'''simple docstring'''







								UpperCAmelCase     	=       self.tokenizer(self.src_text							,      padding=_A							,      return_tensors='''np'''							,      **_A	)
								UpperCAmelCase     	=       self.model.generate(
								    model_inputs.input_ids							,      attention_mask=model_inputs.attention_mask							,      num_beams=2							,      max_length=1_2_8							,      ).sequences
								UpperCAmelCase     	=       self.tokenizer.batch_decode(_A							,      skip_special_tokens=_A	)
								return generated_words

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






								'''simple docstring'''







								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			A_ (a_    ):
	UpperCAmelCase__      =    '''fr'''
	UpperCAmelCase__      =    '''en'''
	UpperCAmelCase__      =    [
	    '''Donnez moi le micro.''',
	    '''Tom et Mary étaient assis à une table.''',  # Accents
	]
	UpperCAmelCase__      =    [
	    '''Give me the microphone.''',
	    '''Tom and Mary were sitting at a table.''',
	]
	@slow
	def   _lowercase       (					self	):






								'''simple docstring'''







								self._assert_generated_batch_equal_expected()

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






								'''simple docstring'''







								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			A_ (a_    ):
	UpperCAmelCase__      =    '''en'''
	UpperCAmelCase__      =    '''de'''
	UpperCAmelCase__      =    [
	    '''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.''',
	]
	UpperCAmelCase__      =    [
	    '''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	):






								'''simple docstring'''







								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			A_ (a_    ):
	UpperCAmelCase__      =    '''en'''
	UpperCAmelCase__      =    '''zh'''
	UpperCAmelCase__      =    ['''My name is Wolfgang and I live in Berlin''']
	UpperCAmelCase__      =    ['''我叫沃尔夫冈 我住在柏林''']
	@slow
	def   _lowercase       (					self	):






								'''simple docstring'''







								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			A_ (a_    ):
	UpperCAmelCase__      =    '''ru'''
	UpperCAmelCase__      =    '''fr'''
	UpperCAmelCase__      =    ['''Он показал мне рукопись своей новой пьесы.''']
	UpperCAmelCase__      =    ['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']
	@slow
	def   _lowercase       (					self	):






								'''simple docstring'''







								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			A_ (a_    ):
	UpperCAmelCase__      =    '''en'''
	UpperCAmelCase__      =    '''ROMANCE'''
	UpperCAmelCase__      =    [
	    '''>>fr<< Don\'t spend so much time watching TV.''',
	    '''>>pt<< Your message has been sent.''',
	    '''>>es<< He\'s two years older than me.''',
	]
	UpperCAmelCase__      =    [
	    '''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	):






								'''simple docstring'''







								self._assert_generated_batch_equal_expected()










import numpy as np





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       int:







			'''simple docstring'''

			UpperCAmelCase     	=       n_draws * class_counts / class_counts.sum()
			# floored means we don't overshoot n_samples, but probably undershoot
			UpperCAmelCase     	=       np.floor(UpperCamelCase__				)
			# we add samples according to how much "left over" probability
			# they had, until we arrive at n_samples
			UpperCAmelCase     	=       int(n_draws - floored.sum()				)
			if need_to_add > 0:
						UpperCAmelCase     	=       continuous - floored
						UpperCAmelCase     	=       np.sort(np.unique(UpperCamelCase__				)				)[::-1]
						# add according to remainder, but break ties
						# randomly to avoid biases
						for value in values:
									((UpperCAmelCase)	,    )     	=       np.where(remainder == value				)
									# if we need_to_add less than what's in inds
									# we draw randomly from them.
									# if we need to add more, we add them all and
									# go to the next value
									UpperCAmelCase     	=       min(len(UpperCamelCase__				)    ,    UpperCamelCase__				)
									UpperCAmelCase     	=       rng.choice(UpperCamelCase__    ,    size=UpperCamelCase__    ,    replace=UpperCamelCase__				)
									floored[inds] += 1
									need_to_add -= add_now
									if need_to_add == 0:
												break
			return floored.astype(np.intaa				)






def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__=10				)						->       Any:







			'''simple docstring'''

			UpperCAmelCase	,    UpperCAmelCase     	=       np.unique(UpperCamelCase__    ,    return_inverse=UpperCamelCase__				)
			UpperCAmelCase     	=       classes.shape[0]
			UpperCAmelCase     	=       np.bincount(UpperCamelCase__				)
			if np.min(UpperCamelCase__				) < 2:
						raise ValueError('''Minimum class count error'''				)
			if n_train < n_classes:
						raise ValueError(
						    '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)				)
			if n_test < n_classes:
						raise ValueError(
						    '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)				)
			UpperCAmelCase     	=       np.split(np.argsort(UpperCamelCase__    ,    kind='''mergesort'''				)    ,    np.cumsum(UpperCamelCase__				)[:-1]				)
			for _ in range(UpperCamelCase__				):
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)
						UpperCAmelCase     	=       class_counts - n_i
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)

						UpperCAmelCase     	=       []
						UpperCAmelCase     	=       []

						for i in range(UpperCamelCase__				):
									UpperCAmelCase     	=       rng.permutation(class_counts[i]				)
									UpperCAmelCase     	=       class_indices[i].take(UpperCamelCase__    ,    mode='''clip'''				)
									train.extend(perm_indices_class_i[: n_i[i]]				)
									test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)

						yield train, test



