




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_     :    Tuple      =			'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  (      snake_case_     ,   snake_case_     ,   snake_case_=None     ,   snake_case_=None     ,   snake_case_=None     ,   snake_case_=None     ,   snake_case_=None     ,   snake_case_=None     ,   ):

						'''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    __lowerCAmelCase			:







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




												"""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(snake_case__   ,   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=snake_case__   ,   )
												_UpperCAmelCase													=					prepare_marian_inputs_dict(snake_case__   ,   snake_case__   ,   snake_case__				)
												return config, inputs_dict







						def UpperCamelCase      (	self   :      Optional[int]				):




												"""simple docstring"""


												_UpperCAmelCase													=					self.prepare_config_and_inputs()
												return config, inputs_dict







						def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      List[Any]   ,   snake_case__   :      str				):




												"""simple docstring"""


												_UpperCAmelCase													=					20
												_UpperCAmelCase													=					model_class_name(snake_case__				)

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

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

												_UpperCAmelCase													=					model.init_cache(decoder_input_ids.shape[0]   ,   snake_case__   ,   snake_case__				)
												_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]   ,   snake_case__   ,   decoder_attention_mask=snake_case__   ,   past_key_values=snake_case__   ,   decoder_position_ids=snake_case__   ,   )

												_UpperCAmelCase													=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,   dtype="i4"				)
												_UpperCAmelCase													=					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__   ,   )

												_UpperCAmelCase													=					model.decode(snake_case__   ,   snake_case__				)

												_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 UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      Any   ,   snake_case__   :      Optional[int]   ,   snake_case__   :      List[str]				):




												"""simple docstring"""


												_UpperCAmelCase													=					20
												_UpperCAmelCase													=					model_class_name(snake_case__				)

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

												_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]   ,   snake_case__   ,   snake_case__				)
												_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]   ,   snake_case__   ,   decoder_attention_mask=snake_case__   ,   past_key_values=snake_case__   ,   decoder_position_ids=snake_case__   ,   )
												_UpperCAmelCase													=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,   dtype="i4"				)
												_UpperCAmelCase													=					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__   ,   )

												_UpperCAmelCase													=					model.decode(snake_case__   ,   snake_case__   ,   decoder_attention_mask=snake_case__				)

												_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			(	UpperCAmelCase__ ,		unittest.TestCase ,		UpperCAmelCase__			):
						snake_case_				:			Optional[Any]								=					True
						snake_case_				:			int								=					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						snake_case_				:			str								=					(FlaxMarianMTModel,) if is_flax_available() else ()







						def UpperCamelCase      (	self   :      Optional[int]				):




												"""simple docstring"""


												_UpperCAmelCase													=					FlaxMarianModelTester(self				)







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




												"""simple docstring"""


												_UpperCAmelCase													=					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   :      Optional[Any]				):




												"""simple docstring"""


												_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(snake_case__   ,   snake_case__   ,   snake_case__				)







						def UpperCamelCase      (	self   :      Dict				):




												"""simple docstring"""


												_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(snake_case__   ,   snake_case__				)
																								_UpperCAmelCase													=					model_class(snake_case__				)

																								@jax.jit
																								def encode_jitted(snake_case__   :      Union[str, Any]   ,   snake_case__   :      int=None   ,   **snake_case__   :      Dict				):
																														return model.encode(input_ids=snake_case__   ,   attention_mask=snake_case__				)

																								with self.subTest("JIT Enabled"				):
																														_UpperCAmelCase													=					encode_jitted(**snake_case__				).to_tuple()

																								with self.subTest("JIT Disabled"				):
																														with jax.disable_jit():
																																				_UpperCAmelCase													=					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   :      str				):




												"""simple docstring"""


												_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(snake_case__				)
																								_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(snake_case__   :      List[str]   ,   snake_case__   :      Optional[int]   ,   snake_case__   :      List[Any]				):
																														return model.decode(
																														    decoder_input_ids=snake_case__   ,   decoder_attention_mask=snake_case__   ,   encoder_outputs=snake_case__   ,   )

																								with self.subTest("JIT Enabled"				):
																														_UpperCAmelCase													=					decode_jitted(**snake_case__				).to_tuple()

																								with self.subTest("JIT Disabled"				):
																														with jax.disable_jit():
																																				_UpperCAmelCase													=					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   :      Optional[int]				):




												"""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(snake_case__				)
																		self.assertIsNotNone(snake_case__				)






@require_flax
@require_sentencepiece
@require_tokenizers
class    __lowerCAmelCase			(	unittest.TestCase			):
						snake_case_				:			int								=					None
						snake_case_				:			Optional[int]								=					None







						@classmethod
						def UpperCamelCase      (	cls   :      Any				):




												"""simple docstring"""


												_UpperCAmelCase													=					F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
												return cls







						@cached_property
						def UpperCamelCase      (	self   :      str				):




												"""simple docstring"""


												return MarianTokenizer.from_pretrained(self.model_name				)







						@property
						def UpperCamelCase      (	self   :      Tuple				):




												"""simple docstring"""


												return self.tokenizer.eos_token_id







						@cached_property
						def UpperCamelCase      (	self   :      Dict				):




												"""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 UpperCamelCase      (	self   :      List[Any]   ,   **snake_case__   :      Optional[Any]				):




												"""simple docstring"""


												_UpperCAmelCase													=					self.translate_src_text(**snake_case__				)
												self.assertListEqual(self.expected_text   ,   snake_case__				)







						def UpperCamelCase      (	self   :      int   ,   **snake_case__   :      List[str]				):




												"""simple docstring"""


												_UpperCAmelCase													=					self.tokenizer(self.src_text   ,   padding=snake_case__   ,   return_tensors="np"   ,   **snake_case__				)
												_UpperCAmelCase													=					self.model.generate(
												    model_inputs.input_ids   ,   attention_mask=model_inputs.attention_mask   ,   num_beams=2   ,   max_length=128   ,   ).sequences
												_UpperCAmelCase													=					self.tokenizer.batch_decode(snake_case__   ,   skip_special_tokens=snake_case__				)
												return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class    __lowerCAmelCase			(	UpperCAmelCase__			):
						snake_case_				:			Tuple								=					'en'
						snake_case_				:			Optional[int]								=					'fr'
						snake_case_				:			List[str]								=					[
						    'I am a small frog.',
						    'Now I can forget the 100 words of german that I know.',
						]
						snake_case_				:			Dict								=					[
						    '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    __lowerCAmelCase			(	UpperCAmelCase__			):
						snake_case_				:			Dict								=					'fr'
						snake_case_				:			Optional[int]								=					'en'
						snake_case_				:			List[Any]								=					[
						    'Donnez moi le micro.',
						    'Tom et Mary étaient assis à une table.',  # Accents
						]
						snake_case_				:			Optional[int]								=					[
						    'Give me the microphone.',
						    'Tom and Mary were sitting at a table.',
						]







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




												"""simple docstring"""


												self._assert_generated_batch_equal_expected()






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







						@slow
						def UpperCamelCase      (	self   :      int				):




												"""simple docstring"""


												self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class    __lowerCAmelCase			(	UpperCAmelCase__			):
						snake_case_				:			Optional[Any]								=					'en'
						snake_case_				:			Any								=					'de'
						snake_case_				:			Dict								=					[
						    '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.',
						]
						snake_case_				:			Tuple								=					[
						    '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   :      str				):




												"""simple docstring"""


												self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class    __lowerCAmelCase			(	UpperCAmelCase__			):
						snake_case_				:			List[Any]								=					'en'
						snake_case_				:			List[str]								=					'zh'
						snake_case_				:			str								=					['My name is Wolfgang and I live in Berlin']
						snake_case_				:			int								=					['我叫沃尔夫冈 我住在柏林']







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




												"""simple docstring"""


												self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class    __lowerCAmelCase			(	UpperCAmelCase__			):
						snake_case_				:			str								=					'ru'
						snake_case_				:			Union[str, Any]								=					'fr'
						snake_case_				:			Dict								=					['Он показал мне рукопись своей новой пьесы.']
						snake_case_				:			Union[str, Any]								=					['Il m\'a montré le manuscrit de sa nouvelle pièce.']







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




												"""simple docstring"""


												self._assert_generated_batch_equal_expected()






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




												"""simple docstring"""


												self._assert_generated_batch_equal_expected()










from __future__ import annotations


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					{}
						for index, val in enumerate(snake_case_						):
												_UpperCAmelCase													=					target - val
												if compl in chk_map:
																		return [chk_map[compl], index]
												_UpperCAmelCase													=					index
						return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_sum([2, 7, 11, 15], 9) = }""")




