




'''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
		_lowerCAmelCase				     = '''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 (   snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				snake_case__=None ,				):
			if attention_mask is None:
						__UpperCamelCase				:      Tuple									=       np.where(input_ids != config.pad_token_id ,				1 ,				0						)
			if decoder_attention_mask is None:
						__UpperCamelCase				:      Optional[Any]									=       np.where(decoder_input_ids != config.pad_token_id ,				1 ,				0						)
			if head_mask is None:
						__UpperCamelCase				:      Dict									=       np.ones((config.encoder_layers, config.encoder_attention_heads)						)
			if decoder_head_mask is None:
						__UpperCamelCase				:      Any									=       np.ones((config.decoder_layers, config.decoder_attention_heads)						)
			if cross_attn_head_mask is None:
						__UpperCamelCase				:      Optional[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  A		:




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase=1_3			,  _UpperCAmelCase=7			,  _UpperCAmelCase=True			,  _UpperCAmelCase=False			,  _UpperCAmelCase=9_9			,  _UpperCAmelCase=1_6			,  _UpperCAmelCase=2			,  _UpperCAmelCase=4			,  _UpperCAmelCase=4			,  _UpperCAmelCase="gelu"			,  _UpperCAmelCase=0.1			,  _UpperCAmelCase=0.1			,  _UpperCAmelCase=3_2			,  _UpperCAmelCase=2			,  _UpperCAmelCase=1			,  _UpperCAmelCase=0			,  _UpperCAmelCase=0.02			,  )     ->	Optional[int]:
							__UpperCamelCase				:      Dict									=       parent
							__UpperCamelCase				:      Optional[Any]									=       batch_size
							__UpperCamelCase				:      int									=       seq_length
							__UpperCamelCase				:      Optional[int]									=       is_training
							__UpperCamelCase				:      Optional[Any]									=       use_labels
							__UpperCamelCase				:      List[Any]									=       vocab_size
							__UpperCamelCase				:      List[str]									=       hidden_size
							__UpperCamelCase				:      Optional[Any]									=       num_hidden_layers
							__UpperCamelCase				:      Any									=       num_attention_heads
							__UpperCamelCase				:      List[str]									=       intermediate_size
							__UpperCamelCase				:      int									=       hidden_act
							__UpperCamelCase				:      List[Any]									=       hidden_dropout_prob
							__UpperCamelCase				:      Any									=       attention_probs_dropout_prob
							__UpperCamelCase				:      Optional[int]									=       max_position_embeddings
							__UpperCamelCase				:      Tuple									=       eos_token_id
							__UpperCamelCase				:      Union[str, Any]									=       pad_token_id
							__UpperCamelCase				:      List[Any]									=       bos_token_id
							__UpperCamelCase				:      Tuple									=       initializer_range
				def 	a_	(self      )     ->	int:
							__UpperCamelCase				:      Union[str, Any]									=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,  self.vocab_size      )			,  3			,  self.vocab_size      )
							__UpperCamelCase				:      str									=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,  dtype=np.intaa      ))			,  -1      )

							__UpperCamelCase				:      Tuple									=       shift_tokens_right(_UpperCAmelCase			,  1			,  2      )

							__UpperCamelCase				:      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=_UpperCAmelCase			,  )
							__UpperCamelCase				:      Optional[int]									=       prepare_marian_inputs_dict(_UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )
							return config, inputs_dict
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      List[Any]									=       self.prepare_config_and_inputs()
							return config, inputs_dict
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Any:
							__UpperCamelCase				:      int									=       2_0
							__UpperCamelCase				:      int									=       model_class_name(_UpperCAmelCase      )

							__UpperCamelCase				:      Tuple									=       model.encode(inputs_dict["input_ids"]      )

							__UpperCamelCase				:      Union[str, Any]									=       (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							__UpperCamelCase				:      Optional[Any]									=       model.init_cache(decoder_input_ids.shape[0]			,  _UpperCAmelCase			,  _UpperCAmelCase      )
							__UpperCamelCase				:      str									=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,  dtype="i4"      )

							__UpperCamelCase				:      int									=       jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]			,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,  )
							__UpperCamelCase				:      Optional[int]									=       model.decode(
							    decoder_input_ids[:, :-1]			,  _UpperCAmelCase			,  decoder_attention_mask=_UpperCAmelCase			,  past_key_values=_UpperCAmelCase			,  decoder_position_ids=_UpperCAmelCase			,  )

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

							__UpperCamelCase				:      List[str]									=       model.decode(_UpperCAmelCase			,  _UpperCAmelCase      )

							__UpperCamelCase				:      Union[str, 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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	List[str]:
							__UpperCamelCase				:      Dict									=       2_0
							__UpperCamelCase				:      List[str]									=       model_class_name(_UpperCAmelCase      )

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

							__UpperCamelCase				:      Optional[Any]									=       (
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							__UpperCamelCase				:      Optional[Any]									=       jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
							    ]			,  axis=-1			,  )

							__UpperCamelCase				:      List[str]									=       model.init_cache(decoder_input_ids.shape[0]			,  _UpperCAmelCase			,  _UpperCAmelCase      )
							__UpperCamelCase				:      List[str]									=       jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]			,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,  )

							__UpperCamelCase				:      Tuple									=       model.decode(
							    decoder_input_ids[:, :-1]			,  _UpperCAmelCase			,  decoder_attention_mask=_UpperCAmelCase			,  past_key_values=_UpperCAmelCase			,  decoder_position_ids=_UpperCAmelCase			,  )
							__UpperCamelCase				:      str									=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,  dtype="i4"      )
							__UpperCamelCase				:      Any									=       model.decode(
							    decoder_input_ids[:, -1:]			,  _UpperCAmelCase			,  past_key_values=outputs_cache.past_key_values			,  decoder_attention_mask=_UpperCAmelCase			,  decoder_position_ids=_UpperCAmelCase			,  )

							__UpperCamelCase				:      Tuple									=       model.decode(_UpperCAmelCase			,  _UpperCAmelCase			,  decoder_attention_mask=_UpperCAmelCase      )

							__UpperCamelCase				:      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  A		(			SCREAMING_SNAKE_CASE__      ,							unittest.TestCase      ,							SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    True
				A				     =    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
				A				     =    (FlaxMarianMTModel,) if is_flax_available() else ()
				def 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      int									=       FlaxMarianModelTester(self      )
				def 	a_	(self      )     ->	Any:
							__UpperCamelCase				:      Optional[Any]									=       self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(_UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )
				def 	a_	(self      )     ->	List[Any]:
							__UpperCamelCase				:      str									=       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(_UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )
				def 	a_	(self      )     ->	List[str]:
							__UpperCamelCase				:      List[Any]									=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__      ):
													__UpperCamelCase				:      int									=       self._prepare_for_class(_UpperCAmelCase			,  _UpperCAmelCase      )
													__UpperCamelCase				:      Optional[int]									=       model_class(_UpperCAmelCase      )

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

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

													with self.subTest("JIT Disabled"      ):
																with jax.disable_jit():
																			__UpperCamelCase				:      List[Any]									=       encode_jitted(**_UpperCAmelCase      ).to_tuple()

													self.assertEqual(len(_UpperCAmelCase      )			,  len(_UpperCAmelCase      )      )
													for jitted_output, output in zip(_UpperCAmelCase			,  _UpperCAmelCase      ):
																self.assertEqual(jitted_output.shape			,  output.shape      )
				def 	a_	(self      )     ->	int:
							__UpperCamelCase				:      Dict									=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__      ):
													__UpperCamelCase				:      str									=       model_class(_UpperCAmelCase      )
													__UpperCamelCase				:      str									=       model.encode(inputs_dict["input_ids"]			,  inputs_dict["attention_mask"]      )

													__UpperCamelCase				:      str									=       {
													    "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(_UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      ):
																return model.decode(
																    decoder_input_ids=_UpperCAmelCase			,  decoder_attention_mask=_UpperCAmelCase			,  encoder_outputs=_UpperCAmelCase			,  )

													with self.subTest("JIT Enabled"      ):
																__UpperCamelCase				:      Union[str, Any]									=       decode_jitted(**_UpperCAmelCase      ).to_tuple()

													with self.subTest("JIT Disabled"      ):
																with jax.disable_jit():
																			__UpperCamelCase				:      Dict									=       decode_jitted(**_UpperCAmelCase      ).to_tuple()

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





				@slow
				def 	a_	(self      )     ->	int:
							for model_class_name in self.all_model_classes:
										__UpperCamelCase				:      Union[str, Any]									=       model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"      )
										# FlaxMarianForSequenceClassification expects eos token in input_ids
										__UpperCamelCase				:      Dict									=       np.ones((1, 1)      ) * model.config.eos_token_id
										__UpperCamelCase				:      List[str]									=       model(_UpperCAmelCase      )
										self.assertIsNotNone(_UpperCAmelCase      )







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




				'''simple docstring'''





				A				     =    None
				A				     =    None
				@classmethod
				def 	a_	(cls      )     ->	None:
							__UpperCamelCase				:      Any									=       f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
							return cls
				@cached_property
				def 	a_	(self      )     ->	Optional[int]:
							return MarianTokenizer.from_pretrained(self.model_name      )
				@property
				def 	a_	(self      )     ->	int:
							return self.tokenizer.eos_token_id
				@cached_property
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      FlaxMarianMTModel									=       FlaxMarianMTModel.from_pretrained(self.model_name      )
							self.assertEqual(model.config.decoder_start_token_id			,  model.config.pad_token_id      )
							return model
				def 	a_	(self			,  **_UpperCAmelCase      )     ->	Optional[int]:
							__UpperCamelCase				:      int									=       self.translate_src_text(**_UpperCAmelCase      )
							self.assertListEqual(self.expected_text			,  _UpperCAmelCase      )





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







@require_flax
@require_sentencepiece
@require_tokenizers
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				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 	a_	(self      )     ->	Tuple:
							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				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 	a_	(self      )     ->	Optional[int]:
							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				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 	a_	(self      )     ->	Tuple:
							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				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 	a_	(self      )     ->	List[str]:
							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				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 	a_	(self      )     ->	int:
							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				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 	a_	(self      )     ->	List[Any]:
							self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				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 	a_	(self      )     ->	Optional[Any]:
							self._assert_generated_batch_equal_expected()









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase				     = {'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase				     = [
				    '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''VisualBertForMultipleChoice''',
				    '''VisualBertForPreTraining''',
				    '''VisualBertForQuestionAnswering''',
				    '''VisualBertForRegionToPhraseAlignment''',
				    '''VisualBertForVisualReasoning''',
				    '''VisualBertLayer''',
				    '''VisualBertModel''',
				    '''VisualBertPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_visual_bert import (
								    VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VisualBertForMultipleChoice,
								    VisualBertForPreTraining,
								    VisualBertForQuestionAnswering,
								    VisualBertForRegionToPhraseAlignment,
								    VisualBertForVisualReasoning,
								    VisualBertLayer,
								    VisualBertModel,
								    VisualBertPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase				     = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



