

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__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							Dict		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]=None		,    SCREAMING_SNAKE_CASE_:							Optional[Any]=None		,    SCREAMING_SNAKE_CASE_:							Tuple=None		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]=None		,    SCREAMING_SNAKE_CASE_:							Any=None		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]=None		,    )     -> Any:




				'''simple docstring'''





				if attention_mask is None:
								A__            =     np.where(input_ids != config.pad_token_id		,    1		,    0 )
				if decoder_attention_mask is None:
								A__            =     np.where(decoder_input_ids != config.pad_token_id		,    1		,    0 )
				if head_mask is None:
								A__            =     np.ones((config.encoder_layers, config.encoder_attention_heads) )
				if decoder_head_mask is None:
								A__            =     np.ones((config.decoder_layers, config.decoder_attention_heads) )
				if cross_attn_head_mask is None:
								A__            =     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 , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=16 , lowercase=2 , lowercase=4 , lowercase=4 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=32 , lowercase=2 , lowercase=1 , lowercase=0 , lowercase=0.02 , )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     parent
							A__            =     batch_size
							A__            =     seq_length
							A__            =     is_training
							A__            =     use_labels
							A__            =     vocab_size
							A__            =     hidden_size
							A__            =     num_hidden_layers
							A__            =     num_attention_heads
							A__            =     intermediate_size
							A__            =     hidden_act
							A__            =     hidden_dropout_prob
							A__            =     attention_probs_dropout_prob
							A__            =     max_position_embeddings
							A__            =     eos_token_id
							A__            =     pad_token_id
							A__            =     bos_token_id
							A__            =     initializer_range


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






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

							A__            =     shift_tokens_right(lowercase , 1 , 2    )

							A__            =     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 , )
							A__            =     prepare_marian_inputs_dict(lowercase , lowercase , lowercase    )
							return config, inputs_dict


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							A__      ,			A__            =     self.prepare_config_and_inputs()
							return config, inputs_dict


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  Optional[int]:
							'''simple docstring'''






							A__            =     20
							A__            =     model_class_name(lowercase    )

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

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

							A__            =     model.init_cache(decoder_input_ids.shape[0] , lowercase , lowercase    )
							A__            =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4"    )

							A__            =     jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
							A__            =     model.decode(
							    decoder_input_ids[:, :-1] , lowercase , decoder_attention_mask=lowercase , past_key_values=lowercase , decoder_position_ids=lowercase , )

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

							A__            =     model.decode(lowercase , lowercase    )

							A__            =     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 , lowercase , lowercase , lowercase    )					->  str:
							'''simple docstring'''






							A__            =     20
							A__            =     model_class_name(lowercase    )

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

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

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

							A__            =     model.init_cache(decoder_input_ids.shape[0] , lowercase , lowercase    )
							A__            =     jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )

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

							A__            =     model.decode(lowercase , lowercase , decoder_attention_mask=lowercase    )

							A__            =     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__  (   snake_case							,		unittest.TestCase							,		snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    True
			__lowerCamelCase	    =    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
			__lowerCamelCase	    =    (FlaxMarianMTModel,) if is_flax_available() else ()


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     FlaxMarianModelTester(self    )


			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							A__      ,			A__            =     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        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							A__      ,			A__            =     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        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							A__      ,			A__            =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__    ):
															A__            =     self._prepare_for_class(lowercase , lowercase    )
															A__            =     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"    ):
																			A__            =     encode_jitted(**lowercase    ).to_tuple()

															with self.subTest("JIT Disabled"    ):
																			with jax.disable_jit():
																							A__            =     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        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__      ,			A__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

															A__            =     {
															    "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"    ):
																			A__            =     decode_jitted(**lowercase    ).to_tuple()

															with self.subTest("JIT Disabled"    ):
																			with jax.disable_jit():
																							A__            =     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        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






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

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   unittest.TestCase     ):
			"""simple docstring"""







			__lowerCamelCase	    =    None
			__lowerCamelCase	    =    None


			@classmethod
			def        UpperCamelCase						(      cls    )					->  None:
							'''simple docstring'''






							A__            =     F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
							return cls


			@cached_property
			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							return MarianTokenizer.from_pretrained(self.model_name    )


			@property
			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							return self.tokenizer.eos_token_id


			@cached_property
			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     FlaxMarianMTModel.from_pretrained(self.model_name    )
							self.assertEqual(model.config.decoder_start_token_id , model.config.pad_token_id    )
							return model


			def        UpperCamelCase						(      self , **lowercase    )					->  Optional[int]:
							'''simple docstring'''






							A__            =     self.translate_src_text(**lowercase    )
							self.assertListEqual(self.expected_text , lowercase    )


			def        UpperCamelCase						(      self , **lowercase    )					->  Any:
							'''simple docstring'''






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

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'en'
			__lowerCamelCase	    =    'fr'
			__lowerCamelCase	    =    [
			    'I am a small frog.',
			    'Now I can forget the 100 words of german that I know.',
			]
			__lowerCamelCase	    =    [
			    'Je suis une petite grenouille.',
			    'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
			]


			@slow
			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'fr'
			__lowerCamelCase	    =    'en'
			__lowerCamelCase	    =    [
			    'Donnez moi le micro.',
			    'Tom et Mary étaient assis à une table.',  # Accents
			]
			__lowerCamelCase	    =    [
			    'Give me the microphone.',
			    'Tom and Mary were sitting at a table.',
			]


			@slow
			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   snake_case     ):
			"""simple docstring"""







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


			@slow
			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'en'
			__lowerCamelCase	    =    'de'
			__lowerCamelCase	    =    [
			    '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.',
			]
			__lowerCamelCase	    =    [
			    '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    )					->  List[str]:
							'''simple docstring'''






							self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'en'
			__lowerCamelCase	    =    'zh'
			__lowerCamelCase	    =    ['My name is Wolfgang and I live in Berlin']
			__lowerCamelCase	    =    ['我叫沃尔夫冈 我住在柏林']


			@slow
			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'ru'
			__lowerCamelCase	    =    'fr'
			__lowerCamelCase	    =    ['Он показал мне рукопись своей новой пьесы.']
			__lowerCamelCase	    =    ['Il m\'a montré le manuscrit de sa nouvelle pièce.']


			@slow
			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






							self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'en'
			__lowerCamelCase	    =    'ROMANCE'
			__lowerCamelCase	    =    [
			    '>>fr<< Don\'t spend so much time watching TV.',
			    '>>pt<< Your message has been sent.',
			    '>>es<< He\'s two years older than me.',
			]
			__lowerCamelCase	    =    [
			    '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    )					->  Union[str, Any]:
							'''simple docstring'''






							self._assert_generated_batch_equal_expected()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__						=	{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	[
	    """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """NatForImageClassification""",
	    """NatModel""",
	    """NatPreTrainedModel""",
	    """NatBackbone""",
	]

if TYPE_CHECKING:
	from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nat import (
		    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NatBackbone,
		    NatForImageClassification,
		    NatModel,
		    NatPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase__						=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

