



"""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
		UpperCAmelCase:  List[str]			     =				"""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   ,				):
		if attention_mask is None:
				_lowercase				: Union[str, Any]     =   np.where(input_ids != config.pad_token_id   ,				1   ,				0      )
		if decoder_attention_mask is None:
				_lowercase				: Dict     =   np.where(decoder_input_ids != config.pad_token_id   ,				1   ,				0      )
		if head_mask is None:
				_lowercase				: Any     =   np.ones((config.encoder_layers, config.encoder_attention_heads)      )
		if decoder_head_mask is None:
				_lowercase				: Union[str, Any]     =   np.ones((config.decoder_layers, config.decoder_attention_heads)      )
		if cross_attn_head_mask is None:
				_lowercase				: Optional[int]     =   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   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_=13				,UpperCAmelCase_=7				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=99				,UpperCAmelCase_=16				,UpperCAmelCase_=2				,UpperCAmelCase_=4				,UpperCAmelCase_=4				,UpperCAmelCase_="gelu"				,UpperCAmelCase_=0.1				,UpperCAmelCase_=0.1				,UpperCAmelCase_=32				,UpperCAmelCase_=2				,UpperCAmelCase_=1				,UpperCAmelCase_=0				,UpperCAmelCase_=0.02				,):
			_lowercase				: List[Any]     =   parent
			_lowercase				: str     =   batch_size
			_lowercase				: Tuple     =   seq_length
			_lowercase				: Optional[Any]     =   is_training
			_lowercase				: Optional[Any]     =   use_labels
			_lowercase				: Optional[int]     =   vocab_size
			_lowercase				: List[Any]     =   hidden_size
			_lowercase				: Dict     =   num_hidden_layers
			_lowercase				: Optional[Any]     =   num_attention_heads
			_lowercase				: Optional[Any]     =   intermediate_size
			_lowercase				: Tuple     =   hidden_act
			_lowercase				: Tuple     =   hidden_dropout_prob
			_lowercase				: Tuple     =   attention_probs_dropout_prob
			_lowercase				: Union[str, Any]     =   max_position_embeddings
			_lowercase				: Any     =   eos_token_id
			_lowercase				: Optional[Any]     =   pad_token_id
			_lowercase				: Dict     =   bos_token_id
			_lowercase				: Any     =   initializer_range






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

			_lowercase				: List[str]     =   shift_tokens_right(UpperCAmelCase_				,1				,2   )

			_lowercase				: Union[str, 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_				,)
			_lowercase				: Union[str, Any]     =   prepare_marian_inputs_dict(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
			return config, inputs_dict






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: List[Any]     =   self.prepare_config_and_inputs()
			return config, inputs_dict






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: Dict     =   20
			_lowercase				: Dict     =   model_class_name(UpperCAmelCase_   )

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

			_lowercase     ,  _lowercase				: int     =   (
			    inputs_dict["""decoder_input_ids"""],
			    inputs_dict["""decoder_attention_mask"""],
			)

			_lowercase				: List[Any]     =   model.init_cache(decoder_input_ids.shape[0]				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Tuple     =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,dtype="""i4"""   )

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

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

			_lowercase				: Optional[int]     =   model.decode(UpperCAmelCase_				,UpperCAmelCase_   )

			_lowercase				: Tuple     =   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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
			_lowercase				: int     =   20
			_lowercase				: Tuple     =   model_class_name(UpperCAmelCase_   )

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

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

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

			_lowercase				: List[Any]     =   model.init_cache(decoder_input_ids.shape[0]				,UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Tuple     =   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]				,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,)

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

			_lowercase				: Tuple     =   model.decode(UpperCAmelCase_				,UpperCAmelCase_				,decoder_attention_mask=UpperCAmelCase_   )

			_lowercase				: Optional[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}"""   )







@require_flax
class   UpperCamelCase	(     snake_case					,   unittest.TestCase					,   snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  True
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	SCREAMING_SNAKE_CASE_							:       str						       =  (FlaxMarianMTModel,) if is_flax_available() else ()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   FlaxMarianModelTester(self   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: 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    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: 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    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: int     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							_lowercase				: Optional[int]     =   self._prepare_for_class(UpperCAmelCase_				,UpperCAmelCase_   )
							_lowercase				: 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"""   ):
									_lowercase				: List[Any]     =   encode_jitted(**UpperCAmelCase_   ).to_tuple()

							with self.subTest("""JIT Disabled"""   ):
									with jax.disable_jit():
											_lowercase				: Union[str, 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    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: int     =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__   ):
							_lowercase				: Any     =   model_class(UpperCAmelCase_   )
							_lowercase				: List[str]     =   model.encode(inputs_dict["""input_ids"""]				,inputs_dict["""attention_mask"""]   )

							_lowercase				: 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"""   ):
									_lowercase				: List[Any]     =   decode_jitted(**UpperCAmelCase_   ).to_tuple()

							with self.subTest("""JIT Disabled"""   ):
									with jax.disable_jit():
											_lowercase				: Optional[Any]     =   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    lowerCamelCase__					(     self   ):
			for model_class_name in self.all_model_classes:
					_lowercase				: List[Any]     =   model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""   )
					# FlaxMarianForSequenceClassification expects eos token in input_ids
					_lowercase				: Union[str, Any]     =   np.ones((1, 1)   ) * model.config.eos_token_id
					_lowercase				: Dict     =   model(UpperCAmelCase_   )
					self.assertIsNotNone(UpperCAmelCase_   )







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




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  None






	@classmethod
	def    lowerCamelCase__					(     cls   ):
			_lowercase				: str     =   f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
			return cls






	@cached_property
	def    lowerCamelCase__					(     self   ):
			return MarianTokenizer.from_pretrained(self.model_name   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.tokenizer.eos_token_id






	@cached_property
	def    lowerCamelCase__					(     self   ):
			_lowercase				: FlaxMarianMTModel     =   FlaxMarianMTModel.from_pretrained(self.model_name   )
			self.assertEqual(model.config.decoder_start_token_id				,model.config.pad_token_id   )
			return model






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			_lowercase				: int     =   self.translate_src_text(**UpperCAmelCase_   )
			self.assertListEqual(self.expected_text				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			_lowercase				: Dict     =   self.tokenizer(self.src_text				,padding=UpperCAmelCase_				,return_tensors="""np"""				,**UpperCAmelCase_   )
			_lowercase				: List[Any]     =   self.model.generate(
			    model_inputs.input_ids				,attention_mask=model_inputs.attention_mask				,num_beams=2				,max_length=1_28				,).sequences
			_lowercase				: int     =   self.tokenizer.batch_decode(UpperCAmelCase_				,skip_special_tokens=UpperCAmelCase_   )
			return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Any						       =  "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[Any]						       =  [
	    "Je suis une petite grenouille.",
	    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
	]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




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






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




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






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Any						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  "de"
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  [
	    "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_							:       Optional[int]						       =  [
	    "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    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  "en"
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "zh"
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["My name is Wolfgang and I live in Berlin"]
	SCREAMING_SNAKE_CASE_							:       Any						       =  ["我叫沃尔夫冈 我住在柏林"]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Dict						       =  "ru"
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  "fr"
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  ["Он показал мне рукопись своей новой пьесы."]
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  ["Il m'a montré le manuscrit de sa nouvelle pièce."]






	@slow
	def    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Dict						       =  "en"
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  "ROMANCE"
	SCREAMING_SNAKE_CASE_							:       List[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_							:       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    lowerCamelCase__					(     self   ):
			self._assert_generated_batch_equal_expected()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

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

