





'''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[Any]		=				"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  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=None      ,   lowerCAmelCase_=None      ,   ):





      """simple docstring"""



      if attention_mask is None:
            _snake_case		:						str         =  np.where(input_ids != config.pad_token_id      ,   1      ,   0						)
      if decoder_attention_mask is None:
            _snake_case		:						int         =  np.where(decoder_input_ids != config.pad_token_id      ,   1      ,   0						)
      if head_mask is None:
            _snake_case		:						Union[str, Any]         =  np.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            _snake_case		:						int         =  np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            _snake_case		:						Dict         =  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  ,  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  ,  )		->    Tuple:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  parent
        _snake_case		:						Optional[int]         =  batch_size
        _snake_case		:						Optional[int]         =  seq_length
        _snake_case		:						int         =  is_training
        _snake_case		:						Optional[Any]         =  use_labels
        _snake_case		:						List[str]         =  vocab_size
        _snake_case		:						int         =  hidden_size
        _snake_case		:						List[str]         =  num_hidden_layers
        _snake_case		:						Union[str, Any]         =  num_attention_heads
        _snake_case		:						Optional[Any]         =  intermediate_size
        _snake_case		:						Tuple         =  hidden_act
        _snake_case		:						str         =  hidden_dropout_prob
        _snake_case		:						Any         =  attention_probs_dropout_prob
        _snake_case		:						int         =  max_position_embeddings
        _snake_case		:						str         =  eos_token_id
        _snake_case		:						Union[str, Any]         =  pad_token_id
        _snake_case		:						List[Any]         =  bos_token_id
        _snake_case		:						Union[str, Any]         =  initializer_range







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





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

        _snake_case		:						Dict         =  shift_tokens_right(_A  ,  1  ,  2		)

        _snake_case		:						Dict         =  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  ,  )
        _snake_case		:						int         =  prepare_marian_inputs_dict(_A  ,  _A  ,  _A		)
        return config, inputs_dict







  def 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        _snake_case  ,				_snake_case		:						Any         =  self.prepare_config_and_inputs()
        return config, inputs_dict







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    List[Any]:






        """simple docstring"""





        _snake_case		:						List[str]         =  20
        _snake_case		:						List[str]         =  model_class_name(_A		)

        _snake_case		:						List[str]         =  model.encode(inputs_dict['''input_ids''']		)

        _snake_case  ,				_snake_case		:						Any         =  (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        _snake_case		:						Optional[int]         =  model.init_cache(decoder_input_ids.shape[0]  ,  _A  ,  _A		)
        _snake_case		:						Any         =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,  dtype='''i4'''		)

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

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

        _snake_case		:						Tuple         =  model.decode(_A  ,  _A		)

        _snake_case		:						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 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__		)		->    List[Any]:






        """simple docstring"""





        _snake_case		:						str         =  20
        _snake_case		:						Optional[Any]         =  model_class_name(_A		)

        _snake_case		:						Dict         =  model.encode(inputs_dict['''input_ids''']		)

        _snake_case  ,				_snake_case		:						Optional[int]         =  (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        _snake_case		:						List[str]         =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
            ]  ,  axis=-1  ,  )

        _snake_case		:						Any         =  model.init_cache(decoder_input_ids.shape[0]  ,  _A  ,  _A		)
        _snake_case		:						Any         =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]  ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,  )

        _snake_case		:						Optional[int]         =  model.decode(
            decoder_input_ids[:, :-1]  ,  _A  ,  decoder_attention_mask=_A  ,  past_key_values=_A  ,  decoder_position_ids=_A  ,  )
        _snake_case		:						Union[str, Any]         =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,  dtype='''i4'''		)
        _snake_case		:						Optional[Any]         =  model.decode(
            decoder_input_ids[:, -1:]  ,  _A  ,  past_key_values=outputs_cache.past_key_values  ,  decoder_attention_mask=_A  ,  decoder_position_ids=_A  ,  )

        _snake_case		:						Optional[Any]         =  model.decode(_A  ,  _A  ,  decoder_attention_mask=_A		)

        _snake_case		:						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 lowerCamelCase    (lowerCAmelCase__    ,   unittest.TestCase    ,   lowerCAmelCase__       ):
  _lowercase	:						Any							       =					True
  _lowercase	:						Dict							       =					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  _lowercase	:						Optional[int]							       =					(FlaxMarianMTModel,) if is_flax_available() else ()







  def 					UpperCAmelCase_					(		self		)		->    str:






        """simple docstring"""





        _snake_case		:						int         =  FlaxMarianModelTester(self		)







  def 					UpperCAmelCase_					(		self		)		->    Optional[int]:






        """simple docstring"""





        _snake_case  ,				_snake_case		:						Optional[int]         =  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 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        _snake_case  ,				_snake_case		:						List[Any]         =  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 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        _snake_case  ,				_snake_case		:						Union[str, Any]         =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__		):
                    _snake_case		:						Dict         =  self._prepare_for_class(_A  ,  _A		)
                    _snake_case		:						Optional[int]         =  model_class(_A		)

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

                    with self.subTest('''JIT Enabled'''		):
                          _snake_case		:						Dict         =  encode_jitted(**_A		).to_tuple()

                    with self.subTest('''JIT Disabled'''		):
                          with jax.disable_jit():
                                _snake_case		:						Tuple         =  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 					UpperCAmelCase_					(		self		)		->    str:






        """simple docstring"""





        _snake_case  ,				_snake_case		:						Dict         =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__		):
                    _snake_case		:						int         =  model_class(_A		)
                    _snake_case		:						Union[str, Any]         =  model.encode(inputs_dict['''input_ids''']  ,  inputs_dict['''attention_mask''']		)

                    _snake_case		:						Optional[int]         =  {
                        '''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=_A  ,  decoder_attention_mask=_A  ,  encoder_outputs=_A  ,  )

                    with self.subTest('''JIT Enabled'''		):
                          _snake_case		:						Union[str, Any]         =  decode_jitted(**_A		).to_tuple()

                    with self.subTest('''JIT Disabled'''		):
                          with jax.disable_jit():
                                _snake_case		:						int         =  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 					UpperCAmelCase_					(		self		)		->    int:






        """simple docstring"""





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





@require_flax
@require_sentencepiece
@require_tokenizers
class lowerCamelCase    (unittest.TestCase       ):
  _lowercase	:						Tuple							       =					None
  _lowercase	:						List[Any]							       =					None







  @classmethod
  def 					UpperCAmelCase_					(		cls		)		->    Dict:






        """simple docstring"""





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







  @cached_property
  def 					UpperCAmelCase_					(		self		)		->    List[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		)		->    Optional[int]:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  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"""





        _snake_case		:						Optional[int]         =  self.translate_src_text(**_A		)
        self.assertListEqual(self.expected_text  ,  _A		)







  def 					UpperCAmelCase_					(		self  ,  **lowercase__		)		->    Optional[Any]:






        """simple docstring"""





        _snake_case		:						int         =  self.tokenizer(self.src_text  ,  padding=_A  ,  return_tensors='''np'''  ,  **_A		)
        _snake_case		:						Union[str, Any]         =  self.model.generate(
            model_inputs.input_ids  ,  attention_mask=model_inputs.attention_mask  ,  num_beams=2  ,  max_length=128  ,  ).sequences
        _snake_case		:						Optional[Any]         =  self.tokenizer.batch_decode(_A  ,  skip_special_tokens=_A		)
        return generated_words





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







  @slow
  def 					UpperCAmelCase_					(		self		)		->    Union[str, Any]:






        """simple docstring"""





        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class lowerCamelCase    (lowerCAmelCase__       ):
  _lowercase	:						List[Any]							       =					'fr'
  _lowercase	:						List[Any]							       =					'en'
  _lowercase	:						str							       =					[
      'Donnez moi le micro.',
      'Tom et Mary étaient assis à une table.',  # Accents
  ]
  _lowercase	:						Dict							       =					[
      'Give me the microphone.',
      'Tom and Mary were sitting at a table.',
  ]







  @slow
  def 					UpperCAmelCase_					(		self		)		->    Union[str, Any]:






        """simple docstring"""





        self._assert_generated_batch_equal_expected()





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







  @slow
  def 					UpperCAmelCase_					(		self		)		->    str:






        """simple docstring"""





        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class lowerCamelCase    (lowerCAmelCase__       ):
  _lowercase	:						Any							       =					'en'
  _lowercase	:						List[Any]							       =					'de'
  _lowercase	:						Optional[int]							       =					[
      '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.',
  ]
  _lowercase	:						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 					UpperCAmelCase_					(		self		)		->    List[Any]:






        """simple docstring"""





        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class lowerCamelCase    (lowerCAmelCase__       ):
  _lowercase	:						Dict							       =					'en'
  _lowercase	:						Union[str, Any]							       =					'zh'
  _lowercase	:						str							       =					['My name is Wolfgang and I live in Berlin']
  _lowercase	:						Union[str, Any]							       =					['我叫沃尔夫冈 我住在柏林']







  @slow
  def 					UpperCAmelCase_					(		self		)		->    List[Any]:






        """simple docstring"""





        self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class lowerCamelCase    (lowerCAmelCase__       ):
  _lowercase	:						Optional[int]							       =					'ru'
  _lowercase	:						str							       =					'fr'
  _lowercase	:						Tuple							       =					['Он показал мне рукопись своей новой пьесы.']
  _lowercase	:						List[str]							       =					['Il m\'a montré le manuscrit de sa nouvelle pièce.']







  @slow
  def 					UpperCAmelCase_					(		self		)		->    Dict:






        """simple docstring"""





        self._assert_generated_batch_equal_expected()





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






        """simple docstring"""





        self._assert_generated_batch_equal_expected()





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__	:   int								=     [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

UpperCamelCase__	:   Any								=     get_logger(__name__)


UpperCamelCase__	:   Union[str, Any]								=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		dict							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    task_template_dict.get('task'							)
    if task_name is None:
        logger.warning(f"""Couldn't find template for task '{task_name}'. Available templates: {list(_SCREAMING_SNAKE_CASE							)}"""							)
        return None
    SCREAMING_SNAKE_CASE_						=    NAMEaTEMPLATE.get(_SCREAMING_SNAKE_CASE							)
    return template.from_dict(_SCREAMING_SNAKE_CASE							)


