




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
 SCREAMING_SNAKE_CASE_						:					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        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_=None   ,    UpperCAmelCase_=None   ,    UpperCAmelCase_=None   ,    UpperCAmelCase_=None   ,    UpperCAmelCase_=None   ,    UpperCAmelCase_=None   ,    )     ->   List[str]:



    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 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       Optional[Any]						,				A :       List[Any]						,				A :       Any=13						,				A :       Tuple=7						,				A :       Optional[int]=True						,				A :       Tuple=False						,				A :       Dict=99						,				A :       Union[str, Any]=16						,				A :       Dict=2						,				A :       Tuple=4						,				A :       Optional[int]=4						,				A :       Dict="gelu"						,				A :       Optional[Any]=0.1						,				A :       str=0.1						,				A :       Dict=32						,				A :       Optional[Any]=2						,				A :       Union[str, Any]=1						,				A :       str=0						,				A :       Tuple=0.0_2						,				)			->			int:






         """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 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			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(A						,				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=A						,				)
         a__      =       prepare_marian_inputs_dict(A						,				A						,				A    )
         return config, inputs_dict

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Union[str, Any]:






         """simple docstring"""
         a__ ,		a__      =       self.prepare_config_and_inputs()
         return config, inputs_dict

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       int						,				A :       Any						,				A :       int    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       20
         a__      =       model_class_name(A    )

         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]						,				A						,				A    )
         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]						,				A						,				decoder_attention_mask=A						,				past_key_values=A						,				decoder_position_ids=A						,				)

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

         a__      =       model.decode(A						,				A    )

         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 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       Any						,				A :       int						,				A :       Optional[Any]    )			->			Dict:






         """simple docstring"""
         a__      =       20
         a__      =       model_class_name(A    )

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

         a__      =       model.decode(A						,				A						,				decoder_attention_mask=A    )

         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 		__UpperCAmelCase(		UpperCAmelCase_       ,	unittest.TestCase       ,	UpperCAmelCase_ ):





     """simple docstring"""



     __UpperCAmelCase	: List[str]						=    True
     __UpperCAmelCase	: Dict						=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     __UpperCAmelCase	: int						=    (FlaxMarianMTModel,) if is_flax_available() else ()

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       FlaxMarianModelTester(self    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			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(A						,				A						,				A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			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_with_attn_mask(A						,				A						,				A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Optional[int]:






         """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(A						,				A    )
                 a__      =       model_class(A    )

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

                 with self.subTest("""JIT Enabled"""    ):
                     a__      =       encode_jitted(**A    ).to_tuple()

                 with self.subTest("""JIT Disabled"""    ):
                     with jax.disable_jit():
                         a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			str:






         """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(A    )
                 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(A :       Optional[int]						,				A :       Union[str, Any]						,				A :       Dict    ):
                     return model.decode(
                         decoder_input_ids=A						,				decoder_attention_mask=A						,				encoder_outputs=A						,				)

                 with self.subTest("""JIT Enabled"""    ):
                     a__      =       decode_jitted(**A    ).to_tuple()

                 with self.subTest("""JIT Disabled"""    ):
                     with jax.disable_jit():
                         a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Optional[Any]:






         """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(A    )
             self.assertIsNotNone(A    )







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





     """simple docstring"""



     __UpperCAmelCase	: Tuple						=    None
     __UpperCAmelCase	: str						=    None

     @classmethod
     def 				__SCREAMING_SNAKE_CASE		(      cls :       int    )			->			None:






         """simple docstring"""
         a__      =       f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
         return cls

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			List[Any]:






         """simple docstring"""
         return MarianTokenizer.from_pretrained(self.model_name    )

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			int:






         """simple docstring"""
         return self.tokenizer.eos_token_id

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Dict:






         """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 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				**A :       int    )			->			List[str]:






         """simple docstring"""
         a__      =       self.translate_src_text(**A    )
         self.assertListEqual(self.expected_text						,				A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				**A :       Union[str, Any]    )			->			Optional[int]:






         """simple docstring"""
         a__      =       self.tokenizer(self.src_text						,				padding=A						,				return_tensors="""np"""						,				**A    )
         a__      =       self.model.generate(
             model_inputs.input_ids						,				attention_mask=model_inputs.attention_mask						,				num_beams=2						,				max_length=1_28						,				).sequences
         a__      =       self.tokenizer.batch_decode(A						,				skip_special_tokens=A    )
         return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		UpperCAmelCase_ ):





     """simple docstring"""



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

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			List[str]:






         """simple docstring"""
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		UpperCAmelCase_ ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    """fr"""
     __UpperCAmelCase	: Any						=    """en"""
     __UpperCAmelCase	: Tuple						=    [
         """Donnez moi le micro.""",
         """Tom et Mary étaient assis à une table.""",  # Accents
     ]
     __UpperCAmelCase	: Dict						=    [
         """Give me the microphone.""",
         """Tom and Mary were sitting at a table.""",
     ]

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






         """simple docstring"""
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		UpperCAmelCase_ ):





     """simple docstring"""



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

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Optional[Any]:






         """simple docstring"""
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		UpperCAmelCase_ ):





     """simple docstring"""



     __UpperCAmelCase	: Optional[int]						=    """en"""
     __UpperCAmelCase	: Any						=    """de"""
     __UpperCAmelCase	: 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.""",
     ]
     __UpperCAmelCase	: str						=    [
         """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 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			Optional[Any]:






         """simple docstring"""
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		UpperCAmelCase_ ):





     """simple docstring"""



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

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			Any:






         """simple docstring"""
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		UpperCAmelCase_ ):





     """simple docstring"""



     __UpperCAmelCase	: str						=    """ru"""
     __UpperCAmelCase	: Union[str, Any]						=    """fr"""
     __UpperCAmelCase	: List[str]						=    ["""Он показал мне рукопись своей новой пьесы."""]
     __UpperCAmelCase	: List[Any]						=    ["""Il m\'a montré le manuscrit de sa nouvelle pièce."""]

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






         """simple docstring"""
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		UpperCAmelCase_ ):





     """simple docstring"""



     __UpperCAmelCase	: Any						=    """en"""
     __UpperCAmelCase	: Optional[Any]						=    """ROMANCE"""
     __UpperCAmelCase	: int						=    [
         """>>fr<< Don\'t spend so much time watching TV.""",
         """>>pt<< Your message has been sent.""",
         """>>es<< He\'s two years older than me.""",
     ]
     __UpperCAmelCase	: 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 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			str:






         """simple docstring"""
         self._assert_generated_batch_equal_expected()












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						:					str			   =       {
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       [
     '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Wav2Vec2ConformerForAudioFrameClassification''',
     '''Wav2Vec2ConformerForCTC''',
     '''Wav2Vec2ConformerForPreTraining''',
     '''Wav2Vec2ConformerForSequenceClassification''',
     '''Wav2Vec2ConformerForXVector''',
     '''Wav2Vec2ConformerModel''',
     '''Wav2Vec2ConformerPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavaveca_conformer import (
     WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     WavaVecaConformerConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavaveca_conformer import (
      WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavaVecaConformerForAudioFrameClassification,
      WavaVecaConformerForCTC,
      WavaVecaConformerForPreTraining,
      WavaVecaConformerForSequenceClassification,
      WavaVecaConformerForXVector,
      WavaVecaConformerModel,
      WavaVecaConformerPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






