'''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
  __SCREAMING_SNAKE_CASE :  int  =						"""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 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   List[str]    ,			_UpperCAmelCase						:   List[str]=None    ,			_UpperCAmelCase						:   List[str]=None    ,			_UpperCAmelCase						:   int=None    ,			_UpperCAmelCase						:   Dict=None    ,			_UpperCAmelCase						:   Optional[int]=None    ,			_UpperCAmelCase						:   Dict=None    ,			)					->					List[str]:


       """simple docstring"""







       if attention_mask is None:
              _UpperCAmelCase  :						Dict							   =					np.where(input_ids != config.pad_token_id    ,			1    ,			0					)
       if decoder_attention_mask is None:
              _UpperCAmelCase  :						Tuple							   =					np.where(decoder_input_ids != config.pad_token_id    ,			1    ,			0					)
       if head_mask is None:
              _UpperCAmelCase  :						Any							   =					np.ones((config.encoder_layers, config.encoder_attention_heads)					)
       if decoder_head_mask is None:
              _UpperCAmelCase  :						int							   =					np.ones((config.decoder_layers, config.decoder_attention_heads)					)
       if cross_attn_head_mask is None:
              _UpperCAmelCase  :						List[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 			lowerCamelCase_			:
      '''simple docstring'''

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




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

             _UpperCAmelCase  :						Optional[Any]							   =					shift_tokens_right(A		,      1		,      2 )

             _UpperCAmelCase  :						Tuple							   =					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		,      )
             _UpperCAmelCase  :						Union[str, Any]							   =					prepare_marian_inputs_dict(A		,      A		,      A )
             return config, inputs_dict




      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					self.prepare_config_and_inputs()
             return config, inputs_dict




      def   _A			( self		:       int		,      A		:       List[str]		,      A		:       Optional[int]		,      A		:       str ):
             _UpperCAmelCase  :						Union[str, Any]							   =					20
             _UpperCAmelCase  :						Dict							   =					model_class_name(A )

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

             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					(
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

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

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

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

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

             _UpperCAmelCase  :						int							   =					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		:       Optional[Any]		,      A		:       int		,      A		:       Optional[int]		,      A		:       Union[str, Any] ):
             _UpperCAmelCase  :						Union[str, Any]							   =					20
             _UpperCAmelCase  :						int							   =					model_class_name(A )

             _UpperCAmelCase  :						int							   =					model.encode(inputs_dict["input_ids"] )

             _UpperCAmelCase   ,						_UpperCAmelCase  :						Tuple							   =					(
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

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

             _UpperCAmelCase  :						str							   =					model.init_cache(decoder_input_ids.shape[0]		,      A		,      A )
             _UpperCAmelCase  :						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)		,      )

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

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

             _UpperCAmelCase  :						Dict							   =					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_			(snake_case__							,				unittest.TestCase							,				snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		True
      __UpperCamelCase:						List[Any]      			=		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      __UpperCamelCase:						Optional[Any]      			=		(FlaxMarianMTModel,) if is_flax_available() else ()




      def   _A			( self		:       int ):
             _UpperCAmelCase  :						int							   =					FlaxMarianModelTester(self )




      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						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   _A			( self		:       int ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						int							   =					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   _A			( self		:       Any ):
             _UpperCAmelCase   ,						_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  :						List[Any]							   =					self._prepare_for_class(A		,      A )
                           _UpperCAmelCase  :						List[Any]							   =					model_class(A )

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

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

                           with self.subTest("JIT Disabled" ):
                                  with jax.disable_jit():
                                         _UpperCAmelCase  :						int							   =					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   _A			( self		:       Any ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						Tuple							   =					self.model_tester.prepare_config_and_inputs_for_common()

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

                           _UpperCAmelCase  :						List[Any]							   =					{
                               "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		:       str		,      A		:       Any		,      A		:       Optional[Any] ):
                                  return model.decode(
                                      decoder_input_ids=A		,      decoder_attention_mask=A		,      encoder_outputs=A		,      )

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

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

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Optional[int]      			=		None
      __UpperCamelCase:						Optional[int]      			=		None




      @classmethod
      def   _A			( cls		:       Tuple ):
             _UpperCAmelCase  :						Tuple							   =					F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
             return cls




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




      @property
      def   _A			( self		:       Dict ):
             return self.tokenizer.eos_token_id




      @cached_property
      def   _A			( self		:       Dict ):
             _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		:       List[str]		,      **A		:       Any ):
             _UpperCAmelCase  :						str							   =					self.translate_src_text(**A )
             self.assertListEqual(self.expected_text		,      A )





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

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

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




      @slow
      def   _A			( self		:       Any ):
             self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

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




      @slow
      def   _A			( self		:       Union[str, Any] ):
             self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		"mt"
      __UpperCamelCase:						Dict      			=		"en"
      __UpperCamelCase:						str      			=		["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   _A			( self		:       List[str] ):
             self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						List[Any]      			=		"en"
      __UpperCamelCase:						Dict      			=		"de"
      __UpperCamelCase:						Optional[Any]      			=		[
          "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   _A			( self		:       Union[str, Any] ):
             self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

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




      @slow
      def   _A			( self		:       List[str] ):
             self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						Optional[Any]      			=		"ru"
      __UpperCamelCase:						str      			=		"fr"
      __UpperCamelCase:						int      			=		["Он показал мне рукопись своей новой пьесы."]
      __UpperCamelCase:						Tuple      			=		["Il m'a montré le manuscrit de sa nouvelle pièce."]




      @slow
      def   _A			( self		:       Optional[int] ):
             self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						str      			=		"en"
      __UpperCamelCase:						List[str]      			=		"ROMANCE"
      __UpperCamelCase:						Dict      			=		[
          ">>fr<< Don't spend so much time watching TV.",
          ">>pt<< Your message has been sent.",
          ">>es<< He's two years older than me.",
      ]
      __UpperCamelCase:						str      			=		[
          "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		:       Tuple ):
             self._assert_generated_batch_equal_expected()


'''simple docstring'''




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__SCREAMING_SNAKE_CASE :  Any  =						TypeVar("""T""", bound="""TaskTemplate""")

@dataclass(frozen=snake_case__					)
class 			lowerCamelCase_			(abc.ABC					):
      '''simple docstring'''

      __UpperCamelCase:						str
      __UpperCamelCase:						ClassVar[Features]
      __UpperCamelCase:						ClassVar[Features]




      def   _A			( self		:       T		,      A		:       Features ):
             return copy.deepcopy(self )




      @property
      def   _A			( self		:       List[str] ):
             return Features(**self.input_schema		,      **self.label_schema )




      @property
      @abc.abstractmethod
      def   _A			( self		:       Optional[int] ):
             raise NotImplementedError





      @classmethod
      def   _A			( cls		:       Type[T]		,      A		:       dict ):
             _UpperCAmelCase  :						Optional[Any]							   =					{f.name for f in dataclasses.fields(cls )}
             return cls(**{k: v for k, v in template_dict.items() if k in field_names} )

