




'''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
      lowercase	:      Union[str, 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   ,				)				->List[str]:






       '''simple docstring'''




       if attention_mask is None:
              lowercase  :		Tuple						=      np.where(input_ids != config.pad_token_id   ,				1   ,				0      )
       if decoder_attention_mask is None:
              lowercase  :		Tuple						=      np.where(decoder_input_ids != config.pad_token_id   ,				1   ,				0      )
       if head_mask is None:
              lowercase  :		List[str]						=      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  :		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   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A   ,		A=13   ,		A=7   ,		A=True   ,		A=False   ,		A=99   ,		A=16   ,		A=2   ,		A=4   ,		A=4   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=32   ,		A=2   ,		A=1   ,		A=0   ,		A=0.0_2   ,		):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		str						=      shift_tokens_right(A   ,		1   ,		2 )

        lowercase  :		Optional[int]						=      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   ,		)
        lowercase  :		int						=      prepare_marian_inputs_dict(A   ,		A   ,		A )
        return config, inputs_dict



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		int						=      20
        lowercase  :		Optional[Any]						=      model_class_name(A )

        lowercase  :		Optional[int]						=      model.encode(inputs_dict['''input_ids'''] )

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

        lowercase  :		Any						=      model.init_cache(decoder_input_ids.shape[0]   ,		A   ,		A )
        lowercase  :		Dict						=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,		dtype='''i4''' )

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

        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:]   ,		A   ,		decoder_attention_mask=A   ,		past_key_values=outputs_cache.past_key_values   ,		decoder_position_ids=A   ,		)

        lowercase  :		str						=      model.decode(A   ,		A )

        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    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      20
        lowercase  :		Tuple						=      model_class_name(A )

        lowercase  :		Union[str, Any]						=      model.encode(inputs_dict['''input_ids'''] )

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

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

        lowercase  :		str						=      model.init_cache(decoder_input_ids.shape[0]   ,		A   ,		A )
        lowercase  :		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  :		Union[str, Any]						=      model.decode(
            decoder_input_ids[:, :-1]   ,		A   ,		decoder_attention_mask=A   ,		past_key_values=A   ,		decoder_position_ids=A   ,		)
        lowercase  :		str						=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,		dtype='''i4''' )
        lowercase  :		Dict						=      model.decode(
            decoder_input_ids[:, -1:]   ,		A   ,		past_key_values=outputs_cache.past_key_values   ,		decoder_attention_mask=A   ,		decoder_position_ids=A   ,		)

        lowercase  :		Union[str, Any]						=      model.decode(A   ,		A   ,		decoder_attention_mask=A )

        lowercase  :		Optional[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}""" )






@require_flax
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     unittest.TestCase		,     lowerCamelCase_				):
 __snake_case	:     List[Any]			   =      True
 __snake_case	:     Optional[Any]			   =      (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 __snake_case	:     int			   =      (FlaxMarianMTModel,) if is_flax_available() else ()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		int						=      FlaxMarianModelTester(self )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        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(A   ,		A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                      lowercase  :		Dict						=      self._prepare_for_class(A   ,		A )
                      lowercase  :		List[str]						=      model_class(A )

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

                      with self.subTest('''JIT Enabled''' ):
                             lowercase  :		List[str]						=      encode_jitted(**A ).to_tuple()

                      with self.subTest('''JIT Disabled''' ):
                             with jax.disable_jit():
                                    lowercase  :		Optional[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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Optional[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                      lowercase  :		List[str]						=      model_class(A )
                      lowercase  :		List[str]						=      model.encode(inputs_dict['''input_ids''']   ,		inputs_dict['''attention_mask'''] )

                      lowercase  :		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(A   ,		A   ,		A ):
                             return model.decode(
                                 decoder_input_ids=A   ,		decoder_attention_mask=A   ,		encoder_outputs=A   ,		)

                      with self.subTest('''JIT Enabled''' ):
                             lowercase  :		List[str]						=      decode_jitted(**A ).to_tuple()

                      with self.subTest('''JIT Disabled''' ):
                             with jax.disable_jit():
                                    lowercase  :		Dict						=      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 ):






        '''simple docstring'''




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






@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):
 __snake_case	:     List[str]			   =      None
 __snake_case	:     List[Any]			   =      None



 @classmethod
 def    SCREAMING_SNAKE_CASE(  cls ):






        '''simple docstring'''




        lowercase  :		int						=      f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
        return cls



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return MarianTokenizer.from_pretrained(self.model_name )



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return self.tokenizer.eos_token_id



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		FlaxMarianMTModel						=      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   ,		**A ):






        '''simple docstring'''




        lowercase  :		Any						=      self.translate_src_text(**A )
        self.assertListEqual(self.expected_text   ,		A )





 def    SCREAMING_SNAKE_CASE(  self   ,		**A ):






        '''simple docstring'''




        lowercase  :		str						=      self.tokenizer(self.src_text   ,		padding=A   ,		return_tensors='''np'''   ,		**A )
        lowercase  :		Tuple						=      self.model.generate(
            model_inputs.input_ids   ,		attention_mask=model_inputs.attention_mask   ,		num_beams=2   ,		max_length=128   ,		).sequences
        lowercase  :		Any						=      self.tokenizer.batch_decode(A   ,		skip_special_tokens=A )
        return generated_words






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



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     Union[str, Any]			   =      "fr"
 __snake_case	:     Tuple			   =      "en"
 __snake_case	:     Optional[Any]			   =      [
     "Donnez moi le micro.",
     "Tom et Mary étaient assis à une table.",  # Accents
 ]
 __snake_case	:     str			   =      [
     "Give me the microphone.",
     "Tom and Mary were sitting at a table.",
 ]



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self._assert_generated_batch_equal_expected()






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



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     Optional[int]			   =      "en"
 __snake_case	:     Tuple			   =      "de"
 __snake_case	:     str			   =      [
     "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.",
 ]
 __snake_case	:     Any			   =      [
     "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 ):






        '''simple docstring'''




        self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     int			   =      "en"
 __snake_case	:     Union[str, Any]			   =      "zh"
 __snake_case	:     Optional[int]			   =      ["My name is Wolfgang and I live in Berlin"]
 __snake_case	:     Dict			   =      ["我叫沃尔夫冈 我住在柏林"]



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self._assert_generated_batch_equal_expected()






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



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     Optional[int]			   =      "en"
 __snake_case	:     Optional[int]			   =      "ROMANCE"
 __snake_case	:     int			   =      [
     ">>fr<< Don't spend so much time watching TV.",
     ">>pt<< Your message has been sent.",
     ">>es<< He's two years older than me.",
 ]
 __snake_case	:     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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self._assert_generated_batch_equal_expected()










'''simple docstring'''






class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      [0, 1]





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if (difference := index - (len(self.sequence ) - 2)) >= 1:
               for _ in range(A ):
                      self.sequence.append(self.sequence[-1] + self.sequence[-2] )
        return self.sequence[:index]






def 		a_( )				->None:






       '''simple docstring'''




       print(
           '''Fibonacci Series Using Dynamic Programming\n'''   ,				'''Enter the index of the Fibonacci number you want to calculate '''   ,				'''in the prompt below. (To exit enter exit or Ctrl-C)\n'''   ,				sep=''''''   ,				)
       lowercase  :		Optional[int]						=      Fibonacci()

       while True:
              lowercase  :		str						=      input('''>> '''      )
              if prompt in {"exit", "quit"}:
                     break

              try:
                     lowercase  :		int						=      int(lowerCamelCase      )
              except ValueError:
                     print('''Enter a number or \'exit\''''      )
                     continue

              print(fibonacci.get(lowerCamelCase      )      )


if __name__ == "__main__":
      main()




