
"""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
   _A      :	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   __magic_name__     (				__snake_case    :	Optional[int]					,			__snake_case    :	str					,			__snake_case    :	Tuple=None					,			__snake_case    :	int=None					,			__snake_case    :	Any=None					,			__snake_case    :	int=None					,			__snake_case    :	Union[str, Any]=None					,			__snake_case    :	Optional[int]=None					,			)			->    List[str]:
       if attention_mask is None:
              lowercase   :    Dict			=							np.where(input_ids != config.pad_token_id					,			1					,			0  )
       if decoder_attention_mask is None:
              lowercase   :    Optional[Any]			=							np.where(decoder_input_ids != config.pad_token_id					,			1					,			0  )
       if head_mask is None:
              lowercase   :    Union[str, 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   :    List[str]			=							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 		a__						:
      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     ,   ):
             lowercase   :    Union[str, Any]			=							parent
             lowercase   :    Dict			=							batch_size
             lowercase   :    Any			=							seq_length
             lowercase   :    Optional[Any]			=							is_training
             lowercase   :    Any			=							use_labels
             lowercase   :    Dict			=							vocab_size
             lowercase   :    List[str]			=							hidden_size
             lowercase   :    Tuple			=							num_hidden_layers
             lowercase   :    Optional[Any]			=							num_attention_heads
             lowercase   :    Union[str, Any]			=							intermediate_size
             lowercase   :    int			=							hidden_act
             lowercase   :    Dict			=							hidden_dropout_prob
             lowercase   :    List[str]			=							attention_probs_dropout_prob
             lowercase   :    List[Any]			=							max_position_embeddings
             lowercase   :    Optional[int]			=							eos_token_id
             lowercase   :    Union[str, Any]			=							pad_token_id
             lowercase   :    Optional[Any]			=							bos_token_id
             lowercase   :    Optional[Any]			=							initializer_range
      def       __magic_name__      (      self    ):
             lowercase   :    str			=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,   self.vocab_size    )     ,   3     ,   self.vocab_size    )
             lowercase   :    str			=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,   dtype=np.intaa    ))     ,   -1    )

             lowercase   :    Dict			=							shift_tokens_right(_a     ,   1     ,   2    )

             lowercase   :    str			=							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   :    Any			=							prepare_marian_inputs_dict(_a     ,   _a     ,   _a    )
             return config, inputs_dict
      def       __magic_name__      (      self    ):
             lowercase   :    Optional[Any]			=							self.prepare_config_and_inputs()
             return config, inputs_dict
      def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
             lowercase   :    Any			=							20
             lowercase   :    Dict			=							model_class_name(_a    )

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

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

             lowercase   :    Any			=							model.init_cache(decoder_input_ids.shape[0]     ,   _a     ,   _a    )
             lowercase   :    int			=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,   dtype="i4"    )

             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   :    Optional[int]			=							model.decode(
                 decoder_input_ids[:, :-1]     ,   _a     ,   decoder_attention_mask=_a     ,   past_key_values=_a     ,   decoder_position_ids=_a     ,   )

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

             lowercase   :    int			=							model.decode(_a     ,   _a    )

             lowercase   :    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}"""    )

      def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
             lowercase   :    Any			=							20
             lowercase   :    int			=							model_class_name(_a    )

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

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

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

             lowercase   :    Tuple			=							model.init_cache(decoder_input_ids.shape[0]     ,   _a     ,   _a    )
             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   :    Any			=							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   :    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   :    int			=							model.decode(_a     ,   _a     ,   decoder_attention_mask=_a    )

             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 		a__						(				a_,      unittest.TestCase,      a_						):
      __lowerCAmelCase						=    True
      __lowerCAmelCase						=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      __lowerCAmelCase						=    (FlaxMarianMTModel,) if is_flax_available() else ()
      def       __magic_name__      (      self    ):
             lowercase   :    Union[str, Any]			=							FlaxMarianModelTester(self    )
      def       __magic_name__      (      self    ):
             lowercase   :    Any			=							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       __magic_name__      (      self    ):
             lowercase   :    Union[str, 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       __magic_name__      (      self    ):
             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   :    Dict			=							self._prepare_for_class(_a     ,   _a    )
                           lowercase   :    Tuple			=							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   :    Union[str, Any]			=							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       __magic_name__      (      self    ):
             lowercase   :    Tuple			=							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]			=							model_class(_a    )
                           lowercase   :    Optional[Any]			=							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(_a     ,   _a     ,   _a    ):
                                  return model.decode(
                                      decoder_input_ids=_a     ,   decoder_attention_mask=_a     ,   encoder_outputs=_a     ,   )

                           with self.subTest("JIT Enabled"    ):
                                  lowercase   :    int			=							decode_jitted(**_a    ).to_tuple()

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


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				unittest.TestCase						):
      __lowerCAmelCase						=    None
      __lowerCAmelCase						=    None
      @classmethod
      def       __magic_name__      (      cls    ):
             lowercase   :    List[str]			=							f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
             return cls
      @cached_property
      def       __magic_name__      (      self    ):
             return MarianTokenizer.from_pretrained(self.model_name    )
      @property
      def       __magic_name__      (      self    ):
             return self.tokenizer.eos_token_id
      @cached_property
      def       __magic_name__      (      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       __magic_name__      (      self     ,   **_a    ):
             lowercase   :    List[Any]			=							self.translate_src_text(**_a    )
             self.assertListEqual(self.expected_text     ,   _a    )

      def       __magic_name__      (      self     ,   **_a    ):
             lowercase   :    List[str]			=							self.tokenizer(self.src_text     ,   padding=_a     ,   return_tensors="np"     ,   **_a    )
             lowercase   :    Union[str, Any]			=							self.model.generate(
                 model_inputs.input_ids     ,   attention_mask=model_inputs.attention_mask     ,   num_beams=2     ,   max_length=128     ,   ).sequences
             lowercase   :    Dict			=							self.tokenizer.batch_decode(_a     ,   skip_special_tokens=_a    )
             return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				a_						):
      __lowerCAmelCase						=    """en"""
      __lowerCAmelCase						=    """fr"""
      __lowerCAmelCase						=    [
          """I am a small frog.""",
          """Now I can forget the 100 words of german that I know.""",
      ]
      __lowerCAmelCase						=    [
          """Je suis une petite grenouille.""",
          """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
      ]
      @slow
      def       __magic_name__      (      self    ):
             self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				a_						):
      __lowerCAmelCase						=    """fr"""
      __lowerCAmelCase						=    """en"""
      __lowerCAmelCase						=    [
          """Donnez moi le micro.""",
          """Tom et Mary étaient assis à une table.""",  # Accents
      ]
      __lowerCAmelCase						=    [
          """Give me the microphone.""",
          """Tom and Mary were sitting at a table.""",
      ]
      @slow
      def       __magic_name__      (      self    ):
             self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				a_						):
      __lowerCAmelCase						=    """mt"""
      __lowerCAmelCase						=    """en"""
      __lowerCAmelCase						=    ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
      __lowerCAmelCase						=    ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
      @slow
      def       __magic_name__      (      self    ):
             self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				a_						):
      __lowerCAmelCase						=    """en"""
      __lowerCAmelCase						=    """de"""
      __lowerCAmelCase						=    [
          """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.""",
      ]
      __lowerCAmelCase						=    [
          """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       __magic_name__      (      self    ):
             self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				a_						):
      __lowerCAmelCase						=    """en"""
      __lowerCAmelCase						=    """zh"""
      __lowerCAmelCase						=    ["""My name is Wolfgang and I live in Berlin"""]
      __lowerCAmelCase						=    ["""我叫沃尔夫冈 我住在柏林"""]
      @slow
      def       __magic_name__      (      self    ):
             self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				a_						):
      __lowerCAmelCase						=    """ru"""
      __lowerCAmelCase						=    """fr"""
      __lowerCAmelCase						=    ["""Он показал мне рукопись своей новой пьесы."""]
      __lowerCAmelCase						=    ["""Il m'a montré le manuscrit de sa nouvelle pièce."""]
      @slow
      def       __magic_name__      (      self    ):
             self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 		a__						(				a_						):
      __lowerCAmelCase						=    """en"""
      __lowerCAmelCase						=    """ROMANCE"""
      __lowerCAmelCase						=    [
          """>>fr<< Don't spend so much time watching TV.""",
          """>>pt<< Your message has been sent.""",
          """>>es<< He's two years older than me.""",
      ]
      __lowerCAmelCase						=    [
          """Ne passez pas autant de temps à regarder la télé.""",
          """A sua mensagem foi enviada.""",
          """Es dos años más viejo que yo.""",
      ]
      @slow
      def       __magic_name__      (      self    ):
             self._assert_generated_batch_equal_expected()




"""simple docstring"""




from __future__ import annotations

_A      :	List[str]       		=	"""Alexander Joslin"""


def   __magic_name__     (				__snake_case    :	int  )			->    int:

       if number <= 0:
              lowercase   :    Optional[Any]			=							f"""n must be greater than 0. Got n = {number}"""
              raise ValueError(__snake_case  )

       lowercase   :    List[str]			=							[number + 1] * (number + 1)

       # starting position
       lowercase   :    Optional[Any]			=							0
       for i in range(1					,			__snake_case  ):
              lowercase   :    str			=							min(table[i + 1]					,			table[i] + 1  )
              # check if out of bounds
              if i * 2 <= number:
                     lowercase   :    int			=							min(table[i * 2]					,			table[i] + 1  )
              # check if out of bounds
              if i * 3 <= number:
                     lowercase   :    Optional[int]			=							min(table[i * 3]					,			table[i] + 1  )
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


