
"""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									=				'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,						):
   if attention_mask is None:
      UpperCAmelCase_   :   Optional[int]     	=       np.where(input_ids != config.pad_token_id,						1,						0    )
   if decoder_attention_mask is None:
      UpperCAmelCase_   :   int     	=       np.where(decoder_input_ids != config.pad_token_id,						1,						0    )
   if head_mask is None:
      UpperCAmelCase_   :   Optional[Any]     	=       np.ones((config.encoder_layers, config.encoder_attention_heads)    )
   if decoder_head_mask is None:
      UpperCAmelCase_   :   str     	=       np.ones((config.decoder_layers, config.decoder_attention_heads)    )
   if cross_attn_head_mask is None:
      UpperCAmelCase_   :   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_      :




  '''simple docstring'''







  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							,  ):

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







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,  self.vocab_size    )							,  3							,  self.vocab_size    )
     UpperCAmelCase_   :   Optional[Any]     	=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,  dtype=np.intaa    ))							,  -1    )

     UpperCAmelCase_   :   List[Any]     	=       shift_tokens_right(lowercase_							,  1							,  2    )

     UpperCAmelCase_   :   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=lowercase_							,  )
     UpperCAmelCase_   :   Optional[Any]     	=       prepare_marian_inputs_dict(lowercase_							,  lowercase_							,  lowercase_    )
     return config, inputs_dict







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   str     	=       self.prepare_config_and_inputs()
     return config, inputs_dict







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       20
     UpperCAmelCase_   :   Optional[Any]     	=       model_class_name(lowercase_    )

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

     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     UpperCAmelCase_   :   Tuple     	=       model.init_cache(decoder_input_ids.shape[0]							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   int     	=       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_   :   int     	=       model.decode(
         decoder_input_ids[:, :-1]							,  lowercase_							,  decoder_attention_mask=lowercase_							,  past_key_values=lowercase_							,  decoder_position_ids=lowercase_							,  )

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

     UpperCAmelCase_   :   Any     	=       model.decode(lowercase_							,  lowercase_    )

     UpperCAmelCase_   :   Union[str, 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}"""    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       20
     UpperCAmelCase_   :   Dict     	=       model_class_name(lowercase_    )

     UpperCAmelCase_   :   Any     	=       model.encode(inputs_dict["input_ids"]    )

     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

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

     UpperCAmelCase_   :   List[str]     	=       model.init_cache(decoder_input_ids.shape[0]							,  lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   Optional[int]     	=       jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]							,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,  )

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

     UpperCAmelCase_   :   Dict     	=       model.decode(lowercase_							,  lowercase_							,  decoder_attention_mask=lowercase_    )

     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 A_      (lowercase__							,unittest.TestCase							,lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						True
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE__     :		str    	=						(FlaxMarianMTModel,) if is_flax_available() else ()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       FlaxMarianModelTester(self    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(lowercase_							,  lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     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_with_attn_mask(lowercase_							,  lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   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__    ):
           UpperCAmelCase_   :   List[Any]     	=       self._prepare_for_class(lowercase_							,  lowercase_    )
           UpperCAmelCase_   :   Optional[int]     	=       model_class(lowercase_    )

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

           with self.subTest("JIT Enabled"    ):
              UpperCAmelCase_   :   Optional[int]     	=       encode_jitted(**lowercase_    ).to_tuple()

           with self.subTest("JIT Disabled"    ):
              with jax.disable_jit():
                 UpperCAmelCase_   :   Any     	=       encode_jitted(**lowercase_    ).to_tuple()

           self.assertEqual(len(lowercase_    )							,  len(lowercase_    )    )
           for jitted_output, output in zip(lowercase_							,  lowercase_    ):
              self.assertEqual(jitted_output.shape							,  output.shape    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     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]     	=       model_class(lowercase_    )
           UpperCAmelCase_   :   Tuple     	=       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(lowercase_							,  lowercase_							,  lowercase_    ):
              return model.decode(
                  decoder_input_ids=lowercase_							,  decoder_attention_mask=lowercase_							,  encoder_outputs=lowercase_							,  )

           with self.subTest("JIT Enabled"    ):
              UpperCAmelCase_   :   Any     	=       decode_jitted(**lowercase_    ).to_tuple()

           with self.subTest("JIT Disabled"    ):
              with jax.disable_jit():
                 UpperCAmelCase_   :   Optional[int]     	=       decode_jitted(**lowercase_    ).to_tuple()

           self.assertEqual(len(lowercase_    )							,  len(lowercase_    )    )
           for jitted_output, output in zip(lowercase_							,  lowercase_    ):
              self.assertEqual(jitted_output.shape							,  output.shape    )







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_   :   Optional[int]     	=       model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"    )
        # FlaxMarianForSequenceClassification expects eos token in input_ids
        UpperCAmelCase_   :   Dict     	=       np.ones((1, 1)    ) * model.config.eos_token_id
        UpperCAmelCase_   :   Optional[int]     	=       model(lowercase_    )
        self.assertIsNotNone(lowercase_    )




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




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						None
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						None







  @classmethod
  def 			UpperCamelCase__   (						cls    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
     return cls







  @cached_property
  def 			UpperCamelCase__   (						self    ):

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







  @property
  def 			UpperCamelCase__   (						self    ):

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







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   FlaxMarianMTModel     	=       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_    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       self.translate_src_text(**lowercase_    )
     self.assertListEqual(self.expected_text							,  lowercase_    )







  def 			UpperCamelCase__   (						self							,  **lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       self.tokenizer(self.src_text							,  padding=lowercase_							,  return_tensors="np"							,  **lowercase_    )
     UpperCAmelCase_   :   Any     	=       self.model.generate(
         model_inputs.input_ids							,  attention_mask=model_inputs.attention_mask							,  num_beams=2							,  max_length=128							,  ).sequences
     UpperCAmelCase_   :   Dict     	=       self.tokenizer.batch_decode(lowercase_							,  skip_special_tokens=lowercase_    )
     return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class A_      (lowercase__ ):




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







  @slow
  def 			UpperCamelCase__   (						self    ):

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




@require_flax
@require_sentencepiece
@require_tokenizers
class A_      (lowercase__ ):




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







  @slow
  def 			UpperCamelCase__   (						self    ):

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




@require_flax
@require_sentencepiece
@require_tokenizers
class A_      (lowercase__ ):




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







  @slow
  def 			UpperCamelCase__   (						self    ):

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




@require_flax
@require_sentencepiece
@require_tokenizers
class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						"""en"""
  SCREAMING_SNAKE_CASE__     :		Any    	=						"""de"""
  SCREAMING_SNAKE_CASE__     :		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.""",
  ]
  SCREAMING_SNAKE_CASE__     :		Dict    	=						[
      """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    ):

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




@require_flax
@require_sentencepiece
@require_tokenizers
class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Any    	=						"""en"""
  SCREAMING_SNAKE_CASE__     :		Dict    	=						"""zh"""
  SCREAMING_SNAKE_CASE__     :		str    	=						["""My name is Wolfgang and I live in Berlin"""]
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						["""我叫沃尔夫冈 我住在柏林"""]







  @slow
  def 			UpperCamelCase__   (						self    ):

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




@require_flax
@require_sentencepiece
@require_tokenizers
class A_      (lowercase__ ):




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







  @slow
  def 			UpperCamelCase__   (						self    ):

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




@require_flax
@require_sentencepiece
@require_tokenizers
class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		int    	=						"""en"""
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						"""ROMANCE"""
  SCREAMING_SNAKE_CASE__     :		str    	=						[
      """>>fr<< Don't spend so much time watching TV.""",
      """>>pt<< Your message has been sent.""",
      """>>es<< He's two years older than me.""",
  ]
  SCREAMING_SNAKE_CASE__     :		Optional[int]    	=						[
      """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    ):

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




'''simple docstring'''







def 				_snake_case (		lowercase						)       ->       Tuple:
				__a       :     Union[str, Any]								=	[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
				for gap in gaps:
								for i in range(lowercase			,    len(lowercase						)						):
												__a       :     Tuple								=	collection[i]
												__a       :     Optional[int]								=	i
												while j >= gap and collection[j - gap] > insert_value:
																__a       :     Tuple								=	collection[j - gap]
																j -= gap
												if j != i:
																__a       :     Dict								=	insert_value
				return collection


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					__SCREAMING_SNAKE_CASE		:  Any        =      input('Enter numbers separated by a comma:\n').strip()
					__SCREAMING_SNAKE_CASE		:  Optional[int]        =      [int(item) for item in user_input.split(',')]
					print(shell_sort(unsorted))