

"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
      lowerCamelCase__			:					Dict							  =					'''platform'''

      import jax
      import jax.numpy as jnp

      from transformers import AutoTokenizer
      from transformers.models.mbart.modeling_flax_mbart import (
          FlaxMBartForConditionalGeneration,
          FlaxMBartForQuestionAnswering,
          FlaxMBartForSequenceClassification,
          FlaxMBartModel,
          shift_tokens_right,
      )
def  UpperCamelCase   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Optional[int]=None,					_lowerCAmelCase       :  str=None,					_lowerCAmelCase       :  Tuple=None,					_lowerCAmelCase       :  str=None,					_lowerCAmelCase       :  List[Any]=None,					_lowerCAmelCase       :  Tuple=None,					)			->     Dict:
 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      :							str       =   np.where(decoder_input_ids != config.pad_token_id,					1,					0	)
 if head_mask is None:
  _UpperCAmelCase      :							int       =   np.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  _UpperCAmelCase      :							Optional[int]       =   np.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  _UpperCAmelCase      :							Union[str, 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": decoder_attention_mask,
 }






class       _UpperCAmelCase     (						unittest.TestCase):
   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=2 ,						_A=0.02 ,						)      ->      Tuple:






    '''simple docstring'''







    _UpperCAmelCase      :							Optional[Any]       =   parent
    _UpperCAmelCase      :							List[str]       =   batch_size
    _UpperCAmelCase      :							Optional[int]       =   seq_length
    _UpperCAmelCase      :							Union[str, Any]       =   is_training
    _UpperCAmelCase      :							Optional[Any]       =   use_labels
    _UpperCAmelCase      :							Optional[Any]       =   vocab_size
    _UpperCAmelCase      :							Tuple       =   hidden_size
    _UpperCAmelCase      :							List[str]       =   num_hidden_layers
    _UpperCAmelCase      :							Dict       =   num_attention_heads
    _UpperCAmelCase      :							int       =   intermediate_size
    _UpperCAmelCase      :							Dict       =   hidden_act
    _UpperCAmelCase      :							str       =   hidden_dropout_prob
    _UpperCAmelCase      :							int       =   attention_probs_dropout_prob
    _UpperCAmelCase      :							Tuple       =   max_position_embeddings
    _UpperCAmelCase      :							str       =   eos_token_id
    _UpperCAmelCase      :							Tuple       =   pad_token_id
    _UpperCAmelCase      :							List[str]       =   bos_token_id
    _UpperCAmelCase      :							Optional[Any]       =   decoder_start_token_id
    _UpperCAmelCase      :							str       =   initializer_range
   def 							__snake_case	(						self       )      ->      Dict:






    '''simple docstring'''







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

    _UpperCAmelCase      :							str       =   shift_tokens_right(_A ,						1       )

    _UpperCAmelCase      :							Optional[Any]       =   MBartConfig(
        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 ,						decoder_start_token_id=self.decoder_start_token_id ,						initializer_range=self.initializer_range ,						use_cache=_A ,						)
    _UpperCAmelCase      :							List[Any]       =   prepare_mbart_inputs_dict(_A ,						_A ,						_A       )
    return config, inputs_dict
   def 							__snake_case	(						self       )      ->      Union[str, Any]:






    '''simple docstring'''







    _UpperCAmelCase      :							Optional[int]       =   self.prepare_config_and_inputs()
    return config, inputs_dict
   def 							__snake_case	(						self ,						_A ,						_A ,						_A       )      ->      Tuple:






    '''simple docstring'''







    _UpperCAmelCase      :							Dict       =   20
    _UpperCAmelCase      :							Any       =   model_class_name(_A       )

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

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

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

    _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      :							Tuple       =   model.decode(
        decoder_input_ids[:, :-1] ,						_A ,						decoder_attention_mask=_A ,						past_key_values=_A ,						decoder_position_ids=_A ,						)

    _UpperCAmelCase      :							List[str]       =   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      :							Optional[int]       =   model.decode(_A ,						_A       )

    _UpperCAmelCase      :							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}'''       )
   def 							__snake_case	(						self ,						_A ,						_A ,						_A       )      ->      Optional[int]:






    '''simple docstring'''







    _UpperCAmelCase      :							Optional[int]       =   20
    _UpperCAmelCase      :							Tuple       =   model_class_name(_A       )

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

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

    _UpperCAmelCase      :							int       =   model.decode(_A ,						_A ,						decoder_attention_mask=_A       )

    _UpperCAmelCase      :							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       _UpperCAmelCase     (						unittest.TestCase):
   __a	:    str			   =							9_9
   def 							__snake_case	(						self       )      ->      Tuple:






    '''simple docstring'''







    _UpperCAmelCase      :							Optional[Any]       =   np.array(
        [
            [71, 82, 18, 33, 46, 91, 2],
            [68, 34, 26, 58, 30, 82, 2],
            [5, 97, 17, 39, 94, 40, 2],
            [76, 83, 94, 25, 70, 78, 2],
            [87, 59, 41, 35, 48, 66, 2],
            [55, 13, 16, 58, 5, 2, 1],  # note padding
            [64, 27, 31, 51, 12, 75, 2],
            [52, 64, 86, 17, 83, 39, 2],
            [48, 61, 9, 24, 71, 82, 2],
            [26, 1, 60, 48, 22, 13, 2],
            [21, 5, 62, 28, 14, 76, 2],
            [45, 98, 37, 86, 59, 48, 2],
            [70, 70, 50, 9, 28, 0, 2],
        ] ,						dtype=np.intaa ,						)

    _UpperCAmelCase      :							Optional[int]       =   input_ids.shape[0]
    _UpperCAmelCase      :							Optional[Any]       =   MBartConfig(
        vocab_size=self.vocab_size ,						d_model=24 ,						encoder_layers=2 ,						decoder_layers=2 ,						encoder_attention_heads=2 ,						decoder_attention_heads=2 ,						encoder_ffn_dim=32 ,						decoder_ffn_dim=32 ,						max_position_embeddings=48 ,						eos_token_id=2 ,						pad_token_id=1 ,						bos_token_id=0 ,						)
    return config, input_ids, batch_size
   def 							__snake_case	(						self       )      ->      str:






    '''simple docstring'''







    _UpperCAmelCase      :							Tuple       =   self._get_config_and_data()
    _UpperCAmelCase      :							Any       =   FlaxMBartForSequenceClassification(_A       )
    _UpperCAmelCase      :							Tuple       =   model(input_ids=_A ,						decoder_input_ids=_A       )
    _UpperCAmelCase      :							str       =   (batch_size, config.num_labels)
    self.assertEqual(outputs["""logits"""].shape ,						_A       )
   def 							__snake_case	(						self       )      ->      Tuple:






    '''simple docstring'''







    _UpperCAmelCase      :							Union[str, Any]       =   self._get_config_and_data()
    _UpperCAmelCase      :							Tuple       =   FlaxMBartForQuestionAnswering(_A       )
    _UpperCAmelCase      :							str       =   model(input_ids=_A       )

    self.assertEqual(outputs["""start_logits"""].shape ,						input_ids.shape       )
    self.assertEqual(outputs["""end_logits"""].shape ,						input_ids.shape       )
   def 							__snake_case	(						self       )      ->      Optional[int]:






    '''simple docstring'''







    _UpperCAmelCase      :							int       =   self._get_config_and_data()
    _UpperCAmelCase      :							Optional[Any]       =   FlaxMBartForConditionalGeneration(_A       )
    _UpperCAmelCase      :							Union[str, Any]       =   lm_model(input_ids=_A       )
    _UpperCAmelCase      :							Tuple       =   (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape ,						_A       )
   def 							__snake_case	(						self       )      ->      Tuple:






    '''simple docstring'''







    _UpperCAmelCase      :							Union[str, Any]       =   MBartConfig(
        vocab_size=self.vocab_size ,						d_model=14 ,						encoder_layers=2 ,						decoder_layers=2 ,						encoder_attention_heads=2 ,						decoder_attention_heads=2 ,						encoder_ffn_dim=8 ,						decoder_ffn_dim=8 ,						max_position_embeddings=48 ,						)
    _UpperCAmelCase      :							Dict       =   FlaxMBartForConditionalGeneration(_A       )
    _UpperCAmelCase      :							List[Any]       =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,						dtype=np.intaa       )
    _UpperCAmelCase      :							str       =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,						dtype=np.intaa       )
    _UpperCAmelCase      :							Union[str, Any]       =   lm_model(input_ids=_A ,						decoder_input_ids=_A       )
    _UpperCAmelCase      :							Optional[int]       =   (*summary.shape, config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape ,						_A       )
   def 							__snake_case	(						self       )      ->      Optional[Any]:






    '''simple docstring'''







    _UpperCAmelCase      :							Tuple       =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,						dtype=np.intaa       )
    _UpperCAmelCase      :							int       =   shift_tokens_right(_A ,						1       )
    _UpperCAmelCase      :							List[Any]       =   np.equal(_A ,						1       ).astype(np.floataa       ).sum()
    _UpperCAmelCase      :							int       =   np.equal(_A ,						1       ).astype(np.floataa       ).sum()
    self.assertEqual(shifted.shape ,						input_ids.shape       )
    self.assertEqual(_A ,						n_pad_before - 1       )
    self.assertTrue(np.equal(shifted[:, 0] ,						2       ).all()       )






@require_flax
class       _UpperCAmelCase     (						__a      ,       unittest.TestCase      ,       __a):
   __a	:    Any			   =							True
   __a	:    Optional[Any]			   =							(
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   __a	:    List[Any]			   =							(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
   def 							__snake_case	(						self       )      ->      List[Any]:






    '''simple docstring'''







    _UpperCAmelCase      :							Union[str, Any]       =   FlaxMBartModelTester(self       )
   def 							__snake_case	(						self       )      ->      int:






    '''simple docstring'''







    _UpperCAmelCase      :							Dict       =   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 							__snake_case	(						self       )      ->      Optional[int]:






    '''simple docstring'''







    _UpperCAmelCase      :							Tuple       =   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 							__snake_case	(						self       )      ->      Optional[Any]:






    '''simple docstring'''







    _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      :							int       =   self._prepare_for_class(_A ,						_A       )
      _UpperCAmelCase      :							Optional[Any]       =   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"""       ):
       _UpperCAmelCase      :							Optional[Any]       =   encode_jitted(**_A       ).to_tuple()

      with self.subTest("""JIT Disabled"""       ):
       with jax.disable_jit():
        _UpperCAmelCase      :							Optional[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 							__snake_case	(						self       )      ->      List[str]:






    '''simple docstring'''







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

      _UpperCAmelCase      :							Tuple       =   {
          """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"""       ):
       _UpperCAmelCase      :							Any       =   decode_jitted(**_A       ).to_tuple()

      with self.subTest("""JIT Disabled"""       ):
       with jax.disable_jit():
        _UpperCAmelCase      :							Optional[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 							__snake_case	(						self       )      ->      str:






    '''simple docstring'''







    for model_class_name in self.all_model_classes:
     _UpperCAmelCase      :							List[Any]       =   model_class_name.from_pretrained("""facebook/mbart-large-cc25""" ,						from_pt=_A       )
     # FlaxMBartForSequenceClassification expects eos token in input_ids
     _UpperCAmelCase      :							List[Any]       =   np.ones((1, 1)       ) * model.config.eos_token_id
     _UpperCAmelCase      :							List[str]       =   model(_A       )
     self.assertIsNotNone(_A       )






@require_flax
@require_sentencepiece
@require_tokenizers
class       _UpperCAmelCase     (						unittest.TestCase):
   __a	:    Any			   =							[
       """ UN Chief Says There Is No Military Solution in Syria""",
   ]
   __a	:    Union[str, Any]			   =							[
       """Şeful ONU declară că nu există o soluţie militară în Siria""",
   ]
   __a	:    Any			   =							"""facebook/mbart-large-en-ro"""
   @cached_property
   def 							__snake_case	(						self       )      ->      Dict:






    '''simple docstring'''







    return AutoTokenizer.from_pretrained(self.model_name       )
   @cached_property
   def 							__snake_case	(						self       )      ->      Tuple:






    '''simple docstring'''







    _UpperCAmelCase      :							List[str]       =   FlaxMBartForConditionalGeneration.from_pretrained(self.model_name ,						from_pt=_A       )
    return model
   def 							__snake_case	(						self ,						**_A       )      ->      Optional[int]:






    '''simple docstring'''







    _UpperCAmelCase      :							Tuple       =   self.translate_src_text(**_A       )
    self.assertListEqual(self.expected_text ,						_A       )
   def 							__snake_case	(						self ,						**_A       )      ->      Dict:






    '''simple docstring'''







    _UpperCAmelCase      :							Any       =   self.tokenizer(self.src_text ,						**_A ,						return_tensors="""np"""       )
    _UpperCAmelCase      :							List[str]       =   self.model.generate(
        model_inputs.input_ids ,						attention_mask=model_inputs.attention_mask ,						decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""] ,						early_stopping=_A ,						num_beams=2 ,						).sequences
    _UpperCAmelCase      :							int       =   self.tokenizer.batch_decode(_A ,						skip_special_tokens=_A       )
    return generated_words
   @slow
   def 							__snake_case	(						self       )      ->      Optional[int]:






    '''simple docstring'''







    self._assert_generated_batch_equal_expected()








"""simple docstring"""






import math
def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5,					int(math.sqrt(_lowerCAmelCase	) + 1	),					6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True
def  UpperCamelCase   (     _lowerCAmelCase       :  int = 2000000	)			->     int:
	return sum(num for num in range(3,					_lowerCAmelCase,					2	) if is_prime(_lowerCAmelCase	)	) + 2 if n > 2 else 0


if __name__ == "__main__":
						print(F'''{solution() = }''')





