





'''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
    a__							:							Optional[Any]			='''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     lowercase__       (       __lowercase						:  Union[str, Any]    ,  __lowercase						:  str    ,  __lowercase						:  Tuple=None    ,  __lowercase						:  Optional[Any]=None    ,  __lowercase						:  Union[str, Any]=None    ,  __lowercase						:  List[str]=None    ,  __lowercase						:  int=None    ,  __lowercase						:  List[str]=None    ,  )			->				List[str]:



       """simple docstring"""






       if attention_mask is None:
              __UpperCamelCase	=  np.where(input_ids != config.pad_token_id    ,  1    ,  0					)
       if decoder_attention_mask is None:
              __UpperCamelCase	=  np.where(decoder_input_ids != config.pad_token_id    ,  1    ,  0					)
       if head_mask is None:
              __UpperCamelCase	=  np.ones((config.encoder_layers, config.encoder_attention_heads)					)
       if decoder_head_mask is None:
              __UpperCamelCase	=  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
       if cross_attn_head_mask is None:
              __UpperCamelCase	=  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 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 def __init__(	self	:							List[str]							,     __A	:							Union[str, Any]							,     __A	:							List[str]=1_3							,     __A	:							int=7							,     __A	:							List[Any]=True							,     __A	:							Optional[Any]=False							,     __A	:							Optional[int]=9_9							,     __A	:							Any=1_6							,     __A	:							List[str]=2							,     __A	:							str=4							,     __A	:							Any=4							,     __A	:							Any="gelu"							,     __A	:							Dict=0.1							,     __A	:							Tuple=0.1							,     __A	:							Optional[int]=3_2							,     __A	:							Any=2							,     __A	:							Optional[int]=1							,     __A	:							Dict=0							,     __A	:							Optional[Any]=2							,     __A	:							Union[str, Any]=0.02							,     ):
        __UpperCamelCase	=  parent
        __UpperCamelCase	=  batch_size
        __UpperCamelCase	=  seq_length
        __UpperCamelCase	=  is_training
        __UpperCamelCase	=  use_labels
        __UpperCamelCase	=  vocab_size
        __UpperCamelCase	=  hidden_size
        __UpperCamelCase	=  num_hidden_layers
        __UpperCamelCase	=  num_attention_heads
        __UpperCamelCase	=  intermediate_size
        __UpperCamelCase	=  hidden_act
        __UpperCamelCase	=  hidden_dropout_prob
        __UpperCamelCase	=  attention_probs_dropout_prob
        __UpperCamelCase	=  max_position_embeddings
        __UpperCamelCase	=  eos_token_id
        __UpperCamelCase	=  pad_token_id
        __UpperCamelCase	=  bos_token_id
        __UpperCamelCase	=  decoder_start_token_id
        __UpperCamelCase	=  initializer_range






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

        __UpperCamelCase	=  shift_tokens_right(__A							,     1     )

        __UpperCamelCase	=  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	=  prepare_mbart_inputs_dict(__A							,     __A							,     __A     )
        return config, inputs_dict






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.prepare_config_and_inputs()
        return config, inputs_dict






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							Union[str, Any]							,     __A	:							Union[str, Any]							,     __A	:							Tuple     ):
        __UpperCamelCase	=  2_0
        __UpperCamelCase	=  model_class_name(__A     )

        __UpperCamelCase	=  model.encode(inputs_dict['input_ids']     )

        __UpperCamelCase	,						__UpperCamelCase	=  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        __UpperCamelCase	=  model.init_cache(decoder_input_ids.shape[0]							,     __A							,     __A     )
        __UpperCamelCase	=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,     dtype='i4'     )

        __UpperCamelCase	=  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]							,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,     )
        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, :-1]							,     __A							,     decoder_attention_mask=__A							,     past_key_values=__A							,     decoder_position_ids=__A							,     )

        __UpperCamelCase	=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,     dtype='i4'     )
        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, -1:]							,     __A							,     decoder_attention_mask=__A							,     past_key_values=outputs_cache.past_key_values							,     decoder_position_ids=__A							,     )

        __UpperCamelCase	=  model.decode(__A							,     __A     )

        __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							Any							,     __A	:							int							,     __A	:							Union[str, Any]     ):
        __UpperCamelCase	=  2_0
        __UpperCamelCase	=  model_class_name(__A     )

        __UpperCamelCase	=  model.encode(inputs_dict['input_ids']     )

        __UpperCamelCase	,						__UpperCamelCase	=  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

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

        __UpperCamelCase	=  model.init_cache(decoder_input_ids.shape[0]							,     __A							,     __A     )
        __UpperCamelCase	=  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]							,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,     )

        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, :-1]							,     __A							,     decoder_attention_mask=__A							,     past_key_values=__A							,     decoder_position_ids=__A							,     )
        __UpperCamelCase	=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,     dtype='i4'     )
        __UpperCamelCase	=  model.decode(
            decoder_input_ids[:, -1:]							,     __A							,     past_key_values=outputs_cache.past_key_values							,     decoder_attention_mask=__A							,     decoder_position_ids=__A							,     )

        __UpperCamelCase	=  model.decode(__A							,     __A							,     decoder_attention_mask=__A     )

        __UpperCamelCase	=  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 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Any      		=99






 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  np.array(
            [
                [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
            ]							,     dtype=np.intaa							,     )

        __UpperCamelCase	=  input_ids.shape[0]
        __UpperCamelCase	=  MBartConfig(
            vocab_size=self.vocab_size							,     d_model=2_4							,     encoder_layers=2							,     decoder_layers=2							,     encoder_attention_heads=2							,     decoder_attention_heads=2							,     encoder_ffn_dim=3_2							,     decoder_ffn_dim=3_2							,     max_position_embeddings=4_8							,     eos_token_id=2							,     pad_token_id=1							,     bos_token_id=0							,     )
        return config, input_ids, batch_size






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  self._get_config_and_data()
        __UpperCamelCase	=  FlaxMBartForSequenceClassification(__A     )
        __UpperCamelCase	=  model(input_ids=__A							,     decoder_input_ids=__A     )
        __UpperCamelCase	=  (batch_size, config.num_labels)
        self.assertEqual(outputs['logits'].shape							,     __A     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  self._get_config_and_data()
        __UpperCamelCase	=  FlaxMBartForQuestionAnswering(__A     )
        __UpperCamelCase	=  model(input_ids=__A     )

        self.assertEqual(outputs['start_logits'].shape							,     input_ids.shape     )
        self.assertEqual(outputs['end_logits'].shape							,     input_ids.shape     )






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  self._get_config_and_data()
        __UpperCamelCase	=  FlaxMBartForConditionalGeneration(__A     )
        __UpperCamelCase	=  lm_model(input_ids=__A     )
        __UpperCamelCase	=  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['logits'].shape							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  MBartConfig(
            vocab_size=self.vocab_size							,     d_model=1_4							,     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=4_8							,     )
        __UpperCamelCase	=  FlaxMBartForConditionalGeneration(__A     )
        __UpperCamelCase	=  np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,     dtype=np.intaa     )
        __UpperCamelCase	=  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,     dtype=np.intaa     )
        __UpperCamelCase	=  lm_model(input_ids=__A							,     decoder_input_ids=__A     )
        __UpperCamelCase	=  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['logits'].shape							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	=  np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,     dtype=np.intaa     )
        __UpperCamelCase	=  shift_tokens_right(__A							,     1     )
        __UpperCamelCase	=  np.equal(__A							,     1     ).astype(np.floataa     ).sum()
        __UpperCamelCase	=  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 					snake_case					(   __lowerCamelCase      ,	unittest.TestCase      ,	__lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=True
 SCREAMING_SNAKE_CASE_     : Tuple      		=(
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_     : Dict      		=(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  FlaxMBartModelTester(self     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  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 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  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 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__     ):
                      __UpperCamelCase	=  self._prepare_for_class(__A							,     __A     )
                      __UpperCamelCase	=  model_class(__A     )

                      @jax.jit
                      def encode_jitted(__A	:							Optional[int]							,     __A	:							List[str]=None							,     **__A	:							List[str]     ):
                             return model.encode(input_ids=__A							,     attention_mask=__A     )

                      with self.subTest('JIT Enabled'     ):
                             __UpperCamelCase	=  encode_jitted(**__A     ).to_tuple()

                      with self.subTest('JIT Disabled'     ):
                             with jax.disable_jit():
                                    __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__     ):
                      __UpperCamelCase	=  model_class(__A     )
                      __UpperCamelCase	=  model.encode(inputs_dict['input_ids']							,     inputs_dict['attention_mask']     )

                      __UpperCamelCase	=  {
                          '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	:							Union[str, Any]							,     __A	:							int							,     __A	:							List[Any]     ):
                             return model.decode(
                                 decoder_input_ids=__A							,     decoder_attention_mask=__A							,     encoder_outputs=__A							,     )

                      with self.subTest('JIT Enabled'     ):
                             __UpperCamelCase	=  decode_jitted(**__A     ).to_tuple()

                      with self.subTest('JIT Disabled'     ):
                             with jax.disable_jit():
                                    __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							str     ):
        for model_class_name in self.all_model_classes:
               __UpperCamelCase	=  model_class_name.from_pretrained('facebook/mbart-large-cc25'							,     from_pt=__A     )
               # FlaxMBartForSequenceClassification expects eos token in input_ids
               __UpperCamelCase	=  np.ones((1, 1)     ) * model.config.eos_token_id
               __UpperCamelCase	=  model(__A     )
               self.assertIsNotNone(__A     )






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




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[Any]      		=[
     " UN Chief Says There Is No Military Solution in Syria",
 ]
 SCREAMING_SNAKE_CASE_     : List[str]      		=[
     "Şeful ONU declară că nu există o soluţie militară în Siria",
 ]
 SCREAMING_SNAKE_CASE_     : Union[str, Any]      		="facebook/mbart-large-en-ro"






 @cached_property
 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        return AutoTokenizer.from_pretrained(self.model_name     )






 @cached_property
 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,     from_pt=__A     )
        return model






 def 	_lowerCamelCase (	self	:							List[Any]							,     **__A	:							Dict     ):
        __UpperCamelCase	=  self.translate_src_text(**__A     )
        self.assertListEqual(self.expected_text							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[int]							,     **__A	:							Optional[int]     ):
        __UpperCamelCase	=  self.tokenizer(self.src_text							,     **__A							,     return_tensors='np'     )
        __UpperCamelCase	=  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	=  self.tokenizer.batch_decode(__A							,     skip_special_tokens=__A     )
        return generated_words






 @slow
 def 	_lowerCamelCase (	self	:							Tuple     ):
        self._assert_generated_batch_equal_expected()











'''simple docstring'''
def     lowercase__       (       __lowercase						:  int = 20					)			->				int:



       """simple docstring"""






       try:
              __UpperCamelCase	=  int(__lowercase					)
       except (TypeError, ValueError):
              raise TypeError('Parameter n must be int or castable to int.'					)
       if n <= 0:
              raise ValueError('Parameter n must be greater than or equal to one.'					)
       __UpperCamelCase	=  0
       while 1:
              i += n * (n - 1)
              __UpperCamelCase	=  0
              for j in range(2    ,  __lowercase					):
                     if i % j != 0:
                            __UpperCamelCase	=  1
                            break
              if nfound == 0:
                     if i == 0:
                            __UpperCamelCase	=  1
                     return i
       return None


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