




'''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_ 			=		"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__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			)						->      Dict:


      if attention_mask is None:
            snake_case__					:      Union[str, Any]  			=						np.where(input_ids != config.pad_token_id							,			1							,			0		)
      if decoder_attention_mask is None:
            snake_case__					:      str  			=						np.where(decoder_input_ids != config.pad_token_id							,			1							,			0		)
      if head_mask is None:
            snake_case__					:      int  			=						np.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
            snake_case__					:      Optional[Any]  			=						np.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
            snake_case__					:      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 					lowercase_			(	unittest.TestCase       ):





     def __init__(    self    :				List[Any]    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Union[str, Any]=13    ,       __lowerCamelCase    :				str=7    ,       __lowerCamelCase    :				Tuple=True    ,       __lowerCamelCase    :				List[str]=False    ,       __lowerCamelCase    :				Optional[int]=99    ,       __lowerCamelCase    :				Optional[Any]=16    ,       __lowerCamelCase    :				int=2    ,       __lowerCamelCase    :				Any=4    ,       __lowerCamelCase    :				Tuple=4    ,       __lowerCamelCase    :				List[Any]="gelu"    ,       __lowerCamelCase    :				Tuple=0.1    ,       __lowerCamelCase    :				List[str]=0.1    ,       __lowerCamelCase    :				int=32    ,       __lowerCamelCase    :				Union[str, Any]=2    ,       __lowerCamelCase    :				Optional[int]=1    ,       __lowerCamelCase    :				Dict=0    ,       __lowerCamelCase    :				Dict=2    ,       __lowerCamelCase    :				List[str]=0.0_2    ,       ):
           snake_case__					:      Union[str, Any]  			=						parent
           snake_case__					:      int  			=						batch_size
           snake_case__					:      Optional[int]  			=						seq_length
           snake_case__					:      Optional[Any]  			=						is_training
           snake_case__					:      Dict  			=						use_labels
           snake_case__					:      Dict  			=						vocab_size
           snake_case__					:      List[str]  			=						hidden_size
           snake_case__					:      int  			=						num_hidden_layers
           snake_case__					:      Optional[Any]  			=						num_attention_heads
           snake_case__					:      Dict  			=						intermediate_size
           snake_case__					:      Tuple  			=						hidden_act
           snake_case__					:      Dict  			=						hidden_dropout_prob
           snake_case__					:      List[Any]  			=						attention_probs_dropout_prob
           snake_case__					:      Optional[Any]  			=						max_position_embeddings
           snake_case__					:      List[Any]  			=						eos_token_id
           snake_case__					:      Union[str, Any]  			=						pad_token_id
           snake_case__					:      int  			=						bos_token_id
           snake_case__					:      str  			=						decoder_start_token_id
           snake_case__					:      Union[str, Any]  			=						initializer_range





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__					:      str  			=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size )    ,       3    ,       self.vocab_size )
           snake_case__					:      str  			=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,       dtype=np.intaa ))    ,       -1 )

           snake_case__					:      Optional[Any]  			=						shift_tokens_right(UpperCamelCase__    ,       1 )

           snake_case__					:      str  			=						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=UpperCamelCase__    ,       )
           snake_case__					:      List[Any]  			=						prepare_mbart_inputs_dict(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__ )
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				List[str] ):
           snake_case__					:      int  			=						self.prepare_config_and_inputs()
           return config, inputs_dict





     def  _lowerCAmelCase			(    self    :				List[str]    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				List[str] ):
           snake_case__					:      Tuple  			=						20
           snake_case__					:      Optional[Any]  			=						model_class_name(UpperCamelCase__ )

           snake_case__					:      str  			=						model.encode(inputs_dict['input_ids'] )

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

           snake_case__					:      Optional[int]  			=						model.init_cache(decoder_input_ids.shape[0]    ,       UpperCamelCase__    ,       UpperCamelCase__ )
           snake_case__					:      Optional[int]  			=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,       dtype='i4' )

           snake_case__					:      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)    ,       )
           snake_case__					:      Any  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       UpperCamelCase__    ,       decoder_attention_mask=UpperCamelCase__    ,       past_key_values=UpperCamelCase__    ,       decoder_position_ids=UpperCamelCase__    ,       )

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

           snake_case__					:      Optional[int]  			=						model.decode(UpperCamelCase__    ,       UpperCamelCase__ )

           snake_case__					:      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}" )





     def  _lowerCAmelCase			(    self    :				str    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Optional[Any] ):
           snake_case__					:      str  			=						20
           snake_case__					:      Dict  			=						model_class_name(UpperCamelCase__ )

           snake_case__					:      Dict  			=						model.encode(inputs_dict['input_ids'] )

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

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

           snake_case__					:      Optional[Any]  			=						model.init_cache(decoder_input_ids.shape[0]    ,       UpperCamelCase__    ,       UpperCamelCase__ )
           snake_case__					:      int  			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,       )

           snake_case__					:      Optional[Any]  			=						model.decode(
               decoder_input_ids[:, :-1]    ,       UpperCamelCase__    ,       decoder_attention_mask=UpperCamelCase__    ,       past_key_values=UpperCamelCase__    ,       decoder_position_ids=UpperCamelCase__    ,       )
           snake_case__					:      Optional[Any]  			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,       dtype='i4' )
           snake_case__					:      List[str]  			=						model.decode(
               decoder_input_ids[:, -1:]    ,       UpperCamelCase__    ,       past_key_values=outputs_cache.past_key_values    ,       decoder_attention_mask=UpperCamelCase__    ,       decoder_position_ids=UpperCamelCase__    ,       )

           snake_case__					:      List[str]  			=						model.decode(UpperCamelCase__    ,       UpperCamelCase__    ,       decoder_attention_mask=UpperCamelCase__ )

           snake_case__					:      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}" )






@require_flax
class 					lowercase_			(	unittest.TestCase       ):
     A_       							= 9_9





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      List[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    ,       )

           snake_case__					:      str  			=						input_ids.shape[0]
           snake_case__					:      Dict  			=						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  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      Optional[Any]  			=						self._get_config_and_data()
           snake_case__					:      List[Any]  			=						FlaxMBartForSequenceClassification(UpperCamelCase__ )
           snake_case__					:      Tuple  			=						model(input_ids=UpperCamelCase__    ,       decoder_input_ids=UpperCamelCase__ )
           snake_case__					:      Any  			=						(batch_size, config.num_labels)
           self.assertEqual(outputs['logits'].shape    ,       UpperCamelCase__ )





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      Union[str, Any]  			=						self._get_config_and_data()
           snake_case__					:      List[str]  			=						FlaxMBartForQuestionAnswering(UpperCamelCase__ )
           snake_case__					:      Optional[int]  			=						model(input_ids=UpperCamelCase__ )

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





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      List[Any]  			=						self._get_config_and_data()
           snake_case__					:      Any  			=						FlaxMBartForConditionalGeneration(UpperCamelCase__ )
           snake_case__					:      List[str]  			=						lm_model(input_ids=UpperCamelCase__ )
           snake_case__					:      int  			=						(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['logits'].shape    ,       UpperCamelCase__ )





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__					:      int  			=						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    ,       )
           snake_case__					:      Optional[int]  			=						FlaxMBartForConditionalGeneration(UpperCamelCase__ )
           snake_case__					:      Tuple  			=						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,       dtype=np.intaa )
           snake_case__					:      Any  			=						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,       dtype=np.intaa )
           snake_case__					:      int  			=						lm_model(input_ids=UpperCamelCase__    ,       decoder_input_ids=UpperCamelCase__ )
           snake_case__					:      List[str]  			=						(*summary.shape, config.vocab_size)
           self.assertEqual(outputs['logits'].shape    ,       UpperCamelCase__ )





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






@require_flax
class 					lowercase_			(	__SCREAMING_SNAKE_CASE	,					unittest.TestCase	,					__SCREAMING_SNAKE_CASE       ):
     A_       							= True
     A_       							= (
         (
             FlaxMBartModel,
             FlaxMBartForConditionalGeneration,
             FlaxMBartForSequenceClassification,
             FlaxMBartForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     A_       							= (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      Dict  			=						FlaxMBartModelTester(self )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      str  			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__ )





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      Optional[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(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__ )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      Optional[int]  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      Tuple  			=						self._prepare_for_class(UpperCamelCase__    ,       UpperCamelCase__ )
                       snake_case__					:      Optional[int]  			=						model_class(UpperCamelCase__ )

                       @jax.jit
                       def encode_jitted(__lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				Tuple=None    ,       **__lowerCamelCase    :				List[Any] ):
                             return model.encode(input_ids=UpperCamelCase__    ,       attention_mask=UpperCamelCase__ )

                       with self.subTest('JIT Enabled' ):
                             snake_case__					:      str  			=						encode_jitted(**UpperCamelCase__ ).to_tuple()

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      int  			=						encode_jitted(**UpperCamelCase__ ).to_tuple()

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





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      int  			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                       snake_case__					:      Tuple  			=						model_class(UpperCamelCase__ )
                       snake_case__					:      Any  			=						model.encode(inputs_dict['input_ids']    ,       inputs_dict['attention_mask'] )

                       snake_case__					:      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(__lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				List[str]    ,       __lowerCamelCase    :				List[Any] ):
                             return model.decode(
                                 decoder_input_ids=UpperCamelCase__    ,       decoder_attention_mask=UpperCamelCase__    ,       encoder_outputs=UpperCamelCase__    ,       )

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

                       with self.subTest('JIT Disabled' ):
                             with jax.disable_jit():
                                   snake_case__					:      Dict  			=						decode_jitted(**UpperCamelCase__ ).to_tuple()

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





     @slow
     def  _lowerCAmelCase			(    self    :				List[str] ):







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






@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowercase_			(	unittest.TestCase       ):
     A_       							= [
         " UN Chief Says There Is No Military Solution in Syria",
     ]
     A_       							= [
         "Şeful ONU declară că nu există o soluţie militară în Siria",
     ]
     A_       							= "facebook/mbart-large-en-ro"





     @cached_property
     def  _lowerCAmelCase			(    self    :				Dict ):
           return AutoTokenizer.from_pretrained(self.model_name )





     @cached_property
     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__					:      str  			=						FlaxMBartForConditionalGeneration.from_pretrained(self.model_name    ,       from_pt=UpperCamelCase__ )
           return model





     def  _lowerCAmelCase			(    self    :				int    ,       **__lowerCamelCase    :				Union[str, Any] ):
           snake_case__					:      Any  			=						self.translate_src_text(**UpperCamelCase__ )
           self.assertListEqual(self.expected_text    ,       UpperCamelCase__ )





     def  _lowerCAmelCase			(    self    :				List[Any]    ,       **__lowerCamelCase    :				str ):
           snake_case__					:      int  			=						self.tokenizer(self.src_text    ,       **UpperCamelCase__    ,       return_tensors='np' )
           snake_case__					:      Any  			=						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=UpperCamelCase__    ,       num_beams=2    ,       ).sequences
           snake_case__					:      List[Any]  			=						self.tokenizer.batch_decode(UpperCamelCase__    ,       skip_special_tokens=UpperCamelCase__ )
           return generated_words





     @slow
     def  _lowerCAmelCase			(    self    :				str ):
           self._assert_generated_batch_equal_expected()







'''simple docstring'''






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 1000		)						->      int:
      snake_case__					:      List[str]  			=						0
      snake_case__					:      int  			=						(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      snake_case__					:      Optional[Any]  			=						(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      snake_case__					:      Tuple  			=						(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total


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