


'''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
      __UpperCAmelCase     :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       _a      (		_lowercase   :      Optional[int]     ,   _lowercase   :      int     ,   _lowercase   :      Union[str, Any]=None     ,   _lowercase   :      Optional[int]=None     ,   _lowercase   :      Tuple=None     ,   _lowercase   :      List[Any]=None     ,   _lowercase   :      Any=None     ,   _lowercase   :      Union[str, Any]=None     ,   ):





     '''simple docstring'''






     if attention_mask is None:
          __UpperCAmelCase      :					List[Any]    				=     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      :					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      :					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": decoder_attention_mask,
     }



class       a							(						unittest.TestCase			):


     """simple docstring"""






     def __init__(							self	:						Tuple				,					snake_case	:						Union[str, Any]				,					snake_case	:						int=13				,					snake_case	:						str=7				,					snake_case	:						List[str]=True				,					snake_case	:						Dict=False				,					snake_case	:						Any=99				,					snake_case	:						Tuple=16				,					snake_case	:						Union[str, Any]=2				,					snake_case	:						Optional[int]=4				,					snake_case	:						Dict=4				,					snake_case	:						Dict="gelu"				,					snake_case	:						List[Any]=0.1				,					snake_case	:						int=0.1				,					snake_case	:						Tuple=32				,					snake_case	:						Any=2				,					snake_case	:						int=1				,					snake_case	:						Tuple=0				,					snake_case	:						Any=2				,					snake_case	:						List[str]=0.02				,					)		->				Union[str, Any]:
          __UpperCAmelCase      :					Optional[int]    				=     parent
          __UpperCAmelCase      :					Dict    				=     batch_size
          __UpperCAmelCase      :					int    				=     seq_length
          __UpperCAmelCase      :					List[Any]    				=     is_training
          __UpperCAmelCase      :					Dict    				=     use_labels
          __UpperCAmelCase      :					Optional[Any]    				=     vocab_size
          __UpperCAmelCase      :					str    				=     hidden_size
          __UpperCAmelCase      :					Any    				=     num_hidden_layers
          __UpperCAmelCase      :					List[str]    				=     num_attention_heads
          __UpperCAmelCase      :					List[str]    				=     intermediate_size
          __UpperCAmelCase      :					List[str]    				=     hidden_act
          __UpperCAmelCase      :					Tuple    				=     hidden_dropout_prob
          __UpperCAmelCase      :					str    				=     attention_probs_dropout_prob
          __UpperCAmelCase      :					str    				=     max_position_embeddings
          __UpperCAmelCase      :					Any    				=     eos_token_id
          __UpperCAmelCase      :					Any    				=     pad_token_id
          __UpperCAmelCase      :					Union[str, Any]    				=     bos_token_id
          __UpperCAmelCase      :					Tuple    				=     decoder_start_token_id
          __UpperCAmelCase      :					Union[str, Any]    				=     initializer_range

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

          __UpperCAmelCase      :					Optional[int]    				=     shift_tokens_right(snake_case				,					1     )

          __UpperCAmelCase      :					List[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=snake_case				,					)
          __UpperCAmelCase      :					Union[str, Any]    				=     prepare_mbart_inputs_dict(snake_case				,					snake_case				,					snake_case     )
          return config, inputs_dict

     def 					lowerCamelCase__					(							self	:						List[str]     )		->				Optional[int]:
          __UpperCAmelCase				,  __UpperCAmelCase      :					List[str]    				=     self.prepare_config_and_inputs()
          return config, inputs_dict

     def 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						Any				,					snake_case	:						Any				,					snake_case	:						Union[str, Any]     )		->				str:
          __UpperCAmelCase      :					Any    				=     20
          __UpperCAmelCase      :					List[Any]    				=     model_class_name(snake_case     )

          __UpperCAmelCase      :					int    				=     model.encode(inputs_dict['''input_ids''']     )

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

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

          __UpperCAmelCase      :					Dict    				=     jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]				,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,					)
          __UpperCAmelCase      :					Optional[int]    				=     model.decode(
              decoder_input_ids[:, :-1]				,					snake_case				,					decoder_attention_mask=snake_case				,					past_key_values=snake_case				,					decoder_position_ids=snake_case				,					)

          __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:]				,					snake_case				,					decoder_attention_mask=snake_case				,					past_key_values=outputs_cache.past_key_values				,					decoder_position_ids=snake_case				,					)

          __UpperCAmelCase      :					Union[str, Any]    				=     model.decode(snake_case				,					snake_case     )

          __UpperCAmelCase      :					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	:						Tuple				,					snake_case	:						int				,					snake_case	:						Tuple				,					snake_case	:						int     )		->				int:
          __UpperCAmelCase      :					Union[str, Any]    				=     20
          __UpperCAmelCase      :					Optional[int]    				=     model_class_name(snake_case     )

          __UpperCAmelCase      :					Any    				=     model.encode(inputs_dict['''input_ids''']     )

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

          __UpperCAmelCase      :					Optional[int]    				=     jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
              ]				,					axis=-1				,					)

          __UpperCAmelCase      :					str    				=     model.init_cache(decoder_input_ids.shape[0]				,					snake_case				,					snake_case     )
          __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      :					int    				=     model.decode(
              decoder_input_ids[:, :-1]				,					snake_case				,					decoder_attention_mask=snake_case				,					past_key_values=snake_case				,					decoder_position_ids=snake_case				,					)
          __UpperCAmelCase      :					Any    				=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,					dtype='''i4'''     )
          __UpperCAmelCase      :					List[Any]    				=     model.decode(
              decoder_input_ids[:, -1:]				,					snake_case				,					past_key_values=outputs_cache.past_key_values				,					decoder_attention_mask=snake_case				,					decoder_position_ids=snake_case				,					)

          __UpperCAmelCase      :					Optional[int]    				=     model.decode(snake_case				,					snake_case				,					decoder_attention_mask=snake_case     )

          __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       a							(						unittest.TestCase			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					Dict				  =  9_9

     def 					lowerCamelCase__					(							self	:						List[str]     )		->				Dict:
          __UpperCAmelCase      :					Dict    				=     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      :					List[str]    				=     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	:						int     )		->				Dict:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					Optional[Any]    				=     self._get_config_and_data()
          __UpperCAmelCase      :					Dict    				=     FlaxMBartForSequenceClassification(snake_case     )
          __UpperCAmelCase      :					Dict    				=     model(input_ids=snake_case				,					decoder_input_ids=snake_case     )
          __UpperCAmelCase      :					Dict    				=     (batch_size, config.num_labels)
          self.assertEqual(outputs['''logits'''].shape				,					snake_case     )

     def 					lowerCamelCase__					(							self	:						Optional[Any]     )		->				str:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					Tuple    				=     self._get_config_and_data()
          __UpperCAmelCase      :					Optional[Any]    				=     FlaxMBartForQuestionAnswering(snake_case     )
          __UpperCAmelCase      :					Union[str, Any]    				=     model(input_ids=snake_case     )

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

     def 					lowerCamelCase__					(							self	:						Optional[int]     )		->				List[str]:
          __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					int    				=     self._get_config_and_data()
          __UpperCAmelCase      :					Tuple    				=     FlaxMBartForConditionalGeneration(snake_case     )
          __UpperCAmelCase      :					Tuple    				=     lm_model(input_ids=snake_case     )
          __UpperCAmelCase      :					Any    				=     (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape				,					snake_case     )

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






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



@require_flax
class       a							(						_a      ,    unittest.TestCase      ,    _a			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					Optional[Any]				  =  True
     SCREAMING_SNAKE_CASE			:					Tuple				  =  (
         (
             FlaxMBartModel,
             FlaxMBartForConditionalGeneration,
             FlaxMBartForSequenceClassification,
             FlaxMBartForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     SCREAMING_SNAKE_CASE			:					Union[str, Any]				  =  (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

     def 					lowerCamelCase__					(							self	:						Union[str, Any]     )		->				Union[str, Any]:
          __UpperCAmelCase      :					Dict    				=     FlaxMBartModelTester(self     )

     def 					lowerCamelCase__					(							self	:						Union[str, Any]     )		->				Optional[Any]:
          __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(snake_case				,					snake_case				,					snake_case     )

     def 					lowerCamelCase__					(							self	:						Union[str, Any]     )		->				Union[str, Any]:
          __UpperCAmelCase				,  __UpperCAmelCase      :					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(snake_case				,					snake_case				,					snake_case     )

     def 					lowerCamelCase__					(							self	:						Dict     )		->				Union[str, Any]:
          __UpperCAmelCase				,  __UpperCAmelCase      :					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      :					Any    				=     self._prepare_for_class(snake_case				,					snake_case     )
                    __UpperCAmelCase      :					Optional[int]    				=     model_class(snake_case     )

                    @jax.jit
                    def encode_jitted(snake_case	:						Tuple				,					snake_case	:						Union[str, Any]=None				,					**snake_case	:						Union[str, Any]     ):
                         return model.encode(input_ids=snake_case				,					attention_mask=snake_case     )

                    with self.subTest('''JIT Enabled'''     ):
                         __UpperCAmelCase      :					Dict    				=     encode_jitted(**snake_case     ).to_tuple()

                    with self.subTest('''JIT Disabled'''     ):
                         with jax.disable_jit():
                              __UpperCAmelCase      :					Tuple    				=     encode_jitted(**snake_case     ).to_tuple()

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

     def 					lowerCamelCase__					(							self	:						Optional[Any]     )		->				Union[str, Any]:
          __UpperCAmelCase				,  __UpperCAmelCase      :					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__     ):
                    __UpperCAmelCase      :					Tuple    				=     model_class(snake_case     )
                    __UpperCAmelCase      :					int    				=     model.encode(inputs_dict['''input_ids''']				,					inputs_dict['''attention_mask''']     )

                    __UpperCAmelCase      :					Dict    				=     {
                        '''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(snake_case	:						int				,					snake_case	:						List[Any]				,					snake_case	:						str     ):
                         return model.decode(
                             decoder_input_ids=snake_case				,					decoder_attention_mask=snake_case				,					encoder_outputs=snake_case				,					)

                    with self.subTest('''JIT Enabled'''     ):
                         __UpperCAmelCase      :					Dict    				=     decode_jitted(**snake_case     ).to_tuple()

                    with self.subTest('''JIT Disabled'''     ):
                         with jax.disable_jit():
                              __UpperCAmelCase      :					Any    				=     decode_jitted(**snake_case     ).to_tuple()

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






     @slow
     def 					lowerCamelCase__					(							self	:						Optional[Any]     )		->				Dict:
          for model_class_name in self.all_model_classes:
               __UpperCAmelCase      :					Optional[Any]    				=     model_class_name.from_pretrained('''facebook/mbart-large-cc25'''				,					from_pt=snake_case     )
               # FlaxMBartForSequenceClassification expects eos token in input_ids
               __UpperCAmelCase      :					Optional[int]    				=     np.ones((1, 1)     ) * model.config.eos_token_id
               __UpperCAmelCase      :					str    				=     model(snake_case     )
               self.assertIsNotNone(snake_case     )



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


     """simple docstring"""






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

     @cached_property
     def 					lowerCamelCase__					(							self	:						Any     )		->				int:
          return AutoTokenizer.from_pretrained(self.model_name     )

     @cached_property
     def 					lowerCamelCase__					(							self	:						Optional[int]     )		->				Any:
          __UpperCAmelCase      :					List[Any]    				=     FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				,					from_pt=snake_case     )
          return model

     def 					lowerCamelCase__					(							self	:						List[Any]				,					**snake_case	:						Union[str, Any]     )		->				Optional[int]:
          __UpperCAmelCase      :					List[Any]    				=     self.translate_src_text(**snake_case     )
          self.assertListEqual(self.expected_text				,					snake_case     )

     def 					lowerCamelCase__					(							self	:						int				,					**snake_case	:						str     )		->				Tuple:
          __UpperCAmelCase      :					Tuple    				=     self.tokenizer(self.src_text				,					**snake_case				,					return_tensors='''np'''     )
          __UpperCAmelCase      :					Optional[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=snake_case				,					num_beams=2				,					).sequences
          __UpperCAmelCase      :					List[Any]    				=     self.tokenizer.batch_decode(snake_case				,					skip_special_tokens=snake_case     )
          return generated_words






     @slow
     def 					lowerCamelCase__					(							self	:						List[Any]     )		->				int:
          self._assert_generated_batch_equal_expected()



'''simple docstring'''



def       _a      (		_lowercase   :      list    ):





     '''simple docstring'''






     if len(_lowercase    ) <= 1:
          return data
     else:
          return [
              *quick_sort([e for e in data[1:] if e <= data[0]]    ),
              data[0],
              *quick_sort([e for e in data[1:] if e > data[0]]    ),
          ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()