



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_		:  List[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 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:str=None			,       UpperCAmelCase__			:Tuple=None			,       UpperCAmelCase__			:List[Any]=None			,       UpperCAmelCase__			:List[str]=None			,       UpperCAmelCase__			:Union[str, Any]=None			,       UpperCAmelCase__			:Optional[int]=None			,       ):






 '''simple docstring'''

 if attention_mask is None:
  a							=		np.where(input_ids != config.pad_token_id			,       1			,       0       )
 if decoder_attention_mask is None:
  a							=		np.where(decoder_input_ids != config.pad_token_id			,       1			,       0       )
 if head_mask is None:
  a							=		np.ones((config.encoder_layers, config.encoder_attention_heads)       )
 if decoder_head_mask is None:
  a							=		np.ones((config.decoder_layers, config.decoder_attention_heads)       )
 if cross_attn_head_mask is None:
  a							=		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      :							Optional[int]				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							str=13				, __lowerCAmelCase      :							List[Any]=7				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Dict=False				, __lowerCAmelCase      :							str=99				, __lowerCAmelCase      :							Any=16				, __lowerCAmelCase      :							List[Any]=2				, __lowerCAmelCase      :							List[str]=4				, __lowerCAmelCase      :							List[str]=4				, __lowerCAmelCase      :							Union[str, Any]="gelu"				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Dict=0.1				, __lowerCAmelCase      :							Optional[int]=32				, __lowerCAmelCase      :							Dict=2				, __lowerCAmelCase      :							int=1				, __lowerCAmelCase      :							str=0				, __lowerCAmelCase      :							Union[str, Any]=2				, __lowerCAmelCase      :							Union[str, Any]=0.0_2				, )							->					List[str]:


    """simple docstring"""






    a							=		parent
    a							=		batch_size
    a							=		seq_length
    a							=		is_training
    a							=		use_labels
    a							=		vocab_size
    a							=		hidden_size
    a							=		num_hidden_layers
    a							=		num_attention_heads
    a							=		intermediate_size
    a							=		hidden_act
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		max_position_embeddings
    a							=		eos_token_id
    a							=		pad_token_id
    a							=		bos_token_id
    a							=		decoder_start_token_id
    a							=		initializer_range

   def 		A				(   self      :							int				)							->					Union[str, Any]:


    """simple docstring"""






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

    a							=		shift_tokens_right(__lowerCAmelCase				, 1				)

    a							=		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=__lowerCAmelCase				, )
    a							=		prepare_mbart_inputs_dict(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)
    return config, inputs_dict

   def 		A				(   self      :							Dict				)							->					Any:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    return config, inputs_dict

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							Any				)							->					List[str]:


    """simple docstring"""






    a							=		20
    a							=		model_class_name(__lowerCAmelCase				)

    a							=		model.encode(inputs_dict["input_ids"]				)

    a							=		(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    a							=		model.init_cache(decoder_input_ids.shape[0]				, __lowerCAmelCase				, __lowerCAmelCase				)
    a							=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				, dtype="i4"				)

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

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

    a							=		model.decode(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		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 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Union[str, Any]				)							->					Optional[int]:


    """simple docstring"""






    a							=		20
    a							=		model_class_name(__lowerCAmelCase				)

    a							=		model.encode(inputs_dict["input_ids"]				)

    a							=		(
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

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

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

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

    a							=		model.decode(__lowerCAmelCase				, __lowerCAmelCase				, decoder_attention_mask=__lowerCAmelCase				)

    a							=		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						):
   _UpperCAmelCase  						=      99

   def 		A				(   self      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		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				, )

    a							=		input_ids.shape[0]
    a							=		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 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self._get_config_and_data()
    a							=		FlaxMBartForSequenceClassification(__lowerCAmelCase				)
    a							=		model(input_ids=__lowerCAmelCase				, decoder_input_ids=__lowerCAmelCase				)
    a							=		(batch_size, config.num_labels)
    self.assertEqual(outputs["logits"].shape				, __lowerCAmelCase				)

   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    a							=		self._get_config_and_data()
    a							=		FlaxMBartForQuestionAnswering(__lowerCAmelCase				)
    a							=		model(input_ids=__lowerCAmelCase				)

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

   def 		A				(   self      :							int				)							->					Optional[int]:


    """simple docstring"""






    a							=		self._get_config_and_data()
    a							=		FlaxMBartForConditionalGeneration(__lowerCAmelCase				)
    a							=		lm_model(input_ids=__lowerCAmelCase				)
    a							=		(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["logits"].shape				, __lowerCAmelCase				)

   def 		A				(   self      :							Any				)							->					Optional[Any]:


    """simple docstring"""






    a							=		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				, )
    a							=		FlaxMBartForConditionalGeneration(__lowerCAmelCase				)
    a							=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				, dtype=np.intaa				)
    a							=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				, dtype=np.intaa				)
    a							=		lm_model(input_ids=__lowerCAmelCase				, decoder_input_ids=__lowerCAmelCase				)
    a							=		(*summary.shape, config.vocab_size)
    self.assertEqual(outputs["logits"].shape				, __lowerCAmelCase				)




   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






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



@require_flax
class 		_lowercase		(							UpperCAmelCase__, unittest.TestCase, UpperCAmelCase__						):
   _UpperCAmelCase  						=      True
   _UpperCAmelCase  						=      (
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   _UpperCAmelCase  						=      (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

   def 		A				(   self      :							Union[str, Any]				)							->					List[str]:


    """simple docstring"""






    a							=		FlaxMBartModelTester(self				)

   def 		A				(   self      :							Union[str, Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

   def 		A				(   self      :							int				)							->					Optional[Any]:


    """simple docstring"""






    a							=		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(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

   def 		A				(   self      :							int				)							->					int:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__				):
      a							=		self._prepare_for_class(__lowerCAmelCase				, __lowerCAmelCase				)
      a							=		model_class(__lowerCAmelCase				)

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

      with self.subTest("JIT Enabled"				):
       a							=		encode_jitted(**__lowerCAmelCase				).to_tuple()

      with self.subTest("JIT Disabled"				):
       with jax.disable_jit():
        a							=		encode_jitted(**__lowerCAmelCase				).to_tuple()

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

   def 		A				(   self      :							Dict				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__				):
      a							=		model_class(__lowerCAmelCase				)
      a							=		model.encode(inputs_dict["input_ids"]				, inputs_dict["attention_mask"]				)

      a							=		{
          """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      :							Optional[int]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							List[Any]				):
       return model.decode(
           decoder_input_ids=__lowerCAmelCase				, decoder_attention_mask=__lowerCAmelCase				, encoder_outputs=__lowerCAmelCase				, )

      with self.subTest("JIT Enabled"				):
       a							=		decode_jitted(**__lowerCAmelCase				).to_tuple()

      with self.subTest("JIT Disabled"				):
       with jax.disable_jit():
        a							=		decode_jitted(**__lowerCAmelCase				).to_tuple()

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




   @slow
   def 		A				(   self      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    for model_class_name in self.all_model_classes:
     a							=		model_class_name.from_pretrained("facebook/mbart-large-cc25"				, from_pt=__lowerCAmelCase				)
     # FlaxMBartForSequenceClassification expects eos token in input_ids
     a							=		np.ones((1, 1)				) * model.config.eos_token_id
     a							=		model(__lowerCAmelCase				)
     self.assertIsNotNone(__lowerCAmelCase				)



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

   @cached_property
   def 		A				(   self      :							Optional[Any]				)							->					Optional[Any]:


    """simple docstring"""






    return AutoTokenizer.from_pretrained(self.model_name				)

   @cached_property
   def 		A				(   self      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				, from_pt=__lowerCAmelCase				)
    return model

   def 		A				(   self      :							str				, **__lowerCAmelCase      :							Optional[Any]				)							->					str:


    """simple docstring"""






    a							=		self.translate_src_text(**__lowerCAmelCase				)
    self.assertListEqual(self.expected_text				, __lowerCAmelCase				)

   def 		A				(   self      :							str				, **__lowerCAmelCase      :							Optional[Any]				)							->					int:


    """simple docstring"""






    a							=		self.tokenizer(self.src_text				, **__lowerCAmelCase				, return_tensors="np"				)
    a							=		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=__lowerCAmelCase				, num_beams=2				, ).sequences
    a							=		self.tokenizer.batch_decode(__lowerCAmelCase				, skip_special_tokens=__lowerCAmelCase				)
    return generated_words




   @slow
   def 		A				(   self      :							Union[str, Any]				)							->					Dict:


    """simple docstring"""






    self._assert_generated_batch_equal_expected()





'''simple docstring'''
from typing import Dict, Optional, Tuple

import torch



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Optional[int]          =					boundaries[1] - boundaries[0]
       snake_case__					:	Optional[Any]          =					boundaries + step / 2
       snake_case__					:	str          =					torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1     )]    ,       dim=0     )
       return bin_centers



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	str          =					_calculate_bin_centers(UpperCAmelCase     )
       return (
           torch.sum(aligned_distance_error_probs * bin_centers    ,       dim=-1     ),
           bin_centers[-1],
       )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	Dict          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	str          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )
       snake_case__       ,   snake_case__					:	List[str]          =					_calculate_expected_aligned_error(
           alignment_confidence_breaks=UpperCAmelCase    ,       aligned_distance_error_probs=UpperCAmelCase    ,       )

       return {
           "aligned_confidence_probs": aligned_confidence_probs,
           "predicted_aligned_error": predicted_aligned_error,
           "max_predicted_aligned_error": max_predicted_aligned_error,
       }






def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = None    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       UpperCAmelCase = 1E-8    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       if residue_weights is None:
              snake_case__					:	List[Any]          =					logits.new_ones(logits.shape[-2]     )

       snake_case__					:	Optional[Any]          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	int          =					_calculate_bin_centers(UpperCAmelCase     )
       torch.sum(UpperCAmelCase     )
       snake_case__					:	Dict          =					logits.shape[-2]
       snake_case__					:	Optional[Any]          =					max(UpperCAmelCase    ,       19     )

       snake_case__					:	Optional[int]          =					1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

       snake_case__					:	Tuple          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )

       snake_case__					:	int          =					1.0 / (1 + (bin_centers**2) / (da**2))
       snake_case__					:	Optional[int]          =					torch.sum(probs * tm_per_bin    ,       dim=-1     )

       snake_case__					:	Optional[Any]          =					residue_weights / (eps + residue_weights.sum())
       snake_case__					:	Any          =					torch.sum(predicted_tm_term * normed_residue_mask    ,       dim=-1     )

       snake_case__					:	Dict          =					per_alignment * residue_weights

       snake_case__					:	List[str]          =					(weighted == torch.max(UpperCAmelCase     )).nonzero()[0]
       return per_alignment[tuple(UpperCAmelCase     )]




