


'''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
       __lowercase:		Tuple    						=  "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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Tuple     ,		_UpperCamelCase   :					Union[str, Any]=None     ,		_UpperCamelCase   :					List[Any]=None     ,		_UpperCamelCase   :					str=None     ,		_UpperCamelCase   :					str=None     ,		_UpperCamelCase   :					str=None     ,		_UpperCamelCase   :					Any=None     ,		)		->       List[Any]:





   '''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 			UpperCAmelCase			(							unittest.TestCase):

 def __init__(			self			:						List[Any],       a_			:						Union[str, Any],       a_			:						str=13,       a_			:						Dict=7,       a_			:						List[Any]=True,       a_			:						Union[str, Any]=False,       a_			:						Tuple=99,       a_			:						List[Any]=16,       a_			:						List[Any]=2,       a_			:						Optional[int]=4,       a_			:						List[Any]=4,       a_			:						str="gelu",       a_			:						Union[str, Any]=0.1,       a_			:						str=0.1,       a_			:						Optional[Any]=32,       a_			:						Tuple=2,       a_			:						List[Any]=1,       a_			:						int=0,       a_			:						str=2,       a_			:						Optional[int]=0.02,       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.prepare_config_and_inputs()
    return config, inputs_dict

 def 		lowercase_    (			self			:						List[Any],       a_			:						Tuple,       a_			:						str,       a_			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    20
    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 		lowercase_    (			self			:						List[str],       a_			:						Optional[int],       a_			:						int,       a_			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    20
    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 			UpperCAmelCase			(							unittest.TestCase):
 _lowerCamelCase			:       Optional[Any]            =       99

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    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__			    =    input_ids.shape[0]
    UpperCamelCase__			    =    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 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    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__			    =    FlaxMBartForConditionalGeneration(a_       )
    UpperCamelCase__			    =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],       dtype=np.intaa       )
    UpperCamelCase__			    =    np.array([[82, 71, 82, 18, 2], [58, 68, 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 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 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 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase		,      SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Optional[int]            =       True
 _lowerCamelCase			:       List[Any]            =       (
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 _lowerCamelCase			:       Dict            =       (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    FlaxMBartModelTester(self       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    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_			:						List[Any],       a_			:						Optional[int]=None,       **a_			:						Any       ):
             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 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    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_			:						List[str],       a_			:						Tuple,       a_			:						Dict       ):
             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 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    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 			UpperCAmelCase			(							unittest.TestCase):
 _lowerCamelCase			:       Tuple            =       [
     ' UN Chief Says There Is No Military Solution in Syria',
 ]
 _lowerCamelCase			:       Union[str, Any]            =       [
     'Şeful ONU declară că nu există o soluţie militară în Siria',
 ]
 _lowerCamelCase			:       int            =       'facebook/mbart-large-en-ro'

 @cached_property
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    return AutoTokenizer.from_pretrained(self.model_name       )

 @cached_property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,       from_pt=a_       )
    return model

 def 		lowercase_    (			self			:						str,       **a_			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.translate_src_text(**a_       )
    self.assertListEqual(self.expected_text,       a_       )

 def 		lowercase_    (			self			:						Optional[Any],       **a_			:						Dict       ):





    """simple docstring"""



    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 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    self._assert_generated_batch_equal_expected()



'''simple docstring'''

from __future__ import annotations



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					list[list[int]]     ,		_UpperCamelCase   :					list[list[int]] )		->       list[int]:





   '''simple docstring'''







   assert len(_UpperCamelCase ) == len(_UpperCamelCase )

   UpperCamelCase__			    =    len(_UpperCamelCase )
   UpperCamelCase__			    =    list(range(_UpperCamelCase ) )
   UpperCamelCase__			    =    [-1] * n  # who the donor has donated to
   UpperCamelCase__			    =    [-1] * n  # who the recipient has received from
   UpperCamelCase__			    =    [0] * n

   while unmatched_donors:
      UpperCamelCase__			    =    unmatched_donors[0]
      UpperCamelCase__			    =    donor_pref[donor]
      UpperCamelCase__			    =    donor_preference[num_donations[donor]]
      num_donations[donor] += 1
      UpperCamelCase__			    =    recipient_pref[recipient]
      UpperCamelCase__			    =    rec_record[recipient]

      if prev_donor != -1:
         if rec_preference.index(_UpperCamelCase ) > rec_preference.index(_UpperCamelCase ):
            UpperCamelCase__			    =    donor
            UpperCamelCase__			    =    recipient
            unmatched_donors.append(_UpperCamelCase )
            unmatched_donors.remove(_UpperCamelCase )
      else:
         UpperCamelCase__			    =    donor
         UpperCamelCase__			    =    recipient
         unmatched_donors.remove(_UpperCamelCase )
   return donor_record