

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_									=    "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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__     :      List[Any]=None       ,     SCREAMING_SNAKE_CASE__     :      Tuple=None       ,     SCREAMING_SNAKE_CASE__     :      str=None       ,     SCREAMING_SNAKE_CASE__     :      Optional[int]=None       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any]=None       ,     )				-> Optional[int]:





    '''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 					A		(				unittest.TestCase					):







    """simple docstring"""





    def __init__( self					:			int,lowercase_					:			int,lowercase_					:			Optional[int]=1_3,lowercase_					:			str=7,lowercase_					:			Tuple=True,lowercase_					:			List[str]=False,lowercase_					:			Any=9_9,lowercase_					:			Any=1_6,lowercase_					:			List[str]=2,lowercase_					:			Optional[int]=4,lowercase_					:			Union[str, Any]=4,lowercase_					:			Dict="gelu",lowercase_					:			Optional[int]=0.1,lowercase_					:			int=0.1,lowercase_					:			Optional[Any]=3_2,lowercase_					:			List[Any]=2,lowercase_					:			Optional[int]=1,lowercase_					:			Any=0,lowercase_					:			int=2,lowercase_					:			List[str]=0.02,)->       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 		snake_case__	( self					:			List[str]						)->       List[str]:





        '''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(lowercase_,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=lowercase_,)
        A__        =  prepare_mbart_inputs_dict(lowercase_,lowercase_,lowercase_						)
        return config, inputs_dict


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





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__        =  2_0
        A__        =  model_class_name(lowercase_						)

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

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

        A__        =  model.init_cache(decoder_input_ids.shape[0],lowercase_,lowercase_						)
        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],lowercase_,decoder_attention_mask=lowercase_,past_key_values=lowercase_,decoder_position_ids=lowercase_,)

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

        A__        =  model.decode(lowercase_,lowercase_						)

        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 		snake_case__	( self					:			Tuple,lowercase_					:			str,lowercase_					:			Optional[Any],lowercase_					:			Optional[int]						)->       List[str]:





        '''simple docstring'''







        A__        =  2_0
        A__        =  model_class_name(lowercase_						)

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

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

        A__        =  model.decode(lowercase_,lowercase_,decoder_attention_mask=lowercase_						)

        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 					A		(				unittest.TestCase					):







    """simple docstring"""





    lowerCamelCase						      =				99


    def 		snake_case__	( self					:			List[Any]						)->       List[Any]:





        '''simple docstring'''







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

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


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





        '''simple docstring'''







        A__ ,		A__ ,		A__        =  self._get_config_and_data()
        A__        =  FlaxMBartForSequenceClassification(lowercase_						)
        A__        =  model(input_ids=lowercase_,decoder_input_ids=lowercase_						)
        A__        =  (batch_size, config.num_labels)
        self.assertEqual(outputs['logits'].shape,lowercase_						)


    def 		snake_case__	( self					:			Dict						)->       Union[str, Any]:





        '''simple docstring'''







        A__ ,		A__ ,		A__        =  self._get_config_and_data()
        A__        =  FlaxMBartForQuestionAnswering(lowercase_						)
        A__        =  model(input_ids=lowercase_						)

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


    def 		snake_case__	( self					:			Dict						)->       Optional[Any]:





        '''simple docstring'''







        A__ ,		A__ ,		A__        =  self._get_config_and_data()
        A__        =  FlaxMBartForConditionalGeneration(lowercase_						)
        A__        =  lm_model(input_ids=lowercase_						)
        A__        =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['logits'].shape,lowercase_						)


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





        '''simple docstring'''







        A__        =  MBartConfig(
            vocab_size=self.vocab_size,d_model=1_4,encoder_layers=2,decoder_layers=2,encoder_attention_heads=2,decoder_attention_heads=2,encoder_ffn_dim=8,decoder_ffn_dim=8,max_position_embeddings=4_8,)
        A__        =  FlaxMBartForConditionalGeneration(lowercase_						)
        A__        =  np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]],dtype=np.intaa						)
        A__        =  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]],dtype=np.intaa						)
        A__        =  lm_model(input_ids=lowercase_,decoder_input_ids=lowercase_						)
        A__        =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['logits'].shape,lowercase_						)


    def 		snake_case__	( self					:			List[str]						)->       int:





        '''simple docstring'''







        A__        =  np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]],dtype=np.intaa						)
        A__        =  shift_tokens_right(lowercase_,1						)
        A__        =  np.equal(lowercase_,1						).astype(np.floataa						).sum()
        A__        =  np.equal(lowercase_,1						).astype(np.floataa						).sum()
        self.assertEqual(shifted.shape,input_ids.shape						)
        self.assertEqual(lowercase_,n_pad_before - 1						)
        self.assertTrue(np.equal(shifted[:, 0],2						).all()						)




@require_flax
class 					A		(				_UpperCAmelCase       ,				unittest.TestCase       ,				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				True
    lowerCamelCase						      =				(
        (
            FlaxMBartModel,
            FlaxMBartForConditionalGeneration,
            FlaxMBartForSequenceClassification,
            FlaxMBartForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    lowerCamelCase						      =				(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


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





        '''simple docstring'''







        A__        =  FlaxMBartModelTester(self						)


    def 		snake_case__	( self					:			List[Any]						)->       List[Any]:





        '''simple docstring'''







        A__ ,		A__        =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(lowercase_,lowercase_,lowercase_						)


    def 		snake_case__	( self					:			str						)->       str:





        '''simple docstring'''







        A__ ,		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(lowercase_,lowercase_,lowercase_						)


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





        '''simple docstring'''







        A__ ,		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(lowercase_,lowercase_						)
                A__        =  model_class(lowercase_						)

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

                with self.subTest('JIT Enabled'						):
                    A__        =  encode_jitted(**lowercase_						).to_tuple()

                with self.subTest('JIT Disabled'						):
                    with jax.disable_jit():
                        A__        =  encode_jitted(**lowercase_						).to_tuple()

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


    def 		snake_case__	( self					:			Tuple						)->       int:





        '''simple docstring'''







        A__ ,		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(lowercase_						)
                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(lowercase_					:			Optional[int],lowercase_					:			Optional[int],lowercase_					:			List[str]						):
                    return model.decode(
                        decoder_input_ids=lowercase_,decoder_attention_mask=lowercase_,encoder_outputs=lowercase_,)

                with self.subTest('JIT Enabled'						):
                    A__        =  decode_jitted(**lowercase_						).to_tuple()

                with self.subTest('JIT Disabled'						):
                    with jax.disable_jit():
                        A__        =  decode_jitted(**lowercase_						).to_tuple()

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


    @slow
    def 		snake_case__	( self					:			Optional[Any]						)->       Dict:





        '''simple docstring'''







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




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







    """simple docstring"""





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


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





        '''simple docstring'''







        return AutoTokenizer.from_pretrained(self.model_name						)


    @cached_property
    def 		snake_case__	( self					:			int						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name,from_pt=lowercase_						)
        return model


    def 		snake_case__	( self					:			List[Any],**lowercase_					:			Dict						)->       Dict:





        '''simple docstring'''







        A__        =  self.translate_src_text(**lowercase_						)
        self.assertListEqual(self.expected_text,lowercase_						)


    def 		snake_case__	( self					:			List[str],**lowercase_					:			str						)->       List[Any]:





        '''simple docstring'''







        A__        =  self.tokenizer(self.src_text,**lowercase_,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=lowercase_,num_beams=2,).sequences
        A__        =  self.tokenizer.batch_decode(lowercase_,skip_special_tokens=lowercase_						)
        return generated_words


    @slow
    def 		snake_case__	( self					:			str						)->       List[Any]:





        '''simple docstring'''







        self._assert_generated_batch_equal_expected()




import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    def __init__( self					:			str,*lowercase_					:			List[Any],**lowercase_					:			Dict						)->       None:





        '''simple docstring'''







        warnings.warn(
            'The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use MobileNetV1ImageProcessor instead.',lowercase_,)
        super().__init__(*lowercase_,**lowercase_						)

