
"""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
  SCREAMING_SNAKE_CASE_  					= '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		(		a_      :		Union[str, Any]     ,	a_      :		Tuple     ,	a_      :		Tuple=None     ,	a_      :		Any=None     ,	a_      :		Optional[int]=None     ,	a_      :		List[str]=None     ,	a_      :		Tuple=None     ,	a_      :		Tuple=None     ,	)  ->							Optional[int]:
   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      __a      (       unittest.TestCase      ):







       def __init__(       self				,lowerCamelCase_				,lowerCamelCase_=13				,lowerCamelCase_=7				,lowerCamelCase_=True				,lowerCamelCase_=False				,lowerCamelCase_=99				,lowerCamelCase_=16				,lowerCamelCase_=2				,lowerCamelCase_=4				,lowerCamelCase_=4				,lowerCamelCase_="gelu"				,lowerCamelCase_=0.1				,lowerCamelCase_=0.1				,lowerCamelCase_=32				,lowerCamelCase_=2				,lowerCamelCase_=1				,lowerCamelCase_=0				,lowerCamelCase_=2				,lowerCamelCase_=0.0_2				,):



          '''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  _snake_case      (       self		):



          '''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(lowerCamelCase_				,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=lowerCamelCase_				,)
          _UpperCAmelCase 						=       prepare_mbart_inputs_dict(lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		)
          return config, inputs_dict







       def  _snake_case      (       self		):



          '''simple docstring'''

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







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       20
          _UpperCAmelCase 						=       model_class_name(lowerCamelCase_		)

          _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]				,lowerCamelCase_				,lowerCamelCase_		)
          _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]				,lowerCamelCase_				,decoder_attention_mask=lowerCamelCase_				,past_key_values=lowerCamelCase_				,decoder_position_ids=lowerCamelCase_				,)

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

          _UpperCAmelCase 						=       model.decode(lowerCamelCase_				,lowerCamelCase_		)

          _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  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       20
          _UpperCAmelCase 						=       model_class_name(lowerCamelCase_		)

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

          _UpperCAmelCase 						=       model.decode(lowerCamelCase_				,lowerCamelCase_				,decoder_attention_mask=lowerCamelCase_		)

          _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      __a      (       unittest.TestCase      ):
       _lowerCamelCase				:				int   			=      99







       def  _snake_case      (       self		):



          '''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  _snake_case      (       self		):



          '''simple docstring'''

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







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       self._get_config_and_data()
          _UpperCAmelCase 						=       FlaxMBartForQuestionAnswering(lowerCamelCase_		)
          _UpperCAmelCase 						=       model(input_ids=lowerCamelCase_		)

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







       def  _snake_case      (       self		):



          '''simple docstring'''

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







       def  _snake_case      (       self		):



          '''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(lowerCamelCase_		)
          _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=lowerCamelCase_				,decoder_input_ids=lowerCamelCase_		)
          _UpperCAmelCase 						=       (*summary.shape, config.vocab_size)
          self.assertEqual(outputs["logits"].shape				,lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''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(lowerCamelCase_				,1		)
          _UpperCAmelCase 						=       np.equal(lowerCamelCase_				,1		).astype(np.floataa		).sum()
          _UpperCAmelCase 						=       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      __a      (       lowerCAmelCase_					,						unittest.TestCase					,						lowerCAmelCase_      ):
       _lowerCamelCase				:				int   			=      True
       _lowerCamelCase				:				Optional[Any]   			=      (
           (
               FlaxMBartModel,
               FlaxMBartForConditionalGeneration,
               FlaxMBartForSequenceClassification,
               FlaxMBartForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       _lowerCamelCase				:				Any   			=      (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       FlaxMBartModelTester(self		)







       def  _snake_case      (       self		):



          '''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(lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''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(lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		)







       def  _snake_case      (       self		):



          '''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(lowerCamelCase_				,lowerCamelCase_		)
                _UpperCAmelCase 						=       model_class(lowerCamelCase_		)

                @jax.jit
                def encode_jitted(lowerCamelCase_				,lowerCamelCase_=None				,**lowerCamelCase_		):
                   return model.encode(input_ids=lowerCamelCase_				,attention_mask=lowerCamelCase_		)

                with self.subTest("JIT Enabled"		):
                   _UpperCAmelCase 						=       encode_jitted(**lowerCamelCase_		).to_tuple()

                with self.subTest("JIT Disabled"		):
                   with jax.disable_jit():
                      _UpperCAmelCase 						=       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  _snake_case      (       self		):



          '''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(lowerCamelCase_		)
                _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(lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):
                   return model.decode(
                       decoder_input_ids=lowerCamelCase_				,decoder_attention_mask=lowerCamelCase_				,encoder_outputs=lowerCamelCase_				,)

                with self.subTest("JIT Enabled"		):
                   _UpperCAmelCase 						=       decode_jitted(**lowerCamelCase_		).to_tuple()

                with self.subTest("JIT Disabled"		):
                   with jax.disable_jit():
                      _UpperCAmelCase 						=       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  _snake_case      (       self		):



          '''simple docstring'''

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






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







       @cached_property
       def  _snake_case      (       self		):



          '''simple docstring'''

          return AutoTokenizer.from_pretrained(self.model_name		)







       @cached_property
       def  _snake_case      (       self		):



          '''simple docstring'''

          _UpperCAmelCase 						=       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				,from_pt=lowerCamelCase_		)
          return model







       def  _snake_case      (       self				,**lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.translate_src_text(**lowerCamelCase_		)
          self.assertListEqual(self.expected_text				,lowerCamelCase_		)







       def  _snake_case      (       self				,**lowerCamelCase_		):



          '''simple docstring'''

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







       @slow
       def  _snake_case      (       self		):



          '''simple docstring'''

          self._assert_generated_batch_equal_expected()



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_  					= {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DetrForObjectDetection',
      'DetrForSegmentation',
      'DetrModel',
      'DetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_detr import DetrFeatureExtractor
    from .image_processing_detr import DetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_detr import (
        DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DetrForObjectDetection,
        DetrForSegmentation,
        DetrModel,
        DetrPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE_  					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

