
'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel




@require_tf
class     UpperCAmelCase_	:






						'''simple docstring'''






						_lowercase						: List[str]										=  MBartConfig
						_lowercase						: Any										=  {}
						_lowercase						: Union[str, Any]										=  '''gelu'''




						def __init__(   self       ,  _lowercase       ,  _lowercase=13       ,  _lowercase=7       ,  _lowercase=True       ,  _lowercase=False       ,  _lowercase=99       ,  _lowercase=32       ,  _lowercase=2       ,  _lowercase=4       ,  _lowercase=37       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=20       ,  _lowercase=2       ,  _lowercase=1       ,  _lowercase=0       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					parent
												_lowerCAmelCase    		=					batch_size
												_lowerCAmelCase    		=					seq_length
												_lowerCAmelCase    		=					is_training
												_lowerCAmelCase    		=					use_labels
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					hidden_dropout_prob
												_lowerCAmelCase    		=					attention_probs_dropout_prob
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					eos_token_id
												_lowerCAmelCase    		=					pad_token_id
												_lowerCAmelCase    		=					bos_token_id




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					ids_tensor([self.batch_size, self.seq_length - 1]       ,  self.vocab_size )
												_lowerCAmelCase    		=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )       ,  1 )
												_lowerCAmelCase    		=					tf.concat([input_ids, eos_tensor]       ,  axis=1 )

												_lowerCAmelCase    		=					ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size )

												_lowerCAmelCase    		=					self.config_cls(
												    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_ids=[2]       ,  bos_token_id=self.bos_token_id       ,  pad_token_id=self.pad_token_id       ,  decoder_start_token_id=self.pad_token_id       ,  **self.config_updates       ,  )
												_lowerCAmelCase    		=					prepare_mbart_inputs_dict(_lowercase       ,  _lowercase       ,  _lowercase )
												return config, inputs_dict




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFMBartModel(config=_lowercase ).get_decoder()
												_lowerCAmelCase    		=					inputs_dict["""input_ids"""]

												_lowerCAmelCase    		=					input_ids[:1, :]
												_lowerCAmelCase    		=					inputs_dict["""attention_mask"""][:1, :]
												_lowerCAmelCase    		=					inputs_dict["""head_mask"""]
												_lowerCAmelCase    		=					1

												# first forward pass
												_lowerCAmelCase    		=					model(_lowercase       ,  attention_mask=_lowercase       ,  head_mask=_lowercase       ,  use_cache=_lowercase )

												_lowerCAmelCase		,       _lowerCAmelCase    		=					outputs.to_tuple()
												_lowerCAmelCase    		=					past_key_values[1]






def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Union[str, Any]=None					,      __lowerCamelCase :Optional[int]=None					,      __lowerCamelCase :Optional[int]=None					,      __lowerCamelCase :List[Any]=None					,      __lowerCamelCase :Optional[Any]=None					,      ):
						if attention_mask is None:
												_lowerCAmelCase    		=					tf.cast(tf.math.not_equal(__lowerCamelCase					,      config.pad_token_id						)					,      tf.inta						)
						if decoder_attention_mask is None:
												_lowerCAmelCase    		=					tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape					,      dtype=tf.inta						),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,      config.pad_token_id						)					,      tf.inta						),
												    ]					,      axis=-1					,      )
						if head_mask is None:
												_lowerCAmelCase    		=					tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
						if decoder_head_mask is None:
												_lowerCAmelCase    		=					tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
						if cross_attn_head_mask is None:
												_lowerCAmelCase    		=					tf.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,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}




@require_tf
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: str										=  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						_lowercase						: Optional[int]										=  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
						_lowercase						: Tuple										=  (
						    {
						        '''conversational''': TFMBartForConditionalGeneration,
						        '''feature-extraction''': TFMBartModel,
						        '''summarization''': TFMBartForConditionalGeneration,
						        '''text2text-generation''': TFMBartForConditionalGeneration,
						        '''translation''': TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowercase						: Any										=  True
						_lowercase						: List[Any]										=  False
						_lowercase						: str										=  False




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																		# Exception encountered when calling layer '...'
																		return True

												return False




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFMBartModelTester(self )
												_lowerCAmelCase    		=					ConfigTester(self       ,  config_class=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_lowercase )






@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






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




						@cached_property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return AutoTokenizer.from_pretrained(self.model_name )




						@cached_property
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
												return model




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.translate_src_text(**_lowercase )
												self.assertListEqual(self.expected_text       ,  _lowercase )




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.tokenizer(self.src_text       ,  **_lowercase       ,  return_tensors="""tf""" )
												_lowerCAmelCase    		=					self.model.generate(
												    model_inputs.input_ids       ,  attention_mask=model_inputs.attention_mask       ,  num_beams=2 )
												_lowerCAmelCase    		=					self.tokenizer.batch_decode(_lowercase       ,  skip_special_tokens=_lowercase )
												return generated_words




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												self._assert_generated_batch_equal_expected()




'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
      import jax

      from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=100			,     lowerCamelCase=13			,     lowerCamelCase=30			,     lowerCamelCase=2			,     lowerCamelCase=3			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=37			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=10			,     lowerCamelCase=0.02			,     lowerCamelCase=3			,     ):


          '''simple docstring'''





          __A	:       Tuple      =							parent
          __A	:       Dict      =							vocab_size
          __A	:       Union[str, Any]      =							batch_size
          __A	:       str      =							image_size
          __A	:       Optional[Any]      =							patch_size
          __A	:       Optional[Any]      =							num_channels
          __A	:       Optional[Any]      =							is_training
          __A	:       Any      =							use_labels
          __A	:       List[str]      =							hidden_size
          __A	:       Union[str, Any]      =							num_hidden_layers
          __A	:       Optional[Any]      =							num_attention_heads
          __A	:       Optional[int]      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       Dict      =							hidden_dropout_prob
          __A	:       str      =							attention_probs_dropout_prob
          __A	:       Optional[int]      =							type_sequence_label_size
          __A	:       Optional[int]      =							initializer_range

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __A	:       Optional[int]      =							(image_size // patch_size) ** 2
          __A	:       Union[str, Any]      =							num_patches + 1


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          __A	:       str      =							None
          if self.use_labels:
               __A	:       List[str]      =							ids_tensor([self.batch_size]			,     self.type_sequence_label_size		)

          __A	:       Dict      =							BeitConfig(
              vocab_size=self.vocab_size			,     image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     is_decoder=lowerCamelCase			,     initializer_range=self.initializer_range			,     )

          return config, pixel_values, labels


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[int]      =							FlaxBeitModel(config=lowerCamelCase		)
          __A	:       Optional[Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Dict      =							FlaxBeitForMaskedImageModeling(config=lowerCamelCase		)
          __A	:       Union[str, Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length - 1, self.vocab_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							self.type_sequence_label_size
          __A	:       List[str]      =							FlaxBeitForImageClassification(config=lowerCamelCase		)
          __A	:       Tuple      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)

          # test greyscale images
          __A	:       Tuple      =							1
          __A	:       Optional[Any]      =							FlaxBeitForImageClassification(lowerCamelCase		)

          __A	:       Union[str, Any]      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          __A	:       str      =							model(lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.prepare_config_and_inputs()
          (
              (
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,
          )	:       Dict      =							config_and_inputs
          __A	:       Dict      =							{"pixel_values": pixel_values}
          return config, inputs_dict







@require_flax
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(
         (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
     )


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							FlaxBeitModelTester(self		)
          __A	:       List[Any]      =							ConfigTester(self			,     config_class=lowerCamelCase			,     has_text_modality=lowerCamelCase			,     hidden_size=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       int      =							model_class(lowerCamelCase		)
               __A	:       Dict      =							inspect.signature(model.__call__		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A	:       List[str]      =							[*signature.parameters.keys()]

               __A	:       Optional[Any]      =							["pixel_values"]
               self.assertListEqual(arg_names[:1]			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       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__		):
                    __A	:       Optional[int]      =							self._prepare_for_class(lowerCamelCase			,     lowerCamelCase		)
                    __A	:       Optional[int]      =							model_class(lowerCamelCase		)

                    @jax.jit
                    def model_jitted(lowerCamelCase			,     **lowerCamelCase		):
                         return model(pixel_values=lowerCamelCase			,     **lowerCamelCase		)

                    with self.subTest("JIT Enabled"		):
                         __A	:       Tuple      =							model_jitted(**lowerCamelCase		).to_tuple()

                    with self.subTest("JIT Disabled"		):
                         with jax.disable_jit():
                              __A	:       Dict      =							model_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 lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for model_class_name in self.all_model_classes:
               __A	:       Any      =							model_class_name.from_pretrained("microsoft/beit-base-patch16-224"		)
               __A	:       Optional[int]      =							model(np.ones((1, 3, 224, 224)		)		)
               self.assertIsNotNone(lowerCamelCase		)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Dict      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image







@require_vision
@require_flax
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"		) if is_vision_available() else None


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"		)

          __A	:       List[Any]      =							self.default_image_processor
          __A	:       Dict      =							prepare_img()
          __A	:       str      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		).pixel_values

          # prepare bool_masked_pos
          __A	:       List[Any]      =							np.ones((1, 196)			,     dtype=lowerCamelCase		)

          # forward pass
          __A	:       Union[str, Any]      =							model(pixel_values=lowerCamelCase			,     bool_masked_pos=lowerCamelCase		)
          __A	:       Dict      =							outputs.logits

          # verify the logits
          __A	:       int      =							(1, 196, 8192)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

          __A	:       Optional[int]      =							np.array(
              [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]		)

          self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]			,     lowerCamelCase			,     atol=1E-2		)		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"		)

          __A	:       Any      =							self.default_image_processor
          __A	:       List[Any]      =							prepare_img()
          __A	:       List[Any]      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		)

          # forward pass
          __A	:       int      =							model(**lowerCamelCase		)
          __A	:       Any      =							outputs.logits

          # verify the logits
          __A	:       str      =							(1, 1000)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

          __A	:       int      =							np.array([-1.2385, -1.0987, -1.0108]		)

          self.assertTrue(np.allclose(logits[0, :3]			,     lowerCamelCase			,     atol=1E-4		)		)

          __A	:       Optional[int]      =							281
          self.assertEqual(logits.argmax(-1		).item()			,     lowerCamelCase		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"		)

          __A	:       Optional[int]      =							self.default_image_processor
          __A	:       Tuple      =							prepare_img()
          __A	:       List[str]      =							image_processor(images=lowerCamelCase			,     return_tensors="np"		)

          # forward pass
          __A	:       List[Any]      =							model(**lowerCamelCase		)
          __A	:       Dict      =							outputs.logits

          # verify the logits
          __A	:       Union[str, Any]      =							(1, 2_1841)
          self.assertEqual(logits.shape			,     lowerCamelCase		)

          __A	:       Tuple      =							np.array([1.6881, -0.2787, 0.5901]		)

          self.assertTrue(np.allclose(logits[0, :3]			,     lowerCamelCase			,     atol=1E-4		)		)

          __A	:       Optional[int]      =							2396
          self.assertEqual(logits.argmax(-1		).item()			,     lowerCamelCase		)

