
'''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       _lowerCAmelCase   :







							'''simple docstring'''

							lowerCAmelCase_ 						=						MBartConfig
							lowerCAmelCase_ 						=						{}
							lowerCAmelCase_ 						=						"gelu"

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=13  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=False  ,  UpperCAmelCase=99  ,  UpperCAmelCase=32  ,  UpperCAmelCase=2  ,  UpperCAmelCase=4  ,  UpperCAmelCase=37  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=20  ,  UpperCAmelCase=2  ,  UpperCAmelCase=1  ,  UpperCAmelCase=0  ,  )					->       Optional[int]:
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  seq_length
														_snake_case       =  is_training
														_snake_case       =  use_labels
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  eos_token_id
														_snake_case       =  pad_token_id
														_snake_case       =  bos_token_id

							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  ids_tensor([self.batch_size, self.seq_length - 1]  ,  self.vocab_size  )
														_snake_case       =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )  ,  1  )
														_snake_case       =  tf.concat([input_ids, eos_tensor]  ,  axis=1  )

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

														_snake_case       =  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  ,  )
														_snake_case       =  prepare_mbart_inputs_dict(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )
														return config, inputs_dict





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  TFMBartModel(config=UpperCAmelCase  ).get_decoder()
														_snake_case       =  inputs_dict["""input_ids"""]

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

														# first forward pass
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  head_mask=UpperCAmelCase  ,  use_cache=UpperCAmelCase  )

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE=None							, _SCREAMING_SNAKE_CASE=None							, ):
							if attention_mask is None:
														_snake_case       =  tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE							, config.pad_token_id				)							, tf.inta				)
							if decoder_attention_mask is None:
														_snake_case       =  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:
														_snake_case       =  tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
							if decoder_head_mask is None:
														_snake_case       =  tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
							if cross_attn_head_mask is None:
														_snake_case       =  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       _lowerCAmelCase   (   __snake_case					,							__snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							lowerCAmelCase_ 						=						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
							lowerCAmelCase_ 						=						(
							    {
							        "conversational": TFMBartForConditionalGeneration,
							        "feature-extraction": TFMBartModel,
							        "summarization": TFMBartForConditionalGeneration,
							        "text2text-generation": TFMBartForConditionalGeneration,
							        "translation": TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCAmelCase_ 						=						True
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
														if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																					# Exception encountered when calling layer '...'
																					return True

														return False

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  TFMBartModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  )

							def 	lowercase							(self  )					->       Optional[Any]:
														self.config_tester.run_common_tests()





							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase  )


@require_sentencepiece
@require_tokenizers
@require_tf
class       _lowerCAmelCase   (   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 	lowercase							(self  )					->       int:
														return AutoTokenizer.from_pretrained(self.model_name  )

							@cached_property
							def 	lowercase							(self  )					->       str:
														_snake_case       =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
														return model

							def 	lowercase							(self  ,  **UpperCAmelCase  )					->       Any:
														_snake_case       =  self.translate_src_text(**UpperCAmelCase  )
														self.assertListEqual(self.expected_text  ,  UpperCAmelCase  )

							def 	lowercase							(self  ,  **UpperCAmelCase  )					->       Union[str, Any]:
														_snake_case       =  self.tokenizer(self.src_text  ,  **UpperCAmelCase  ,  return_tensors="""tf"""  )
														_snake_case       =  self.model.generate(
														    model_inputs.input_ids  ,  attention_mask=model_inputs.attention_mask  ,  num_beams=2  )
														_snake_case       =  self.tokenizer.batch_decode(UpperCAmelCase  ,  skip_special_tokens=UpperCAmelCase  )
														return generated_words





							@slow
							def 	lowercase							(self  )					->       Optional[int]:
														self._assert_generated_batch_equal_expected()

'''simple docstring'''
import logging

from transformers.configuration_utils import PretrainedConfig


__lowerCAmelCase												=	logging.getLogger(__name__)


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"masked_bert"

							def __init__(self  ,  UpperCAmelCase=30522  ,  UpperCAmelCase=768  ,  UpperCAmelCase=12  ,  UpperCAmelCase=12  ,  UpperCAmelCase=3072  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-1_2  ,  UpperCAmelCase=0  ,  UpperCAmelCase="topK"  ,  UpperCAmelCase="constant"  ,  UpperCAmelCase=0.0  ,  **UpperCAmelCase  ,  )					->       int:
														super().__init__(pad_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )

														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_act
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  initializer_range
														_snake_case       =  layer_norm_eps
														_snake_case       =  pruning_method
														_snake_case       =  mask_init
														_snake_case       =  mask_scale