

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				:
	_lowercase  :   int       				=  MBartConfig
	_lowercase  :   Optional[Any]       				=  {}
	_lowercase  :   Any       				=  "gelu"







	def __init__(   self: Dict  , __UpperCamelCase: Dict  , __UpperCamelCase: Optional[int]=13  , __UpperCamelCase: Any=7  , __UpperCamelCase: Any=True  , __UpperCamelCase: List[Any]=False  , __UpperCamelCase: Optional[int]=99  , __UpperCamelCase: Union[str, Any]=32  , __UpperCamelCase: Any=2  , __UpperCamelCase: Dict=4  , __UpperCamelCase: Optional[int]=37  , __UpperCamelCase: int=0.1  , __UpperCamelCase: List[str]=0.1  , __UpperCamelCase: int=20  , __UpperCamelCase: Dict=2  , __UpperCamelCase: Any=1  , __UpperCamelCase: Tuple=0  , ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





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

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

						__magic_name__							=    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  , )
						__magic_name__							=    prepare_mbart_inputs_dict(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						return config, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    TFMBartModel(config=__UpperCamelCase      ).get_decoder()
						__magic_name__							=    inputs_dict['input_ids']

						__magic_name__							=    input_ids[:1, :]
						__magic_name__							=    inputs_dict['attention_mask'][:1, :]
						__magic_name__							=    inputs_dict['head_mask']
						__magic_name__							=    1

						# first forward pass
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase  , head_mask=__UpperCamelCase  , use_cache=__UpperCamelCase      )

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




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Optional[int]					,a_				:					List[str]					,a_				:					Optional[int]=None					,a_				:					Tuple=None					,a_				:					Tuple=None					,a_				:					Union[str, Any]=None					,a_				:					List[str]=None					,)				->	Optional[Any]:




					'''simple docstring'''







					if attention_mask is None:
										__magic_name__							=    tf.cast(tf.math.not_equal(a_					,config.pad_token_id				)					,tf.inta				)
					if decoder_attention_mask is None:
										__magic_name__							=    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:
										__magic_name__							=    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
					if decoder_head_mask is None:
										__magic_name__							=    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
					if cross_attn_head_mask is None:
										__magic_name__							=    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			):
	_lowercase  :   int       				=  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	_lowercase  :   Union[str, Any]       				=  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
	_lowercase  :   int       				=  (
	    {
	        "conversational": TFMBartForConditionalGeneration,
	        "feature-extraction": TFMBartModel,
	        "summarization": TFMBartForConditionalGeneration,
	        "text2text-generation": TFMBartForConditionalGeneration,
	        "translation": TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	_lowercase  :   int       				=  True
	_lowercase  :   Any       				=  False
	_lowercase  :   Optional[Any]       				=  False







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: List[str]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str      ):

						'''simple docstring'''





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

						return False







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    TFMBartModelTester(self      )
						__magic_name__							=    ConfigTester(self  , config_class=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						self.config_tester.run_common_tests()







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    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 				__UpperCamelCase				(					unittest.TestCase			):
	_lowercase  :   Optional[int]       				=  [
	    " UN Chief Says There Is No Military Solution in Syria",
	]
	_lowercase  :   str       				=  [
	    "Şeful ONU declară că nu există o soluţie militară în Siria",
	]
	_lowercase  :   List[str]       				=  "facebook/mbart-large-en-ro"







	@cached_property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						return AutoTokenizer.from_pretrained(self.model_name      )







	@cached_property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.translate_src_text(**__UpperCamelCase      )
						self.assertListEqual(self.expected_text  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , **__UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer(self.src_text  , **__UpperCamelCase  , return_tensors='tf'      )
						__magic_name__							=    self.model.generate(
						    model_inputs.input_ids  , attention_mask=model_inputs.attention_mask  , num_beams=2      )
						__magic_name__							=    self.tokenizer.batch_decode(__UpperCamelCase  , skip_special_tokens=__UpperCamelCase      )
						return generated_words







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						self._assert_generated_batch_equal_expected()







from typing import TYPE_CHECKING

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


A__						=							{"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "VanForImageClassification",
						    "VanModel",
						    "VanPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						A__						=							_LazyModule(__name__, globals()["__file__"], _import_structure)




