




'''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     _a						:
       __a       :  int            =      MBartConfig
       __a       :  Tuple            =      {}
       __a       :  Dict            =      """gelu"""
       def __init__(			self		:			Any     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Any=13     ,       lowercase		:			List[Any]=7     ,       lowercase		:			List[str]=True     ,       lowercase		:			Optional[int]=False     ,       lowercase		:			List[str]=99     ,       lowercase		:			str=32     ,       lowercase		:			Any=2     ,       lowercase		:			Tuple=4     ,       lowercase		:			Optional[Any]=37     ,       lowercase		:			Union[str, Any]=0.1     ,       lowercase		:			List[str]=0.1     ,       lowercase		:			Tuple=20     ,       lowercase		:			int=2     ,       lowercase		:			Tuple=1     ,       lowercase		:			int=0     ,       ):



           '''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_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	eos_token_id
           UpperCAmelCase     =	pad_token_id
           UpperCAmelCase     =	bos_token_id
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

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

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

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







       def 					A   (			self		:			Tuple     ,       lowercase		:			str     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFMBartModel(config=lowercase       ).get_decoder()
           UpperCAmelCase     =	inputs_dict['''input_ids''']

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

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

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






def 		snake_case_							(_a			:							str					,     _a			:							Any					,     _a			:							Tuple					,     _a			:							List[Any]=None					,     _a			:							Any=None					,     _a			:							str=None					,     _a			:							int=None					,     _a			:							Union[str, Any]=None					,     ):
    if attention_mask is None:
        UpperCAmelCase     =	tf.cast(tf.math.not_equal(_a					,     config.pad_token_id  )					,     tf.inta  )
    if decoder_attention_mask is None:
        UpperCAmelCase     =	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:
        UpperCAmelCase     =	tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
    if decoder_head_mask is None:
        UpperCAmelCase     =	tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
    if cross_attn_head_mask is None:
        UpperCAmelCase     =	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     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  Tuple            =      (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
       __a       :  Tuple            =      (TFMBartForConditionalGeneration,) if is_tf_available() else ()
       __a       :  Any            =      (
           {
               """conversational""": TFMBartForConditionalGeneration,
               """feature-extraction""": TFMBartModel,
               """summarization""": TFMBartForConditionalGeneration,
               """text2text-generation""": TFMBartForConditionalGeneration,
               """translation""": TFMBartForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       __a       :  Union[str, Any]            =      True
       __a       :  Optional[int]            =      False
       __a       :  str            =      False
       def 					A   (			self		:			Any     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			Tuple     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

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

           return False
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFMBartModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()







       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	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     _a						(			unittest.TestCase				):
       __a       :  Tuple            =      [
           """ UN Chief Says There Is No Military Solution in Syria""",
       ]
       __a       :  List[Any]            =      [
           """Şeful ONU declară că nu există o soluţie militară în Siria""",
       ]
       __a       :  Any            =      """facebook/mbart-large-en-ro"""
       @cached_property
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           return AutoTokenizer.from_pretrained(self.model_name       )
       @cached_property
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
           return model
       def 					A   (			self		:			Optional[Any]     ,       **lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.translate_src_text(**lowercase       )
           self.assertListEqual(self.expected_text     ,       lowercase       )
       def 					A   (			self		:			int     ,       **lowercase		:			Tuple       ):



           '''simple docstring'''

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







       @slow
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()










'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


A									=logging.get_logger(__name__)


class     _a						(			__a				):
       def __init__(			self		:			List[str]     ,       *lowercase		:			List[str]     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

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




