





'''simple docstring'''

from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel






@require_tf
class      UpperCAmelCase			:
 __lowercase     	=       MarianConfig
 __lowercase     	=       {}
 __lowercase     	=       """gelu"""
 def __init__(       self   :Any		, lowercase_   :Union[str, Any]		, lowercase_   :List[str]=13		, lowercase_   :str=7		, lowercase_   :Optional[Any]=True		, lowercase_   :Optional[int]=False		, lowercase_   :Tuple=99		, lowercase_   :str=32		, lowercase_   :Dict=2		, lowercase_   :int=4		, lowercase_   :Union[str, Any]=37		, lowercase_   :int=0.1		, lowercase_   :Optional[Any]=0.1		, lowercase_   :List[str]=20		, lowercase_   :List[str]=2		, lowercase_   :List[str]=1		, lowercase_   :Tuple=0		, )->							Dict:
     A__       =    parent
     A__       =    batch_size
     A__       =    seq_length
     A__       =    is_training
     A__       =    use_labels
     A__       =    vocab_size
     A__       =    hidden_size
     A__       =    num_hidden_layers
     A__       =    num_attention_heads
     A__       =    intermediate_size

     A__       =    hidden_dropout_prob
     A__       =    attention_probs_dropout_prob
     A__       =    max_position_embeddings
     A__       =    eos_token_id
     A__       =    pad_token_id
     A__       =    bos_token_id
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[int]:
     A__       =    ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size							)
     A__       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)		, 1							)
     A__       =    tf.concat([input_ids, eos_tensor]		, axis=1							)

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

     A__       =    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		, )
     A__       =    prepare_marian_inputs_dict(lowercase_		, lowercase_		, lowercase_							)
     return config, inputs_dict



 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :str		, lowercase_   :Optional[int]							)->							Dict:
     A__       =    TFMarianModel(config=lowercase_							).get_decoder()
     A__       =    inputs_dict["input_ids"]

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

     # first forward pass
     A__       =    model(lowercase_		, attention_mask=lowercase_		, head_mask=lowercase_		, use_cache=lowercase_							)

     A__       =    outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     A__       =    ids_tensor((self.batch_size, 3)		, config.vocab_size							)
     A__       =    tf.cast(ids_tensor((self.batch_size, 3)		, 2							)		, tf.inta							)

     # append to next input_ids and
     A__       =    tf.concat([input_ids, next_tokens]		, axis=-1							)
     A__       =    tf.concat([attention_mask, next_attn_mask]		, axis=-1							)

     A__       =    model(lowercase_		, attention_mask=lowercase_							)[0]
     A__       =    model(lowercase_		, attention_mask=lowercase_		, past_key_values=lowercase_							)[0]

     self.parent.assertEqual(next_tokens.shape[1]		, output_from_past.shape[1]							)

     # select random slice
     A__       =    int(ids_tensor((1,)		, output_from_past.shape[-1]							)							)
     A__       =    output_from_no_past[:, -3:, random_slice_idx]
     A__       =    output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(lowercase_		, lowercase_		, rtol=1E-3							)





def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      int		, _lowerCamelCase      :      Dict		, _lowerCamelCase      :      int=None		, _lowerCamelCase      :      int=None		, _lowerCamelCase      :      Optional[int]=None		, _lowerCamelCase      :      Any=None		, _lowerCamelCase      :      Optional[int]=None		, ):
    if attention_mask is None:
        A__       =    tf.cast(tf.math.not_equal(lowercase__		, config.pad_token_id )		, tf.inta )
    if decoder_attention_mask is None:
        A__       =    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:
        A__       =    tf.ones((config.encoder_layers, config.encoder_attention_heads) )
    if decoder_head_mask is None:
        A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads) )
    if cross_attn_head_mask is None:
        A__       =    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			( lowerCamelCase_    , lowerCamelCase_    , unittest.TestCase       ):
 __lowercase     	=       (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 __lowercase     	=       (TFMarianMTModel,) if is_tf_available() else ()
 __lowercase     	=       (
     {
         """conversational""": TFMarianMTModel,
         """feature-extraction""": TFMarianModel,
         """summarization""": TFMarianMTModel,
         """text2text-generation""": TFMarianMTModel,
         """translation""": TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 __lowercase     	=       True
 __lowercase     	=       False
 __lowercase     	=       False
 def   UpperCAmelCase_							(       self   :Dict							)->							Optional[int]:
     A__       =    TFMarianModelTester(self							)
     A__       =    ConfigTester(self		, config_class=lowercase_							)
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Any:
     self.config_tester.run_common_tests()



 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Union[str, Any]:
     A__       =    self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowercase_							)






@require_tf
class      UpperCAmelCase			( unittest.TestCase       ):
 __lowercase     	=       1000  # show more chars for failing integration tests
 @classmethod
 def   UpperCAmelCase_							(       cls   :Any							)->							List[str]:
     A__       =    F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
     return cls
 @cached_property
 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							str:
     return AutoTokenizer.from_pretrained(self.model_name							)
 @property
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Union[str, Any]:
     return self.tokenizer.eos_token_id
 @cached_property
 def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[int]:
     warnings.simplefilter("error"							)
     A__       =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
     assert isinstance(lowercase_		, lowercase_							)
     A__       =    model.config
     self.assertListEqual(c.bad_words_ids		, [[c.pad_token_id]]							)
     self.assertEqual(c.max_length		, 5_12							)
     self.assertEqual(c.decoder_start_token_id		, c.pad_token_id							)
     return model
 def   UpperCAmelCase_							(       self   :Tuple		, **lowercase_   :Any							)->							Union[str, Any]:
     A__       =    self.translate_src_text(**lowercase_							)
     self.assertListEqual(self.expected_text		, lowercase_							)



 def   UpperCAmelCase_							(       self   :int		, **lowercase_   :List[Any]							)->							List[Any]:
     A__       =    self.tokenizer(self.src_text		, **lowercase_		, padding=lowercase_		, return_tensors="tf"							)
     A__       =    self.model.generate(
         model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, num_beams=2		, max_length=1_28							)
     A__       =    self.tokenizer.batch_decode(generated_ids.numpy()		, skip_special_tokens=lowercase_							)
     return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class      UpperCAmelCase			( lowerCamelCase_       ):
 __lowercase     	=       """mt"""
 __lowercase     	=       """en"""
 __lowercase     	=       ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
 __lowercase     	=       ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
 @unittest.skip("Skipping until #12647 is resolved."							)
 @slow
 def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
     self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class      UpperCAmelCase			( lowerCamelCase_       ):
 __lowercase     	=       """en"""
 __lowercase     	=       """zh"""
 __lowercase     	=       ["""My name is Wolfgang and I live in Berlin"""]
 __lowercase     	=       ["""我叫沃尔夫冈 我住在柏林"""]
 @unittest.skip("Skipping until #12647 is resolved."							)
 @slow
 def   UpperCAmelCase_							(       self   :Any							)->							List[str]:
     self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class      UpperCAmelCase			( lowerCamelCase_       ):
 __lowercase     	=       """en"""
 __lowercase     	=       """ROMANCE"""
 __lowercase     	=       [
     """>>fr<< Don't spend so much time watching TV.""",
     """>>pt<< Your message has been sent.""",
     """>>es<< He's two years older than me.""",
 ]
 __lowercase     	=       [
     """Ne passez pas autant de temps à regarder la télé.""",
     """A sua mensagem foi enviada.""",
     """Es dos años más viejo que yo.""",
 ]
 @unittest.skip("Skipping until #12647 is resolved."							)
 @slow
 def   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
     self._assert_generated_batch_equal_expected()



 @unittest.skip("Skipping until #12647 is resolved."							)
 @slow
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							List[str]:
     A__       =    TranslationPipeline(self.model		, self.tokenizer		, framework="tf"							)
     A__       =    pipeline(self.src_text							)
     self.assertEqual(self.expected_text		, [x["translation_text"] for x in output]							)










'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


__lowerCAmelCase							:			Dict				=logging.get_logger(__name__)






class      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :List[str]		, *lowercase_   :Dict		, **lowercase_   :int							)->							None:
     warnings.warn(
         "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use ViltImageProcessor instead."		, lowercase_		, )
     super().__init__(*lowercase_		, **lowercase_							)



