




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 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    Dict									=				MarianConfig
     lowerCAmelCase_     :    Optional[Any]									=				{}
     lowerCAmelCase_     :    Dict									=				"""gelu"""







     def __init__(							self       ,__a       ,__a=13       ,__a=7       ,__a=True       ,__a=False       ,__a=99       ,__a=32       ,__a=2       ,__a=4       ,__a=37       ,__a=0.1       ,__a=0.1       ,__a=20       ,__a=2       ,__a=1       ,__a=0       ,)			->							Optional[int]:
          snake_case :			Union[str, Any]					   = parent
          snake_case :			int					   = batch_size
          snake_case :			int					   = seq_length
          snake_case :			Any					   = is_training
          snake_case :			Dict					   = use_labels
          snake_case :			List[Any]					   = vocab_size
          snake_case :			Any					   = hidden_size
          snake_case :			int					   = num_hidden_layers
          snake_case :			Optional[Any]					   = num_attention_heads
          snake_case :			str					   = intermediate_size

          snake_case :			Tuple					   = hidden_dropout_prob
          snake_case :			List[Any]					   = attention_probs_dropout_prob
          snake_case :			Dict					   = max_position_embeddings
          snake_case :			Union[str, Any]					   = eos_token_id
          snake_case :			List[str]					   = pad_token_id
          snake_case :			List[str]					   = bos_token_id







     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case :			List[str]					   = ids_tensor([self.batch_size, self.seq_length - 1]       ,self.vocab_size       )
          snake_case :			List[str]					   = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )       ,1       )
          snake_case :			str					   = tf.concat([input_ids, eos_tensor]       ,axis=1       )

          snake_case :			Tuple					   = ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size       )

          snake_case :			str					   = 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 :			Optional[Any]					   = prepare_marian_inputs_dict(lowercase_       ,lowercase_       ,lowercase_       )
          return config, inputs_dict







     def  snake_case_			(							self       ,__a       ,__a       )			->							List[Any]:
          snake_case :			Any					   = TFMarianModel(config=lowercase_       ).get_decoder()
          snake_case :			int					   = inputs_dict['''input_ids''']

          snake_case :			Optional[Any]					   = input_ids[:1, :]
          snake_case :			int					   = inputs_dict['''attention_mask'''][:1, :]
          snake_case :			Tuple					   = inputs_dict['''head_mask''']
          snake_case :			List[Any]					   = 1

          # first forward pass
          snake_case :			int					   = model(lowercase_       ,attention_mask=lowercase_       ,head_mask=lowercase_       ,use_cache=lowercase_       )

          snake_case :			Optional[int]					   = outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          snake_case :			Union[str, Any]					   = ids_tensor((self.batch_size, 3)       ,config.vocab_size       )
          snake_case :			Union[str, Any]					   = tf.cast(ids_tensor((self.batch_size, 3)       ,2       )       ,tf.inta       )

          # append to next input_ids and
          snake_case :			Dict					   = tf.concat([input_ids, next_tokens]       ,axis=-1       )
          snake_case :			Optional[int]					   = tf.concat([attention_mask, next_attn_mask]       ,axis=-1       )

          snake_case :			List[Any]					   = model(lowercase_       ,attention_mask=lowercase_       )[0]
          snake_case :			str					   = 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
          snake_case :			int					   = int(ids_tensor((1,)       ,output_from_past.shape[-1]       )       )
          snake_case :			Union[str, Any]					   = output_from_no_past[:, -3:, random_slice_idx]
          snake_case :			str					   = output_from_past[:, :, random_slice_idx]

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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=None    ,      __lowercase=None    ,      __lowercase=None    ,      __lowercase=None    ,      __lowercase=None    ,      ):
     if attention_mask is None:
          snake_case :			str					   = tf.cast(tf.math.not_equal(lowercase_    ,      config.pad_token_id							)    ,      tf.inta							)
     if decoder_attention_mask is None:
          snake_case :			Any					   = 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 :			List[Any]					   = tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
     if decoder_head_mask is None:
          snake_case :			Union[str, Any]					   = tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
     if cross_attn_head_mask is None:
          snake_case :			List[Any]					   = 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   (__UpperCAmelCase,		__UpperCAmelCase,		unittest.TestCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    int									=				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     lowerCAmelCase_     :    List[str]									=				(TFMarianMTModel,) if is_tf_available() else ()
     lowerCAmelCase_     :    Dict									=				(
         {
             """conversational""": TFMarianMTModel,
             """feature-extraction""": TFMarianModel,
             """summarization""": TFMarianMTModel,
             """text2text-generation""": TFMarianMTModel,
             """translation""": TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase_     :    List[str]									=				True
     lowerCAmelCase_     :    int									=				False
     lowerCAmelCase_     :    List[str]									=				False







     def  snake_case_			(							self       )			->							List[str]:
          snake_case :			Union[str, Any]					   = TFMarianModelTester(self       )
          snake_case :			List[Any]					   = ConfigTester(self       ,config_class=lowercase_       )







     def  snake_case_			(							self       )			->							Any:
          self.config_tester.run_common_tests()







     def  snake_case_			(							self       )			->							Any:
          snake_case :			Optional[int]					   = self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*lowercase_       )







@require_tf
class 				_a   (unittest.TestCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    int									=				1000  # show more chars for failing integration tests







     @classmethod
     def  snake_case_			(							cls       )			->							None:
          snake_case :			str					   = F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
          return cls







     @cached_property
     def  snake_case_			(							self       )			->							MarianTokenizer:
          return AutoTokenizer.from_pretrained(self.model_name       )







     @property
     def  snake_case_			(							self       )			->							int:
          return self.tokenizer.eos_token_id







     @cached_property
     def  snake_case_			(							self       )			->							List[str]:
          warnings.simplefilter("""error"""       )
          snake_case :			TFMarianMTModel					   = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
          assert isinstance(lowercase_       ,lowercase_       )
          snake_case :			Dict					   = model.config
          self.assertListEqual(c.bad_words_ids       ,[[c.pad_token_id]]       )
          self.assertEqual(c.max_length       ,512       )
          self.assertEqual(c.decoder_start_token_id       ,c.pad_token_id       )
          return model







     def  snake_case_			(							self       ,**__a       )			->							List[Any]:
          snake_case :			Dict					   = self.translate_src_text(**lowercase_       )
          self.assertListEqual(self.expected_text       ,lowercase_       )







     def  snake_case_			(							self       ,**__a       )			->							int:
          snake_case :			Any					   = self.tokenizer(self.src_text       ,**lowercase_       ,padding=lowercase_       ,return_tensors="""tf"""       )
          snake_case :			str					   = self.model.generate(
              model_inputs.input_ids       ,attention_mask=model_inputs.attention_mask       ,num_beams=2       ,max_length=128       )
          snake_case :			str					   = self.tokenizer.batch_decode(generated_ids.numpy()       ,skip_special_tokens=lowercase_       )
          return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 				_a   (__UpperCAmelCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    str									=				"""mt"""
     lowerCAmelCase_     :    Dict									=				"""en"""
     lowerCAmelCase_     :    Optional[Any]									=				["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
     lowerCAmelCase_     :    Optional[int]									=				["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]







     @unittest.skip("""Skipping until #12647 is resolved."""       )
     @slow
     def  snake_case_			(							self       )			->							str:
          self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 				_a   (__UpperCAmelCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[Any]									=				"""en"""
     lowerCAmelCase_     :    int									=				"""zh"""
     lowerCAmelCase_     :    int									=				["""My name is Wolfgang and I live in Berlin"""]
     lowerCAmelCase_     :    List[str]									=				["""我叫沃尔夫冈 我住在柏林"""]







     @unittest.skip("""Skipping until #12647 is resolved."""       )
     @slow
     def  snake_case_			(							self       )			->							Any:
          self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 				_a   (__UpperCAmelCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				"""en"""
     lowerCAmelCase_     :    List[str]									=				"""ROMANCE"""
     lowerCAmelCase_     :    Optional[Any]									=				[
         """>>fr<< Don't spend so much time watching TV.""",
         """>>pt<< Your message has been sent.""",
         """>>es<< He's two years older than me.""",
     ]
     lowerCAmelCase_     :    List[Any]									=				[
         """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  snake_case_			(							self       )			->							Union[str, Any]:
          self._assert_generated_batch_equal_expected()







     @unittest.skip("""Skipping until #12647 is resolved."""       )
     @slow
     def  snake_case_			(							self       )			->							Tuple:
          snake_case :			Dict					   = TranslationPipeline(self.model       ,self.tokenizer       ,framework="""tf"""       )
          snake_case :			Optional[Any]					   = pipeline(self.src_text       )
          self.assertEqual(self.expected_text       ,[x["""translation_text"""] for x in output]       )









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase			:      Any			=					{"""configuration_visual_bert""": ["""VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VisualBertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      Optional[int]			=					[
          """VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VisualBertForMultipleChoice""",
          """VisualBertForPreTraining""",
          """VisualBertForQuestionAnswering""",
          """VisualBertForRegionToPhraseAlignment""",
          """VisualBertForVisualReasoning""",
          """VisualBertLayer""",
          """VisualBertModel""",
          """VisualBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

      lowercase			:      List[Any]			=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



