

'''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 					lowerCAmelCase       :
       lowerCAmelCase_				=	MarianConfig
       lowerCAmelCase_				=	{}
       lowerCAmelCase_				=	"gelu"







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







            """simple docstring"""







            __lowercase     						=parent
            __lowercase     						=batch_size
            __lowercase     						=seq_length
            __lowercase     						=is_training
            __lowercase     						=use_labels
            __lowercase     						=vocab_size
            __lowercase     						=hidden_size
            __lowercase     						=num_hidden_layers
            __lowercase     						=num_attention_heads
            __lowercase     						=intermediate_size

            __lowercase     						=hidden_dropout_prob
            __lowercase     						=attention_probs_dropout_prob
            __lowercase     						=max_position_embeddings
            __lowercase     						=eos_token_id
            __lowercase     						=pad_token_id
            __lowercase     						=bos_token_id







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







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

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

            __lowercase     						=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  , )
            __lowercase     						=prepare_marian_inputs_dict(__lowercase  , __lowercase  , __lowercase )
            return config, inputs_dict







       def      snake_case				(							self     :	Any  , __lowercase     :	Optional[int]  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=TFMarianModel(config=__lowercase ).get_decoder()
            __lowercase     						=inputs_dict['input_ids']

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

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

            __lowercase						,       __lowercase     						=outputs.to_tuple()

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

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

            __lowercase     						=model(__lowercase  , attention_mask=__lowercase )[0]
            __lowercase     						=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
            __lowercase     						=int(ids_tensor((1,)  , output_from_past.shape[-1] ) )
            __lowercase     						=output_from_no_past[:, -3:, random_slice_idx]
            __lowercase     						=output_from_past[:, :, random_slice_idx]

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






def 			__UpperCamelCase							(		lowercase__  :					Optional[int],   lowercase__  :					Optional[Any],   lowercase__  :					Dict,   lowercase__  :					Optional[int]=None,   lowercase__  :					Any=None,   lowercase__  :					str=None,   lowercase__  :					Optional[int]=None,   lowercase__  :					Union[str, Any]=None,   ):






     '''simple docstring'''





     if attention_mask is None:
          __lowercase     						=tf.cast(tf.math.not_equal(lowercase__,   config.pad_token_id	),   tf.inta	)
     if decoder_attention_mask is None:
          __lowercase     						=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:
          __lowercase     						=tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
     if decoder_head_mask is None:
          __lowercase     						=tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
     if cross_attn_head_mask is None:
          __lowercase     						=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       ( A				,     A				,     unittest.TestCase				):
       lowerCAmelCase_				=	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
       lowerCAmelCase_				=	(TFMarianMTModel,) if is_tf_available() else ()
       lowerCAmelCase_				=	(
           {
               "conversational": TFMarianMTModel,
               "feature-extraction": TFMarianModel,
               "summarization": TFMarianMTModel,
               "text2text-generation": TFMarianMTModel,
               "translation": TFMarianMTModel,
           }
           if is_tf_available()
           else {}
       )
       lowerCAmelCase_				=	True
       lowerCAmelCase_				=	False
       lowerCAmelCase_				=	False







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=TFMarianModelTester(self )
            __lowercase     						=ConfigTester(self  , config_class=__lowercase )







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*__lowercase )






@require_tf
class 					lowerCAmelCase       ( unittest.TestCase				):
       lowerCAmelCase_				=	1_0_0_0  # show more chars for failing integration tests







       @classmethod
       def      snake_case				(							cls     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
            return cls







       @cached_property
       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            return AutoTokenizer.from_pretrained(self.model_name )







       @property
       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            return self.tokenizer.eos_token_id







       @cached_property
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            warnings.simplefilter('error' )
            __lowercase     						=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
            assert isinstance(__lowercase  , __lowercase )
            __lowercase     						=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     :	int  , **__lowercase     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.translate_src_text(**__lowercase )
            self.assertListEqual(self.expected_text  , __lowercase )







       def      snake_case				(							self     :	Any  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.tokenizer(self.src_text  , **__lowercase  , padding=__lowercase  , return_tensors='tf' )
            __lowercase     						=self.model.generate(
                model_inputs.input_ids  , attention_mask=model_inputs.attention_mask  , num_beams=2  , max_length=128 )
            __lowercase     						=self.tokenizer.batch_decode(generated_ids.numpy()  , skip_special_tokens=__lowercase )
            return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"mt"
       lowerCAmelCase_				=	"en"
       lowerCAmelCase_				=	["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
       lowerCAmelCase_				=	["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     :	Optional[int] ):







            """simple docstring"""







            self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"en"
       lowerCAmelCase_				=	"zh"
       lowerCAmelCase_				=	["My name is Wolfgang and I live in Berlin"]
       lowerCAmelCase_				=	["我叫沃尔夫冈 我住在柏林"]







       @unittest.skip('Skipping until #12647 is resolved.' )
       @slow
       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"en"
       lowerCAmelCase_				=	"ROMANCE"
       lowerCAmelCase_				=	[
           ">>fr<< Don't spend so much time watching TV.",
           ">>pt<< Your message has been sent.",
           ">>es<< He's two years older than me.",
       ]
       lowerCAmelCase_				=	[
           "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     :	Optional[Any] ):







            """simple docstring"""







            self._assert_generated_batch_equal_expected()







       @unittest.skip('Skipping until #12647 is resolved.' )
       @slow
       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=TranslationPipeline(self.model  , self.tokenizer  , framework='tf' )
            __lowercase     						=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, is_vision_available


UpperCAmelCase							=					{
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''VideoMAEForPreTraining''',
           '''VideoMAEModel''',
           '''VideoMAEPreTrainedModel''',
           '''VideoMAEForVideoClassification''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''VideoMAEFeatureExtractor''']
       UpperCAmelCase							=					['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
       from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_videomae import (
                  VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VideoMAEForPreTraining,
                  VideoMAEForVideoClassification,
                  VideoMAEModel,
                  VideoMAEPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_videomae import VideoMAEFeatureExtractor
              from .image_processing_videomae import VideoMAEImageProcessor

else:
       import sys

       UpperCAmelCase							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


