




'''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					:
  a    :Optional[int]      					=				MarianConfig
  a    :List[str]      					=				{}
  a    :List[str]      					=				'gelu'



  def __init__( self		,    __lowerCAmelCase		,    __lowerCAmelCase=13		,    __lowerCAmelCase=7		,    __lowerCAmelCase=True		,    __lowerCAmelCase=False		,    __lowerCAmelCase=99		,    __lowerCAmelCase=32		,    __lowerCAmelCase=2		,    __lowerCAmelCase=4		,    __lowerCAmelCase=37		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=20		,    __lowerCAmelCase=2		,    __lowerCAmelCase=1		,    __lowerCAmelCase=0		,    ):







      '''simple docstring'''


      _a							:       Tuple       					=			parent
      _a							:       Dict       					=			batch_size
      _a							:       Optional[Any]       					=			seq_length
      _a							:       Tuple       					=			is_training
      _a							:       Optional[Any]       					=			use_labels
      _a							:       str       					=			vocab_size
      _a							:       Optional[Any]       					=			hidden_size
      _a							:       Dict       					=			num_hidden_layers
      _a							:       Union[str, Any]       					=			num_attention_heads
      _a							:       Dict       					=			intermediate_size

      _a							:       Optional[Any]       					=			hidden_dropout_prob
      _a							:       Optional[Any]       					=			attention_probs_dropout_prob
      _a							:       Optional[Any]       					=			max_position_embeddings
      _a							:       Any       					=			eos_token_id
      _a							:       Any       					=			pad_token_id
      _a							:       Optional[Any]       					=			bos_token_id



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       int       					=			ids_tensor([self.batch_size, self.seq_length - 1]		,    self.vocab_size   )
      _a							:       Optional[Any]       					=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )		,    1   )
      _a							:       Dict       					=			tf.concat([input_ids, eos_tensor]		,    axis=1   )

      _a							:       List[str]       					=			ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size   )

      _a							:       int       					=			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							:       Dict       					=			prepare_marian_inputs_dict(__lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   )
      return config, inputs_dict



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			TFMarianModel(config=__lowerCAmelCase   ).get_decoder()
      _a							:       str       					=			inputs_dict["""input_ids"""]

      _a							:       Union[str, Any]       					=			input_ids[:1, :]
      _a							:       Tuple       					=			inputs_dict["""attention_mask"""][:1, :]
      _a							:       Tuple       					=			inputs_dict["""head_mask"""]
      _a							:       Any       					=			1

      # first forward pass
      _a							:       Dict       					=			model(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase		,    head_mask=__lowerCAmelCase		,    use_cache=__lowerCAmelCase   )

      _a					,   _a							:       List[str]       					=			outputs.to_tuple()

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

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

      _a							:       Any       					=			model(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase   )[0]
      _a							:       Dict       					=			model(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase		,    past_key_values=__lowerCAmelCase   )[0]

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

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

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






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_=None      ,  UpperCAmelCase_=None      ,  UpperCAmelCase_=None      ,  UpperCAmelCase_=None      ,  UpperCAmelCase_=None      ,  ):
    if attention_mask is None:
        _a							:       Dict       					=			tf.cast(tf.math.not_equal(UpperCAmelCase_      ,  config.pad_token_id	)      ,  tf.inta	)
    if decoder_attention_mask is None:
        _a							:       List[str]       					=			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							:       Optional[int]       					=			tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
    if decoder_head_mask is None:
        _a							:       Tuple       					=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
    if cross_attn_head_mask is None:
        _a							:       Union[str, 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     UpperCamelCase					(  snake_case__			,		snake_case__			,		unittest.TestCase					):
  a    :Optional[int]      					=				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  a    :str      					=				(TFMarianMTModel,) if is_tf_available() else ()
  a    :List[str]      					=				(
      {
          'conversational': TFMarianMTModel,
          'feature-extraction': TFMarianModel,
          'summarization': TFMarianMTModel,
          'text2text-generation': TFMarianMTModel,
          'translation': TFMarianMTModel,
      }
      if is_tf_available()
      else {}
  )
  a    :str      					=				True
  a    :Union[str, Any]      					=				False
  a    :Dict      					=				False



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			TFMarianModelTester(self   )
      _a							:       Dict       					=			ConfigTester(self		,    config_class=__lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      self.config_tester.run_common_tests()



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase   )






@require_tf
class     UpperCamelCase					(  unittest.TestCase					):
  a    :Union[str, Any]      					=				1_0_0_0  # show more chars for failing integration tests



  @classmethod
  def 						a_       ( cls   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls



  @cached_property
  def 						a_       ( self   ):







      '''simple docstring'''


      return AutoTokenizer.from_pretrained(self.model_name   )



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      return self.tokenizer.eos_token_id



  @cached_property
  def 						a_       ( self   ):







      '''simple docstring'''


      warnings.simplefilter("""error"""   )
      _a							:       TFMarianMTModel       					=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
      assert isinstance(__lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       int       					=			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 						a_       ( self		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       int       					=			self.translate_src_text(**__lowerCAmelCase   )
      self.assertListEqual(self.expected_text		,    __lowerCAmelCase   )



  def 						a_       ( self		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       str       					=			self.tokenizer(self.src_text		,    **__lowerCAmelCase		,    padding=__lowerCAmelCase		,    return_tensors="""tf"""   )
      _a							:       Dict       					=			self.model.generate(
          model_inputs.input_ids		,    attention_mask=model_inputs.attention_mask		,    num_beams=2		,    max_length=128   )
      _a							:       Optional[Any]       					=			self.tokenizer.batch_decode(generated_ids.numpy()		,    skip_special_tokens=__lowerCAmelCase   )
      return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCamelCase					(  snake_case__					):
  a    :Any      					=				'mt'
  a    :Tuple      					=				'en'
  a    :str      					=				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
  a    :List[Any]      					=				['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']



  @unittest.skip("""Skipping until #12647 is resolved."""   )
  @slow
  def 						a_       ( self   ):







      '''simple docstring'''


      self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCamelCase					(  snake_case__					):
  a    :Tuple      					=				'en'
  a    :int      					=				'zh'
  a    :Union[str, Any]      					=				['My name is Wolfgang and I live in Berlin']
  a    :Dict      					=				['我叫沃尔夫冈 我住在柏林']



  @unittest.skip("""Skipping until #12647 is resolved."""   )
  @slow
  def 						a_       ( self   ):







      '''simple docstring'''


      self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCamelCase					(  snake_case__					):
  a    :Any      					=				'en'
  a    :List[Any]      					=				'ROMANCE'
  a    :int      					=				[
      '>>fr<< Don\'t spend so much time watching TV.',
      '>>pt<< Your message has been sent.',
      '>>es<< He\'s two years older than me.',
  ]
  a    :Tuple      					=				[
      '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 						a_       ( self   ):







      '''simple docstring'''


      self._assert_generated_batch_equal_expected()



  @unittest.skip("""Skipping until #12647 is resolved."""   )
  @slow
  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Union[str, Any]       					=			TranslationPipeline(self.model		,    self.tokenizer		,    framework="""tf"""   )
      _a							:       str       					=			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,
)


a		:  Dict   			=			{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MobileViTV2ForImageClassification''',
      '''MobileViTV2ForSemanticSegmentation''',
      '''MobileViTV2Model''',
      '''MobileViTV2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  a		:  Dict   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


