






"""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		:
    __lowerCamelCase         =  MarianConfig
    __lowerCamelCase         =  {}
    __lowerCamelCase         =  "gelu"






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__=1_3						,      lowerCamelCase__=7						,      lowerCamelCase__=True						,      lowerCamelCase__=False						,      lowerCamelCase__=9_9						,      lowerCamelCase__=3_2						,      lowerCamelCase__=2						,      lowerCamelCase__=4						,      lowerCamelCase__=3_7						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0.1						,      lowerCamelCase__=2_0						,      lowerCamelCase__=2						,      lowerCamelCase__=1						,      lowerCamelCase__=0						,      ):


      """simple docstring"""
      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  A    (self   ):


      """simple docstring"""
      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(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   )
      return config, inputs_dict






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							TFMarianModel(config=lowerCamelCase__   ).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(lowerCamelCase__						,      attention_mask=lowerCamelCase__						,      head_mask=lowerCamelCase__						,      use_cache=lowerCamelCase__   )

      A__						,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(lowerCamelCase__						,      attention_mask=lowerCamelCase__   )[0]
      A__											=							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__											=							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(lowerCamelCase__						,      lowerCamelCase__						,      rtol=1E-3   )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple		,				UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	Any=None		,				UpperCamelCase     :	Union[str, Any]=None		,				UpperCamelCase     :	List[str]=None		,				UpperCamelCase     :	int=None		,				UpperCamelCase     :	Dict=None		,				):
  if attention_mask is None:
    A__											=							tf.cast(tf.math.not_equal(UpperCamelCase		,				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		( __snake_case						, __snake_case						, 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  A    (self   ):


      """simple docstring"""
      A__											=							TFMarianModelTester(self   )
      A__											=							ConfigTester(self						,      config_class=lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      self.config_tester.run_common_tests()






    def  A    (self   ):


      """simple docstring"""
      A__											=							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):
    __lowerCamelCase         =  1_0_0_0  # show more chars for failing integration tests






    @classmethod
    def  A    (cls   ):


      """simple docstring"""
      A__											=							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__											=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
      assert isinstance(lowerCamelCase__						,      lowerCamelCase__   )
      A__											=							model.config
      self.assertListEqual(c.bad_words_ids						,      [[c.pad_token_id]]   )
      self.assertEqual(c.max_length						,      5_1_2   )
      self.assertEqual(c.decoder_start_token_id						,      c.pad_token_id   )
      return model






    def  A    (self						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.translate_src_text(**lowerCamelCase__   )
      self.assertListEqual(self.expected_text						,      lowerCamelCase__   )






    def  A    (self						,      **lowerCamelCase__   ):


      """simple docstring"""
      A__											=							self.tokenizer(self.src_text						,      **lowerCamelCase__						,      padding=lowerCamelCase__						,      return_tensors="""tf"""   )
      A__											=							self.model.generate(
          model_inputs.input_ids						,      attention_mask=model_inputs.attention_mask						,      num_beams=2						,      max_length=1_2_8   )
      A__											=							self.tokenizer.batch_decode(generated_ids.numpy()						,      skip_special_tokens=lowerCamelCase__   )
      return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 					_UpperCamelCase		( __snake_case):
    __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  A    (self   ):


      """simple docstring"""
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 					_UpperCamelCase		( __snake_case):
    __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  A    (self   ):


      """simple docstring"""
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 					_UpperCamelCase		( __snake_case):
    __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  A    (self   ):


      """simple docstring"""
      self._assert_generated_batch_equal_expected()






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


      """simple docstring"""
      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"""




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime







class 					_UpperCamelCase		( __snake_case):






    def __init__(self						,      *lowerCamelCase__						,      **lowerCamelCase__   ):


      """simple docstring"""
      super().__init__(*lowerCamelCase__						,      **lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							(
          next_prime(value % self.size_table   )
          if not is_prime(value % self.size_table   )
          else value % self.size_table
      )  # gt = bigger than
      return next_prime_gt - (data % next_prime_gt)






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      return (increment * self.__hash_function_a(lowerCamelCase__						,      lowerCamelCase__   )) % self.size_table






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=None   ):


      """simple docstring"""
      A__											=							1
      A__											=							self.hash_function(lowerCamelCase__   )

      while self.values[new_key] is not None and self.values[new_key] != key:
        A__											=							(
            self.__hash_double_function(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   )
            if self.balanced_factor() >= self.lim_charge
            else None
        )
        if new_key is None:
          break
        else:
          i += 1

      return new_key




