"""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   __snake_case			:
      a__								=						MarianConfig
      a__								=						{}
      a__								=						"""gelu"""



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





        '''simple docstring'''





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

        a__:	List[Any]     				=      hidden_dropout_prob
        a__:	List[str]     				=      attention_probs_dropout_prob
        a__:	Tuple     				=      max_position_embeddings
        a__:	int     				=      eos_token_id
        a__:	Optional[int]     				=      pad_token_id
        a__:	Dict     				=      bos_token_id



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





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

        a__:	Union[str, Any]     				=      ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size)

        a__:	Optional[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(lowercase       ,     lowercase       ,     lowercase)
        return config, inputs_dict



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        a__:	List[Any]     				=      TFMarianModel(config=lowercase).get_decoder()
        a__:	List[Any]     				=      inputs_dict['input_ids']

        a__:	Union[str, Any]     				=      input_ids[:1, :]
        a__:	Optional[int]     				=      inputs_dict['attention_mask'][:1, :]
        a__:	str     				=      inputs_dict['head_mask']
        a__:	Optional[Any]     				=      1

        # first forward pass
        a__:	Optional[int]     				=      model(lowercase       ,     attention_mask=lowercase       ,     head_mask=lowercase       ,     use_cache=lowercase)

        a__				,       a__:	List[Any]     				=      outputs.to_tuple()

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

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

        a__:	Any     				=      model(lowercase       ,     attention_mask=lowercase)[0]
        a__:	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
        a__:	Optional[int]     				=      int(ids_tensor((1,)       ,     output_from_past.shape[-1]))
        a__:	Optional[Any]     				=      output_from_no_past[:, -3:, random_slice_idx]
        a__:	Tuple     				=      output_from_past[:, :, random_slice_idx]

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







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None						,	)      ->Optional[Any]:
  if attention_mask is None:
    a__:	Union[str, Any]     				=      tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE						,	config.pad_token_id  )						,	tf.inta  )
  if decoder_attention_mask is None:
    a__:	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__:	List[str]     				=      tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
  if decoder_head_mask is None:
    a__:	Any     				=      tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
  if cross_attn_head_mask is None:
    a__:	str     				=      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   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase			, unittest.TestCase							):
      a__								=						(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      a__								=						(TFMarianMTModel,) if is_tf_available() else ()
      a__								=						(
          {
              """conversational""": TFMarianMTModel,
              """feature-extraction""": TFMarianModel,
              """summarization""": TFMarianMTModel,
              """text2text-generation""": TFMarianMTModel,
              """translation""": TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      a__								=						True
      a__								=						False
      a__								=						False



      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      TFMarianModelTester(self)
        a__:	List[Any]     				=      ConfigTester(self       ,     config_class=lowercase)



      def     lowerCamelCase_  (		self)      ->     Optional[Any]:





        '''simple docstring'''





        self.config_tester.run_common_tests()



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Dict     				=      self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase)







@require_tf
class   __snake_case			(	unittest.TestCase							):
      a__								=						1000  # show more chars for failing integration tests



      @classmethod
      def     lowerCamelCase_  (		cls)      ->     None:





        '''simple docstring'''





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



      @cached_property
      def     lowerCamelCase_  (		self)      ->     MarianTokenizer:





        '''simple docstring'''





        return AutoTokenizer.from_pretrained(self.model_name)



      @property
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        return self.tokenizer.eos_token_id



      @cached_property
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        warnings.simplefilter('error')
        a__:	TFMarianMTModel     				=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
        assert isinstance(lowercase       ,     lowercase)
        a__:	str     				=      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     lowerCamelCase_  (		self       ,     **lowercase)      ->     Any:





        '''simple docstring'''





        a__:	str     				=      self.translate_src_text(**lowercase)
        self.assertListEqual(self.expected_text       ,     lowercase)



      def     lowerCamelCase_  (		self       ,     **lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	List[Any]     				=      self.tokenizer(self.src_text       ,     **lowercase       ,     padding=lowercase       ,     return_tensors='tf')
        a__:	Optional[int]     				=      self.model.generate(
            model_inputs.input_ids       ,     attention_mask=model_inputs.attention_mask       ,     num_beams=2       ,     max_length=1_28)
        a__:	Dict     				=      self.tokenizer.batch_decode(generated_ids.numpy()       ,     skip_special_tokens=lowercase)
        return generated_words







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



      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        self._assert_generated_batch_equal_expected()







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



      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""en"""
      a__								=						"""ROMANCE"""
      a__								=						[
          """>>fr<< Don't spend so much time watching TV.""",
          """>>pt<< Your message has been sent.""",
          """>>es<< He's two years older than me.""",
      ]
      a__								=						[
          """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     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        self._assert_generated_batch_equal_expected()



      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Any     				=      TranslationPipeline(self.model       ,     self.tokenizer       ,     framework='tf')
        a__:	Dict     				=      pipeline(self.src_text)
        self.assertEqual(self.expected_text       ,     [x['translation_text'] for x in output])










import numpy as np







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   np.exp(UpperCAmelCase )

				# Add up the all the exponentials
				A									=   np.sum(UpperCAmelCase )

				# Divide every exponent by the sum of all exponents
				A									=   exponent_vector / sum_of_exponents

				return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))





