






'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase      :       str      			=      get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

__UpperCamelCase      :       Optional[int]      			=      250004
__UpperCamelCase      :       Optional[Any]      			=      250020
@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=MBartTokenizer
      __a  					=MBartTokenizerFast
      __a  					=True
      __a  					=True




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         super().setUp()

         # We have a SentencePiece fixture for testing
         __a				     =    MBartTokenizer(lowerCamelCase ,   keep_accents=lowerCamelCase      )
         tokenizer.save_pretrained(self.tmpdirname      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    MBartTokenizer(lowerCamelCase ,   keep_accents=lowerCamelCase      )

         __a				     =    tokenizer.tokenize('This is a test'      )
         self.assertListEqual(lowerCamelCase ,   ['▁This', '▁is', '▁a', '▁t', 'est']      )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCamelCase      ) ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,   )

         __a				     =    tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
         self.assertListEqual(
             lowerCamelCase ,   [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ] ,   )
         __a				     =    tokenizer.convert_tokens_to_ids(lowerCamelCase      )
         self.assertListEqual(
             lowerCamelCase ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ] ,   )

         __a				     =    tokenizer.convert_ids_to_tokens(lowerCamelCase      )
         self.assertListEqual(
             lowerCamelCase ,   [
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '<unk>',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '<unk>',
                 '.',
             ] ,   )






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



         '''simple docstring'''
         if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

         __a				     =    (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
               __a				     =    self.rust_tokenizer_class.from_pretrained(lowerCamelCase ,   **lowerCamelCase      )
               __a				     =    self.tokenizer_class.from_pretrained(lowerCamelCase ,   **lowerCamelCase      )

               __a				     =    tempfile.mkdtemp()

               __a				     =    tokenizer_r.save_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.save_pretrained(lowerCamelCase      )

               # Checks it save with the same files + the tokenizer.json file for the fast one
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )
               __a				     =    tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
               self.assertSequenceEqual(lowerCamelCase ,   lowerCamelCase      )

               # Checks everything loads correctly in the same way
               __a				     =    tokenizer_r.from_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.from_pretrained(lowerCamelCase      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(lowerCamelCase ,   lowerCamelCase      )      )
                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

               shutil.rmtree(lowerCamelCase      )

               # Save tokenizer rust, legacy_format=True
               __a				     =    tempfile.mkdtemp()

               __a				     =    tokenizer_r.save_pretrained(lowerCamelCase ,   legacy_format=lowerCamelCase      )
               __a				     =    tokenizer_p.save_pretrained(lowerCamelCase      )

               # Checks it save with the same files
               self.assertSequenceEqual(lowerCamelCase ,   lowerCamelCase      )

               # Checks everything loads correctly in the same way
               __a				     =    tokenizer_r.from_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.from_pretrained(lowerCamelCase      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(lowerCamelCase ,   lowerCamelCase      )      )

               shutil.rmtree(lowerCamelCase      )

               # Save tokenizer rust, legacy_format=False
               __a				     =    tempfile.mkdtemp()

               __a				     =    tokenizer_r.save_pretrained(lowerCamelCase ,   legacy_format=lowerCamelCase      )
               __a				     =    tokenizer_p.save_pretrained(lowerCamelCase      )

               # Checks it saved the tokenizer.json file
               self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )

               # Checks everything loads correctly in the same way
               __a				     =    tokenizer_r.from_pretrained(lowerCamelCase      )
               __a				     =    tokenizer_p.from_pretrained(lowerCamelCase      )

               # Check special tokens are set accordingly on Rust and Python
               for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(lowerCamelCase ,   lowerCamelCase      )      )

               shutil.rmtree(lowerCamelCase      )







@require_torch
@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):
      __a  					="facebook/mbart-large-en-ro"
      __a  					=[
          " UN Chief Says There Is No Military Solution in Syria",
          " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
      ]
      __a  					=[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
          "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
          " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
          " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
      ]
      __a  					=[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]




      @classmethod
      def  __UpperCamelCase							(  cls      )							->List[str]:



         '''simple docstring'''
         __a				     =    MBartTokenizer.from_pretrained(
             cls.checkpoint_name ,   src_lang='en_XX' ,   tgt_lang='ro_RO'      )
         __a				     =    1
         return cls




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,   25_0001      )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,   25_0004      )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,   25_0020      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         self.assertIn(lowerCamelCase ,   self.tokenizer.all_special_ids      )
         __a				     =    [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
         __a				     =    self.tokenizer.decode(lowerCamelCase ,   skip_special_tokens=lowerCamelCase      )
         __a				     =    self.tokenizer.decode(generated_ids[1:] ,   skip_special_tokens=lowerCamelCase      )
         self.assertEqual(lowerCamelCase ,   lowerCamelCase      )
         self.assertNotIn(self.tokenizer.eos_token ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    ['this is gunna be a long sentence ' * 20]
         assert isinstance(src_text[0] ,   lowerCamelCase      )
         __a				     =    10
         __a				     =    self.tokenizer(lowerCamelCase ,   max_length=lowerCamelCase ,   truncation=lowerCamelCase      ).input_ids[0]
         self.assertEqual(ids[-2] ,   2      )
         self.assertEqual(ids[-1] ,   lowerCamelCase      )
         self.assertEqual(len(lowerCamelCase      ) ,   lowerCamelCase      )




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



         '''simple docstring'''
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      ) ,   [25_0026, 25_0001]      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    tempfile.mkdtemp()
         __a				     =    self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(lowerCamelCase      )
         __a				     =    MBartTokenizer.from_pretrained(lowerCamelCase      )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,   lowerCamelCase      )




      @require_torch
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.tokenizer(self.src_text ,   text_target=self.tgt_text ,   padding=lowerCamelCase ,   return_tensors='pt'      )
         __a				     =    shift_tokens_right(batch['labels'] ,   self.tokenizer.pad_token_id      )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
         assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
         assert batch.decoder_input_ids[1][-1] == 2
         assert batch.labels[1][-2:].tolist() == [2, RO_CODE]




      @require_torch
      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.tokenizer(
             self.src_text ,   text_target=self.tgt_text ,   padding=lowerCamelCase ,   truncation=lowerCamelCase ,   max_length=len(self.expected_src_tokens      ) ,   return_tensors='pt' ,   )

         __a				     =    shift_tokens_right(batch['labels'] ,   self.tokenizer.pad_token_id      )

         self.assertIsInstance(lowerCamelCase ,   lowerCamelCase      )

         self.assertEqual((2, 14) ,   batch.input_ids.shape      )
         self.assertEqual((2, 14) ,   batch.attention_mask.shape      )
         __a				     =    batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens ,   lowerCamelCase      )
         self.assertEqual(2 ,   batch.decoder_input_ids[0, -1]      )  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens ,   []      )
         self.assertEqual(self.tokenizer.suffix_tokens ,   [self.tokenizer.eos_token_id, EN_CODE]      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.tokenizer(self.src_text ,   padding=lowerCamelCase ,   truncation=lowerCamelCase ,   max_length=3 ,   return_tensors='pt'      )
         __a				     =    self.tokenizer(
             text_target=self.tgt_text ,   padding=lowerCamelCase ,   truncation=lowerCamelCase ,   max_length=10 ,   return_tensors='pt'      )
         __a				     =    targets['input_ids']
         __a				     =    shift_tokens_right(lowerCamelCase ,   self.tokenizer.pad_token_id      )

         self.assertEqual(batch.input_ids.shape[1] ,   3      )
         self.assertEqual(batch.decoder_input_ids.shape[1] ,   10      )






      @require_torch
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    self.tokenizer._build_translation_inputs(
             'A test' ,   return_tensors='pt' ,   src_lang='en_XX' ,   tgt_lang='ar_AR'      )

         self.assertEqual(
             nested_simplify(lowerCamelCase      ) ,   {
                 # A, test, EOS, en_XX
                 'input_ids': [[62, 3034, 2, 25_0004]],
                 'attention_mask': [[1, 1, 1, 1]],
                 # ar_AR
                 'forced_bos_token_id': 25_0001,
             } ,   )







'''simple docstring'''



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
 from datasets import load_dataset
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
          "text to read (in English) and returns a waveform object containing the sound."
      )
      __a  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         if self.post_processor is None:
            __a				     =    'microsoft/speecht5_hifigan'
         super().setup()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



         '''simple docstring'''
         __a				     =    self.pre_processor(text=lowerCamelCase ,   return_tensors='pt' ,   truncation=lowerCamelCase      )

         if speaker_embeddings is None:
            if not is_datasets_available():
               raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'      )

            __a				     =    load_dataset('Matthijs/cmu-arctic-xvectors' ,   split='validation'      )
            __a				     =    torch.tensor(embeddings_dataset[7305]['xvector']      ).unsqueeze(0      )

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()