





'''simple docstring'''



import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_ :	Dict             =   get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_sentencepiece_available():
			import sentencepiece as sp


snake_case_ :	str             =   5
snake_case_ :	int             =   10

@require_sentencepiece
@require_tokenizers
class      A_			(	UpperCAmelCase__     ,      unittest.TestCase	):

			'''simple docstring'''





			_lowerCAmelCase							=     SpeechaTextTokenizer
			_lowerCAmelCase							=     False
			_lowerCAmelCase							=     True




			def    a						(       self ):
								super().setUp()

								_UpperCamelCase									= sp.SentencePieceProcessor()
								spm_model.Load(UpperCAmelCase__ )
								_UpperCamelCase									= ["<s>", "<pad>", "</s>", "<unk>"]

								vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(UpperCAmelCase__ ) )]
								_UpperCamelCase									= dict(zip(UpperCAmelCase__      ,  range(len(UpperCAmelCase__ ) ) ) )

								_UpperCamelCase									= Path(self.tmpdirname )
								save_json(UpperCAmelCase__      ,  save_dir / VOCAB_FILES_NAMES["vocab_file"] )
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
													copyfile(UpperCAmelCase__      ,  save_dir / VOCAB_FILES_NAMES["spm_file"] )

								_UpperCamelCase									= SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
								tokenizer.save_pretrained(self.tmpdirname )




			def    a						(       self ):
								_UpperCamelCase									= "<pad>"
								_UpperCamelCase									= 1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ )      ,  UpperCAmelCase__ )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ )      ,  UpperCAmelCase__ )




			def    a						(       self ):
								_UpperCamelCase									= list(self.get_tokenizer().get_vocab().keys() )

								self.assertEqual(vocab_keys[0]      ,  "<s>" )
								self.assertEqual(vocab_keys[1]      ,  "<pad>" )
								self.assertEqual(vocab_keys[-1]      ,  "j" )
								self.assertEqual(len(UpperCAmelCase__ )      ,  10_01 )




			def    a						(       self ):
								self.assertEqual(self.get_tokenizer().vocab_size      ,  10_01 )




			def    a						(       self ):
								_UpperCamelCase									= SpeechaTextTokenizer.from_pretrained(self.tmpdirname )

								_UpperCamelCase									= tokenizer.tokenize("This is a test" )
								self.assertListEqual(UpperCAmelCase__      ,  ["▁This", "▁is", "▁a", "▁t", "est"] )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )      ,  [2_89, 50, 14, 1_74, 3_86]      ,  )

								_UpperCamelCase									= tokenizer.tokenize("I was born in 92000, and this is falsé." )
								self.assertListEqual(
								    UpperCAmelCase__      ,  [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", "."]      ,  )
								_UpperCamelCase									= tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )
								self.assertListEqual(UpperCAmelCase__      ,  [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8] )

								_UpperCamelCase									= tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
								self.assertListEqual(
								    UpperCAmelCase__      ,  [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>", "."]      ,  )




			@slow
			def    a						(       self ):
								_UpperCamelCase									= {"input_ids": [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCAmelCase__      ,  model_name="facebook/s2t-small-mustc-en-de-st"      ,  revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad"      ,  )




@require_sentencepiece
class      A_			(	unittest.TestCase	):

			'''simple docstring'''





			_lowerCAmelCase							=     """valhalla/s2t_mustc_multilinguial_medium"""

			_lowerCAmelCase							=     """C\'est trop cool"""
			_lowerCAmelCase							=     """Esto es genial"""




			@classmethod
			def    a						(       cls ):
								_UpperCamelCase									= SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
								return cls




			def    a						(       self ):
								self.assertEqual(self.tokenizer.lang_code_to_id["pt"]      ,  4 )
								self.assertEqual(self.tokenizer.lang_code_to_id["ru"]      ,  6 )
								self.assertEqual(self.tokenizer.lang_code_to_id["it"]      ,  9 )
								self.assertEqual(self.tokenizer.lang_code_to_id["de"]      ,  11 )




			def    a						(       self ):
								self.assertEqual(self.tokenizer.vocab_size      ,  1_00_00 )




			def    a						(       self ):
								self.assertIn(UpperCAmelCase__      ,  self.tokenizer.all_special_ids )
								_UpperCamelCase									= [ES_CODE, 4, 16_01, 47, 76_47, 2]
								_UpperCamelCase									= self.tokenizer.decode(UpperCAmelCase__      ,  skip_special_tokens=UpperCAmelCase__ )
								_UpperCamelCase									= self.tokenizer.decode(generated_ids[1:]      ,  skip_special_tokens=UpperCAmelCase__ )
								self.assertEqual(UpperCAmelCase__      ,  UpperCAmelCase__ )
								self.assertNotIn(self.tokenizer.eos_token      ,  UpperCAmelCase__ )




			def    a						(       self ):
								_UpperCamelCase									= "fr"
								_UpperCamelCase									= self.tokenizer(self.french_text ).input_ids
								self.assertEqual(encoded[0]      ,  UpperCAmelCase__ )
								self.assertEqual(encoded[-1]      ,  self.tokenizer.eos_token_id )




			def    a						(       self ):
								_UpperCamelCase									= "fr"
								self.assertListEqual(self.tokenizer.prefix_tokens      ,  [FR_CODE] )

								_UpperCamelCase									= "es"
								self.assertListEqual(self.tokenizer.prefix_tokens      ,  [ES_CODE] )









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor


class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    def __init__(     self :      Tuple   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int=13   ,					UpperCAmelCase__ :      Union[str, Any]=3   ,					UpperCAmelCase__ :      str=224   ,					UpperCAmelCase__ :      str=30   ,					UpperCAmelCase__ :      Tuple=400   ,					UpperCAmelCase__ :      int=True   ,					UpperCAmelCase__ :      Dict=None   ,					UpperCAmelCase__ :      List[Any]=True   ,					UpperCAmelCase__ :      Union[str, Any]=[0.5, 0.5, 0.5]   ,					UpperCAmelCase__ :      Tuple=[0.5, 0.5, 0.5]   ,					)		->str:





           '''simple docstring'''

           A__ 					=       size if size is not None else {'''height''': 18, '''width''': 18}
           A__ 					=       parent
           A__ 					=       batch_size
           A__ 					=       num_channels
           A__ 					=       image_size
           A__ 					=       min_resolution
           A__ 					=       max_resolution
           A__ 					=       do_resize
           A__ 					=       size
           A__ 					=       do_normalize
           A__ 					=       image_mean
           A__ 					=       image_std





    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Optional[int]:





           '''simple docstring'''

           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }


@require_torch
@require_vision
class 							UpperCamelCase_       (					UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = ViTImageProcessor if is_vision_available() else None


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->str:





           '''simple docstring'''

           A__ 					=       EfficientFormerImageProcessorTester(self)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           return self.image_proc_tester.prepare_image_processor_dict()


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Dict:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''image_mean'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''image_std'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''do_normalize'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__   ,					'''size'''))


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Dict:





           '''simple docstring'''

           pass


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__ 					=       prepare_image_inputs(self.image_proc_tester   ,					equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__   ,					Image.Image)

           # Test not batched input
           A__ 					=       image_processor(image_inputs[0]   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)

           # Test batched
           A__ 					=       image_processor(UpperCAmelCase__   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->List[Any]:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__ 					=       prepare_image_inputs(self.image_proc_tester   ,					equal_resolution=UpperCAmelCase__   ,					numpify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__   ,					np.ndarray)

           # Test not batched input
           A__ 					=       image_processor(image_inputs[0]   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)

           # Test batched
           A__ 					=       image_processor(UpperCAmelCase__   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)





    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__ 					=       prepare_image_inputs(self.image_proc_tester   ,					equal_resolution=UpperCAmelCase__   ,					torchify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__   ,					torch.Tensor)

           # Test not batched input
           A__ 					=       image_processor(image_inputs[0]   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   1,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)

           # Test batched
           A__ 					=       image_processor(UpperCAmelCase__   ,					return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape   ,					(
                   self.image_proc_tester.batch_size,
                   self.image_proc_tester.num_channels,
                   self.image_proc_tester.size['''height'''],
                   self.image_proc_tester.size['''width'''],
               )   ,					)


