





'''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


lowerCAmelCase       		=      get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_sentencepiece_available():
					import sentencepiece as sp


lowerCAmelCase       		=      5
lowerCAmelCase       		=      10
@require_sentencepiece
@require_tokenizers
class 		lowerCamelCase  (      __a      ,   unittest.TestCase       ):
	snake_case_      =   SpeechaTextTokenizer
	snake_case_      =   False
	snake_case_      =   True



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

			lowerCAmelCase     :    List[Any]						=   sp.SentencePieceProcessor()
			spm_model.Load(snake_case__	)
			lowerCAmelCase     :    Tuple						=   ["<s>", "<pad>", "</s>", "<unk>"]

			vocab += [spm_model.IdToPiece(id_	) for id_ in range(len(snake_case__	)	)]
			lowerCAmelCase     :    str						=   dict(zip(snake_case__		,      range(len(snake_case__	)	)	)	)

			lowerCAmelCase     :    List[str]						=   Path(self.tmpdirname	)
			save_json(snake_case__		,      save_dir / VOCAB_FILES_NAMES["vocab_file"]	)
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(snake_case__		,      save_dir / VOCAB_FILES_NAMES["spm_file"]	)

			lowerCAmelCase     :    List[Any]						=   SpeechaTextTokenizer.from_pretrained(self.tmpdirname	)
			tokenizer.save_pretrained(self.tmpdirname	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   "<pad>"
			lowerCAmelCase     :    Tuple						=   1

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   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(snake_case__	)		,      1_001	)



	def   _lowerCamelCase    (   self	):
			self.assertEqual(self.get_tokenizer().vocab_size		,      1_001	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   SpeechaTextTokenizer.from_pretrained(self.tmpdirname	)

			lowerCAmelCase     :    List[Any]						=   tokenizer.tokenize("This is a test"	)
			self.assertListEqual(snake_case__		,      ["▁This", "▁is", "▁a", "▁t", "est"]	)

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(snake_case__	)		,      [289, 50, 14, 174, 386]		,      )

			lowerCAmelCase     :    Tuple						=   tokenizer.tokenize("I was born in 92000, and this is falsé."	)
			self.assertListEqual(
			    snake_case__		,      [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", "."]		,      )
			lowerCAmelCase     :    List[Any]						=   tokenizer.convert_tokens_to_ids(snake_case__	)
			self.assertListEqual(snake_case__		,      [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8]	)

			lowerCAmelCase     :    Dict						=   tokenizer.convert_ids_to_tokens(snake_case__	)
			self.assertListEqual(
			    snake_case__		,      [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   _lowerCamelCase    (   self	):
			# fmt: off
			lowerCAmelCase     :    Tuple						=   {"input_ids": [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 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], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 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=snake_case__		,      model_name="facebook/s2t-small-mustc-en-de-st"		,      revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad"		,      )






@require_sentencepiece
class 		lowerCamelCase  (      unittest.TestCase       ):
	snake_case_      =   '''valhalla/s2t_mustc_multilinguial_medium'''

	snake_case_      =   '''C\'est trop cool'''
	snake_case_      =   '''Esto es genial'''



	@classmethod
	def   _lowerCamelCase    (   cls	):
			lowerCAmelCase     :    Tuple						=   SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name	)
			return cls



	def   _lowerCamelCase    (   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   _lowerCamelCase    (   self	):
			self.assertEqual(self.tokenizer.vocab_size		,      10_000	)



	def   _lowerCamelCase    (   self	):
			self.assertIn(snake_case__		,      self.tokenizer.all_special_ids	)
			lowerCAmelCase     :    Tuple						=   [ES_CODE, 4, 1_601, 47, 7_647, 2]
			lowerCAmelCase     :    List[str]						=   self.tokenizer.decode(snake_case__		,      skip_special_tokens=snake_case__	)
			lowerCAmelCase     :    Union[str, Any]						=   self.tokenizer.decode(generated_ids[1:]		,      skip_special_tokens=snake_case__	)
			self.assertEqual(snake_case__		,      snake_case__	)
			self.assertNotIn(self.tokenizer.eos_token		,      snake_case__	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   "fr"
			lowerCAmelCase     :    int						=   self.tokenizer(self.french_text	).input_ids
			self.assertEqual(encoded[0]		,      snake_case__	)
			self.assertEqual(encoded[-1]		,      self.tokenizer.eos_token_id	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   "fr"
			self.assertListEqual(self.tokenizer.prefix_tokens		,      [FR_CODE]	)

			lowerCAmelCase     :    Any						=   "es"
			self.assertListEqual(self.tokenizer.prefix_tokens		,      [ES_CODE]	)










'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   ["image_processor", "tokenizer"]
	snake_case_      =   "ChineseCLIPImageProcessor"
	snake_case_      =   ("BertTokenizer", "BertTokenizerFast")



	def __init__(   self		,      a_=None		,      a_=None		,      **a_	):
			lowerCAmelCase     :    Any						=   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,      a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   kwargs.pop("feature_extractor"	)

			lowerCAmelCase     :    int						=   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."	)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."	)

			super().__init__(a_		,      a_	)
			lowerCAmelCase     :    Dict						=   self.image_processor



	def __call__(   self		,      a_=None		,      a_=None		,      a_=None		,      **a_	):

			if text is None and images is None:
					raise ValueError("You have to specify either text or images. Both cannot be none."	)

			if text is not None:
					lowerCAmelCase     :    List[str]						=   self.tokenizer(a_		,      return_tensors=a_		,      **a_	)

			if images is not None:
					lowerCAmelCase     :    List[Any]						=   self.image_processor(a_		,      return_tensors=a_		,      **a_	)

			if text is not None and images is not None:
					lowerCAmelCase     :    str						=   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**a_	)		,      tensor_type=a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.batch_decode(*a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.decode(*a_		,      **a_	)



	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.tokenizer.model_input_names
			lowerCAmelCase     :    List[Any]						=   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





	@property
	def   _lowerCamelCase    (   self	):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,      a_		,      )
			return self.image_processor_class



