






'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor






@require_vision
class     a_						(      unittest.TestCase    ):

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = tempfile.mkdtemp()

        UpperCamelCase      = [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """的""",
            """价""",
            """格""",
            """是""",
            """15""",
            """便""",
            """alex""",
            """##andra""",
            """，""",
            """。""",
            """-""",
            """t""",
            """shirt""",
        ]
        UpperCamelCase      = os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["""vocab_file"""]				)
        with open(self.vocab_file      ,		"""w"""      ,		encoding="""utf-8"""				) as vocab_writer:
              vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

        UpperCamelCase      = {
            """do_resize""": True,
            """size""": {"""height""": 224, """width""": 224},
            """do_center_crop""": True,
            """crop_size""": {"""height""": 18, """width""": 18},
            """do_normalize""": True,
            """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
            """do_convert_rgb""": True,
        }
        UpperCamelCase      = os.path.join(self.tmpdirname      ,		_SCREAMING_SNAKE_CASE				)
        with open(self.image_processor_file      ,		"""w"""      ,		encoding="""utf-8"""				) as fp:
              json.dump(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return BertTokenizer.from_pretrained(self.tmpdirname      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        return BertTokenizerFast.from_pretrained(self.tmpdirname      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        shutil.rmtree(self.tmpdirname				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = [np.random.randint(255      ,		size=(3, 30, 400)      ,		dtype=np.uinta				)]

        UpperCamelCase      = [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE      ,		0      ,		-1				)				) for x in image_inputs]

        return image_inputs

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.get_tokenizer()
        UpperCamelCase      = self.get_rust_tokenizer()
        UpperCamelCase      = self.get_image_processor()

        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,		image_processor=_SCREAMING_SNAKE_CASE				)
        processor_slow.save_pretrained(self.tmpdirname				)
        UpperCamelCase      = ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,		use_fast=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,		image_processor=_SCREAMING_SNAKE_CASE				)
        processor_fast.save_pretrained(self.tmpdirname				)
        UpperCamelCase      = ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor_slow.tokenizer.get_vocab()      ,		tokenizer_slow.get_vocab()				)
        self.assertEqual(processor_fast.tokenizer.get_vocab()      ,		tokenizer_fast.get_vocab()				)
        self.assertEqual(tokenizer_slow.get_vocab()      ,		tokenizer_fast.get_vocab()				)
        self.assertIsInstance(processor_slow.tokenizer      ,		_SCREAMING_SNAKE_CASE				)
        self.assertIsInstance(processor_fast.tokenizer      ,		_SCREAMING_SNAKE_CASE				)

        self.assertEqual(processor_slow.image_processor.to_json_string()      ,		image_processor.to_json_string()				)
        self.assertEqual(processor_fast.image_processor.to_json_string()      ,		image_processor.to_json_string()				)
        self.assertIsInstance(processor_slow.image_processor      ,		_SCREAMING_SNAKE_CASE				)
        self.assertIsInstance(processor_fast.image_processor      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,		image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        UpperCamelCase      = self.get_tokenizer(cls_token="""(CLS)"""      ,		sep_token="""(SEP)"""				)
        UpperCamelCase      = self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname      ,		cls_token="""(CLS)"""      ,		sep_token="""(SEP)"""      ,		do_normalize=_SCREAMING_SNAKE_CASE				)

        self.assertEqual(processor.tokenizer.get_vocab()      ,		tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.tokenizer      ,		_SCREAMING_SNAKE_CASE				)

        self.assertEqual(processor.image_processor.to_json_string()      ,		image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,		image_processor=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.prepare_image_inputs()

        UpperCamelCase      = image_processor(_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				)
        UpperCamelCase      = processor(images=_SCREAMING_SNAKE_CASE      ,		return_tensors="""np"""				)

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()      ,		input_processor[key].sum()      ,		delta=1e-2				)

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,		image_processor=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = """Alexandra，T-shirt的价格是15便士。"""

        UpperCamelCase      = processor(text=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = tokenizer(_SCREAMING_SNAKE_CASE				)

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]      ,		encoded_processor[key]				)

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,		image_processor=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = """Alexandra，T-shirt的价格是15便士。"""
        UpperCamelCase      = self.prepare_image_inputs()

        UpperCamelCase      = processor(text=_SCREAMING_SNAKE_CASE      ,		images=_SCREAMING_SNAKE_CASE				)

        self.assertListEqual(list(inputs.keys()				)      ,		["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

        # test if it raises when no input is passed
        with pytest.raises(_SCREAMING_SNAKE_CASE				):
              processor()

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,		image_processor=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCamelCase      = processor.batch_decode(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)

        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)


  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.get_image_processor()
        UpperCamelCase      = self.get_tokenizer()

        UpperCamelCase      = ChineseCLIPProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,		image_processor=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = """Alexandra，T-shirt的价格是15便士。"""
        UpperCamelCase      = self.prepare_image_inputs()

        UpperCamelCase      = processor(text=_SCREAMING_SNAKE_CASE      ,		images=_SCREAMING_SNAKE_CASE				)

        self.assertListEqual(list(inputs.keys()				)      ,		processor.model_input_names				)








'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'spm_char.model'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

SCREAMING_SNAKE_CASE__     =       {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="<s>"      ,		_SCREAMING_SNAKE_CASE="</s>"      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        UpperCamelCase      = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_SCREAMING_SNAKE_CASE      ,		eos_token=_SCREAMING_SNAKE_CASE      ,		unk_token=_SCREAMING_SNAKE_CASE      ,		pad_token=_SCREAMING_SNAKE_CASE      ,		sp_model_kwargs=self.sp_model_kwargs      ,		**_SCREAMING_SNAKE_CASE      ,		)

        UpperCamelCase      = vocab_file

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.get_piece_size()

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
        vocab.update(self.added_tokens_encoder				)
        return vocab

  def __getstate__(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.__dict__.copy()
        UpperCamelCase      = None
        return state

  def __setstate__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = d

        # for backward compatibility
        if not hasattr(self      ,		"""sp_model_kwargs"""				):
              UpperCamelCase      = {}

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.vocab_file				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,		out_type=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE				)
        return token

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = []
        UpperCamelCase      = """"""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				) + token
                    UpperCamelCase      = []
              else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
        out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
        return out_string.strip()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None				)  ->				List[int]:



        """simple docstring"""






        if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_SCREAMING_SNAKE_CASE      ,		token_ids_a=_SCREAMING_SNAKE_CASE      ,		already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = [1]
        if token_ids_a is None:
              return ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones
        return ([0] * len(_SCREAMING_SNAKE_CASE				)) + ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				Tuple[str]:



        """simple docstring"""






        if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
              return
        UpperCamelCase      = os.path.join(
            _SCREAMING_SNAKE_CASE      ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

        if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
              copyfile(self.vocab_file      ,		_SCREAMING_SNAKE_CASE				)
        elif not os.path.isfile(self.vocab_file				):
              with open(_SCREAMING_SNAKE_CASE      ,		"""wb"""				) as fi:
                    UpperCamelCase      = self.sp_model.serialized_model_proto()
                    fi.write(_SCREAMING_SNAKE_CASE				)

        return (out_vocab_file,)
