



'''simple docstring'''






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Dict			     = """ylacombe/bark-small"""
        _UpperCAmelCase  :						Tuple			     = tempfile.mkdtemp()
        _UpperCAmelCase  :						Union[str, Any]			     = """en_speaker_1"""
        _UpperCAmelCase  :						List[str]			     = """This is a test string"""
        _UpperCAmelCase  :						Dict			     = """speaker_embeddings_path.json"""
        _UpperCAmelCase  :						List[Any]			     = """speaker_embeddings"""







 def _snake_case (							self  ,**a_							)		->   Any:
        return AutoTokenizer.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self							)		->   Any:
        shutil.rmtree(self.tmpdirname							)







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						Tuple			     = self.get_tokenizer()

        _UpperCAmelCase  :						Union[str, Any]			     = BarkProcessor(tokenizer=a_							)

        processor.save_pretrained(self.tmpdirname							)
        _UpperCAmelCase  :						str			     = BarkProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()							)







 @slow
 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						Dict			     = BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint  ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,)
        processor.save_pretrained(
            self.tmpdirname  ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,speaker_embeddings_directory=self.speaker_embeddings_directory  ,)

        _UpperCAmelCase  :						List[Any]			     = self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""							)

        _UpperCAmelCase  :						List[str]			     = BarkProcessor.from_pretrained(
            self.tmpdirname  ,self.speaker_embeddings_dict_path  ,bos_token="""(BOS)"""  ,eos_token="""(EOS)"""  ,)

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







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Any			     = BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint  ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,)

        _UpperCAmelCase  :						int			     = 35
        _UpperCAmelCase  :						int			     = 2
        _UpperCAmelCase  :						int			     = 8

        _UpperCAmelCase  :						str			     = {
            """semantic_prompt""": np.ones(a_							),
            """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)							),
            """fine_prompt""": np.ones((nb_codebooks_total, seq_len)							),
        }

        # test providing already loaded voice_preset
        _UpperCAmelCase  :						Dict			     = processor(text=self.input_string  ,voice_preset=a_							)

        _UpperCAmelCase  :						int			     = inputs["""history_prompt"""]
        for key in voice_preset:
               self.assertListEqual(voice_preset[key].tolist()  ,processed_voice_preset.get(a_  ,np.array([]							)							).tolist()							)

        # test loading voice preset from npz file
        _UpperCAmelCase  :						List[Any]			     = os.path.join(self.tmpdirname  ,"""file.npz"""							)
        np.savez(a_  ,**a_							)
        _UpperCAmelCase  :						Dict			     = processor(text=self.input_string  ,voice_preset=a_							)
        _UpperCAmelCase  :						List[str]			     = inputs["""history_prompt"""]

        for key in voice_preset:
               self.assertListEqual(voice_preset[key].tolist()  ,processed_voice_preset.get(a_  ,np.array([]							)							).tolist()							)

        # test loading voice preset from the hub
        _UpperCAmelCase  :						List[str]			     = processor(text=self.input_string  ,voice_preset=self.voice_preset							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Union[str, Any]			     = self.get_tokenizer()

        _UpperCAmelCase  :						Tuple			     = BarkProcessor(tokenizer=a_							)

        _UpperCAmelCase  :						Any			     = processor(text=self.input_string							)

        _UpperCAmelCase  :						int			     = tokenizer(
            self.input_string  ,padding="""max_length"""  ,max_length=256  ,add_special_tokens=a_  ,return_attention_mask=a_  ,return_token_type_ids=a_  ,)

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








'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							: Any						 =	logging.get_logger(__name__)

A_							: Any						 =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """markuplm"""







 def __init__(							self  ,a_=30_522  ,a_=768  ,a_=12  ,a_=12  ,a_=3_072  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=512  ,a_=2  ,a_=0.02  ,a_=1E-1_2  ,a_=0  ,a_=0  ,a_=2  ,a_=256  ,a_=1_024  ,a_=216  ,a_=1_001  ,a_=32  ,a_=50  ,a_="absolute"  ,a_=True  ,a_=None  ,**a_  ,)		->   Union[str, Any]:
        super().__init__(
            pad_token_id=a_  ,bos_token_id=a_  ,eos_token_id=a_  ,**a_  ,)
        _UpperCAmelCase  :						Optional[int]			     = vocab_size
        _UpperCAmelCase  :						Tuple			     = hidden_size
        _UpperCAmelCase  :						str			     = num_hidden_layers
        _UpperCAmelCase  :						Dict			     = num_attention_heads
        _UpperCAmelCase  :						int			     = hidden_act
        _UpperCAmelCase  :						Optional[Any]			     = intermediate_size
        _UpperCAmelCase  :						Tuple			     = hidden_dropout_prob
        _UpperCAmelCase  :						List[str]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Optional[int]			     = max_position_embeddings
        _UpperCAmelCase  :						Tuple			     = type_vocab_size
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						List[Any]			     = layer_norm_eps
        _UpperCAmelCase  :						Optional[Any]			     = position_embedding_type
        _UpperCAmelCase  :						Any			     = use_cache
        _UpperCAmelCase  :						List[Any]			     = classifier_dropout
        # additional properties
        _UpperCAmelCase  :						Dict			     = max_depth
        _UpperCAmelCase  :						Union[str, Any]			     = max_xpath_tag_unit_embeddings
        _UpperCAmelCase  :						Optional[int]			     = max_xpath_subs_unit_embeddings
        _UpperCAmelCase  :						List[Any]			     = tag_pad_id
        _UpperCAmelCase  :						Tuple			     = subs_pad_id
        _UpperCAmelCase  :						List[str]			     = xpath_unit_hidden_size



