


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       __magic_name__	(     unittest.TestCase):


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





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   '''ylacombe/bark-small'''
      UpperCamelCase__					:    Dict          =   tempfile.mkdtemp()
      UpperCamelCase__					:    Dict          =   '''en_speaker_1'''
      UpperCamelCase__					:    Optional[int]          =   '''This is a test string'''
      UpperCamelCase__					:    Union[str, Any]          =   '''speaker_embeddings_path.json'''
      UpperCamelCase__					:    str          =   '''speaker_embeddings'''


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	**lowerCamelCase__							:     Dict			)  ->      Optional[Any]:





      '''simple docstring'''






      return AutoTokenizer.from_pretrained(self.checkpoint							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      str:





      '''simple docstring'''






      shutil.rmtree(self.tmpdirname			)


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.get_tokenizer()

      UpperCamelCase__					:    str          =   BarkProcessor(tokenizer=lowerCamelCase__			)

      processor.save_pretrained(self.tmpdirname			)
      UpperCamelCase__					:    Optional[Any]          =   BarkProcessor.from_pretrained(self.tmpdirname			)

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


     @slow
     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   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__					:    Union[str, Any]          =   self.get_tokenizer(bos_token='''(BOS)'''							,	eos_token='''(EOS)'''			)

      UpperCamelCase__					:    Union[str, Any]          =   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      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint							,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,	)

      UpperCamelCase__					:    str          =   35
      UpperCamelCase__					:    int          =   2
      UpperCamelCase__					:    List[Any]          =   8

      UpperCamelCase__					:    str          =   {
          '''semantic_prompt''': np.ones(lowerCamelCase__			),
          '''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__					:    int          =   processor(text=self.input_string							,	voice_preset=lowerCamelCase__			)

      UpperCamelCase__					:    Dict          =   inputs['''history_prompt''']
      for key in voice_preset:
       self.assertListEqual(voice_preset[key].tolist()							,	processed_voice_preset.get(lowerCamelCase__							,	np.array([]			)			).tolist()			)

      # test loading voice preset from npz file
      UpperCamelCase__					:    Tuple          =   os.path.join(self.tmpdirname							,	'''file.npz'''			)
      np.savez(lowerCamelCase__							,	**lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   processor(text=self.input_string							,	voice_preset=lowerCamelCase__			)
      UpperCamelCase__					:    Optional[Any]          =   inputs['''history_prompt''']

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

      # test loading voice preset from the hub
      UpperCamelCase__					:    Dict          =   processor(text=self.input_string							,	voice_preset=self.voice_preset			)


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   self.get_tokenizer()

      UpperCamelCase__					:    Tuple          =   BarkProcessor(tokenizer=lowerCamelCase__			)

      UpperCamelCase__					:    List[str]          =   processor(text=self.input_string			)

      UpperCamelCase__					:    str          =   tokenizer(
          self.input_string							,	padding='''max_length'''							,	max_length=256							,	add_special_tokens=lowerCamelCase__							,	return_attention_mask=lowerCamelCase__							,	return_token_type_ids=lowerCamelCase__							,	)

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






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[Any]			=      ["image_processor", "tokenizer"]
     A:		Optional[Any]			=      "LayoutLMv2ImageProcessor"
     A:		List[str]			=      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


     def __init__(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]=None							,	lowerCamelCase__							:     List[str]=None							,	**lowerCamelCase__							:     Any			)  ->      Optional[Any]:





      '''simple docstring'''






      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''							,	lowerCamelCase__							,	)
       UpperCamelCase__					:    str          =   kwargs.pop('''feature_extractor'''			)

      UpperCamelCase__					:    List[Any]          =   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__(lowerCamelCase__							,	lowerCamelCase__			)


     def __call__(      self							:     Optional[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	lowerCamelCase__							:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None							,	lowerCamelCase__							:     Union[List[List[int]], List[List[List[int]]]] = None							,	lowerCamelCase__							:     Optional[Union[List[int], List[List[int]]]] = None							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Union[bool, str, PaddingStrategy] = False							,	lowerCamelCase__							:     Union[bool, str, TruncationStrategy] = None							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     int = 0							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Optional[Union[str, TensorType]] = None							,	**lowerCamelCase__							:     Any							,	)  ->      BatchEncoding:





      '''simple docstring'''






      if self.image_processor.apply_ocr and (boxes is not None):
       raise ValueError(
           '''You cannot provide bounding boxes '''
           '''if you initialized the image processor with apply_ocr set to True.'''			)

      if self.image_processor.apply_ocr and (word_labels is not None):
       raise ValueError(
           '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''			)

      if return_overflowing_tokens is True and return_offsets_mapping is False:
       raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''			)

      # first, apply the image processor
      UpperCamelCase__					:    Optional[Any]          =   self.image_processor(images=lowerCamelCase__							,	return_tensors=lowerCamelCase__			)

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
       if isinstance(lowerCamelCase__							,	lowerCamelCase__			):
        UpperCamelCase__					:    Optional[int]          =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
       UpperCamelCase__					:    Optional[int]          =   features['''words''']

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          text=text if text is not None else features['''words''']							,	text_pair=text_pair if text_pair is not None else None							,	boxes=boxes if boxes is not None else features['''boxes''']							,	word_labels=lowerCamelCase__							,	add_special_tokens=lowerCamelCase__							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	max_length=lowerCamelCase__							,	stride=lowerCamelCase__							,	pad_to_multiple_of=lowerCamelCase__							,	return_token_type_ids=lowerCamelCase__							,	return_attention_mask=lowerCamelCase__							,	return_overflowing_tokens=lowerCamelCase__							,	return_special_tokens_mask=lowerCamelCase__							,	return_offsets_mapping=lowerCamelCase__							,	return_length=lowerCamelCase__							,	verbose=lowerCamelCase__							,	return_tensors=lowerCamelCase__							,	**lowerCamelCase__							,	)

      # add pixel values
      UpperCamelCase__					:    Optional[Any]          =   features.pop('''pixel_values'''			)
      if return_overflowing_tokens is True:
       UpperCamelCase__					:    Union[str, Any]          =   self.get_overflowing_images(lowerCamelCase__							,	encoded_inputs['''overflow_to_sample_mapping''']			)
      UpperCamelCase__					:    Tuple          =   images

      return encoded_inputs


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     List[Any]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   []
      for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx]			)

      if len(lowerCamelCase__			) != len(lowerCamelCase__			):
       raise ValueError(
           '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
           F" {len(lowerCamelCase__			)} and {len(lowerCamelCase__			)}"			)

      return images_with_overflow


     def      UpperCAmelCase__	(      self							:     int							,	*lowerCamelCase__							:     Dict							,	**lowerCamelCase__							:     str			)  ->      str:





      '''simple docstring'''






      return self.tokenizer.batch_decode(*lowerCamelCase__							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     List[str]							,	*lowerCamelCase__							:     int							,	**lowerCamelCase__							:     str			)  ->      Tuple:





      '''simple docstring'''






      return self.tokenizer.decode(*lowerCamelCase__							,	**lowerCamelCase__			)


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''simple docstring'''






      return ["input_ids", "bbox", "attention_mask", "image"]


     @property
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      warnings.warn(
          '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,	lowerCamelCase__							,	)
      return self.image_processor_class


     @property
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[int]:





      '''simple docstring'''






      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,	lowerCamelCase__							,	)
      return self.image_processor


