


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        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__				:			Dict						=		'''LayoutLMv2ImageProcessor'''
				SCREAMING_SNAKE_CASE__				:			Dict						=		('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


				def __init__(       self			:Union[str, Any]			,			lowerCAmelCase__			:int=None			,			lowerCAmelCase__			:List[str]=None			,			**lowerCAmelCase__			:Optional[int]	)		->			Union[str, Any]:
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''			,			lowerCAmelCase__			,			)
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		kwargs.pop('''feature_extractor'''	)

										__SCREAMING_SNAKE_CASE						:     Optional[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__(lowerCAmelCase__			,			lowerCAmelCase__	)


				def __call__(       self			:Optional[int]			,			lowerCAmelCase__			:List[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__			:List[Any]			,			)		->			BatchEncoding:
										# verify input
										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
										__SCREAMING_SNAKE_CASE						:     str					=		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__	):
																						__SCREAMING_SNAKE_CASE						:     Any					=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
																__SCREAMING_SNAKE_CASE						:     List[str]					=		features['''words''']

										__SCREAMING_SNAKE_CASE						:     str					=		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
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		features.pop('''pixel_values'''	)
										if return_overflowing_tokens is True:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		self.get_overflowing_images(lowerCAmelCase__			,			encoded_inputs['''overflow_to_sample_mapping''']	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		images

										return encoded_inputs


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Optional[int]	)		->			Optional[int]:
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										__SCREAMING_SNAKE_CASE						:     Dict					=		[]
										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 		__magic_name__(       self			:Any			,			*lowerCAmelCase__			:Optional[int]			,			**lowerCAmelCase__			:Optional[Any]	)		->			Union[str, Any]:
										return self.tokenizer.batch_decode(*lowerCAmelCase__			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:Union[str, Any]			,			*lowerCAmelCase__			:Tuple			,			**lowerCAmelCase__			:Union[str, Any]	)		->			str:
										return self.tokenizer.decode(*lowerCAmelCase__			,			**lowerCAmelCase__	)


				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			List[Any]:
										return ["input_ids", "bbox", "attention_mask", "image"]


				@property
				def 		__magic_name__(       self			:str	)		->			Dict:
										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 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,			lowerCAmelCase__			,			)
										return self.image_processor




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


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			int:
										shutil.rmtree(self.tmpdirname	)


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

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

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


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     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			,			)

										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''	)

										__SCREAMING_SNAKE_CASE						:     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 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
										    '''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
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		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
										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, 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
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		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()	)
