


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase		=						logging.get_logger(__name__)

lowerCamelCase		=						{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class    _a				(							_lowercase):
	_a    :    List[Any]        =				'''oneformer'''
	_a    :    Dict        =				{'''hidden_size''': '''hidden_dim'''}




	def __init__(   self		:						Optional[Any]						,   _SCREAMING_SNAKE_CASE		:						Optional[Dict] = None						,   _SCREAMING_SNAKE_CASE		:						int = 255						,   _SCREAMING_SNAKE_CASE		:						int = 150						,   _SCREAMING_SNAKE_CASE		:						int = 0.1						,   _SCREAMING_SNAKE_CASE		:						float = 2.0						,   _SCREAMING_SNAKE_CASE		:						float = 5.0						,   _SCREAMING_SNAKE_CASE		:						float = 5.0						,   _SCREAMING_SNAKE_CASE		:						float = 0.5						,   _SCREAMING_SNAKE_CASE		:						float = 0.07						,   _SCREAMING_SNAKE_CASE		:						int = 1_2544						,   _SCREAMING_SNAKE_CASE		:						float = 3.0						,   _SCREAMING_SNAKE_CASE		:						float = 0.75						,   _SCREAMING_SNAKE_CASE		:						float = 0.02						,   _SCREAMING_SNAKE_CASE		:						float = 1.0						,   _SCREAMING_SNAKE_CASE		:						float = 1E-05						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Optional[list] = [4, 8, 16, 32]						,   _SCREAMING_SNAKE_CASE		:						int = 77						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 77						,   _SCREAMING_SNAKE_CASE		:						int = 6						,   _SCREAMING_SNAKE_CASE		:						int = 4_9408						,   _SCREAMING_SNAKE_CASE		:						int = 2						,   _SCREAMING_SNAKE_CASE		:						int = 16						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 256						,   _SCREAMING_SNAKE_CASE		:						int = 1024						,   _SCREAMING_SNAKE_CASE		:						str = "GN"						,   _SCREAMING_SNAKE_CASE		:						int = 6						,   _SCREAMING_SNAKE_CASE		:						int = 10						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						int = 8						,   _SCREAMING_SNAKE_CASE		:						float = 0.1						,   _SCREAMING_SNAKE_CASE		:						int = 2048						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						bool = False						,   _SCREAMING_SNAKE_CASE		:						int = 2						,   _SCREAMING_SNAKE_CASE		:						int = 4						,   **_SCREAMING_SNAKE_CASE		:						Any						,   )->			Tuple:
			if backbone_config is None:
					logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''						)
					lowerCAmelCase__  :     Optional[int]       	=   CONFIG_MAPPING['''swin'''](
					    image_size=224						,   in_channels=3						,   patch_size=4						,   embed_dim=96						,   depths=[2, 2, 6, 2]						,   num_heads=[3, 6, 12, 24]						,   window_size=7						,   drop_path_rate=0.3						,   use_absolute_embeddings=_lowerCAmelCase						,   out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						,   )
			elif isinstance(_lowerCAmelCase						,   _lowerCAmelCase						):
					lowerCAmelCase__  :     Union[str, Any]       	=   backbone_config.get('''model_type'''						)
					lowerCAmelCase__  :     Union[str, Any]       	=   CONFIG_MAPPING[backbone_model_type]
					lowerCAmelCase__  :     Dict       	=   config_class.from_dict(_lowerCAmelCase						)

			lowerCAmelCase__  :     str       	=   backbone_config

			lowerCAmelCase__  :     Tuple       	=   ignore_value
			lowerCAmelCase__  :     Any       	=   num_queries
			lowerCAmelCase__  :     Optional[Any]       	=   no_object_weight
			lowerCAmelCase__  :     Any       	=   class_weight
			lowerCAmelCase__  :     Dict       	=   mask_weight
			lowerCAmelCase__  :     List[str]       	=   dice_weight
			lowerCAmelCase__  :     Union[str, Any]       	=   contrastive_weight
			lowerCAmelCase__  :     Optional[Any]       	=   contrastive_temperature
			lowerCAmelCase__  :     Any       	=   train_num_points
			lowerCAmelCase__  :     Optional[int]       	=   oversample_ratio
			lowerCAmelCase__  :     Tuple       	=   importance_sample_ratio
			lowerCAmelCase__  :     List[str]       	=   init_std
			lowerCAmelCase__  :     Optional[int]       	=   init_xavier_std
			lowerCAmelCase__  :     Optional[int]       	=   layer_norm_eps
			lowerCAmelCase__  :     Optional[Any]       	=   is_training
			lowerCAmelCase__  :     Dict       	=   use_auxiliary_loss
			lowerCAmelCase__  :     str       	=   output_auxiliary_logits
			lowerCAmelCase__  :     List[str]       	=   strides
			lowerCAmelCase__  :     Optional[Any]       	=   task_seq_len
			lowerCAmelCase__  :     Tuple       	=   text_encoder_width
			lowerCAmelCase__  :     Any       	=   text_encoder_context_length
			lowerCAmelCase__  :     Any       	=   text_encoder_num_layers
			lowerCAmelCase__  :     List[Any]       	=   text_encoder_vocab_size
			lowerCAmelCase__  :     Any       	=   text_encoder_proj_layers
			lowerCAmelCase__  :     List[str]       	=   text_encoder_n_ctx
			lowerCAmelCase__  :     Tuple       	=   conv_dim
			lowerCAmelCase__  :     Any       	=   mask_dim
			lowerCAmelCase__  :     Union[str, Any]       	=   hidden_dim
			lowerCAmelCase__  :     Any       	=   encoder_feedforward_dim
			lowerCAmelCase__  :     Any       	=   norm
			lowerCAmelCase__  :     int       	=   encoder_layers
			lowerCAmelCase__  :     Optional[int]       	=   decoder_layers
			lowerCAmelCase__  :     int       	=   use_task_norm
			lowerCAmelCase__  :     Any       	=   num_attention_heads
			lowerCAmelCase__  :     Any       	=   dropout
			lowerCAmelCase__  :     str       	=   dim_feedforward
			lowerCAmelCase__  :     Any       	=   pre_norm
			lowerCAmelCase__  :     str       	=   enforce_input_proj
			lowerCAmelCase__  :     int       	=   query_dec_layers
			lowerCAmelCase__  :     Tuple       	=   common_stride
			lowerCAmelCase__  :     str       	=   decoder_layers

			super().__init__(**_lowerCAmelCase						)




	def 				UpperCAmelCase__(   self		:						List[str]						)->			Dict[str, any]:
			lowerCAmelCase__  :     Tuple       	=   copy.deepcopy(self.__dict__						)
			lowerCAmelCase__  :     Optional[Any]       	=   self.backbone_config.to_dict()
			lowerCAmelCase__  :     List[str]       	=   self.__class__.model_type
			return output






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
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_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
       from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


_UpperCamelCase:					str     =get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Any:
         _lowerCAmelCase						=		tempfile.mkdtemp()

         _lowerCAmelCase						=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         _lowerCAmelCase						=		dict(zip(_lowerCAmelCase  ,      range(len(_lowerCAmelCase       )       )       )       )
         _lowerCAmelCase						=		Path(self.tmpdirname       )
         save_json(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']       )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
           copyfile(_lowerCAmelCase  ,      save_dir / VOCAB_FILES_NAMES['spm_file']       )

         _lowerCAmelCase						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
         tokenizer.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		{
             'feature_size': 24,
             'num_mel_bins': 24,
             'padding_value': 0.0,
             'sampling_rate': 1_6000,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_lowerCAmelCase  ,      save_dir / FEATURE_EXTRACTOR_NAME       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   List[str]:
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      **_lowerCAmelCase					:			Dict       )					->   Tuple:
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Any:
         _lowerCAmelCase						=		self.get_tokenizer()
         _lowerCAmelCase						=		self.get_feature_extractor()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         processor.save_pretrained(self.tmpdirname       )
         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

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

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   int:
         _lowerCAmelCase						=		SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         _lowerCAmelCase						=		self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'       )
         _lowerCAmelCase						=		self.get_feature_extractor(do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

         _lowerCAmelCase						=		SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_lowerCAmelCase  ,      padding_value=1.0       )

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

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor  ,      _lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		floats_list((3, 1000)       )

         _lowerCAmelCase						=		feature_extractor(_lowerCAmelCase  ,      return_tensors='np'       )
         _lowerCAmelCase						=		processor(_lowerCAmelCase  ,      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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         _lowerCAmelCase						=		'This is a test string'

         _lowerCAmelCase						=		processor(text=_lowerCAmelCase       )

         _lowerCAmelCase						=		tokenizer(_lowerCAmelCase       )

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



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Tuple:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

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

         _lowerCAmelCase						=		processor.batch_decode(_lowerCAmelCase       )
         _lowerCAmelCase						=		tokenizer.batch_decode(_lowerCAmelCase       )

         self.assertListEqual(_lowerCAmelCase  ,      _lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Union[str, Any]:
         _lowerCAmelCase						=		self.get_feature_extractor()
         _lowerCAmelCase						=		self.get_tokenizer()

         _lowerCAmelCase						=		SpeechaTextProcessor(tokenizer=_lowerCAmelCase  ,      feature_extractor=_lowerCAmelCase       )

         self.assertListEqual(
             processor.model_input_names  ,      feature_extractor.model_input_names  ,      msg='`processor` and `feature_extractor` model input names do not match'  ,      )
