
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


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





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



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     tempfile.mkdtemp()

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

								UpperCAmelCase:					Tuple 						=     SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
								tokenizer.save_pretrained(self.tmpdirname							)

								UpperCAmelCase:					int 						=     {
								    "feature_size": 2_4,
								    "num_mel_bins": 2_4,
								    "padding_value": 0.0,
								    "sampling_rate": 1_6_0_0_0,
								    "return_attention_mask": False,
								    "do_normalize": True,
								}
								save_json(__lowercase	,				save_dir / FEATURE_EXTRACTOR_NAME							)



							def     A__ (     self	,				**__snake_case							)						->     str:






								"""simple docstring"""



								return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,				**__lowercase							)



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






								"""simple docstring"""



								return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,				**__lowercase							)



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






								"""simple docstring"""



								shutil.rmtree(self.tmpdirname							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     self.get_tokenizer()
								UpperCAmelCase:					int 						=     self.get_feature_extractor()

								UpperCAmelCase:					int 						=     SpeechaTextProcessor(tokenizer=__lowercase	,				feature_extractor=__lowercase							)

								processor.save_pretrained(self.tmpdirname							)
								UpperCAmelCase:					Tuple 						=     SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

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

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



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     SpeechaTextProcessor(
								    tokenizer=self.get_tokenizer()	,				feature_extractor=self.get_feature_extractor()							)
								processor.save_pretrained(self.tmpdirname							)

								UpperCAmelCase:					Dict 						=     self.get_tokenizer(bos_token="(BOS)"	,				eos_token="(EOS)"							)
								UpperCAmelCase:					int 						=     self.get_feature_extractor(do_normalize=__lowercase	,				padding_value=1.0							)

								UpperCAmelCase:					List[str] 						=     SpeechaTextProcessor.from_pretrained(
								    self.tmpdirname	,				bos_token="(BOS)"	,				eos_token="(EOS)"	,				do_normalize=__lowercase	,				padding_value=1.0							)

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

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



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.get_feature_extractor()
								UpperCAmelCase:					Any 						=     self.get_tokenizer()

								UpperCAmelCase:					List[Any] 						=     SpeechaTextProcessor(tokenizer=__lowercase	,				feature_extractor=__lowercase							)

								UpperCAmelCase:					Optional[int] 						=     floats_list((3, 1_0_0_0)							)

								UpperCAmelCase:					Dict 						=     feature_extractor(__lowercase	,				return_tensors="np"							)
								UpperCAmelCase:					Any 						=     processor(__lowercase	,				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							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.get_feature_extractor()
								UpperCAmelCase:					List[str] 						=     self.get_tokenizer()

								UpperCAmelCase:					Union[str, Any] 						=     SpeechaTextProcessor(tokenizer=__lowercase	,				feature_extractor=__lowercase							)

								UpperCAmelCase:					Union[str, Any] 						=     "This is a test string"

								UpperCAmelCase:					Optional[Any] 						=     processor(text=__lowercase							)

								UpperCAmelCase:					Any 						=     tokenizer(__lowercase							)

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



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.get_feature_extractor()
								UpperCAmelCase:					List[str] 						=     self.get_tokenizer()

								UpperCAmelCase:					int 						=     SpeechaTextProcessor(tokenizer=__lowercase	,				feature_extractor=__lowercase							)

								UpperCAmelCase:					Union[str, Any] 						=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								UpperCAmelCase:					Dict 						=     processor.batch_decode(__lowercase							)
								UpperCAmelCase:					Union[str, Any] 						=     tokenizer.batch_decode(__lowercase							)

								self.assertListEqual(__lowercase	,				__lowercase							)




							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     self.get_feature_extractor()
								UpperCAmelCase:					str 						=     self.get_tokenizer()

								UpperCAmelCase:					int 						=     SpeechaTextProcessor(tokenizer=__lowercase	,				feature_extractor=__lowercase							)

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




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_UpperCAmelCase					:			Optional[int]				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			str				     =					{
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
	import detectrona


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		List[Any]						=						'layoutlmv2'




				def __init__(self							,      __lowercase=3_05_22							,      __lowercase=7_68							,      __lowercase=12							,      __lowercase=12							,      __lowercase=30_72							,      __lowercase="gelu"							,      __lowercase=0.1							,      __lowercase=0.1							,      __lowercase=5_12							,      __lowercase=2							,      __lowercase=0.0_2							,      __lowercase=1e-12							,      __lowercase=0							,      __lowercase=10_24							,      __lowercase=1_28							,      __lowercase=32							,      __lowercase=True							,      __lowercase=2_56							,      __lowercase=64							,      __lowercase=True							,      __lowercase=[7, 7, 2_56]							,      __lowercase=1_28							,      __lowercase=1_28							,      __lowercase=True							,      __lowercase=True							,      __lowercase=False							,      __lowercase=None							,      **__lowercase							,      ):
										super().__init__(
										    vocab_size=__lowercase							,      hidden_size=__lowercase							,      num_hidden_layers=__lowercase							,      num_attention_heads=__lowercase							,      intermediate_size=__lowercase							,      hidden_act=__lowercase							,      hidden_dropout_prob=__lowercase							,      attention_probs_dropout_prob=__lowercase							,      max_position_embeddings=__lowercase							,      type_vocab_size=__lowercase							,      initializer_range=__lowercase							,      layer_norm_eps=__lowercase							,      pad_token_id=__lowercase							,      **__lowercase							,      )
										__lowerCAmelCase											=					max_ad_position_embeddings
										__lowerCAmelCase											=					max_rel_pos
										__lowerCAmelCase											=					rel_pos_bins
										__lowerCAmelCase											=					fast_qkv
										__lowerCAmelCase											=					max_rel_ad_pos
										__lowerCAmelCase											=					rel_ad_pos_bins
										__lowerCAmelCase											=					convert_sync_batchnorm
										__lowerCAmelCase											=					image_feature_pool_shape
										__lowerCAmelCase											=					coordinate_size
										__lowerCAmelCase											=					shape_size
										__lowerCAmelCase											=					has_relative_attention_bias
										__lowerCAmelCase											=					has_spatial_attention_bias
										__lowerCAmelCase											=					has_visual_segment_embedding
										__lowerCAmelCase											=					(
										    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
										)




				@classmethod
				def        _snake_case	(self		):
										return {
										    "MODEL.MASK_ON": True,
										    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
										    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
										    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
										    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
										    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
										    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
										    "MODEL.POST_NMS_TOPK_TEST": 10_00,
										    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
										    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
										    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
										    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
										    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
										    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
										    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
										    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
										    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
										    "MODEL.RESNETS.DEPTH": 1_01,
										    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
										    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.RESNETS.NUM_GROUPS": 32,
										    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
										    "MODEL.RESNETS.STRIDE_IN_1X1": False,
										}




				def        _snake_case	(self		):
										__lowerCAmelCase											=					detectrona.config.get_cfg()
										for k, v in self.detectrona_config_args.items():
																__lowerCAmelCase											=					k.split('''.'''		)
																__lowerCAmelCase											=					detectrona_config
																for attribute in attributes[:-1]:
																						__lowerCAmelCase											=					getattr(__lowercase							,      __lowercase		)
																setattr(__lowercase							,      attributes[-1]							,      __lowercase		)

										return detectrona_config

