



"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
				from huggingface_hub import snapshot_download
				from pyctcdecode import BeamSearchDecoderCTC

				from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
				from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
				from transformers import WavaVecaForCTC







@require_pyctcdecode
class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Union[str, Any]:
												UpperCAmelCase					=			'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
												UpperCAmelCase					=			dict(zip(lowercase_       ,  range(len(lowercase_ ) ) ) )

												UpperCAmelCase					=			{
												    'unk_token': '<unk>',
												    'bos_token': '<s>',
												    'eos_token': '</s>',
												}
												UpperCAmelCase					=			{
												    'feature_size': 1,
												    'padding_value': 0.0,
												    'sampling_rate': 1_60_00,
												    'return_attention_mask': False,
												    'do_normalize': True,
												}

												UpperCAmelCase					=			tempfile.mkdtemp()
												UpperCAmelCase					=			os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['vocab_file'] )
												UpperCAmelCase					=			os.path.join(self.tmpdirname       ,  lowercase_ )
												with open(self.vocab_file       ,  'w'       ,  encoding='utf-8' ) as fp:
																		fp.write(json.dumps(lowercase_ ) + '\n' )

												with open(self.feature_extraction_file       ,  'w'       ,  encoding='utf-8' ) as fp:
																		fp.write(json.dumps(lowercase_ ) + '\n' )

												# load decoder from hub
												UpperCAmelCase					=			'hf-internal-testing/ngram-beam-search-decoder'






						def      UpperCAmelCase__					( self			:int       ,  **lowercase_			:Union[str, Any] )    ->      Optional[Any]:
												UpperCAmelCase					=			self.add_kwargs_tokens_map.copy()
												kwargs.update(lowercase_ )
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  **lowercase_			:Optional[int] )    ->      int:
												return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  **lowercase_			:List[str] )    ->      List[Any]:
												return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name       ,  **lowercase_ )






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






						def      UpperCAmelCase__					( self			:Dict )    ->      List[str]:
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

												processor.save_pretrained(self.tmpdirname )
												UpperCAmelCase					=			WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )

												# tokenizer
												self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer.get_vocab() )
												self.assertIsInstance(processor.tokenizer       ,  lowercase_ )

												# feature extractor
												self.assertEqual(processor.feature_extractor.to_json_string()       ,  feature_extractor.to_json_string() )
												self.assertIsInstance(processor.feature_extractor       ,  lowercase_ )

												# decoder
												self.assertEqual(processor.decoder._alphabet.labels       ,  decoder._alphabet.labels )
												self.assertEqual(
												    processor.decoder.model_container[decoder._model_key]._unigram_set       ,  decoder.model_container[decoder._model_key]._unigram_set       ,  )
												self.assertIsInstance(processor.decoder       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Union[str, Any]:
												UpperCAmelCase					=			WavaVecaProcessorWithLM(
												    tokenizer=self.get_tokenizer()       ,  feature_extractor=self.get_feature_extractor()       ,  decoder=self.get_decoder() )
												processor.save_pretrained(self.tmpdirname )

												# make sure that error is thrown when decoder alphabet doesn't match
												UpperCAmelCase					=			WavaVecaProcessorWithLM.from_pretrained(
												    self.tmpdirname       ,  alpha=5.0       ,  beta=3.0       ,  score_boundary=-7.0       ,  unk_score_offset=3 )

												# decoder
												self.assertEqual(processor.language_model.alpha       ,  5.0 )
												self.assertEqual(processor.language_model.beta       ,  3.0 )
												self.assertEqual(processor.language_model.score_boundary       ,  -7.0 )
												self.assertEqual(processor.language_model.unk_score_offset       ,  3 )






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Optional[int]:
												UpperCAmelCase					=			self.get_tokenizer()
												# add token to trigger raise
												tokenizer.add_tokens(['xx'] )
												with self.assertRaisesRegex(lowercase_       ,  'include' ):
																		WavaVecaProcessorWithLM(
																		    tokenizer=lowercase_       ,  feature_extractor=self.get_feature_extractor()       ,  decoder=self.get_decoder() )






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

												UpperCAmelCase					=			floats_list((3, 10_00) )

												UpperCAmelCase					=			feature_extractor(lowercase_       ,  return_tensors='np' )
												UpperCAmelCase					=			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      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

												UpperCAmelCase					=			'This is a test string'

												UpperCAmelCase					=			processor(text=lowercase_ )

												UpperCAmelCase					=			tokenizer(lowercase_ )

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






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:List[str]=(2, 10, 16)       ,  lowercase_			:str=77 )    ->      Union[str, Any]:
												np.random.seed(lowercase_ )
												return np.random.rand(*lowercase_ )






						def      UpperCAmelCase__					( self			:Any )    ->      Dict:
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

												UpperCAmelCase					=			self._get_dummy_logits(shape=(10, 16)       ,  seed=13 )

												UpperCAmelCase					=			processor.decode(lowercase_ )

												UpperCAmelCase					=			decoder.decode_beams(lowercase_ )[0]

												self.assertEqual(decoded_decoder[0]       ,  decoded_processor.text )
												self.assertEqual('</s> <s> </s>'       ,  decoded_processor.text )
												self.assertEqual(decoded_decoder[-2]       ,  decoded_processor.logit_score )
												self.assertEqual(decoded_decoder[-1]       ,  decoded_processor.lm_score )






						@parameterized.expand([[None], ['fork'], ['spawn']] )
						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Optional[Any] )    ->      str:
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

												UpperCAmelCase					=			self._get_dummy_logits()

												# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
												#       otherwise, the LM won't be available to the pool's sub-processes.
												# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
												if pool_context is None:
																		UpperCAmelCase					=			processor.batch_decode(lowercase_ )
												else:
																		with get_context(lowercase_ ).Pool() as pool:
																								UpperCAmelCase					=			processor.batch_decode(lowercase_       ,  lowercase_ )

												UpperCAmelCase					=			list(lowercase_ )

												with get_context('fork' ).Pool() as p:
																		UpperCAmelCase					=			decoder.decode_beams_batch(lowercase_       ,  lowercase_ )

												UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			[], [], []
												for beams in decoded_beams:
																		texts_decoder.append(beams[0][0] )
																		logit_scores_decoder.append(beams[0][-2] )
																		lm_scores_decoder.append(beams[0][-1] )

												self.assertListEqual(lowercase_       ,  decoded_processor.text )
												self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']       ,  decoded_processor.text )
												self.assertListEqual(lowercase_       ,  decoded_processor.logit_score )
												self.assertListEqual(lowercase_       ,  decoded_processor.lm_score )






						def      UpperCAmelCase__					( self			:List[Any] )    ->      Optional[int]:
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

												UpperCAmelCase					=			self._get_dummy_logits()

												UpperCAmelCase					=			15
												UpperCAmelCase					=			-20.0
												UpperCAmelCase					=			-4.0

												UpperCAmelCase					=			processor.batch_decode(
												    lowercase_       ,  beam_width=lowercase_       ,  beam_prune_logp=lowercase_       ,  token_min_logp=lowercase_       ,  )
												UpperCAmelCase					=			decoded_processor_out.text

												UpperCAmelCase					=			list(lowercase_ )

												with get_context('fork' ).Pool() as pool:
																		UpperCAmelCase					=			decoder.decode_beams_batch(
																		    lowercase_       ,  lowercase_       ,  beam_width=lowercase_       ,  beam_prune_logp=lowercase_       ,  token_min_logp=lowercase_       ,  )

												UpperCAmelCase					=			[d[0][0] for d in decoded_decoder_out]
												UpperCAmelCase					=			[d[0][2] for d in decoded_decoder_out]
												UpperCAmelCase					=			[d[0][3] for d in decoded_decoder_out]

												self.assertListEqual(lowercase_       ,  lowercase_ )
												self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']       ,  lowercase_ )

												self.assertTrue(np.array_equal(lowercase_       ,  decoded_processor_out.logit_score ) )
												self.assertTrue(np.allclose([-20.054, -18.447]       ,  lowercase_       ,  atol=1E-3 ) )

												self.assertTrue(np.array_equal(lowercase_       ,  decoded_processor_out.lm_score ) )
												self.assertTrue(np.allclose([-15.554, -13.9474]       ,  lowercase_       ,  atol=1E-3 ) )






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

												UpperCAmelCase					=			self._get_dummy_logits()

												UpperCAmelCase					=			2.0
												UpperCAmelCase					=			5.0
												UpperCAmelCase					=			-20.0
												UpperCAmelCase					=			True

												UpperCAmelCase					=			processor.batch_decode(
												    lowercase_       ,  alpha=lowercase_       ,  beta=lowercase_       ,  unk_score_offset=lowercase_       ,  lm_score_boundary=lowercase_       ,  )
												UpperCAmelCase					=			decoded_processor_out.text

												UpperCAmelCase					=			list(lowercase_ )
												decoder.reset_params(
												    alpha=lowercase_       ,  beta=lowercase_       ,  unk_score_offset=lowercase_       ,  lm_score_boundary=lowercase_       ,  )

												with get_context('fork' ).Pool() as pool:
																		UpperCAmelCase					=			decoder.decode_beams_batch(
																		    lowercase_       ,  lowercase_       ,  )

												UpperCAmelCase					=			[d[0][0] for d in decoded_decoder_out]

												self.assertListEqual(lowercase_       ,  lowercase_ )
												self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']       ,  lowercase_ )
												UpperCAmelCase					=			processor.decoder.model_container[processor.decoder._model_key]
												self.assertEqual(lm_model.alpha       ,  2.0 )
												self.assertEqual(lm_model.beta       ,  5.0 )
												self.assertEqual(lm_model.unk_score_offset       ,  -20.0 )
												self.assertEqual(lm_model.score_boundary       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Any )    ->      int:
												UpperCAmelCase					=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )

												UpperCAmelCase					=			processor.decoder.model_container[processor.decoder._model_key]
												UpperCAmelCase					=			Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()

												UpperCAmelCase					=			os.listdir(lowercase_ )
												UpperCAmelCase					=			['alphabet.json', 'language_model']

												downloaded_decoder_files.sort()
												expected_decoder_files.sort()

												# test that only decoder relevant files from
												# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
												# are downloaded and none of the rest (e.g. README.md, ...)
												self.assertListEqual(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:List[str] )    ->      int:
												UpperCAmelCase					=			snapshot_download('hf-internal-testing/processor_with_lm' )

												UpperCAmelCase					=			WavaVecaProcessorWithLM.from_pretrained(lowercase_ )

												UpperCAmelCase					=			processor.decoder.model_container[processor.decoder._model_key]
												UpperCAmelCase					=			Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()

												UpperCAmelCase					=			os.listdir(lowercase_ )
												UpperCAmelCase					=			os.listdir(lowercase_ )

												local_decoder_files.sort()
												expected_decoder_files.sort()

												# test that both decoder form hub and local files in cache are the same
												self.assertListEqual(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Tuple )    ->      List[str]:
												UpperCAmelCase					=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
												UpperCAmelCase					=			AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm' )

												UpperCAmelCase					=			floats_list((3, 10_00) )

												UpperCAmelCase					=			processor_wavaveca(lowercase_       ,  return_tensors='np' )
												UpperCAmelCase					=			processor_auto(lowercase_       ,  return_tensors='np' )

												for key in input_wavaveca.keys():
																		self.assertAlmostEqual(input_wavaveca[key].sum()       ,  input_auto[key].sum()       ,  delta=1E-2 )

												UpperCAmelCase					=			self._get_dummy_logits()

												UpperCAmelCase					=			processor_wavaveca.batch_decode(lowercase_ )
												UpperCAmelCase					=			processor_auto.batch_decode(lowercase_ )

												self.assertListEqual(decoded_wavaveca.text       ,  decoded_auto.text )






						def      UpperCAmelCase__					( self			:str )    ->      Dict:
												UpperCAmelCase					=			self.get_feature_extractor()
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_decoder()

												UpperCAmelCase					=			WavaVecaProcessorWithLM(tokenizer=lowercase_       ,  feature_extractor=lowercase_       ,  decoder=lowercase_ )

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






						@staticmethod
						def      UpperCAmelCase__					( lowercase_			:List[str]       ,  lowercase_			:Any )    ->      List[Any]:
												UpperCAmelCase					=			[d[key] for d in offsets]
												return retrieved_list






						def      UpperCAmelCase__					( self			:str )    ->      Tuple:
												UpperCAmelCase					=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
												UpperCAmelCase					=			self._get_dummy_logits()[0]

												UpperCAmelCase					=			processor.decode(lowercase_       ,  output_word_offsets=lowercase_ )
												# check Wav2Vec2CTCTokenizerOutput keys for word
												self.assertEqual(len(outputs.keys() )       ,  4 )
												self.assertTrue('text' in outputs )
												self.assertTrue('word_offsets' in outputs )
												self.assertTrue(isinstance(lowercase_       ,  lowercase_ ) )

												self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets']       ,  'word' ) )       ,  outputs.text )
												self.assertListEqual(self.get_from_offsets(outputs['word_offsets']       ,  'word' )       ,  ['<s>', '<s>', '</s>'] )
												self.assertListEqual(self.get_from_offsets(outputs['word_offsets']       ,  'start_offset' )       ,  [0, 2, 4] )
												self.assertListEqual(self.get_from_offsets(outputs['word_offsets']       ,  'end_offset' )       ,  [1, 3, 5] )






						def      UpperCAmelCase__					( self			:int )    ->      List[str]:
												UpperCAmelCase					=			WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
												UpperCAmelCase					=			self._get_dummy_logits()

												UpperCAmelCase					=			processor.batch_decode(lowercase_       ,  output_word_offsets=lowercase_ )

												# check Wav2Vec2CTCTokenizerOutput keys for word
												self.assertEqual(len(outputs.keys() )       ,  4 )
												self.assertTrue('text' in outputs )
												self.assertTrue('word_offsets' in outputs )
												self.assertTrue(isinstance(lowercase_       ,  lowercase_ ) )

												self.assertListEqual(
												    [' '.join(self.get_from_offsets(lowercase_       ,  'word' ) ) for o in outputs['word_offsets']]       ,  outputs.text )
												self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]       ,  'word' )       ,  ['<s>', '<s>', '</s>'] )
												self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]       ,  'start_offset' )       ,  [0, 2, 4] )
												self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0]       ,  'end_offset' )       ,  [1, 3, 5] )






						@slow
						@require_torch
						@require_torchaudio
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      str:
												import torch

												UpperCAmelCase					=			load_dataset('common_voice'       ,  'en'       ,  split='train'       ,  streaming=lowercase_ )
												UpperCAmelCase					=			ds.cast_column('audio'       ,  datasets.Audio(sampling_rate=1_60_00 ) )
												UpperCAmelCase					=			iter(lowercase_ )
												UpperCAmelCase					=			next(lowercase_ )

												UpperCAmelCase					=			AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
												UpperCAmelCase					=			WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )

												# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
												UpperCAmelCase					=			processor(sample['audio']['array']       ,  return_tensors='pt' ).input_values

												with torch.no_grad():
																		UpperCAmelCase					=			model(lowercase_ ).logits.cpu().numpy()

												UpperCAmelCase					=			processor.decode(logits[0]       ,  output_word_offsets=lowercase_ )

												UpperCAmelCase					=			model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
												UpperCAmelCase					=			[
												    {
												        'start_time': d['start_offset'] * time_offset,
												        'end_time': d['end_offset'] * time_offset,
												        'word': d['word'],
												    }
												    for d in output['word_offsets']
												]

												UpperCAmelCase					=			'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'

												# output words
												self.assertEqual(' '.join(self.get_from_offsets(lowercase_       ,  'word' ) )       ,  lowercase_ )
												self.assertEqual(' '.join(self.get_from_offsets(lowercase_       ,  'word' ) )       ,  output.text )

												# output times
												UpperCAmelCase					=			torch.tensor(self.get_from_offsets(lowercase_       ,  'start_time' ) )
												UpperCAmelCase					=			torch.tensor(self.get_from_offsets(lowercase_       ,  'end_time' ) )

												# fmt: off
												UpperCAmelCase					=			torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
												UpperCAmelCase					=			torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
												# fmt: on

												self.assertTrue(torch.allclose(lowercase_       ,  lowercase_       ,  atol=0.01 ) )
												self.assertTrue(torch.allclose(lowercase_       ,  lowercase_       ,  atol=0.01 ) )







"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


snake_case_    					=	{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

snake_case_    					=	logging.get_logger(__name__)







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""maskformer"""
						__UpperCamelCase		    =							{"""hidden_size""": """mask_feature_size"""}
						__UpperCamelCase		    =							["""resnet""", """swin"""]
						__UpperCamelCase		    =							["""detr"""]






						def __init__( self			:Dict       ,  lowercase_			:int = 2_56       ,  lowercase_			:int = 2_56       ,  lowercase_			:float = 0.1       ,  lowercase_			:bool = False       ,  lowercase_			:Optional[Dict] = None       ,  lowercase_			:Optional[Dict] = None       ,  lowercase_			:float = 0.02       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 1.0       ,  lowercase_			:float = 20.0       ,  lowercase_			:Optional[bool] = None       ,  **lowercase_			:List[str]       ,  )    ->      Optional[int]:
												if backbone_config is None:
																		# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																		UpperCAmelCase					=			SwinConfig(
																		    image_size=3_84       ,  in_channels=3       ,  patch_size=4       ,  embed_dim=1_28       ,  depths=[2, 2, 18, 2]       ,  num_heads=[4, 8, 16, 32]       ,  window_size=12       ,  drop_path_rate=0.3       ,  out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,  )

												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			backbone_config.pop('model_type' )
																		UpperCAmelCase					=			CONFIG_MAPPING[backbone_model_type]
																		UpperCAmelCase					=			config_class.from_dict(lowercase_ )

												# verify that the backbone is supported
												if backbone_config.model_type not in self.backbones_supported:
																		logger.warning_once(
																		    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
																		    f"""Supported model types: {','.join(self.backbones_supported )}""" )

												if decoder_config is None:
																		# fall back to https://huggingface.co/facebook/detr-resnet-50
																		UpperCAmelCase					=			DetrConfig()
												else:
																		# verify that the decoder is supported
																		UpperCAmelCase					=			(
																		    decoder_config.pop('model_type' ) if isinstance(lowercase_       ,  lowercase_ ) else decoder_config.model_type
																		)
																		if decoder_type not in self.decoders_supported:
																								raise ValueError(
																								    f"""Transformer Decoder {decoder_type} not supported, please use one of"""
																								    f""" {','.join(self.decoders_supported )}""" )
																		if isinstance(lowercase_       ,  lowercase_ ):
																								UpperCAmelCase					=			CONFIG_MAPPING[decoder_type]
																								UpperCAmelCase					=			config_class.from_dict(lowercase_ )

												UpperCAmelCase					=			backbone_config
												UpperCAmelCase					=			decoder_config
												# main feature dimension for the model
												UpperCAmelCase					=			fpn_feature_size
												UpperCAmelCase					=			mask_feature_size
												# initializer
												UpperCAmelCase					=			init_std
												UpperCAmelCase					=			init_xavier_std
												# Hungarian matcher && loss
												UpperCAmelCase					=			cross_entropy_weight
												UpperCAmelCase					=			dice_weight
												UpperCAmelCase					=			mask_weight
												UpperCAmelCase					=			use_auxiliary_loss
												UpperCAmelCase					=			no_object_weight
												UpperCAmelCase					=			output_auxiliary_logits

												UpperCAmelCase					=			self.decoder_config.encoder_attention_heads
												UpperCAmelCase					=			self.decoder_config.num_hidden_layers
												super().__init__(**lowercase_ )






						@classmethod
						def      UpperCAmelCase__					( cls			:int       ,  lowercase_			:PretrainedConfig       ,  lowercase_			:PretrainedConfig       ,  **lowercase_			:int )    ->      List[Any]:
												return cls(
												    backbone_config=lowercase_       ,  decoder_config=lowercase_       ,  **lowercase_       ,  )






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





