





'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

snake_case_   :  Tuple								=      {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def  lowercase__(		_UpperCamelCase  : int ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Any ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Tuple				)->   int:






					"""simple docstring"""

					for attribute in key.split("."				):
										_UpperCamelCase											=   getattr(_UpperCamelCase ,   _UpperCamelCase				)

					if weight_type is not None:
										_UpperCamelCase											=   getattr(_UpperCamelCase ,   _UpperCamelCase				).shape
					else:
										_UpperCamelCase											=   hf_pointer.shape

					assert hf_shape == value.shape, (
					    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
					    f" {value.shape} for {full_name}"
					)

					if weight_type == "weight":
										_UpperCamelCase											=   value
					elif weight_type == "weight_g":
										_UpperCamelCase											=   value
					elif weight_type == "weight_v":
										_UpperCamelCase											=   value
					elif weight_type == "bias":
										_UpperCamelCase											=   value
					else:
										_UpperCamelCase											=   value

					logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."				)



def  lowercase__(		_UpperCamelCase  : List[str] ,   _UpperCamelCase  : List[str] ,   _UpperCamelCase  : Optional[int]				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   []
					_UpperCamelCase											=   fairseq_model.state_dict()

					_UpperCamelCase											=   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										_UpperCamelCase											=   False
										if "conv_layers" in name:
															load_conv_layer(
															    _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   hf_model.config.feat_extract_norm == "group" ,   )
															_UpperCamelCase											=   True
										else:
															for key, mapped_key in MAPPING.items():
																				_UpperCamelCase											=   "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

																				if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
																									_UpperCamelCase											=   True
																									if "*" in mapped_key:
																														_UpperCamelCase											=   name.split(_UpperCamelCase				)[0].split("."				)[-2]
																														if not layer_index.isnumeric():
																																			continue
																														_UpperCamelCase											=   mapped_key.replace("*" ,   _UpperCamelCase				)
																									if "weight_g" in name:
																														_UpperCamelCase											=   "weight_g"
																									elif "weight_v" in name:
																														_UpperCamelCase											=   "weight_v"
																									elif "weight" in name:
																														_UpperCamelCase											=   "weight"
																									elif "bias" in name:
																														_UpperCamelCase											=   "bias"
																									else:
																														_UpperCamelCase											=   None
																									set_recursively(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
																				continue
										if not is_used:
															unused_weights.append(_UpperCamelCase				)

					logger.warning(f"Unused weights: {unused_weights}"				)



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : str ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Any				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   full_name.split("conv_layers."				)[-1]
					_UpperCamelCase											=   name.split("."				)
					_UpperCamelCase											=   int(items[0]				)
					_UpperCamelCase											=   int(items[1]				)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    f"{full_name} has size {value.shape}, but"
															    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
															)
															_UpperCamelCase											=   value
															logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."				)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    f"{full_name} has size {value.shape}, but"
															    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
															)
															_UpperCamelCase											=   value
															logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."				)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
															    " found."
															)
															_UpperCamelCase											=   value
															logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    f"{full_name} has size {value.shape}, but"
															    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
															)
															_UpperCamelCase											=   value
															logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
					else:
										unused_weights.append(_UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : Dict ,   _UpperCamelCase  : Optional[int]				)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   SEWDConfig()
					if is_finetuned:
										_UpperCamelCase											=   model.wav_encoder.wav_model.cfg
					else:
										_UpperCamelCase											=   model.cfg

					_UpperCamelCase											=   fs_config.conv_bias
					_UpperCamelCase											=   eval(fs_config.conv_feature_layers				)
					_UpperCamelCase											=   [x[0] for x in conv_layers]
					_UpperCamelCase											=   [x[1] for x in conv_layers]
					_UpperCamelCase											=   [x[2] for x in conv_layers]
					_UpperCamelCase											=   "gelu"
					_UpperCamelCase											=   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
					_UpperCamelCase											=   0.0
					_UpperCamelCase											=   fs_config.activation_fn.name
					_UpperCamelCase											=   fs_config.encoder_embed_dim
					_UpperCamelCase											=   0.02
					_UpperCamelCase											=   fs_config.encoder_ffn_embed_dim
					_UpperCamelCase											=   1E-5
					_UpperCamelCase											=   fs_config.encoder_layerdrop
					_UpperCamelCase											=   fs_config.encoder_attention_heads
					_UpperCamelCase											=   fs_config.conv_pos_groups
					_UpperCamelCase											=   fs_config.conv_pos
					_UpperCamelCase											=   len(_UpperCamelCase				)
					_UpperCamelCase											=   fs_config.encoder_layers
					_UpperCamelCase											=   fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					_UpperCamelCase											=   fs_config.max_position_embeddings
					_UpperCamelCase											=   fs_config.position_buckets
					_UpperCamelCase											=   fs_config.share_att_key
					_UpperCamelCase											=   fs_config.relative_attention
					_UpperCamelCase											=   fs_config.position_biased_input
					_UpperCamelCase											=   tuple(fs_config.pos_att_type.split("|"				)				)
					_UpperCamelCase											=   fs_config.norm_rel_ebd

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										_UpperCamelCase											=   model.cfg
										_UpperCamelCase											=   fs_config.final_dropout
										_UpperCamelCase											=   fs_config.layerdrop
					_UpperCamelCase											=   fs_config.activation_dropout
					_UpperCamelCase											=   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					_UpperCamelCase											=   fs_config.attention_dropout
					_UpperCamelCase											=   fs_config.dropout_input
					_UpperCamelCase											=   fs_config.dropout
					_UpperCamelCase											=   fs_config.mask_channel_length
					_UpperCamelCase											=   fs_config.mask_channel_prob
					_UpperCamelCase											=   fs_config.mask_length
					_UpperCamelCase											=   fs_config.mask_prob

					_UpperCamelCase											=   "Wav2Vec2FeatureExtractor"
					_UpperCamelCase											=   "Wav2Vec2CTCTokenizer"

					return config



@torch.no_grad()
def  lowercase__(		_UpperCamelCase  : Dict ,   _UpperCamelCase  : Any ,   _UpperCamelCase  : List[Any]=None ,   _UpperCamelCase  : Optional[int]=None ,   _UpperCamelCase  : List[str]=True				)->   int:






					"""simple docstring"""

					if is_finetuned:
										_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path] ,   arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
					else:
										_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

					if config_path is not None:
										_UpperCamelCase											=   SEWDConfig.from_pretrained(_UpperCamelCase				)
					else:
										_UpperCamelCase											=   convert_config(model[0] ,   _UpperCamelCase				)
					_UpperCamelCase											=   model[0].eval()

					_UpperCamelCase											=   True if config.feat_extract_norm == "layer" else False
					_UpperCamelCase											=   WavaVecaFeatureExtractor(
					    feature_size=1 ,   sampling_rate=16000 ,   padding_value=0 ,   do_normalize=_UpperCamelCase ,   return_attention_mask=_UpperCamelCase ,   )

					if is_finetuned:
										if dict_path:
															_UpperCamelCase											=   Dictionary.load(_UpperCamelCase				)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_UpperCamelCase											=   target_dict.pad_index
															_UpperCamelCase											=   target_dict.bos_index
															_UpperCamelCase											=   target_dict.pad_index
															_UpperCamelCase											=   target_dict.bos_index
															_UpperCamelCase											=   target_dict.eos_index
															_UpperCamelCase											=   len(target_dict.symbols				)
															_UpperCamelCase											=   os.path.join(_UpperCamelCase ,   "vocab.json"				)
															if not os.path.isdir(_UpperCamelCase				):
																				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_UpperCamelCase				)				)
																				return
															os.makedirs(_UpperCamelCase ,   exist_ok=_UpperCamelCase				)
															with open(_UpperCamelCase ,   "w" ,   encoding="utf-8"				) as vocab_handle:
																				json.dump(target_dict.indices ,   _UpperCamelCase				)
															_UpperCamelCase											=   WavaVecaCTCTokenizer(
															    _UpperCamelCase ,   unk_token=target_dict.unk_word ,   pad_token=target_dict.pad_word ,   bos_token=target_dict.bos_word ,   eos_token=target_dict.eos_word ,   word_delimiter_token="|" ,   do_lower_case=_UpperCamelCase ,   )
															_UpperCamelCase											=   WavaVecaProcessor(feature_extractor=_UpperCamelCase ,   tokenizer=_UpperCamelCase				)
															processor.save_pretrained(_UpperCamelCase				)

										_UpperCamelCase											=   SEWDForCTC(_UpperCamelCase				)
					else:
										_UpperCamelCase											=   SEWDModel(_UpperCamelCase				)
										feature_extractor.save_pretrained(_UpperCamelCase				)

					recursively_load_weights(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)

					hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
			snake_case_   :  Any								=      argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument(
			    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			snake_case_   :  Tuple								=      parser.parse_args()
			convert_sew_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
			)










'''simple docstring'''



import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import SegformerImageProcessor

class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=7			,     A_=3			,     A_=30			,     A_=4_00			,     A_=True			,     A_=None			,     A_=True			,     A_=[0.5, 0.5, 0.5]			,     A_=[0.5, 0.5, 0.5]			,     A_=False			,     ):
								_UpperCamelCase											=   size if size is not None else {"height": 30, "width": 30}
								_UpperCamelCase											=   parent
								_UpperCamelCase											=   batch_size
								_UpperCamelCase											=   num_channels
								_UpperCamelCase											=   min_resolution
								_UpperCamelCase											=   max_resolution
								_UpperCamelCase											=   do_resize
								_UpperCamelCase											=   size
								_UpperCamelCase											=   do_normalize
								_UpperCamelCase											=   image_mean
								_UpperCamelCase											=   image_std
								_UpperCamelCase											=   do_reduce_labels




			def       a	(					self   ):
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_reduce_labels": self.do_reduce_labels,
								}




def  lowercase__(		)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   load_dataset("hf-internal-testing/fixtures_ade20k" ,   split="test"				)

					_UpperCamelCase											=   Image.open(dataset[0]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[1]["file"]				)

					return image, map



def  lowercase__(		)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   load_dataset("hf-internal-testing/fixtures_ade20k" ,   split="test"				)

					_UpperCamelCase											=   Image.open(dataset[0]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[1]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[2]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[3]["file"]				)

					return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class     A_ (  lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    SegformerImageProcessor if is_vision_available() else None




			def       a	(					self   ):
								_UpperCamelCase											=   SegformerImageProcessingTester(self   )




			@property
			def       a	(					self   ):
								return self.image_processor_tester.prepare_image_processor_dict()




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(A_			,     "do_resize"   )   )
								self.assertTrue(hasattr(A_			,     "size"   )   )
								self.assertTrue(hasattr(A_			,     "do_normalize"   )   )
								self.assertTrue(hasattr(A_			,     "image_mean"   )   )
								self.assertTrue(hasattr(A_			,     "image_std"   )   )
								self.assertTrue(hasattr(A_			,     "do_reduce_labels"   )   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class.from_dict(self.image_processor_dict   )
								self.assertEqual(image_processor.size			,     {"height": 30, "width": 30}   )
								self.assertEqual(image_processor.do_reduce_labels			,     A_   )

								_UpperCamelCase											=   self.image_processing_class.from_dict(self.image_processor_dict			,     size=42			,     reduce_labels=A_   )
								self.assertEqual(image_processor.size			,     {"height": 42, "width": 42}   )
								self.assertEqual(image_processor.do_reduce_labels			,     A_   )




			def       a	(					self   ):
								pass




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     Image.Image   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     numpify=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     np.ndarray   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     torchify=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     torch.Tensor   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     torchify=A_   )
								_UpperCamelCase											=   []
								for image in image_inputs:
													self.assertIsInstance(A_			,     torch.Tensor   )
													maps.append(torch.zeros(image.shape[-2:]   ).long()   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     maps[0]			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        1,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )

								# Test not batched input (PIL images)
								_UpperCamelCase				,	_UpperCamelCase											=   prepare_semantic_single_inputs()

								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        1,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )

								# Test batched input (PIL images)
								_UpperCamelCase				,	_UpperCamelCase											=   prepare_semantic_batch_inputs()

								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        2,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        2,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )

								# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
								_UpperCamelCase				,	_UpperCamelCase											=   prepare_semantic_single_inputs()
								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 1_50   )

								_UpperCamelCase											=   True
								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )



