


'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A						:						List[Any]  =  logging.get_logger(__name__)





def   UpperCAmelCase   (		lowerCamelCase_	:Union[str, Any]					,   lowerCamelCase_	:Any=False			):







		'''simple docstring'''
		snake_case_		:				Optional[Any]      							=    OrderedDict()
		for key, value in state_dict.items():
				if encoder_only and not key.startswith("""head"""			):
						snake_case_		:				List[Any]      							=    """segformer.encoder.""" + key
				if key.startswith("""backbone"""			):
						snake_case_		:				Union[str, Any]      							=    key.replace("""backbone"""					,   """segformer.encoder"""			)
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						snake_case_		:				Any      							=    key[key.find("""patch_embed"""			) + len("""patch_embed"""			)]
						snake_case_		:				Dict      							=    key.replace(F'''patch_embed{idx}'''					,   F'''patch_embeddings.{int(lowerCamelCase_			)-1}'''			)
				if "norm" in key:
						snake_case_		:				str      							=    key.replace("""norm"""					,   """layer_norm"""			)
				if "segformer.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						snake_case_		:				Union[str, Any]      							=    key[key.find("""segformer.encoder.layer_norm"""			) + len("""segformer.encoder.layer_norm"""			)]
						snake_case_		:				Optional[Any]      							=    key.replace(F'''layer_norm{idx}'''					,   F'''layer_norm.{int(lowerCamelCase_			)-1}'''			)
				if "layer_norm1" in key:
						snake_case_		:				str      							=    key.replace("""layer_norm1"""					,   """layer_norm_1"""			)
				if "layer_norm2" in key:
						snake_case_		:				str      							=    key.replace("""layer_norm2"""					,   """layer_norm_2"""			)
				if "block" in key:
						# replace for example block1 by block.0
						snake_case_		:				Dict      							=    key[key.find("""block"""			) + len("""block"""			)]
						snake_case_		:				List[Any]      							=    key.replace(F'''block{idx}'''					,   F'''block.{int(lowerCamelCase_			)-1}'''			)
				if "attn.q" in key:
						snake_case_		:				Union[str, Any]      							=    key.replace("""attn.q"""					,   """attention.self.query"""			)
				if "attn.proj" in key:
						snake_case_		:				Optional[Any]      							=    key.replace("""attn.proj"""					,   """attention.output.dense"""			)
				if "attn" in key:
						snake_case_		:				Optional[Any]      							=    key.replace("""attn"""					,   """attention.self"""			)
				if "fc1" in key:
						snake_case_		:				List[Any]      							=    key.replace("""fc1"""					,   """dense1"""			)
				if "fc2" in key:
						snake_case_		:				int      							=    key.replace("""fc2"""					,   """dense2"""			)
				if "linear_pred" in key:
						snake_case_		:				List[Any]      							=    key.replace("""linear_pred"""					,   """classifier"""			)
				if "linear_fuse" in key:
						snake_case_		:				Optional[Any]      							=    key.replace("""linear_fuse.conv"""					,   """linear_fuse"""			)
						snake_case_		:				Optional[Any]      							=    key.replace("""linear_fuse.bn"""					,   """batch_norm"""			)
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						snake_case_		:				Optional[int]      							=    key[key.find("""linear_c"""			) + len("""linear_c"""			)]
						snake_case_		:				int      							=    key.replace(F'''linear_c{idx}'''					,   F'''linear_c.{int(lowerCamelCase_			)-1}'''			)
				if key.startswith("""head"""			):
						snake_case_		:				List[str]      							=    key.replace("""head"""					,   """classifier"""			)
				snake_case_		:				Optional[int]      							=    value

		return new_state_dict





def   UpperCAmelCase   (		lowerCamelCase_	:Any					,   lowerCamelCase_	:Union[str, Any]			):







		'''simple docstring'''




		for i in range(config.num_encoder_blocks			):
				for j in range(config.depths[i]			):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						snake_case_		:				Tuple      							=    state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''			)
						snake_case_		:				int      							=    state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias'''			)
						# next, add keys and values (in that order) to the state dict
						snake_case_		:				Union[str, Any]      							=    kv_weight[
						    : config.hidden_sizes[i], :
						]
						snake_case_		:				Optional[int]      							=    kv_bias[: config.hidden_sizes[i]]
						snake_case_		:				List[str]      							=    kv_weight[
						    config.hidden_sizes[i] :, :
						]
						snake_case_		:				List[str]      							=    kv_bias[
						    config.hidden_sizes[i] :
						]





def   UpperCAmelCase   (		):







		'''simple docstring'''
		snake_case_		:				Union[str, Any]      							=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
		snake_case_		:				Optional[int]      							=    Image.open(requests.get(lowerCamelCase_					,   stream=lowerCamelCase_			).raw			)

		return image





@torch.no_grad()
def   UpperCAmelCase   (		lowerCamelCase_	:List[Any]					,   lowerCamelCase_	:Dict					,   lowerCamelCase_	:Union[str, Any]			):







		'''simple docstring'''
		snake_case_		:				int      							=    SegformerConfig()
		snake_case_		:				Dict      							=    False

		# set attributes based on model_name
		snake_case_		:				Optional[Any]      							=    """huggingface/label-files"""
		if "segformer" in model_name:
				snake_case_		:				Tuple      							=    model_name[len("""segformer."""			) : len("""segformer."""			) + 2]
				if "ade" in model_name:
						snake_case_		:				Optional[int]      							=    1_50
						snake_case_		:				Optional[int]      							=    """ade20k-id2label.json"""
						snake_case_		:				int      							=    (1, 1_50, 1_28, 1_28)
				elif "city" in model_name:
						snake_case_		:				int      							=    19
						snake_case_		:				Optional[int]      							=    """cityscapes-id2label.json"""
						snake_case_		:				str      							=    (1, 19, 1_28, 1_28)
				else:
						raise ValueError(F'''Model {model_name} not supported'''			)
		elif "mit" in model_name:
				snake_case_		:				Optional[Any]      							=    True
				snake_case_		:				Optional[int]      							=    model_name[4:6]
				snake_case_		:				int      							=    10_00
				snake_case_		:				str      							=    """imagenet-1k-id2label.json"""
				snake_case_		:				Tuple      							=    (1, 10_00)
		else:
				raise ValueError(F'''Model {model_name} not supported'''			)

		# set config attributes
		snake_case_		:				int      							=    json.load(open(hf_hub_download(lowerCamelCase_					,   lowerCamelCase_					,   repo_type="""dataset"""			)					,   """r"""			)			)
		snake_case_		:				List[str]      							=    {int(lowerCamelCase_			): v for k, v in idalabel.items()}
		snake_case_		:				List[Any]      							=    idalabel
		snake_case_		:				int      							=    {v: k for k, v in idalabel.items()}
		if size == "b0":
				pass
		elif size == "b1":
				snake_case_		:				Tuple      							=    [64, 1_28, 3_20, 5_12]
				snake_case_		:				List[str]      							=    2_56
		elif size == "b2":
				snake_case_		:				Any      							=    [64, 1_28, 3_20, 5_12]
				snake_case_		:				Optional[int]      							=    7_68
				snake_case_		:				Optional[int]      							=    [3, 4, 6, 3]
		elif size == "b3":
				snake_case_		:				int      							=    [64, 1_28, 3_20, 5_12]
				snake_case_		:				Any      							=    7_68
				snake_case_		:				str      							=    [3, 4, 18, 3]
		elif size == "b4":
				snake_case_		:				List[str]      							=    [64, 1_28, 3_20, 5_12]
				snake_case_		:				List[str]      							=    7_68
				snake_case_		:				List[str]      							=    [3, 8, 27, 3]
		elif size == "b5":
				snake_case_		:				Optional[Any]      							=    [64, 1_28, 3_20, 5_12]
				snake_case_		:				Optional[Any]      							=    7_68
				snake_case_		:				Union[str, Any]      							=    [3, 6, 40, 3]
		else:
				raise ValueError(F'''Size {size} not supported'''			)

		# load image processor (only resize + normalize)
		snake_case_		:				List[str]      							=    SegformerImageProcessor(
		    image_scale=(5_12, 5_12)					,   keep_ratio=lowerCamelCase_					,   align=lowerCamelCase_					,   do_random_crop=lowerCamelCase_			)

		# prepare image
		snake_case_		:				str      							=    prepare_img()
		snake_case_		:				List[Any]      							=    image_processor(images=lowerCamelCase_					,   return_tensors="""pt"""			).pixel_values

		logger.info(F'''Converting model {model_name}...'''			)

		# load original state dict
		if encoder_only:
				snake_case_		:				Optional[Any]      							=    torch.load(lowerCamelCase_					,   map_location=torch.device("""cpu"""			)			)
		else:
				snake_case_		:				Union[str, Any]      							=    torch.load(lowerCamelCase_					,   map_location=torch.device("""cpu"""			)			)["""state_dict"""]

		# rename keys
		snake_case_		:				str      							=    rename_keys(lowerCamelCase_					,   encoder_only=lowerCamelCase_			)
		if not encoder_only:
				del state_dict["decode_head.conv_seg.weight"]
				del state_dict["decode_head.conv_seg.bias"]

		# key and value matrices need special treatment
		read_in_k_v(lowerCamelCase_					,   lowerCamelCase_			)

		# create HuggingFace model and load state dict
		if encoder_only:
				snake_case_		:				Tuple      							=    False
				snake_case_		:				str      							=    SegformerForImageClassification(lowerCamelCase_			)
		else:
				snake_case_		:				Optional[Any]      							=    SegformerForSemanticSegmentation(lowerCamelCase_			)
		model.load_state_dict(lowerCamelCase_			)
		model.eval()

		# forward pass
		snake_case_		:				int      							=    model(lowerCamelCase_			)
		snake_case_		:				Optional[int]      							=    outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				snake_case_		:				Optional[Any]      							=    torch.tensor(
				    [
				        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
				        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
				        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
				    ]			)
		elif model_name == "segformer.b1.512x512.ade.160k":
				snake_case_		:				int      							=    torch.tensor(
				    [
				        [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.35_29, -10.03_04], [-7.5_208, -9.4_103, -9.6_239]],
				        [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
				        [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
				    ]			)
		elif model_name == "segformer.b2.512x512.ade.160k":
				snake_case_		:				Any      							=    torch.tensor(
				    [
				        [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
				        [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
				        [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
				    ]			)
		elif model_name == "segformer.b3.512x512.ade.160k":
				snake_case_		:				Tuple      							=    torch.tensor(
				    [
				        [[-9.0_878, -10.20_81, -10.18_91], [-9.3_144, -10.79_41, -10.98_43], [-9.2_294, -10.38_55, -10.57_04]],
				        [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
				        [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
				    ]			)
		elif model_name == "segformer.b4.512x512.ade.160k":
				snake_case_		:				Dict      							=    torch.tensor(
				    [
				        [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
				        [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
				        [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
				    ]			)
		elif model_name == "segformer.b5.640x640.ade.160k":
				snake_case_		:				Optional[int]      							=    torch.tensor(
				    [
				        [[-9.5_524, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.5_842, -12.88_51, -13.94_14]],
				        [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
				        [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
				    ]			)
		# Cityscapes checkpoints
		elif model_name == "segformer.b0.1024x1024.city.160k":
				snake_case_		:				Dict      							=    torch.tensor(
				    [
				        [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
				        [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
				        [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
				    ]			)
		elif model_name == "segformer.b0.512x1024.city.160k":
				snake_case_		:				Optional[Any]      							=    torch.tensor(
				    [
				        [[-7.8_217, -9.8_767, -10.17_17], [-9.4_438, -10.90_58, -11.40_47], [-9.7_939, -12.34_95, -12.10_79]],
				        [[-7.1_514, -9.5_336, -10.08_60], [-9.7_776, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
				        [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
				    ]			)
		elif model_name == "segformer.b0.640x1280.city.160k":
				snake_case_		:				Optional[Any]      							=    torch.tensor(
				    [
				        [
				            [-1.13_72E01, -1.27_87E01, -1.34_77E01],
				            [-1.25_36E01, -1.41_94E01, -1.44_09E01],
				            [-1.32_17E01, -1.48_88E01, -1.53_27E01],
				        ],
				        [
				            [-1.47_91E01, -1.71_22E01, -1.82_77E01],
				            [-1.71_63E01, -1.91_92E01, -1.95_33E01],
				            [-1.78_97E01, -1.99_91E01, -2.03_15E01],
				        ],
				        [
				            [7.67_23E-01, 4.19_21E-01, -7.78_78E-02],
				            [4.77_72E-01, 9.55_57E-03, -2.80_82E-01],
				            [3.60_32E-01, -2.48_26E-01, -5.11_68E-01],
				        ],
				    ]			)
		elif model_name == "segformer.b0.768x768.city.160k":
				snake_case_		:				Union[str, Any]      							=    torch.tensor(
				    [
				        [[-9.4_959, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
				        [[-9.8_905, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
				        [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
				    ]			)
		elif model_name == "segformer.b1.1024x1024.city.160k":
				snake_case_		:				Any      							=    torch.tensor(
				    [
				        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
				        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
				        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
				    ]			)
		elif model_name == "segformer.b2.1024x1024.city.160k":
				snake_case_		:				int      							=    torch.tensor(
				    [
				        [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
				        [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
				        [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
				    ]			)
		elif model_name == "segformer.b3.1024x1024.city.160k":
				snake_case_		:				str      							=    torch.tensor(
				    [
				        [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
				        [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
				        [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
				    ]			)
		elif model_name == "segformer.b4.1024x1024.city.160k":
				snake_case_		:				int      							=    torch.tensor(
				    [
				        [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
				        [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
				        [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
				    ]			)
		elif model_name == "segformer.b5.1024x1024.city.160k":
				snake_case_		:				Union[str, Any]      							=    torch.tensor(
				    [
				        [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
				        [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
				        [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
				    ]			)
		else:
				snake_case_		:				Optional[Any]      							=    logits.argmax(-1			).item()
				print("""Predicted class:"""					,   model.config.idalabel[predicted_class_idx]			)

		# verify logits
		if not encoder_only:
				assert logits.shape == expected_shape
				assert torch.allclose(logits[0, :3, :3, :3]					,   lowerCamelCase_					,   atol=1E-2			)

		# finally, save model and image processor
		logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''			)
		Path(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
		model.save_pretrained(lowerCamelCase_			)
		image_processor.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
							__A						:						List[str]  =  argparse.ArgumentParser()

							parser.add_argument(
							    '--model_name',
							    default='segformer.b0.512x512.ade.160k',
							    type=str,
							    help='Name of the model you\'d like to convert.',
							)
							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
							)
							__A						:						int  =  parser.parse_args()
							convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						"""ylacombe/bark-small"""
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						"""en_speaker_1"""
							lowercase__	     =						"""This is a test string"""
							lowercase__	     =						"""speaker_embeddings_path.json"""
							lowercase__	     =						"""speaker_embeddings"""

					def     lowerCamelCase__	(self       :		str ,   **_UpperCAmelCase       :		Optional[int]						)		->  str:




							"""simple docstring"""


							return AutoTokenizer.from_pretrained(self.checkpoint ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							shutil.rmtree(self.tmpdirname						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)
							lowercase__	     =						BarkProcessor.from_pretrained(self.tmpdirname						)

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

					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						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 ,   )

							lowercase__	     =						self.get_tokenizer(bos_token="""(BOS)""" ,   eos_token="""(EOS)"""						)

							lowercase__	     =						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     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )

							lowercase__	     =						35
							lowercase__	     =						2
							lowercase__	     =						8

							lowercase__	     =						{
							    """semantic_prompt""": np.ones(_UpperCAmelCase						),
							    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)						),
							    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)						),
							}

							# test providing already loaded voice_preset
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)

							lowercase__	     =						inputs["""history_prompt"""]
							for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(_UpperCAmelCase ,   np.array([]						)						).tolist()						)

							# test loading voice preset from npz file
							lowercase__	     =						os.path.join(self.tmpdirname ,   """file.npz"""						)
							np.savez(_UpperCAmelCase ,   **_UpperCAmelCase						)
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)
							lowercase__	     =						inputs["""history_prompt"""]

							for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(_UpperCAmelCase ,   np.array([]						)						).tolist()						)

							# test loading voice preset from the hub
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=self.voice_preset						)


					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							lowercase__	     =						processor(text=self.input_string						)

							lowercase__	     =						tokenizer(
							    self.input_string ,   padding="""max_length""" ,   max_length=256 ,   add_special_tokens=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   return_token_type_ids=_UpperCAmelCase ,   )

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

