





import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						SwinaSRConfig()

							if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Any     =						4
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      str     =						4
														SCREAMING_SNAKE_CASE__       :      str     =						48
														SCREAMING_SNAKE_CASE__       :      Dict     =						"pixelshuffle_aux"
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[6, 6, 6, 6]
														SCREAMING_SNAKE_CASE__       :      int     =						60
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[6, 6, 6, 6]
														SCREAMING_SNAKE_CASE__       :      Any     =						"pixelshuffledirect"
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						4
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"nearest+conv"
							elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						1_26
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						7
														SCREAMING_SNAKE_CASE__       :      Tuple     =						2_5_5.0
														SCREAMING_SNAKE_CASE__       :      str     =						""

							return config




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[int]			,				SCREAMING_SNAKE_CASE__      :  Any       )	->    List[str]:

							'''simple docstring'''






							if "patch_embed.proj" in name and "layers" not in name:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						name.replace("patch_embed.proj"			,				"embeddings.patch_embeddings.projection"       )
							if "patch_embed.norm" in name:
														SCREAMING_SNAKE_CASE__       :      str     =						name.replace("patch_embed.norm"			,				"embeddings.patch_embeddings.layernorm"       )
							if "layers" in name:
														SCREAMING_SNAKE_CASE__       :      Tuple     =						name.replace("layers"			,				"encoder.stages"       )
							if "residual_group.blocks" in name:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						name.replace("residual_group.blocks"			,				"layers"       )
							if "attn.proj" in name:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						name.replace("attn.proj"			,				"attention.output.dense"       )
							if "attn" in name:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						name.replace("attn"			,				"attention.self"       )
							if "norm1" in name:
														SCREAMING_SNAKE_CASE__       :      str     =						name.replace("norm1"			,				"layernorm_before"       )
							if "norm2" in name:
														SCREAMING_SNAKE_CASE__       :      int     =						name.replace("norm2"			,				"layernorm_after"       )
							if "mlp.fc1" in name:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						name.replace("mlp.fc1"			,				"intermediate.dense"       )
							if "mlp.fc2" in name:
														SCREAMING_SNAKE_CASE__       :      int     =						name.replace("mlp.fc2"			,				"output.dense"       )
							if "q_bias" in name:
														SCREAMING_SNAKE_CASE__       :      str     =						name.replace("q_bias"			,				"query.bias"       )
							if "k_bias" in name:
														SCREAMING_SNAKE_CASE__       :      int     =						name.replace("k_bias"			,				"key.bias"       )
							if "v_bias" in name:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						name.replace("v_bias"			,				"value.bias"       )
							if "cpb_mlp" in name:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						name.replace("cpb_mlp"			,				"continuous_position_bias_mlp"       )
							if "patch_embed.proj" in name:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						name.replace("patch_embed.proj"			,				"patch_embed.projection"       )

							if name == "norm.weight":
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						"layernorm.weight"
							if name == "norm.bias":
														SCREAMING_SNAKE_CASE__       :      int     =						"layernorm.bias"

							if "conv_first" in name:
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						name.replace("conv_first"			,				"first_convolution"       )

							if (
							    "upsample" in name
							    or "conv_before_upsample" in name
							    or "conv_bicubic" in name
							    or "conv_up" in name
							    or "conv_hr" in name
							    or "conv_last" in name
							    or "aux" in name
							):
														# heads
														if "conv_last" in name:
																					SCREAMING_SNAKE_CASE__       :      Any     =						name.replace("conv_last"			,				"final_convolution"       )
														if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
																					if "conv_before_upsample.0" in name:
																												SCREAMING_SNAKE_CASE__       :      int     =						name.replace("conv_before_upsample.0"			,				"conv_before_upsample"       )
																					if "upsample.0" in name:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						name.replace("upsample.0"			,				"upsample.convolution_0"       )
																					if "upsample.2" in name:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						name.replace("upsample.2"			,				"upsample.convolution_1"       )
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						"upsample." + name
														elif config.upsampler == "pixelshuffledirect":
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						name.replace("upsample.0.weight"			,				"upsample.conv.weight"       )
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						name.replace("upsample.0.bias"			,				"upsample.conv.bias"       )
														else:
																					pass
							else:
														SCREAMING_SNAKE_CASE__       :      Dict     =						"swin2sr." + name

							return name




def _a (  SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  str       )	->    Optional[int]:

							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__       :      int     =						orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

														if "qkv" in key:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						key.split("."       )
																					SCREAMING_SNAKE_CASE__       :      int     =						int(key_split[1]       )
																					SCREAMING_SNAKE_CASE__       :      Any     =						int(key_split[4]       )
																					SCREAMING_SNAKE_CASE__       :      int     =						config.embed_dim

																					if "weight" in key:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						val[:dim, :]
																												SCREAMING_SNAKE_CASE__       :      str     =						val[dim : dim * 2, :]
																												SCREAMING_SNAKE_CASE__       :      int     =						val[-dim:, :]
																					else:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						val[:dim]
																												SCREAMING_SNAKE_CASE__       :      int     =						val[dim : dim * 2]
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						val[-dim:]
																					pass
														else:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						val

							return orig_state_dict




def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]       )	->    Optional[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						get_config(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[str]     =						SwinaSRForImageSuperResolution(SCREAMING_SNAKE_CASE__       )
							model.eval()

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__			,				map_location="cpu"       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						convert_state_dict(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						model.load_state_dict(SCREAMING_SNAKE_CASE__			,				strict=SCREAMING_SNAKE_CASE__       )

							if len(SCREAMING_SNAKE_CASE__       ) > 0:
														raise ValueError("Missing keys when converting: {}".format(SCREAMING_SNAKE_CASE__       )       )
							for key in unexpected_keys:
														if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
																					raise ValueError(f'''Unexpected key {key} in state_dict'''       )

    # verify values
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						Image.open(requests.get(SCREAMING_SNAKE_CASE__			,				stream=SCREAMING_SNAKE_CASE__       ).raw       ).convert("RGB"       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						SwinaSRImageProcessor()
							# pixel_values = processor(image, return_tensors="pt").pixel_values

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1_26 if "Jpeg" in checkpoint_url else 2_56
							SCREAMING_SNAKE_CASE__       :      List[str]     =						Compose(
							    [
							        Resize((image_size, image_size)       ),
							        ToTensor(),
							        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]			,				std=[0.2_2_9, 0.2_2_4, 0.2_2_5]       ),
							    ]       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						transforms(SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )

							if config.num_channels == 1:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						pixel_values[:, 0, :, :].unsqueeze(1       )

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(SCREAMING_SNAKE_CASE__       )

							# assert values
							if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Dict     =						torch.Size([1, 3, 5_12, 5_12]       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						torch.tensor(
														    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]       )
							elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Dict     =						torch.Size([1, 3, 10_24, 10_24]       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.tensor(
														    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]       )
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														# TODO values didn't match exactly here
														SCREAMING_SNAKE_CASE__       :      str     =						torch.Size([1, 3, 10_24, 10_24]       )
														SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor(
														    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]       )
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.Size([1, 3, 5_12, 5_12]       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.tensor(
														    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]       )
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.Size([1, 3, 10_24, 10_24]       )
														SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor(
														    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]       )

							assert (
							    outputs.reconstruction.shape == expected_shape
							), f'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
							assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]			,				SCREAMING_SNAKE_CASE__			,				atol=1E-3       )
							print("Looks ok!"       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						{
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
							        "swin2SR-classical-sr-x2-64"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
							        "swin2SR-classical-sr-x4-64"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
							        "swin2SR-compressed-sr-x4-48"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
							        "swin2SR-lightweight-x2-64"
							    ),
							    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
							        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
							    ),
							}
							SCREAMING_SNAKE_CASE__       :      str     =						url_to_name[checkpoint_url]

							if pytorch_dump_folder_path is not None:
														print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
														model.save_pretrained(SCREAMING_SNAKE_CASE__       )
														print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
														processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

							if push_to_hub:
														model.push_to_hub(f'''caidas/{model_name}'''       )
														processor.push_to_hub(f'''caidas/{model_name}'''       )


if __name__ == "__main__":
		_lowerCamelCase :    Dict  							= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
		    type=str,
		    help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

		_lowerCamelCase :    Tuple  							= parser.parse_args()
		convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)













import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


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


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "AutoTokenizer"
							UpperCAmelCase_      						=   ["tokenizer"]

							UpperCAmelCase_      						=   {
							    "semantic_prompt": 1,
							    "coarse_prompt": 2,
							    "fine_prompt": 2,
							}






							def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]=None						)				->			Union[str, Any]:





														"""simple docstring"""
														super().__init__(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						speaker_embeddings






							@classmethod
							def  A_     ( cls     :					Any,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Dict="speaker_embeddings_path.json",			**_UpperCAmelCase     :					Optional[Any]						)				->			List[Any]:





														"""simple docstring"""

														if speaker_embeddings_dict_path is not None:
																					SCREAMING_SNAKE_CASE__       :      Any     =						get_file_from_repo(
																					    _UpperCAmelCase,			_UpperCAmelCase,			subfolder=kwargs.pop("subfolder",			_UpperCAmelCase						),			cache_dir=kwargs.pop("cache_dir",			_UpperCAmelCase						),			force_download=kwargs.pop("force_download",			_UpperCAmelCase						),			proxies=kwargs.pop("proxies",			_UpperCAmelCase						),			resume_download=kwargs.pop("resume_download",			_UpperCAmelCase						),			local_files_only=kwargs.pop("local_files_only",			_UpperCAmelCase						),			use_auth_token=kwargs.pop("use_auth_token",			_UpperCAmelCase						),			revision=kwargs.pop("revision",			_UpperCAmelCase						),			)
																					if speaker_embeddings_path is None:
																												logger.warning(
																												    F'''`{os.path.join(_UpperCAmelCase,			_UpperCAmelCase						)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''						)
																												SCREAMING_SNAKE_CASE__       :      Dict     =						None
																					else:
																												with open(_UpperCAmelCase						) as speaker_embeddings_json:
																																			SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						json.load(_UpperCAmelCase						)
														else:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						None

														SCREAMING_SNAKE_CASE__       :      List[str]     =						AutoTokenizer.from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)

														return cls(tokenizer=_UpperCAmelCase,			speaker_embeddings=_UpperCAmelCase						)






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[str]="speaker_embeddings_path.json",			_UpperCAmelCase     :					Optional[Any]="speaker_embeddings",			_UpperCAmelCase     :					bool = False,			**_UpperCAmelCase     :					List[str],			)				->			Union[str, Any]:





														"""simple docstring"""
														if self.speaker_embeddings is not None:
																					os.makedirs(os.path.join(_UpperCAmelCase,			_UpperCAmelCase,			"v2"						),			exist_ok=_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{}

																					SCREAMING_SNAKE_CASE__       :      Any     =						save_directory

																					for prompt_key in self.speaker_embeddings:
																												if prompt_key != "repo_or_path":
																																			SCREAMING_SNAKE_CASE__       :      List[Any]     =						self._load_voice_preset(_UpperCAmelCase						)

																																			SCREAMING_SNAKE_CASE__       :      int     =						{}
																																			for key in self.speaker_embeddings[prompt_key]:
																																										np.save(
																																										    os.path.join(
																																										        embeddings_dict["repo_or_path"],			_UpperCAmelCase,			F'''{prompt_key}_{key}'''						),			voice_preset[key],			allow_pickle=_UpperCAmelCase,			)
																																										SCREAMING_SNAKE_CASE__       :      List[str]     =						os.path.join(_UpperCAmelCase,			F'''{prompt_key}_{key}.npy'''						)

																																			SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tmp_dict

																					with open(os.path.join(_UpperCAmelCase,			_UpperCAmelCase						),			"w"						) as fp:
																												json.dump(_UpperCAmelCase,			_UpperCAmelCase						)

														super().save_pretrained(_UpperCAmelCase,			_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					str = None,			**_UpperCAmelCase     :					List[Any]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.speaker_embeddings[voice_preset]

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{}
														for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																					if key not in voice_preset_paths:
																												raise ValueError(
																												    F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''						)

																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						get_file_from_repo(
																					    self.speaker_embeddings.get("repo_or_path",			"/"						),			voice_preset_paths[key],			subfolder=kwargs.pop("subfolder",			_UpperCAmelCase						),			cache_dir=kwargs.pop("cache_dir",			_UpperCAmelCase						),			force_download=kwargs.pop("force_download",			_UpperCAmelCase						),			proxies=kwargs.pop("proxies",			_UpperCAmelCase						),			resume_download=kwargs.pop("resume_download",			_UpperCAmelCase						),			local_files_only=kwargs.pop("local_files_only",			_UpperCAmelCase						),			use_auth_token=kwargs.pop("use_auth_token",			_UpperCAmelCase						),			revision=kwargs.pop("revision",			_UpperCAmelCase						),			)
																					if path is None:
																												raise ValueError(
																												    F'''`{os.path.join(self.speaker_embeddings.get("repo_or_path",			"/"						),			voice_preset_paths[key]						)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''						)

																					SCREAMING_SNAKE_CASE__       :      int     =						np.load(_UpperCAmelCase						)

														return voice_preset_dict






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[dict] = None						)				->			Optional[int]:





														"""simple docstring"""
														for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
																					if key not in voice_preset:
																												raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.'''						)

																					if not isinstance(voice_preset[key],			np.ndarray						):
																												raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]						)}D ndarray.'''						)

																					if len(voice_preset[key].shape						) != self.preset_shape[key]:
																												raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key]						)}D ndarray.'''						)






							def __call__( self     :					List[Any],			_UpperCAmelCase     :					Optional[Any]=None,			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					Optional[int]="pt",			_UpperCAmelCase     :					List[str]=2_5_6,			_UpperCAmelCase     :					int=False,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Any=False,			**_UpperCAmelCase     :					List[str],			)				->			List[Any]:





														"""simple docstring"""
														if voice_preset is not None and not isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																					if (
																					    isinstance(_UpperCAmelCase,			_UpperCAmelCase						)
																					    and self.speaker_embeddings is not None
																					    and voice_preset in self.speaker_embeddings
																					):
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						self._load_voice_preset(_UpperCAmelCase						)

																					else:
																												if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) and not voice_preset.endswith(".npz"						):
																																			SCREAMING_SNAKE_CASE__       :      Optional[int]     =						voice_preset + ".npz"

																												SCREAMING_SNAKE_CASE__       :      List[str]     =						np.load(_UpperCAmelCase						)

														if voice_preset is not None:
																					self._validate_voice_preset_dict(_UpperCAmelCase,			**_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      Any     =						BatchFeature(data=_UpperCAmelCase,			tensor_type=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						self.tokenizer(
														    _UpperCAmelCase,			return_tensors=_UpperCAmelCase,			padding="max_length",			max_length=_UpperCAmelCase,			return_attention_mask=_UpperCAmelCase,			return_token_type_ids=_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase,			**_UpperCAmelCase,			)

														if voice_preset is not None:
																					SCREAMING_SNAKE_CASE__       :      str     =						voice_preset

														return encoded_text






