
'''simple docstring'''






import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A			      =      logging.get_logger(__name__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict):
				# initialize config
				if "resnet-50" in model_name:
								lowerCamelCase					:			str									= ResNetConfig.from_pretrained('microsoft/resnet-50')
				elif "resnet-101" in model_name:
								lowerCamelCase					:			List[Any]									= ResNetConfig.from_pretrained('microsoft/resnet-101')
				else:
								raise ValueError('Model name should include either resnet50 or resnet101')

				lowerCamelCase					:			Optional[int]									= DetrConfig(use_timm_backbone=UpperCAmelCase__       ,							backbone_config=UpperCAmelCase__)

				# set label attributes
				lowerCamelCase					:			int									= 'panoptic' in model_name
				if is_panoptic:
								lowerCamelCase					:			List[str]									= 2_50
				else:
								lowerCamelCase					:			str									= 91
								lowerCamelCase					:			Dict									= 'huggingface/label-files'
								lowerCamelCase					:			Any									= 'coco-detection-id2label.json'
								lowerCamelCase					:			Any									= json.load(open(hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__       ,							repo_type='dataset')       ,							'r'))
								lowerCamelCase					:			Any									= {int(UpperCAmelCase__): v for k, v in idalabel.items()}
								lowerCamelCase					:			List[str]									= idalabel
								lowerCamelCase					:			Tuple									= {v: k for k, v in idalabel.items()}

				return config, is_panoptic





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				# here we list all keys to be renamed (original name on the left, our name on the right)
				lowerCamelCase					:			int									= []

				# stem
				# fmt: off
				rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight'))
				rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight'))
				rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias'))
				rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'))
				rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var'))
				# stages
				for stage_idx in range(len(config.backbone_config.depths)):
								for layer_idx in range(config.backbone_config.depths[stage_idx]):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																    ))
												# 3 convs
												for i in range(3):
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																    ))
																rename_keys.append(
																    (
																        F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																        F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																    ))
    # fmt: on

				for i in range(config.encoder_layers):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (
								        F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
								        F'''encoder.layers.{i}.self_attn.out_proj.weight''',
								    ))
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias'''))
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight'''))
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
								rename_keys.append(
								    (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
								# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
								rename_keys.append(
								    (
								        F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
								        F'''decoder.layers.{i}.self_attn.out_proj.weight''',
								    ))
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias'''))
								rename_keys.append(
								    (
								        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
								        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
								    ))
								rename_keys.append(
								    (
								        F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
								        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
								    ))
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight'''))
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight'''))
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias'''))
								rename_keys.append(
								    (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

				# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
				rename_keys.extend(
				    [
				        ('input_proj.weight', 'input_projection.weight'),
				        ('input_proj.bias', 'input_projection.bias'),
				        ('query_embed.weight', 'query_position_embeddings.weight'),
				        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
				        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
				        ('class_embed.weight', 'class_labels_classifier.weight'),
				        ('class_embed.bias', 'class_labels_classifier.bias'),
				        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
				        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
				        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
				        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
				        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
				        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
				    ])

				return rename_keys





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Dict):
				lowerCamelCase					:			Optional[int]									= state_dict.pop(UpperCAmelCase__)
				lowerCamelCase					:			Union[str, Any]									= val





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[int]=False):
				lowerCamelCase					:			Optional[int]									= ''
				if is_panoptic:
								lowerCamelCase					:			Dict									= 'detr.'

				# first: transformer encoder
				for i in range(6):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								lowerCamelCase					:			List[str]									= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
								lowerCamelCase					:			Any									= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''')
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase					:			int									= in_proj_weight[:2_56, :]
								lowerCamelCase					:			int									= in_proj_bias[:2_56]
								lowerCamelCase					:			List[Any]									= in_proj_weight[2_56:5_12, :]
								lowerCamelCase					:			Tuple									= in_proj_bias[2_56:5_12]
								lowerCamelCase					:			str									= in_proj_weight[-2_56:, :]
								lowerCamelCase					:			Optional[int]									= in_proj_bias[-2_56:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6):
								# read in weights + bias of input projection layer of self-attention
								lowerCamelCase					:			str									= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
								lowerCamelCase					:			Optional[Any]									= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''')
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase					:			str									= in_proj_weight[:2_56, :]
								lowerCamelCase					:			str									= in_proj_bias[:2_56]
								lowerCamelCase					:			List[Any]									= in_proj_weight[2_56:5_12, :]
								lowerCamelCase					:			Union[str, Any]									= in_proj_bias[2_56:5_12]
								lowerCamelCase					:			int									= in_proj_weight[-2_56:, :]
								lowerCamelCase					:			Any									= in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								lowerCamelCase					:			int									= state_dict.pop(
								    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
								lowerCamelCase					:			Union[str, Any]									= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''')
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								lowerCamelCase					:			Any									= in_proj_weight_cross_attn[:2_56, :]
								lowerCamelCase					:			Optional[Any]									= in_proj_bias_cross_attn[:2_56]
								lowerCamelCase					:			Union[str, Any]									= in_proj_weight_cross_attn[2_56:5_12, :]
								lowerCamelCase					:			List[Any]									= in_proj_bias_cross_attn[2_56:5_12]
								lowerCamelCase					:			List[Any]									= in_proj_weight_cross_attn[-2_56:, :]
								lowerCamelCase					:			int									= in_proj_bias_cross_attn[-2_56:]





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			Tuple									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				lowerCamelCase					:			int									= Image.open(requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__).raw)

				return im





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							List[Any]=None       ,							UpperCAmelCase__							:							Union[str, Any]=False):
				lowerCamelCase					:			str									= get_detr_config(UpperCAmelCase__)

				# load original model from torch hub
				lowerCamelCase					:			Optional[Any]									= {
				    'detr-resnet-50': 'detr_resnet50',
				    'detr-resnet-101': 'detr_resnet101',
				}
				logger.info(F'''Converting model {model_name}...''')
				lowerCamelCase					:			Tuple									= torch.hub.load('facebookresearch/detr'       ,							model_name_to_original_name[model_name]       ,							pretrained=UpperCAmelCase__).eval()
				lowerCamelCase					:			Optional[Any]									= detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(UpperCAmelCase__):
								if is_panoptic:
												lowerCamelCase					:			str									= 'detr.' + src
								rename_key(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
				# query, key and value matrices need special treatment
				read_in_q_k_v(UpperCAmelCase__       ,							is_panoptic=UpperCAmelCase__)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowerCamelCase					:			Tuple									= 'detr.model.' if is_panoptic else 'model.'
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('detr')
												    and not key.startswith('class_labels_classifier')
												    and not key.startswith('bbox_predictor')
												):
																lowerCamelCase					:			Tuple									= state_dict.pop(UpperCAmelCase__)
																lowerCamelCase					:			List[str]									= val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																lowerCamelCase					:			Tuple									= state_dict.pop(UpperCAmelCase__)
																lowerCamelCase					:			Dict									= val
												elif key.startswith('bbox_attention') or key.startswith('mask_head'):
																continue
												else:
																lowerCamelCase					:			List[str]									= state_dict.pop(UpperCAmelCase__)
																lowerCamelCase					:			Dict									= val
								else:
												if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
																lowerCamelCase					:			int									= state_dict.pop(UpperCAmelCase__)
																lowerCamelCase					:			Optional[int]									= val

    # finally, create HuggingFace model and load state dict
				lowerCamelCase					:			Dict									= DetrForSegmentation(UpperCAmelCase__) if is_panoptic else DetrForObjectDetection(UpperCAmelCase__)
				model.load_state_dict(UpperCAmelCase__)
				model.eval()

				# verify our conversion on an image
				lowerCamelCase					:			Optional[Any]									= 'coco_panoptic' if is_panoptic else 'coco_detection'
				lowerCamelCase					:			int									= DetrImageProcessor(format=UpperCAmelCase__)

				lowerCamelCase					:			List[Any]									= processor(images=prepare_img()       ,							return_tensors='pt')
				lowerCamelCase					:			Dict									= encoding['pixel_values']

				lowerCamelCase					:			str									= detr(UpperCAmelCase__)
				lowerCamelCase					:			str									= model(UpperCAmelCase__)

				assert torch.allclose(outputs.logits       ,							original_outputs['pred_logits']       ,							atol=1E-3)
				assert torch.allclose(outputs.pred_boxes       ,							original_outputs['pred_boxes']       ,							atol=1E-3)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks       ,							original_outputs['pred_masks']       ,							atol=1E-4)
				print('Looks ok!')

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
								Path(UpperCAmelCase__).mkdir(exist_ok=UpperCAmelCase__)
								model.save_pretrained(UpperCAmelCase__)
								processor.save_pretrained(UpperCAmelCase__)

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info('Uploading PyTorch model and image processor to the hub...')
								model.push_to_hub(F'''nielsr/{model_name}''')
								processor.push_to_hub(F'''nielsr/{model_name}''')


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()

					parser.add_argument(
					    '--model_name',
					    default='detr-resnet-50',
					    type=str,
					    choices=['detr-resnet-50', 'detr-resnet-101'],
					    help='Name of the DETR model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
					)
					parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
					A			      =      parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''






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


A			      =      logging.get_logger(__name__)




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''AutoTokenizer'''
					_lowerCAmelCase			     =	['''tokenizer''']

					_lowerCAmelCase			     =	{
					    '''semantic_prompt''': 1,
					    '''coarse_prompt''': 2,
					    '''fine_prompt''': 2,
					}
					def __init__(     self,    A,    A=None      ):

									"""simple docstring"""


									super().__init__(A      )

									lowerCamelCase					:			Union[str, Any]									= speaker_embeddings
					@classmethod
					def     UpperCAmelCase_							(     cls,    A,    A="speaker_embeddings_path.json",    **A      ):

									"""simple docstring"""


									if speaker_embeddings_dict_path is not None:
													lowerCamelCase					:			List[Any]									= get_file_from_repo(
													    A,    A,    subfolder=kwargs.pop('subfolder',    A      ),    cache_dir=kwargs.pop('cache_dir',    A      ),    force_download=kwargs.pop('force_download',    A      ),    proxies=kwargs.pop('proxies',    A      ),    resume_download=kwargs.pop('resume_download',    A      ),    local_files_only=kwargs.pop('local_files_only',    A      ),    use_auth_token=kwargs.pop('use_auth_token',    A      ),    revision=kwargs.pop('revision',    A      ),    )
													if speaker_embeddings_path is None:
																	logger.warning(
																	    F'''`{os.path.join(A,    A      )}` 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`.'''      )
																	lowerCamelCase					:			Union[str, Any]									= None
													else:
																	with open(A      ) as speaker_embeddings_json:
																					lowerCamelCase					:			Union[str, Any]									= json.load(A      )
									else:
													lowerCamelCase					:			Dict									= None

									lowerCamelCase					:			int									= AutoTokenizer.from_pretrained(A,    **A      )

									return cls(tokenizer=A,    speaker_embeddings=A      )
					def     UpperCAmelCase_							(     self,    A,    A="speaker_embeddings_path.json",    A="speaker_embeddings",    A = False,    **A,    ):

									"""simple docstring"""


									if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(A,    A,    'v2'      ),    exist_ok=A      )

													lowerCamelCase					:			Any									= {}

													lowerCamelCase					:			Tuple									= save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					lowerCamelCase					:			Optional[int]									= self._load_voice_preset(A      )

																					lowerCamelCase					:			Optional[Any]									= {}
																					for key in self.speaker_embeddings[prompt_key]:
																									np.save(
																									    os.path.join(
																									        embeddings_dict['repo_or_path'],    A,    F'''{prompt_key}_{key}'''      ),    voice_preset[key],    allow_pickle=A,    )
																									lowerCamelCase					:			Tuple									= os.path.join(A,    F'''{prompt_key}_{key}.npy'''      )

																					lowerCamelCase					:			Optional[Any]									= tmp_dict

													with open(os.path.join(A,    A      ),    'w'      ) as fp:
																	json.dump(A,    A      )

									super().save_pretrained(A,    A,    **A      )
					def     UpperCAmelCase_							(     self,    A = None,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.speaker_embeddings[voice_preset]

									lowerCamelCase					:			Optional[Any]									= {}
									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}].'''      )

													lowerCamelCase					:			Optional[Any]									= get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path',    '/'      ),    voice_preset_paths[key],    subfolder=kwargs.pop('subfolder',    A      ),    cache_dir=kwargs.pop('cache_dir',    A      ),    force_download=kwargs.pop('force_download',    A      ),    proxies=kwargs.pop('proxies',    A      ),    resume_download=kwargs.pop('resume_download',    A      ),    local_files_only=kwargs.pop('local_files_only',    A      ),    use_auth_token=kwargs.pop('use_auth_token',    A      ),    revision=kwargs.pop('revision',    A      ),    )
													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.'''      )

													lowerCamelCase					:			Tuple									= np.load(A      )

									return voice_preset_dict
					def     UpperCAmelCase_							(     self,    A = None      ):

									"""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,    A=None,    A=None,    A="pt",    A=256,    A=False,    A=True,    A=False,    **A,    ):

									"""simple docstring"""


									if voice_preset is not None and not isinstance(A,    A      ):
													if (
													    isinstance(A,    A      )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																	lowerCamelCase					:			Dict									= self._load_voice_preset(A      )

													else:
																	if isinstance(A,    A      ) and not voice_preset.endswith('.npz'      ):
																					lowerCamelCase					:			List[Any]									= voice_preset + '.npz'

																	lowerCamelCase					:			Tuple									= np.load(A      )

									if voice_preset is not None:
													self._validate_voice_preset_dict(A,    **A      )
													lowerCamelCase					:			List[Any]									= BatchFeature(data=A,    tensor_type=A      )

									lowerCamelCase					:			str									= self.tokenizer(
									    A,    return_tensors=A,    padding='max_length',    max_length=A,    return_attention_mask=A,    return_token_type_ids=A,    add_special_tokens=A,    **A,    )

									if voice_preset is not None:
													lowerCamelCase					:			Dict									= voice_preset

									return encoded_text
