



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()
SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Any:
		# initialize config
		if "resnet-50" in model_name:
				UpperCAmelCase_						=				ResNetConfig.from_pretrained("microsoft/resnet-50" )
		elif "resnet-101" in model_name:
				UpperCAmelCase_						=				ResNetConfig.from_pretrained("microsoft/resnet-101" )
		else:
				raise ValueError("Model name should include either resnet50 or resnet101" )

		UpperCAmelCase_						=				DetrConfig(use_timm_backbone=__SCREAMING_SNAKE_CASE			,     backbone_config=__SCREAMING_SNAKE_CASE )

		# set label attributes
		UpperCAmelCase_						=				"panoptic" in model_name
		if is_panoptic:
				UpperCAmelCase_						=				250
		else:
				UpperCAmelCase_						=				91
				UpperCAmelCase_						=				"huggingface/label-files"
				UpperCAmelCase_						=				"coco-detection-id2label.json"
				UpperCAmelCase_						=				json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     repo_type="dataset" )			,     "r" ) )
				UpperCAmelCase_						=				{int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
				UpperCAmelCase_						=				idalabel
				UpperCAmelCase_						=				{v: k for k, v in idalabel.items()}

		return config, is_panoptic







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
		# here we list all keys to be renamed (original name on the left, our name on the right)
		UpperCAmelCase_						=				[]

		# 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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				state_dict.pop(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				val







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=False )		->							Optional[Any]:
		UpperCAmelCase_						=				""
		if is_panoptic:
				UpperCAmelCase_						=				"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)
				UpperCAmelCase_						=				state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
				UpperCAmelCase_						=				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
				UpperCAmelCase_						=				in_proj_weight[:256, :]
				UpperCAmelCase_						=				in_proj_bias[:256]
				UpperCAmelCase_						=				in_proj_weight[256:512, :]
				UpperCAmelCase_						=				in_proj_bias[256:512]
				UpperCAmelCase_						=				in_proj_weight[-256:, :]
				UpperCAmelCase_						=				in_proj_bias[-256:]
		# 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
				UpperCAmelCase_						=				state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
				UpperCAmelCase_						=				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
				UpperCAmelCase_						=				in_proj_weight[:256, :]
				UpperCAmelCase_						=				in_proj_bias[:256]
				UpperCAmelCase_						=				in_proj_weight[256:512, :]
				UpperCAmelCase_						=				in_proj_bias[256:512]
				UpperCAmelCase_						=				in_proj_weight[-256:, :]
				UpperCAmelCase_						=				in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				UpperCAmelCase_						=				state_dict.pop(
				    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
				UpperCAmelCase_						=				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
				UpperCAmelCase_						=				in_proj_weight_cross_attn[:256, :]
				UpperCAmelCase_						=				in_proj_bias_cross_attn[:256]
				UpperCAmelCase_						=				in_proj_weight_cross_attn[256:512, :]
				UpperCAmelCase_						=				in_proj_bias_cross_attn[256:512]
				UpperCAmelCase_						=				in_proj_weight_cross_attn[-256:, :]
				UpperCAmelCase_						=				in_proj_bias_cross_attn[-256:]







def snake_case__  (			)		->							str:
		UpperCAmelCase_						=				"http://images.cocodataset.org/val2017/000000039769.jpg"
		UpperCAmelCase_						=				Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw )

		return im







@torch.no_grad()
def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=False )		->							str:
		UpperCAmelCase_					,						UpperCAmelCase_						=				get_detr_config(__SCREAMING_SNAKE_CASE )

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

    # finally, create HuggingFace model and load state dict
		UpperCAmelCase_						=				DetrForSegmentation(__SCREAMING_SNAKE_CASE ) if is_panoptic else DetrForObjectDetection(__SCREAMING_SNAKE_CASE )
		model.load_state_dict(__SCREAMING_SNAKE_CASE )
		model.eval()

		# verify our conversion on an image
		UpperCAmelCase_						=				"coco_panoptic" if is_panoptic else "coco_detection"
		UpperCAmelCase_						=				DetrImageProcessor(format=__SCREAMING_SNAKE_CASE )

		UpperCAmelCase_						=				processor(images=prepare_img()			,     return_tensors="pt" )
		UpperCAmelCase_						=				encoding["pixel_values"]

		UpperCAmelCase_						=				detr(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				model(__SCREAMING_SNAKE_CASE )

		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(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
				model.save_pretrained(__SCREAMING_SNAKE_CASE )
				processor.save_pretrained(__SCREAMING_SNAKE_CASE )

		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__":
			SCREAMING_SNAKE_CASE     				=						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.")
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "yjernite/retribert-base-uncased": 512,
}


SCREAMING_SNAKE_CASE     				=						{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    int									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    Any									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Dict									=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_				:    List[str]									=  RetriBertTokenizer
					lowerCAmelCase_				:    Union[str, Any]									=  ['input_ids', 'attention_mask']



					def __init__(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase="[UNK]"    ,	lowerCAmelCase="[SEP]"    ,	lowerCAmelCase="[PAD]"    ,	lowerCAmelCase="[CLS]"    ,	lowerCAmelCase="[MASK]"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    lowerCAmelCase    ,	tokenizer_file=lowerCAmelCase    ,	do_lower_case=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	sep_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	cls_token=lowerCAmelCase    ,	mask_token=lowerCAmelCase    ,	tokenize_chinese_chars=lowerCAmelCase    ,	strip_accents=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
							if (
							    normalizer_state.get("lowercase"    ,	lowerCAmelCase     ) != do_lower_case
							    or normalizer_state.get("strip_accents"    ,	lowerCAmelCase     ) != strip_accents
							    or normalizer_state.get("handle_chinese_chars"    ,	lowerCAmelCase     ) != tokenize_chinese_chars
							):
									UpperCAmelCase_						=				getattr(lowerCAmelCase    ,	normalizer_state.pop("type"     )     )
									UpperCAmelCase_						=				do_lower_case
									UpperCAmelCase_						=				strip_accents
									UpperCAmelCase_						=				tokenize_chinese_chars
									UpperCAmelCase_						=				normalizer_class(**lowerCAmelCase     )

							UpperCAmelCase_						=				do_lower_case



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				[self.sep_token_id]
							UpperCAmelCase_						=				[self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep     ) * [0]
							return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )

