
"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


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






def    _snake_case					(		snake_case__ :  int  ):
	A     = SwinConfig(
	    embed_dim=192						,   depths=(2, 2, 18, 2)						,   num_heads=(6, 12, 24, 48)						,   window_size=12						,   out_features=['stage2', 'stage3', 'stage4']						,   )

	A     = DetaConfig(
	    backbone_config=snake_case__						,   num_queries=900						,   encoder_ffn_dim=2048						,   decoder_ffn_dim=2048						,   num_feature_levels=5						,   assign_first_stage=snake_case__						,   with_box_refine=snake_case__						,   two_stage=snake_case__						,   )

	# set labels
	A     = 'huggingface/label-files'
	if "o365" in model_name:
		A     = 366
		A     = 'object365-id2label.json'
	else:
		A     = 91
		A     = 'coco-detection-id2label.json'

	A     = num_labels
	A     = json.load(open(cached_download(hf_hub_url(snake_case__						,   snake_case__						,   repo_type='dataset'  )  )						,   'r'  )  )
	A     = {int(snake_case__  ): v for k, v in idalabel.items()}
	A     = idalabel
	A     = {v: k for k, v in idalabel.items()}

	return config






def    _snake_case					(		snake_case__ :  Any  ):
	A     = []

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')  )
	rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')  )
	rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')  )
	rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')  )
	# stages
	for i in range(len(config.backbone_config.depths  )  ):
		for j in range(config.backbone_config.depths[i]  ):
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')  )

		if i < 3:
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')  )
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')  )

	rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')  )
	rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')  )
	rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')  )
	rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')  )
	rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')  )
	rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')  )

	# transformer encoder
	for i in range(config.encoder_layers  ):
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')  )
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')  )

	# transformer decoder
	for i in range(config.decoder_layers  ):
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')  )
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')  )

	# fmt: on

	return rename_keys






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  List[Any]						,   snake_case__ :  Any  ):
	A     = dct.pop(snake_case__  )
	A     = val






def    _snake_case					(		snake_case__ :  Any						,   snake_case__ :  Optional[int]  ):
	A     = [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
	for i in range(len(backbone_config.depths  )  ):
		A     = num_features[i]
		for j in range(backbone_config.depths[i]  ):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			A     = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'  )
			A     = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'  )
			# next, add query, keys and values (in that order) to the state dict
			A     = in_proj_weight[:dim, :]
			A     = in_proj_bias[: dim]
			A     = in_proj_weight[
			    dim : dim * 2, :
			]
			A     = in_proj_bias[
			    dim : dim * 2
			]
			A     = in_proj_weight[
			    -dim :, :
			]
			A     = in_proj_bias[-dim :]
			# fmt: on






def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  Union[str, Any]  ):
	# transformer decoder self-attention layers
	A     = config.d_model
	for i in range(config.decoder_layers  ):
		# read in weights + bias of input projection layer of self-attention
		A     = state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
		A     = state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias'  )
		# next, add query, keys and values (in that order) to the state dict
		A     = in_proj_weight[:hidden_size, :]
		A     = in_proj_bias[:hidden_size]
		A     = in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		A     = in_proj_bias[hidden_size : hidden_size * 2]
		A     = in_proj_weight[-hidden_size:, :]
		A     = in_proj_bias[-hidden_size:]






def    _snake_case					(		):
	A     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
	A     = Image.open(requests.get(snake_case__						,   stream=snake_case__  ).raw  )

	return im






@torch.no_grad()
def    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  Dict						,   snake_case__ :  List[str]  ):
	A     = get_deta_config(snake_case__  )

	# load original state dict
	if model_name == "deta-swin-large":
		A     = hf_hub_download(repo_id='nielsr/deta-checkpoints'						,   filename='adet_swin_ft.pth'  )
	elif model_name == "deta-swin-large-o365":
		A     = hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'						,   filename='deta_swin_pt_o365.pth'  )
	else:
		raise ValueError(F'Model name {model_name} not supported'  )

	A     = torch.load(snake_case__						,   map_location='cpu'  )['model']

	# original state dict
	for name, param in state_dict.items():
		print(snake_case__						,   param.shape  )

	# rename keys
	A     = create_rename_keys(snake_case__  )
	for src, dest in rename_keys:
		rename_key(snake_case__						,   snake_case__						,   snake_case__  )
	read_in_swin_q_k_v(snake_case__						,   config.backbone_config  )
	read_in_decoder_q_k_v(snake_case__						,   snake_case__  )

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			A     = state_dict.pop(snake_case__  )
			A     = val
		if "input_proj" in key:
			A     = state_dict.pop(snake_case__  )
			A     = val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			A     = state_dict.pop(snake_case__  )
			A     = val

    # finally, create HuggingFace model and load state dict
	A     = DetaForObjectDetection(snake_case__  )
	model.load_state_dict(snake_case__  )
	model.eval()

	A     = 'cuda' if torch.cuda.is_available() else 'cpu'
	model.to(snake_case__  )

	# load image processor
	A     = DetaImageProcessor(format='coco_detection'  )

	# verify our conversion on image
	A     = prepare_img()
	A     = processor(images=snake_case__						,   return_tensors='pt'  )
	A     = encoding['pixel_values']
	A     = model(pixel_values.to(snake_case__  )  )

	# verify logits
	print('Logits:'						,   outputs.logits[0, :3, :3]  )
	print('Boxes:'						,   outputs.pred_boxes[0, :3, :3]  )
	if model_name == "deta-swin-large":
		A     = torch.tensor(
		    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]  )
		A     = torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]  )
	elif model_name == "deta-swin-large-o365":
		A     = torch.tensor(
		    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]  )
		A     = torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]  )
	assert torch.allclose(outputs.logits[0, :3, :3]						,   expected_logits.to(snake_case__  )						,   atol=1e-4  )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,   expected_boxes.to(snake_case__  )						,   atol=1e-4  )
	print('Everything ok!'  )

	if pytorch_dump_folder_path:
		# Save model and processor
		logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'  )
		Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
		model.save_pretrained(snake_case__  )
		processor.save_pretrained(snake_case__  )

	# Push to hub
	if push_to_hub:
		print('Pushing model and processor to hub...'  )
		model.push_to_hub(F'jozhang97/{model_name}'  )
		processor.push_to_hub(F'jozhang97/{model_name}'  )


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

		parser.add_argument(
		    '''--model_name''',
		    type=str,
		    default='''deta-swin-large''',
		    choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
		    help='''Name of the 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 or not to push the converted model to the 🤗 hub.'''
		)
		_lowercase				 =					parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       '''Wav2Vec2FeatureExtractor'''
			__lowerCamelCase      :			str							     =       '''AutoTokenizer'''






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Tuple:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase				,      __lowerCAmelCase  )
					A__     =  self.feature_extractor
					A__     =  False






			@classmethod
			def   a_			(     cls  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					try:
							return super().from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
					except OSError:
							warnings.warn(
							    f'Loading a tokenizer inside {cls.__name__} from a config that does not'
							    """ include a `tokenizer_class` attribute is deprecated and will be """
							    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
							    """ attribute to either your `config.json` or `tokenizer_config.json` """
							    """file to suppress this warning: """				,      __lowerCAmelCase				,      )

							A__     =  WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
							A__     =  WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )

							return cls(feature_extractor=__lowerCAmelCase				,      tokenizer=__lowerCAmelCase  )






			def __call__(     self  :       Any				,      *__lowerCAmelCase  :       Union[str, Any]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*__lowerCAmelCase				,      **__lowerCAmelCase  )

					if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
							A__     =  kwargs.pop("""raw_speech"""  )
					else:
							A__     =  kwargs.pop("""audio"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""sampling_rate"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""text"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 0:
							A__     =  args[0]
							A__     =  args[1:]

					if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

					if audio is not None:
							A__     =  self.feature_extractor(__lowerCAmelCase				,      *__lowerCAmelCase				,      sampling_rate=__lowerCAmelCase				,      **__lowerCAmelCase  )
					if text is not None:
							A__     =  self.tokenizer(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if text is None:
							return inputs
					elif audio is None:
							return encodings
					else:
							A__     =  encodings["""input_ids"""]
							return inputs






			def   a_			(     self  :       List[str]				,      *__lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Tuple  )      ->      List[str]:







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor.pad(*__lowerCAmelCase				,      **__lowerCAmelCase  )

					A__     =  kwargs.pop("""input_features"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""labels"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 0:
							A__     =  args[0]
							A__     =  args[1:]

					if input_features is not None:
							A__     =  self.feature_extractor.pad(__lowerCAmelCase				,      *__lowerCAmelCase				,      **__lowerCAmelCase  )
					if labels is not None:
							A__     =  self.tokenizer.pad(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if labels is None:
							return input_features
					elif input_features is None:
							return labels
					else:
							A__     =  labels["""input_ids"""]
							return input_features






			def   a_			(     self  :       Optional[int]				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Dict:







					"""simple docstring"""






					return self.tokenizer.batch_decode(*__lowerCAmelCase				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Any				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






					return self.tokenizer.decode(*__lowerCAmelCase				,      **__lowerCAmelCase  )







			@contextmanager
			def   a_			(     self  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					warnings.warn(
					    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
					    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
					    """your audio inputs, or in a separate call."""  )
					A__     =  True
					A__     =  self.tokenizer
					yield
					A__     =  self.feature_extractor
					A__     =  False



