code: 
  """simple docstring"""
  import argparse
  import collections
  
  import jax
  import jax.numpy as jnp
  import torch
  import torch.nn as nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  
  from transformers import (
      CLIPTokenizer,
      OwlViTConfig,
      OwlViTForObjectDetection,
      OwlViTImageProcessor,
      OwlViTModel,
      OwlViTProcessor,
  )
  
  
  __magic_name__		       =				{
      '''vit_b32''': {
          '''embed_dim''': 5_12,
          '''image_resolution''': 7_68,
          '''context_length''': 16,
          '''vocab_size''': 4_94_08,
          '''vision_layers''': 12,
          '''vision_width''': 7_68,
          '''vision_patch_size''': 32,
          '''transformer_width''': 5_12,
          '''transformer_heads''': 8,
          '''transformer_layers''': 12,
      },
      '''vit_b16''': {
          '''embed_dim''': 5_12,
          '''image_resolution''': 7_68,
          '''context_length''': 16,
          '''vocab_size''': 4_94_08,
          '''vision_layers''': 12,
          '''vision_width''': 7_68,
          '''vision_patch_size''': 16,
          '''transformer_width''': 5_12,
          '''transformer_heads''': 8,
          '''transformer_layers''': 12,
      },
      '''vit_l14''': {
          '''embed_dim''': 7_68,
          '''image_resolution''': 8_40,
          '''context_length''': 16,
          '''vocab_size''': 4_94_08,
          '''vision_layers''': 24,
          '''vision_width''': 10_24,
          '''vision_patch_size''': 14,
          '''transformer_width''': 7_68,
          '''transformer_heads''': 12,
          '''transformer_layers''': 12,
      },
  }
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__=""				,     UpperCamelCase__="/"      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						[]
  
  	for k, v in params.items():
  		_UpperCAmelCase =						parent_key + sep + k if parent_key else k
  
  		if isinstance(UpperCamelCase__				,     collections.MutableMapping      ):
  			items.extend(flatten_nested_dict(UpperCamelCase__				,     UpperCamelCase__				,     sep=UpperCamelCase__      ).items()      )
  		else:
  			items.append((new_key, v)      )
  	return dict(UpperCamelCase__      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	return jax.tree_util.tree_map(lambda UpperCamelCase__      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x				,     UpperCamelCase__      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						pt_attn_layer.in_proj_weight.chunk(3				,     dim=0      )
  	_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						pt_attn_layer.in_proj_bias.chunk(3				,     dim=0      )
  
  	_UpperCAmelCase =						pt_attn_layer.out_proj.weight
  	_UpperCAmelCase =						pt_attn_layer.out_proj.bias
  
  	_UpperCAmelCase =						q_proj
  	_UpperCAmelCase =						q_proj_bias
  
  	_UpperCAmelCase =						k_proj
  	_UpperCAmelCase =						k_proj_bias
  
  	_UpperCAmelCase =						v_proj
  	_UpperCAmelCase =						v_proj_bias
  
  	_UpperCAmelCase =						out_proj_weights
  	_UpperCAmelCase =						out_proj_bias
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	copy_linear(hf_mlp.fca				,     pt_mlp.c_fc      )
  	copy_linear(hf_mlp.fca				,     pt_mlp.c_proj      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						pt_linear.weight
  	_UpperCAmelCase =						pt_linear.bias
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	copy_linear(hf_layer.layer_norma				,     pt_layer.ln_a      )
  	copy_linear(hf_layer.layer_norma				,     pt_layer.ln_a      )
  
  	# copy MLP
  	copy_mlp(hf_layer.mlp				,     pt_layer.mlp      )
  
  	# copy attn
  	copy_attn_layer(hf_layer.self_attn				,     pt_layer.attn      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	for hf_layer, pt_layer in zip(UpperCamelCase__				,     UpperCamelCase__      ):
  		copy_layer(UpperCamelCase__				,     UpperCamelCase__      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						pt_model.token_embedding.weight
  	_UpperCAmelCase =						pt_model.positional_embedding
  
  	# copy layer norm
  	copy_linear(hf_encoder.final_layer_norm				,     pt_model.ln_final      )
  
  	# copy hidden layers
  	copy_layers(hf_encoder.encoder.layers				,     pt_model.transformer.resblocks      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						pt_model.text_projection.data.T
  
  	# copy text encoder
  	copy_encoder(hf_model.text_model				,     UpperCamelCase__      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						pt_model.visual.proj.data.T
  
  	# copy layer norms
  	copy_linear(hf_model.vision_model.pre_layernorm				,     pt_model.visual.ln_pre      )
  	copy_linear(hf_model.vision_model.post_layernorm				,     pt_model.visual.ln_post      )
  
  	# copy embeds
  	_UpperCAmelCase =						pt_model.visual.conva.weight.data
  	_UpperCAmelCase =						pt_model.visual.class_embedding
  	_UpperCAmelCase =						pt_model.visual.positional_embedding.data
  
  	# copy encoder
  	copy_layers(hf_model.vision_model.encoder.layers				,     pt_model.visual.transformer.resblocks      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						flatten_nested_dict(flax_params["backbone"]["merged_class_token"]      )
  
  	_UpperCAmelCase =						torch.from_numpy(flax_class_token_params["scale"]      )
  	_UpperCAmelCase =						torch.from_numpy(flax_class_token_params["bias"]      )
  	_UpperCAmelCase =						nn.Parameter(UpperCamelCase__      )
  	_UpperCAmelCase =						nn.Parameter(UpperCamelCase__      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						hf_model.state_dict()
  	_UpperCAmelCase =						{}
  
  	# Rename class prediction head flax params to pytorch HF
  	_UpperCAmelCase =						flatten_nested_dict(flax_params["class_head"]      )
  
  	for flax_key, v in flax_class_params.items():
  		_UpperCAmelCase =						flax_key.replace("/"				,     "."      )
  		_UpperCAmelCase =						torch_key.replace(".kernel"				,     ".weight"      )
  		_UpperCAmelCase =						torch_key.replace("Dense_0"				,     "dense0"      )
  		_UpperCAmelCase =						"class_head." + torch_key
  
  		if "weight" in torch_key and v.ndim == 2:
  			_UpperCAmelCase =						v.T
  
  		_UpperCAmelCase =						nn.Parameter(torch.from_numpy(UpperCamelCase__      )      )
  
  	# Rename box prediction box flax params to pytorch HF
  	_UpperCAmelCase =						flatten_nested_dict(flax_params["obj_box_head"]      )
  
  	for flax_key, v in flax_box_params.items():
  		_UpperCAmelCase =						flax_key.replace("/"				,     "."      )
  		_UpperCAmelCase =						torch_key.replace(".kernel"				,     ".weight"      )
  		_UpperCAmelCase =						torch_key.replace("_"				,     ""      ).lower()
  		_UpperCAmelCase =						"box_head." + torch_key
  
  		if "weight" in torch_key and v.ndim == 2:
  			_UpperCAmelCase =						v.T
  
  		_UpperCAmelCase =						nn.Parameter(torch.from_numpy(UpperCamelCase__      )      )
  
  	# Copy flax params to PyTorch params
  	for name, param in new_params.items():
  		if name in pt_params.keys():
  			pt_params[name].copy_(UpperCamelCase__      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	for k, v in flax_attn_params.items():
  		if k.startswith("transformer"      ):
  			_UpperCAmelCase =						k.replace("transformer.resblocks"				,     "text_model.encoder.layers"      )
  		else:
  			_UpperCAmelCase =						k.replace("visual.transformer.resblocks"				,     "vision_model.encoder.layers"      )
  
  		_UpperCAmelCase =						torch_key.replace("attn"				,     "self_attn"      )
  		_UpperCAmelCase =						torch_key.replace("key"				,     "k_proj"      )
  		_UpperCAmelCase =						torch_key.replace("value"				,     "v_proj"      )
  		_UpperCAmelCase =						torch_key.replace("query"				,     "q_proj"      )
  		_UpperCAmelCase =						torch_key.replace("out"				,     "out_proj"      )
  
  		if "bias" in torch_key and v.ndim == 2:
  			_UpperCAmelCase =						v.shape[0] * v.shape[1]
  			_UpperCAmelCase =						v.reshape(UpperCamelCase__      )
  
  		if "weight" in torch_key and "out" in torch_key:
  			_UpperCAmelCase =						(v.shape[0] * v.shape[1], v.shape[2])
  			_UpperCAmelCase =						v.reshape(UpperCamelCase__      ).T
  
  		if "weight" in torch_key and "out" not in torch_key:
  			_UpperCAmelCase =						(v.shape[0], v.shape[1] * v.shape[2])
  			_UpperCAmelCase =						v.reshape(UpperCamelCase__      ).T
  
  		# Copy flax CLIP attn params to HF PyTorch params
  		_UpperCAmelCase =						torch.from_numpy(UpperCamelCase__      )
  		hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase__      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						{}
  	_UpperCAmelCase =						[]
  
  	for k, v in params.items():
  		if "attn." in k:
  			_UpperCAmelCase =						k[: k.rindex("attn."      ) + 5]
  			if base in processed_attn_layers:
  				continue
  
  			processed_attn_layers.append(UpperCamelCase__      )
  			_UpperCAmelCase =						params[base + "out.weight"].shape[-1]
  			_UpperCAmelCase =						params[base + "out.weight"].reshape(UpperCamelCase__				,     UpperCamelCase__      ).T
  			_UpperCAmelCase =						params[base + "out.bias"]
  		else:
  			_UpperCAmelCase =						v
  	return new_params
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						CLIP(**UpperCamelCase__      )
  	torch_model.eval()
  	_UpperCAmelCase =						torch_model.state_dict()
  
  	_UpperCAmelCase =						flatten_nested_dict(flax_params["backbone"]["clip"]      )
  	_UpperCAmelCase =						{}
  
  	for flax_key, v in flax_clip_params.items():
  		_UpperCAmelCase =						flax_key.replace("/"				,     "."      )
  		_UpperCAmelCase =						torch_key.replace("text.token_embedding.embedding"				,     "token_embedding.kernel"      )
  
  		if (
  		    torch_key.startswith("text.transformer"      )
  		    or torch_key.startswith("text.text_projection"      )
  		    or torch_key.startswith("text.ln_final"      )
  		    or torch_key.startswith("text.positional_embedding"      )
  		):
  			_UpperCAmelCase =						torch_key[5:]
  
  		_UpperCAmelCase =						torch_key.replace("text_projection.kernel"				,     "text_projection"      )
  		_UpperCAmelCase =						torch_key.replace("visual.proj.kernel"				,     "visual.proj"      )
  		_UpperCAmelCase =						torch_key.replace(".scale"				,     ".weight"      )
  		_UpperCAmelCase =						torch_key.replace(".kernel"				,     ".weight"      )
  
  		if "conv" in torch_key or "downsample.0.weight" in torch_key:
  			_UpperCAmelCase =						v.transpose(3				,     2				,     0				,     1      )
  
  		elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
  			# Fully connected layers are transposed, embeddings are not
  			_UpperCAmelCase =						v.T
  
  		_UpperCAmelCase =						v
  
  	_UpperCAmelCase =						_convert_attn_layers(UpperCamelCase__      )
  	new_torch_params.update(UpperCamelCase__      )
  	_UpperCAmelCase =						{}
  
  	# Copy flax CLIP backbone params to PyTorch params
  	for name, param in new_torch_params.items():
  		if name in torch_clip_params.keys():
  			_UpperCAmelCase =						torch.from_numpy(new_torch_params[name]      )
  			torch_clip_params[name].copy_(UpperCamelCase__      )
  		else:
  			_UpperCAmelCase =						param
  
  	return torch_clip_params, torch_model, attn_params
  
  
  
  
  @torch.no_grad()
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__=None      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						Repository(UpperCamelCase__				,     clone_from=f"google/{pytorch_dump_folder_path}"      )
  	repo.git_pull()
  
  	if config_path is not None:
  		_UpperCAmelCase =						OwlViTConfig.from_pretrained(UpperCamelCase__      )
  	else:
  		_UpperCAmelCase =						OwlViTConfig()
  
  	_UpperCAmelCase =						OwlViTModel(UpperCamelCase__      ).eval()
  	_UpperCAmelCase =						OwlViTForObjectDetection(UpperCamelCase__      ).eval()
  
  	copy_text_model_and_projection(UpperCamelCase__				,     UpperCamelCase__      )
  	copy_vision_model_and_projection(UpperCamelCase__				,     UpperCamelCase__      )
  	_UpperCAmelCase =						pt_backbone.logit_scale
  	copy_flax_attn_params(UpperCamelCase__				,     UpperCamelCase__      )
  
  	_UpperCAmelCase =						hf_backbone
  	copy_class_merge_token(UpperCamelCase__				,     UpperCamelCase__      )
  	copy_class_box_heads(UpperCamelCase__				,     UpperCamelCase__      )
  
  	# Save HF model
  	hf_model.save_pretrained(repo.local_dir      )
  
  	# Initialize image processor
  	_UpperCAmelCase =						OwlViTImageProcessor(
  	    size=config.vision_config.image_size				,     crop_size=config.vision_config.image_size      )
  	# Initialize tokenizer
  	_UpperCAmelCase =						CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"				,     pad_token="!"				,     model_max_length=16      )
  
  	# Initialize processor
  	_UpperCAmelCase =						OwlViTProcessor(image_processor=UpperCamelCase__				,     tokenizer=UpperCamelCase__      )
  	image_processor.save_pretrained(repo.local_dir      )
  	processor.save_pretrained(repo.local_dir      )
  
  	repo.git_add()
  	repo.git_commit("Upload model and processor"      )
  	repo.git_push()
  
  
  if __name__ == "__main__":
  			__magic_name__		       =				argparse.ArgumentParser()
  			# Required parameters
  			parser.add_argument(
  			    '''--owlvit_version''',
  			    default=None,
  			    type=str,
  			    required=True,
  			    help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
  			)
  			parser.add_argument(
  			    '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
  			)
  			parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
  			parser.add_argument(
  			    '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
  			)
  			__magic_name__		       =				parser.parse_args()
  
  			# Initialize PyToch clip model
  			__magic_name__		       =				args.owlvit_version
  			if model_name == "clip_b16":
  						__magic_name__		       =				CONFIGS['''vit_b16''']
  			elif model_name == "clip_b32":
  						__magic_name__		       =				CONFIGS['''vit_b32''']
  			elif model_name == "clip_l14":
  						__magic_name__		       =				CONFIGS['''vit_l14''']
  
  			# Load from checkpoint and convert params to float-32
  			__magic_name__		       =				checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
  			__magic_name__		       =				jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
  			del variables
  
  			# Convert CLIP backbone
  			__magic_name__       ,  __magic_name__       ,  __magic_name__		       =				convert_clip_backbone(flax_params, torch_config)
  
  			convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)
  
  
  
  
  
style_context: 
  """simple docstring"""
  import subprocess
  from typing import Union
  
  import numpy as np
  import requests
  
  from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
  from .base import PIPELINE_INIT_ARGS, Pipeline
  
  
  if is_torch_available():
  			from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  
  __magic_name__		       =				logging.get_logger(__name__)
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						f"{sampling_rate}"
  	_UpperCAmelCase =						"1"
  	_UpperCAmelCase =						"f32le"
  	_UpperCAmelCase =						[
  	    "ffmpeg",
  	    "-i",
  	    "pipe:0",
  	    "-ac",
  	    ac,
  	    "-ar",
  	    ar,
  	    "-f",
  	    format_for_conversion,
  	    "-hide_banner",
  	    "-loglevel",
  	    "quiet",
  	    "pipe:1",
  	]
  
  	try:
  		_UpperCAmelCase =						subprocess.Popen(UpperCamelCase__				,     stdin=subprocess.PIPE				,     stdout=subprocess.PIPE      )
  	except FileNotFoundError:
  		raise ValueError("ffmpeg was not found but is required to load audio files from filename"      )
  	_UpperCAmelCase =						ffmpeg_process.communicate(UpperCamelCase__      )
  	_UpperCAmelCase =						output_stream[0]
  
  	_UpperCAmelCase =						np.frombuffer(UpperCamelCase__				,     np.floataa      )
  	if audio.shape[0] == 0:
  		raise ValueError("Malformed soundfile"      )
  	return audio
  
  
  
  @add_end_docstrings(lowerCamelCase						)
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							def __init__(      self ,						*a_ ,						**a_   )							->							Union[str, Any]:
  								# Default, might be overriden by the model.config.
  								_UpperCAmelCase =						5
  								super().__init__(*a_ ,						**a_   )
  
  								if self.framework != "pt":
  									raise ValueError(f"The {self.__class__} is only available in PyTorch."   )
  
  								self.check_model_type(a_   )
  
  
  
  							def __call__(      self ,						a_ ,						**a_ ,						)							->							int:
  								return super().__call__(a_ ,						**a_   )
  
  
  
  							def       _a   (      self ,						a_=None ,						**a_   )							->							Dict:
  								# No parameters on this pipeline right now
  								_UpperCAmelCase =						{}
  								if top_k is not None:
  									if top_k > self.model.config.num_labels:
  										_UpperCAmelCase =						self.model.config.num_labels
  									_UpperCAmelCase =						top_k
  								return {}, {}, postprocess_params
  
  
  
  							def       _a   (      self ,						a_   )							->							str:
  								if isinstance(a_ ,						a_   ):
  									if inputs.startswith("http://"   ) or inputs.startswith("https://"   ):
  										# We need to actually check for a real protocol, otherwise it's impossible to use a local file
  										# like http_huggingface_co.png
  										_UpperCAmelCase =						requests.get(a_   ).content
  									else:
  										with open(a_ ,						"rb"   ) as f:
  											_UpperCAmelCase =						f.read()
  
  								if isinstance(a_ ,						a_   ):
  									_UpperCAmelCase =						ffmpeg_read(a_ ,						self.feature_extractor.sampling_rate   )
  
  								if isinstance(a_ ,						a_   ):
  									# Accepting `"array"` which is the key defined in `datasets` for
  									# better integration
  									if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
  										raise ValueError(
  										    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
  										    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
  										    "containing the sampling_rate associated with that array"   )
  
  									_UpperCAmelCase =						inputs.pop("raw" ,						a_   )
  									if _inputs is None:
  										# Remove path which will not be used from `datasets`.
  										inputs.pop("path" ,						a_   )
  										_UpperCAmelCase =						inputs.pop("array" ,						a_   )
  									_UpperCAmelCase =						inputs.pop("sampling_rate"   )
  									_UpperCAmelCase =						_inputs
  									if in_sampling_rate != self.feature_extractor.sampling_rate:
  										import torch
  
  										if is_torchaudio_available():
  											from torchaudio import functional as F
  										else:
  											raise ImportError(
  											    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
  											    "The torchaudio package can be installed through: `pip install torchaudio`."   )
  
  										_UpperCAmelCase =						F.resample(
  										    torch.from_numpy(a_   ) ,						a_ ,						self.feature_extractor.sampling_rate   ).numpy()
  
  								if not isinstance(a_ ,						np.ndarray   ):
  									raise ValueError("We expect a numpy ndarray as input"   )
  								if len(inputs.shape   ) != 1:
  									raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"   )
  
  								_UpperCAmelCase =						self.feature_extractor(
  								    a_ ,						sampling_rate=self.feature_extractor.sampling_rate ,						return_tensors="pt"   )
  								return processed
  
  
  
  							def       _a   (      self ,						a_   )							->							List[str]:
  								_UpperCAmelCase =						self.model(**a_   )
  								return model_outputs
  
  
  
  							def       _a   (      self ,						a_ ,						a_=5   )							->							int:
  								_UpperCAmelCase =						model_outputs.logits[0].softmax(-1   )
  								_UpperCAmelCase ,      _UpperCAmelCase =						probs.topk(a_   )
  
  								_UpperCAmelCase =						scores.tolist()
  								_UpperCAmelCase =						ids.tolist()
  
  								_UpperCAmelCase =						[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a_ ,						a_   )]
  
  								return labels
  
  
  
  
  