






'''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()
a_					=	logging.get_logger(__name__)





def 	_a(					UpperCamelCase__			:  Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[int]													=SwinConfig(
          embed_dim=1_9_2,		depths=(2, 2, 1_8, 2),		num_heads=(6, 1_2, 2_4, 4_8),		window_size=1_2,		out_features=['''stage2''', '''stage3''', '''stage4'''],		)

      SCREAMING_SNAKE_CASE__       :			str													=DetaConfig(
          backbone_config=lowercase__,		num_queries=9_0_0,		encoder_ffn_dim=2_0_4_8,		decoder_ffn_dim=2_0_4_8,		num_feature_levels=5,		assign_first_stage=lowercase__,		with_box_refine=lowercase__,		two_stage=lowercase__,		)

      # set labels
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''huggingface/label-files'''
      if "o365" in model_name:
            SCREAMING_SNAKE_CASE__       :			Dict													=3_6_6
            SCREAMING_SNAKE_CASE__       :			int													='''object365-id2label.json'''
      else:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=9_1
            SCREAMING_SNAKE_CASE__       :			Tuple													='''coco-detection-id2label.json'''

      SCREAMING_SNAKE_CASE__       :			int													=num_labels
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=json.load(open(cached_download(hf_hub_url(lowercase__,		lowercase__,		repo_type='''dataset'''							)							),		'''r'''							)							)
      SCREAMING_SNAKE_CASE__       :			int													={int(lowercase__							): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=idalabel
      SCREAMING_SNAKE_CASE__       :			Dict													={v: k for k, v in idalabel.items()}

      return config





def 	_a(					UpperCamelCase__			:  Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=[]

      # 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 	_a(					UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=dct.pop(lowercase__							)
      SCREAMING_SNAKE_CASE__       :			Any													=val





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=[int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
      for i in range(len(backbone_config.depths							)							):
            SCREAMING_SNAKE_CASE__       :			List[Any]													=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)
                  SCREAMING_SNAKE_CASE__       :			Tuple													=state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"							)
                  SCREAMING_SNAKE_CASE__       :			Tuple													=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
                  SCREAMING_SNAKE_CASE__       :			Any													=in_proj_weight[:dim, :]
                  SCREAMING_SNAKE_CASE__       :			Dict													=in_proj_bias[: dim]
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=in_proj_weight[
                      dim : dim * 2, :
                  ]
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=in_proj_bias[
                      dim : dim * 2
                  ]
                  SCREAMING_SNAKE_CASE__       :			List[str]													=in_proj_weight[
                      -dim :, :
                  ]
                  SCREAMING_SNAKE_CASE__       :			Optional[int]													=in_proj_bias[-dim :]
                  # fmt: on





def 	_a(					UpperCamelCase__			:  Any,		UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=config.d_model
      for i in range(config.decoder_layers							):
            # read in weights + bias of input projection layer of self-attention
            SCREAMING_SNAKE_CASE__       :			List[Any]													=state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"							)
            SCREAMING_SNAKE_CASE__       :			Dict													=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
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=in_proj_weight[:hidden_size, :]
            SCREAMING_SNAKE_CASE__       :			str													=in_proj_bias[:hidden_size]
            SCREAMING_SNAKE_CASE__       :			Dict													=in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE__       :			Dict													=in_proj_bias[hidden_size : hidden_size * 2]
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=in_proj_weight[-hidden_size:, :]
            SCREAMING_SNAKE_CASE__       :			List[Any]													=in_proj_bias[-hidden_size:]





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__       :			Dict													=Image.open(requests.get(lowercase__,		stream=lowercase__							).raw							)

      return im





@torch.no_grad()
def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  str,		UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													=get_deta_config(lowercase__							)

      # load original state dict
      if model_name == "deta-swin-large":
            SCREAMING_SNAKE_CASE__       :			List[str]													=hf_hub_download(repo_id='''nielsr/deta-checkpoints''',		filename='''adet_swin_ft.pth'''							)
      elif model_name == "deta-swin-large-o365":
            SCREAMING_SNAKE_CASE__       :			Dict													=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"							)

      SCREAMING_SNAKE_CASE__       :			Dict													=torch.load(lowercase__,		map_location='''cpu'''							)['''model''']

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

      # rename keys
      SCREAMING_SNAKE_CASE__       :			List[Any]													=create_rename_keys(lowercase__							)
      for src, dest in rename_keys:
            rename_key(lowercase__,		lowercase__,		lowercase__							)
      read_in_swin_q_k_v(lowercase__,		config.backbone_config							)
      read_in_decoder_q_k_v(lowercase__,		lowercase__							)

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=state_dict.pop(lowercase__							)
                  SCREAMING_SNAKE_CASE__       :			int													=val
            if "input_proj" in key:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=state_dict.pop(lowercase__							)
                  SCREAMING_SNAKE_CASE__       :			Any													=val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=state_dict.pop(lowercase__							)
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=val

    # finally, create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE__       :			List[Any]													=DetaForObjectDetection(lowercase__							)
      model.load_state_dict(lowercase__							)
      model.eval()

      SCREAMING_SNAKE_CASE__       :			Tuple													='''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(lowercase__							)

      # load image processor
      SCREAMING_SNAKE_CASE__       :			List[str]													=DetaImageProcessor(format='''coco_detection'''							)

      # verify our conversion on image
      SCREAMING_SNAKE_CASE__       :			Any													=prepare_img()
      SCREAMING_SNAKE_CASE__       :			Tuple													=processor(images=lowercase__,		return_tensors='''pt'''							)
      SCREAMING_SNAKE_CASE__       :			List[str]													=encoding['''pixel_values''']
      SCREAMING_SNAKE_CASE__       :			Tuple													=model(pixel_values.to(lowercase__							)							)

      # verify logits
      print('''Logits:''',		outputs.logits[0, :3, :3]							)
      print('''Boxes:''',		outputs.pred_boxes[0, :3, :3]							)
      if model_name == "deta-swin-large":
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.tensor(
                [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]							)
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]							)
      elif model_name == "deta-swin-large-o365":
            SCREAMING_SNAKE_CASE__       :			Any													=torch.tensor(
                [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]							)
            SCREAMING_SNAKE_CASE__       :			int													=torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]							)
      assert torch.allclose(outputs.logits[0, :3, :3],		expected_logits.to(lowercase__							),		atol=1e-4							)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3],		expected_boxes.to(lowercase__							),		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(lowercase__							).mkdir(exist_ok=lowercase__							)
            model.save_pretrained(lowercase__							)
            processor.save_pretrained(lowercase__							)

      # 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__":
  a_					=	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.'
  )
  a_					=	parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""facebook/bart-large-mnli"""
 snake_case_												=							(
     """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
     """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
     """It returns the most likely label in the list of provided `labels` for the input text."""
 )
 snake_case_												=							"""text_classifier"""
 snake_case_												=							AutoTokenizer
 snake_case_												=							AutoModelForSequenceClassification

 snake_case_												=							["""text""", ["""text"""]]
 snake_case_												=							["""text"""]




 def        __magic_name__		( self		:							Any )		->  Any:
       super().setup()
       SCREAMING_SNAKE_CASE__       :			int													=self.model.config
       SCREAMING_SNAKE_CASE__       :			int													=-1
       for idx, label in config.idalabel.items():
             if label.lower().startswith('''entail''' ):
                   SCREAMING_SNAKE_CASE__       :			Any													=int(__lowercase )
       if self.entailment_id == -1:
             raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''' )




 def        __magic_name__		( self		:							str   , __lowercase		:							Any   , __lowercase		:							Optional[Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Dict													=labels
       return self.pre_processor(
           [text] * len(__lowercase )   , [F"This example is {label}" for label in labels]   , return_tensors='''pt'''   , padding='''max_length'''   , )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							str )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=outputs.logits
       SCREAMING_SNAKE_CASE__       :			Any													=torch.argmax(logits[:, 2] ).item()
       return self._labels[label_id]