





'''simple docstring'''







import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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

def    __UpperCamelCase	(	a    : str     ) ->int:
 snake_case				=			SwinConfig.from_pretrained(
     '''microsoft/swin-tiny-patch4-window7-224'''					,   out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     )
 snake_case				=			MaskFormerConfig(backbone_config=a     )

 snake_case				=			'''huggingface/label-files'''
 if "ade20k-full" in model_name:
  # this should be ok
  snake_case				=			847
  snake_case				=			'''maskformer-ade20k-full-id2label.json'''
 elif "ade" in model_name:
  # this should be ok
  snake_case				=			150
  snake_case				=			'''ade20k-id2label.json'''
 elif "coco-stuff" in model_name:
  # this should be ok
  snake_case				=			171
  snake_case				=			'''maskformer-coco-stuff-id2label.json'''
 elif "coco" in model_name:
  # TODO
  snake_case				=			133
  snake_case				=			'''coco-panoptic-id2label.json'''
 elif "cityscapes" in model_name:
  # this should be ok
  snake_case				=			19
  snake_case				=			'''cityscapes-id2label.json'''
 elif "vistas" in model_name:
  # this should be ok
  snake_case				=			65
  snake_case				=			'''mapillary-vistas-id2label.json'''

 snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
 snake_case				=			{int(a     ): v for k, v in idalabel.items()}

 return config

def    __UpperCamelCase	(	a    : List[str]     ) ->Optional[int]:
 snake_case				=			[]
 # stem
 # fmt: off
 rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')     )
 rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')     )
 rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')     )
 rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
   rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

  if i < 3:
   rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")     )
   rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")     )
   rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")     )
  rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")     )
  rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")     )

 # FPN
 rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')     )
 rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')     )
 rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')     )
 for source_index, target_index in zip(range(3					,   0					,   -1     )					,   range(0					,   3     )     ):
  rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")     )
  rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")     )
  rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")     )
  rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")     )
  rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")     )
  rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")     )
 rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')     )
 rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')     )

 # Transformer decoder
 for idx in range(config.decoder_config.decoder_layers     ):
  # self-attention out projection
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")     )
  # cross-attention out projection
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")     )
  # MLP 1
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")     )
  # MLP 2
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")     )
  # layernorm 1 (self-attention layernorm)
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")     )
  # layernorm 2 (cross-attention layernorm)
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")     )
  # layernorm 3 (final layernorm)
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")     )

 rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')     )
 rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')     )

 # heads on top
 rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')     )

 rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')     )
 rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')     )

 rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')     )
 rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')     )

 for i in range(3     ):
  rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")     )
  rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")     )
 # fmt: on

 return rename_keys

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : List[str]					,   a    : int     ) ->Tuple:
 snake_case				=			dct.pop(a     )
 snake_case				=			val

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : Tuple     ) ->str:
 snake_case				=			[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
 for i in range(len(backbone_config.depths     )     ):
  snake_case				=			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)
   snake_case				=			state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
   snake_case				=			state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
   # next, add query, keys and values (in that order) to the state dict
   snake_case				=			in_proj_weight[:dim, :]
   snake_case				=			in_proj_bias[: dim]
   snake_case				=			in_proj_weight[
       dim : dim * 2, :
   ]
   snake_case				=			in_proj_bias[
       dim : dim * 2
   ]
   snake_case				=			in_proj_weight[
       -dim :, :
   ]
   snake_case				=			in_proj_bias[-dim :]
   # fmt: on

def    __UpperCamelCase	(	a    : List[Any]					,   a    : int     ) ->Optional[int]:
 # fmt: off
 snake_case				=			config.decoder_config.hidden_size
 for idx in range(config.decoder_config.decoder_layers     ):
  # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
  snake_case				=			state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""     )
  snake_case				=			state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""     )
  # next, add query, keys and values (in that order) to the state dict
  snake_case				=			in_proj_weight[: hidden_size, :]
  snake_case				=			in_proj_bias[:config.hidden_size]
  snake_case				=			in_proj_weight[hidden_size : hidden_size * 2, :]
  snake_case				=			in_proj_bias[hidden_size : hidden_size * 2]
  snake_case				=			in_proj_weight[-hidden_size :, :]
  snake_case				=			in_proj_bias[-hidden_size :]
  # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
  snake_case				=			state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""     )
  snake_case				=			state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""     )
  # next, add query, keys and values (in that order) to the state dict
  snake_case				=			in_proj_weight[: hidden_size, :]
  snake_case				=			in_proj_bias[:config.hidden_size]
  snake_case				=			in_proj_weight[hidden_size : hidden_size * 2, :]
  snake_case				=			in_proj_bias[hidden_size : hidden_size * 2]
  snake_case				=			in_proj_weight[-hidden_size :, :]
  snake_case				=			in_proj_bias[-hidden_size :]
 # fmt: on

def    __UpperCamelCase	(	) ->torch.Tensor:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )
 return im

@torch.no_grad()
def    __UpperCamelCase	(	a    : str					,   a    : str					,   a    : str					,   a    : bool = False     ) ->int:
 snake_case				=			get_maskformer_config(a     )

 # load original state_dict
 with open(a					,   '''rb'''     ) as f:
  snake_case				=			pickle.load(a     )
 snake_case				=			data['''model''']

 # for name, param in state_dict.items():
 #     print(name, param.shape)

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

 # update to torch tensors
 for key, value in state_dict.items():
  snake_case				=			torch.from_numpy(a     )

 # load 🤗 model
 snake_case				=			MaskFormerForInstanceSegmentation(a     )
 model.eval()

 for name, param in model.named_parameters():
  print(a					,   param.shape     )

 snake_case				,		snake_case				=			model.load_state_dict(a					,   strict=a     )
 assert missing_keys == [
     "model.pixel_level_module.encoder.model.layernorm.weight",
     "model.pixel_level_module.encoder.model.layernorm.bias",
 ]
 assert len(a     ) == 0, f"""Unexpected keys: {unexpected_keys}"""

 # verify results
 snake_case				=			prepare_img()
 if "vistas" in model_name:
  snake_case				=			65
 elif "cityscapes" in model_name:
  snake_case				=			6_5535
 else:
  snake_case				=			255
 snake_case				=			True if '''ade''' in model_name else False
 snake_case				=			MaskFormerImageProcessor(ignore_index=a					,   reduce_labels=a     )

 snake_case				=			image_processor(a					,   return_tensors='''pt'''     )

 snake_case				=			model(**a     )

 print('''Logits:'''					,   outputs.class_queries_logits[0, :3, :3]     )

 if model_name == "maskformer-swin-tiny-ade":
  snake_case				=			torch.tensor(
      [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]     )
 assert torch.allclose(outputs.class_queries_logits[0, :3, :3]					,   a					,   atol=1e-4     )
 print('''Looks ok!'''     )

 if pytorch_dump_folder_path is not None:
  print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
  Path(a     ).mkdir(exist_ok=a     )
  model.save_pretrained(a     )
  image_processor.save_pretrained(a     )

 if push_to_hub:
  print('''Pushing model and image processor to the hub...'''     )
  model.push_to_hub(f"""nielsr/{model_name}"""     )
  image_processor.push_to_hub(f"""nielsr/{model_name}"""     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='maskformer-swin-tiny-ade',
       type=str,
       help=('Name of the MaskFormer model you\'d like to convert',),
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
       type=str,
       help='Path to the original state dict (.pth file).',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   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_maskformer_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )








'''simple docstring'''







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''char'''
     _UpperCAmelCase										=					'''bpe'''
     _UpperCAmelCase										=					'''wp'''




_lowercase       						=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					['''image_processor''', '''char_tokenizer''']
     _UpperCAmelCase										=					'''ViTImageProcessor'''
     _UpperCAmelCase										=					'''MgpstrTokenizer'''







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[Any]:
      snake_case				=			None
      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''    ,   A__    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      snake_case				=			image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''    )
      if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''    )

      snake_case				=			tokenizer
      snake_case				=			AutoTokenizer.from_pretrained('''gpt2'''    )
      snake_case				=			AutoTokenizer.from_pretrained('''bert-base-uncased'''    )

      super().__init__(A__    ,   A__    )







     def __call__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[str]:
      if images is None and text is None:
       raise ValueError('''You need to specify either an `images` or `text` input to process.'''    )

      if images is not None:
       snake_case				=			self.image_processor(A__    ,   return_tensors=A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.char_tokenizer(A__    ,   return_tensors=A__    ,   **A__    )

      if text is None:
       return inputs
      elif images is None:
       return encodings
      else:
       snake_case				=			encodings['''input_ids''']
       return inputs







     def     UpperCamelCase			( self    ,   A__    ) ->	Dict:
      snake_case				,		snake_case				,		snake_case				=			sequences
      snake_case				=			char_preds.size(0    )

      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''char'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''bpe'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''wp'''    )

      snake_case				=			[]
      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
       snake_case				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
       snake_case				=			scores.index(max(A__    )    )
       final_strs.append(strs[max_score_index]    )
       final_scores.append(scores[max_score_index]    )

      snake_case				=			{}
      snake_case				=			final_strs
      snake_case				=			final_scores
      snake_case				=			char_strs
      snake_case				=			bpe_strs
      snake_case				=			wp_strs
      return out







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Optional[Any]:
      if format == DecodeType.CHARACTER:
       snake_case				=			self.char_decode
       snake_case				=			1
       snake_case				=			'''[s]'''
      elif format == DecodeType.BPE:
       snake_case				=			self.bpe_decode
       snake_case				=			2
       snake_case				=			'''#'''
      elif format == DecodeType.WORDPIECE:
       snake_case				=			self.wp_decode
       snake_case				=			1_02
       snake_case				=			'''[SEP]'''
      else:
       raise ValueError(F"""Format {format} is not supported."""    )

      snake_case				,		snake_case				=			[], []
      snake_case				=			pred_logits.size(0    )
      snake_case				=			pred_logits.size(1    )
      snake_case				,		snake_case				=			pred_logits.topk(1    ,   dim=-1    ,   largest=A__    ,   sorted=A__    )
      snake_case				=			preds_index.view(-1    ,   A__    )[:, 1:]
      snake_case				=			decoder(A__    )
      snake_case				,		snake_case				=			torch.nn.functional.softmax(A__    ,   dim=2    ).max(dim=2    )
      snake_case				=			preds_max_prob[:, 1:]

      for index in range(A__    ):
       snake_case				=			preds_str[index].find(A__    )
       snake_case				=			preds_str[index][:pred_eos]
       snake_case				=			preds_index[index].cpu().tolist()
       snake_case				=			pred_index.index(A__    ) if eos_token in pred_index else -1
       snake_case				=			preds_max_prob[index][: pred_eos_index + 1]
       snake_case				=			pred_max_prob.cumprod(dim=0    )[-1] if pred_max_prob.nelement() != 0 else 0.0
       dec_strs.append(A__    )
       conf_scores.append(A__    )

      return dec_strs, conf_scores







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.char_tokenizer.batch_decode(A__    )]
      return decode_strs







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      return self.bpe_tokenizer.batch_decode(A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.wp_tokenizer.batch_decode(A__    )]
      return decode_strs

