






'''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()
UpperCAmelCase__						:List[Any]          =			logging.get_logger(__name__)
def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				SwinConfig.from_pretrained(
         """microsoft/swin-tiny-patch4-window7-224""",						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     )
     __lowerCamelCase						:       Union[str, Any]				   =				MaskFormerConfig(backbone_config=_lowercase     )

     __lowerCamelCase						:       List[str]				   =				"""huggingface/label-files"""
     if "ade20k-full" in model_name:
          # this should be ok
          __lowerCamelCase						:       int				   =				847
          __lowerCamelCase						:       List[Any]				   =				"""maskformer-ade20k-full-id2label.json"""
     elif "ade" in model_name:
          # this should be ok
          __lowerCamelCase						:       List[str]				   =				150
          __lowerCamelCase						:       List[Any]				   =				"""ade20k-id2label.json"""
     elif "coco-stuff" in model_name:
          # this should be ok
          __lowerCamelCase						:       Any				   =				171
          __lowerCamelCase						:       Union[str, Any]				   =				"""maskformer-coco-stuff-id2label.json"""
     elif "coco" in model_name:
          # TODO
          __lowerCamelCase						:       Dict				   =				133
          __lowerCamelCase						:       Tuple				   =				"""coco-panoptic-id2label.json"""
     elif "cityscapes" in model_name:
          # this should be ok
          __lowerCamelCase						:       Union[str, Any]				   =				19
          __lowerCamelCase						:       Optional[int]				   =				"""cityscapes-id2label.json"""
     elif "vistas" in model_name:
          # this should be ok
          __lowerCamelCase						:       Tuple				   =				65
          __lowerCamelCase						:       Tuple				   =				"""mapillary-vistas-id2label.json"""

     __lowerCamelCase						:       Optional[Any]				   =				json.load(open(hf_hub_download(_lowercase,						_lowercase,						repo_type="""dataset"""     ),						"""r"""     )     )
     __lowerCamelCase						:       List[str]				   =				{int(_lowercase     ): v for k, v in idalabel.items()}

     return config
def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				[]
     # 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     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				dct.pop(_lowercase     )
     __lowerCamelCase						:       Dict				   =				val
def     __lowercase  (_lowercase,						_lowercase     )   ->     Dict:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
     for i in range(len(backbone_config.depths     )     ):
          __lowerCamelCase						:       Optional[int]				   =				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)
               __lowerCamelCase						:       Optional[Any]				   =				state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"     )
               __lowerCamelCase						:       Union[str, Any]				   =				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
               __lowerCamelCase						:       Any				   =				in_proj_weight[:dim, :]
               __lowerCamelCase						:       str				   =				in_proj_bias[: dim]
               __lowerCamelCase						:       Dict				   =				in_proj_weight[
                   dim : dim * 2, :
               ]
               __lowerCamelCase						:       Union[str, Any]				   =				in_proj_bias[
                   dim : dim * 2
               ]
               __lowerCamelCase						:       Dict				   =				in_proj_weight[
                   -dim :, :
               ]
               __lowerCamelCase						:       Optional[Any]				   =				in_proj_bias[-dim :]
               # fmt: on
def     __lowercase  (_lowercase,						_lowercase     )   ->     Optional[int]:


     """simple docstring"""
     # fmt: off
     __lowerCamelCase						:       List[str]				   =				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)
          __lowerCamelCase						:       Dict				   =				state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"     )
          __lowerCamelCase						:       str				   =				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
          __lowerCamelCase						:       Dict				   =				in_proj_weight[: hidden_size, :]
          __lowerCamelCase						:       Union[str, Any]				   =				in_proj_bias[:config.hidden_size]
          __lowerCamelCase						:       Dict				   =				in_proj_weight[hidden_size : hidden_size * 2, :]
          __lowerCamelCase						:       List[Any]				   =				in_proj_bias[hidden_size : hidden_size * 2]
          __lowerCamelCase						:       Optional[int]				   =				in_proj_weight[-hidden_size :, :]
          __lowerCamelCase						:       Optional[Any]				   =				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)
          __lowerCamelCase						:       Union[str, Any]				   =				state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"     )
          __lowerCamelCase						:       Tuple				   =				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
          __lowerCamelCase						:       Tuple				   =				in_proj_weight[: hidden_size, :]
          __lowerCamelCase						:       Optional[Any]				   =				in_proj_bias[:config.hidden_size]
          __lowerCamelCase						:       Optional[Any]				   =				in_proj_weight[hidden_size : hidden_size * 2, :]
          __lowerCamelCase						:       Any				   =				in_proj_bias[hidden_size : hidden_size * 2]
          __lowerCamelCase						:       Any				   =				in_proj_weight[-hidden_size :, :]
          __lowerCamelCase						:       Any				   =				in_proj_bias[-hidden_size :]
     # fmt: on
def     __lowercase  ()   ->     torch.Tensor:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __lowerCamelCase						:       Dict				   =				Image.open(requests.get(_lowercase,						stream=_lowercase     ).raw     )
     return im
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase = False     )   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				get_maskformer_config(_lowercase     )

     # load original state_dict
     with open(_lowercase,						"""rb"""     ) as f:
          __lowerCamelCase						:       Any				   =				pickle.load(_lowercase     )
     __lowerCamelCase						:       Tuple				   =				data["""model"""]

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

     # rename keys
     __lowerCamelCase						:       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     )

     # update to torch tensors
     for key, value in state_dict.items():
          __lowerCamelCase						:       Dict				   =				torch.from_numpy(_lowercase     )

     # load 🤗 model
     __lowerCamelCase						:       Optional[Any]				   =				MaskFormerForInstanceSegmentation(_lowercase     )
     model.eval()

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

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

     # verify results
     __lowerCamelCase						:       Optional[Any]				   =				prepare_img()
     if "vistas" in model_name:
          __lowerCamelCase						:       str				   =				65
     elif "cityscapes" in model_name:
          __lowerCamelCase						:       Optional[Any]				   =				65_535
     else:
          __lowerCamelCase						:       Dict				   =				255
     __lowerCamelCase						:       Dict				   =				True if """ade""" in model_name else False
     __lowerCamelCase						:       List[str]				   =				MaskFormerImageProcessor(ignore_index=_lowercase,						reduce_labels=_lowercase     )

     __lowerCamelCase						:       Tuple				   =				image_processor(_lowercase,						return_tensors="""pt"""     )

     __lowerCamelCase						:       Any				   =				model(**_lowercase     )

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

     if model_name == "maskformer-swin-tiny-ade":
          __lowerCamelCase						:       Any				   =				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],						_lowercase,						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(_lowercase     ).mkdir(exist_ok=_lowercase     )
          model.save_pretrained(_lowercase     )
          image_processor.save_pretrained(_lowercase     )

     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__":
   UpperCAmelCase__						:Union[str, Any]          =			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."""
   )

   UpperCAmelCase__						:Optional[Any]          =			parser.parse_args()
   convert_maskformer_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )











'''simple docstring'''

from ...processing_utils import ProcessorMixin







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     List[Any]   		=			'SpeechT5FeatureExtractor'
       snake_case__    :     Union[str, Any]   		=			'SpeechT5Tokenizer'




       def __init__(					self      : Union[str, Any]	,    A__      : str	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__(A__	,    A__					)




       def __call__(					self      : List[Any]	,    *A__      : Any	,    **A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				kwargs.pop("""audio"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""text"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""text_target"""	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				kwargs.pop("""audio_target"""	,    A__					)
            __lowerCamelCase						:       Any				   =				kwargs.pop("""sampling_rate"""	,    A__					)

            if audio is not None and text is not None:
                 raise ValueError(
                     """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""					)
            if audio_target is not None and text_target is not None:
                 raise ValueError(
                     """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""					)
            if audio is None and audio_target is None and text is None and text_target is None:
                 raise ValueError(
                     """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""					)

            if audio is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(A__	,    *A__	,    sampling_rate=A__	,    **A__					)
            elif text is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.tokenizer(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Tuple				   =				None

            if audio_target is not None:
                 __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor(audio_target=A__	,    *A__	,    sampling_rate=A__	,    **A__					)
                 __lowerCamelCase						:       Union[str, Any]				   =				targets["""input_values"""]
            elif text_target is not None:
                 __lowerCamelCase						:       Tuple				   =				self.tokenizer(A__	,    **A__					)
                 __lowerCamelCase						:       List[str]				   =				targets["""input_ids"""]
            else:
                 __lowerCamelCase						:       Dict				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       str				   =				labels

                 __lowerCamelCase						:       int				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       Optional[int]				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Any	,    *A__      : Union[str, Any]	,    **A__      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				kwargs.pop("""input_values"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""input_ids"""	,    A__					)
            __lowerCamelCase						:       List[Any]				   =				kwargs.pop("""labels"""	,    A__					)

            if input_values is not None and input_ids is not None:
                 raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""					)
            if input_values is None and input_ids is None and labels is None:
                 raise ValueError(
                     """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""					)

            if input_values is not None:
                 __lowerCamelCase						:       List[Any]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
            elif input_ids is not None:
                 __lowerCamelCase						:       Any				   =				self.tokenizer.pad(A__	,    **A__					)
            else:
                 __lowerCamelCase						:       Any				   =				None

            if labels is not None:
                 if "input_ids" in labels or (isinstance(A__	,    A__					) and "input_ids" in labels[0]):
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer.pad(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				targets["""input_ids"""]
                 else:
                      __lowerCamelCase						:       Optional[int]				   =				self.feature_extractor.feature_size
                      __lowerCamelCase						:       Any				   =				self.feature_extractor.num_mel_bins
                      __lowerCamelCase						:       List[str]				   =				self.feature_extractor.pad(A__	,    *A__	,    **A__					)
                      __lowerCamelCase						:       Dict				   =				feature_size_hack
                      __lowerCamelCase						:       List[Any]				   =				targets["""input_values"""]
            else:
                 __lowerCamelCase						:       Optional[int]				   =				None

            if inputs is None:
                 return targets

            if targets is not None:
                 __lowerCamelCase						:       Dict				   =				labels

                 __lowerCamelCase						:       List[str]				   =				targets.get("""attention_mask"""					)
                 if decoder_attention_mask is not None:
                      __lowerCamelCase						:       int				   =				decoder_attention_mask

            return inputs




       def 					a_					(					self      : Optional[Any]	,    *A__      : List[Any]	,    **A__      : Optional[int]					):


            """simple docstring"""







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




       def 					a_					(					self      : Any	,    *A__      : int	,    **A__      : Dict					):


            """simple docstring"""







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



