import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_lowercase							:      List[Any]      							=[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

_lowercase							:      str      							=[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

_lowercase							:      Optional[int]      							=[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
  # loop over downblocks/upblocks

  for j in range(2):
    # loop over resnets/attentions for downblocks
    _lowercase							:      Optional[Any]      							=f'''down_blocks.{i}.resnets.{j}.'''
    _lowercase							:      Optional[Any]      							=f'''input_blocks.{3*i + j + 1}.0.'''
    unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

    if i < 3:
      # no attention layers in down_blocks.3
      _lowercase							:      Tuple      							=f'''down_blocks.{i}.attentions.{j}.'''
      _lowercase							:      Optional[Any]      							=f'''input_blocks.{3*i + j + 1}.1.'''
      unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

  for j in range(3):
    # loop over resnets/attentions for upblocks
    _lowercase							:      Optional[int]      							=f'''up_blocks.{i}.resnets.{j}.'''
    _lowercase							:      int      							=f'''output_blocks.{3*i + j}.0.'''
    unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

    if i > 0:
      # no attention layers in up_blocks.0
      _lowercase							:      Optional[Any]      							=f'''up_blocks.{i}.attentions.{j}.'''
      _lowercase							:      Any      							=f'''output_blocks.{3*i + j}.1.'''
      unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

  if i < 3:
    # no downsample in down_blocks.3
    _lowercase							:      List[Any]      							=f'''down_blocks.{i}.downsamplers.0.conv.'''
    _lowercase							:      Any      							=f'''input_blocks.{3*(i+1)}.0.op.'''
    unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

    # no upsample in up_blocks.3
    _lowercase							:      Any      							=f'''up_blocks.{i}.upsamplers.0.'''
    _lowercase							:      Any      							=f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
    unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowercase							:      List[str]      							='''mid_block.attentions.0.'''
_lowercase							:      Optional[Any]      							='''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
  _lowercase							:      Optional[int]      							=f'''mid_block.resnets.{j}.'''
  _lowercase							:      Union[str, Any]      							=f'''middle_block.{2*j}.'''
  unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def A__  (			lowercase:     str					)				->		str:
       # buyer beware: this is a *brittle* function,
       # and correct output requires that all of these pieces interact in
       # the exact order in which I have arranged them.
       A						:     Optional[int]         ={k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              A						:     Optional[Any]         =sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            A						:     str         =v.replace(lowercase,		lowercase					)
                     A						:     Union[str, Any]         =v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     A						:     int         =v.replace(lowercase,		lowercase					)
              A						:     Tuple         =v
       A						:     List[Any]         ={v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


# ================#
# VAE Conversion #
# ================#

_lowercase							:      str      							=[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
  # down_blocks have two resnets
  for j in range(2):
    _lowercase							:      int      							=f'''encoder.down_blocks.{i}.resnets.{j}.'''
    _lowercase							:      Any      							=f'''encoder.down.{i}.block.{j}.'''
    vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

  if i < 3:
    _lowercase							:      Dict      							=f'''down_blocks.{i}.downsamplers.0.'''
    _lowercase							:      str      							=f'''down.{i}.downsample.'''
    vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

    _lowercase							:      int      							=f'''up_blocks.{i}.upsamplers.0.'''
    _lowercase							:      int      							=f'''up.{3-i}.upsample.'''
    vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

  # up_blocks have three resnets
  # also, up blocks in hf are numbered in reverse from sd
  for j in range(3):
    _lowercase							:      Optional[Any]      							=f'''decoder.up_blocks.{i}.resnets.{j}.'''
    _lowercase							:      int      							=f'''decoder.up.{3-i}.block.{j}.'''
    vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
  _lowercase							:      List[str]      							=f'''mid_block.resnets.{i}.'''
  _lowercase							:      Any      							=f'''mid.block_{i+1}.'''
  vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_lowercase							:      Tuple      							=[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]



def A__  (			lowercase:     Optional[int]					)				->		Tuple:
       # convert HF linear weights to SD conv2d weights
       return w.reshape(*w.shape,		1,		1					)



def A__  (			lowercase:     List[Any]					)				->		List[str]:
       A						:     Optional[int]         ={k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     A						:     int         =v.replace(lowercase,		lowercase					)
              A						:     List[Any]         =v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            A						:     Union[str, Any]         =v.replace(lowercase,		lowercase					)
                     A						:     Dict         =v
       A						:     Union[str, Any]         ={v: vae_state_dict[k] for k, v in mapping.items()}
       A						:     Dict         =['q', 'k', 'v', 'proj_out']
       for k, v in new_state_dict.items():
              for weight_name in weights_to_convert:
                     if F'mid.attn_1.{weight_name}.weight' in k:
                            print(F'Reshaping {k} for SD format'					)
                            A						:     Optional[Any]         =reshape_weight_for_sd(lowercase					)
       return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_lowercase							:      Dict      							=[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
_lowercase							:      str      							={re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowercase							:      int      							=re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowercase							:      Optional[int]      							={'''q''': 0, '''k''': 1, '''v''': 2}



def A__  (			lowercase:     Tuple					)				->		Any:
       A						:     Any         ={}
       A						:     Dict         ={}
       A						:     Optional[int]         ={}
       for k, v in text_enc_dict.items():
              if (
                  k.endswith('.self_attn.q_proj.weight'					)
                  or k.endswith('.self_attn.k_proj.weight'					)
                  or k.endswith('.self_attn.v_proj.weight'					)
              ):
                     A						:     Tuple         =k[: -len('.q_proj.weight'					)]
                     A						:     Optional[int]         =k[-len('q_proj.weight'					)]
                     if k_pre not in capture_qkv_weight:
                            A						:     Optional[Any]         =[None, None, None]
                     A						:     Tuple         =v
                     continue

              if (
                  k.endswith('.self_attn.q_proj.bias'					)
                  or k.endswith('.self_attn.k_proj.bias'					)
                  or k.endswith('.self_attn.v_proj.bias'					)
              ):
                     A						:     int         =k[: -len('.q_proj.bias'					)]
                     A						:     List[Any]         =k[-len('q_proj.bias'					)]
                     if k_pre not in capture_qkv_bias:
                            A						:     Optional[int]         =[None, None, None]
                     A						:     Optional[Any]         =v
                     continue

              A						:     List[Any]         =textenc_pattern.sub(lambda lowercase					: protected[re.escape(m.group(0					)					)],		lowercase					)
              A						:     Optional[int]         =v

       for k_pre, tensors in capture_qkv_weight.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'					)
              A						:     Optional[int]         =textenc_pattern.sub(lambda lowercase					: protected[re.escape(m.group(0					)					)],		lowercase					)
              A						:     List[Any]         =torch.cat(lowercase					)

       for k_pre, tensors in capture_qkv_bias.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'					)
              A						:     int         =textenc_pattern.sub(lambda lowercase					: protected[re.escape(m.group(0					)					)],		lowercase					)
              A						:     Tuple         =torch.cat(lowercase					)

       return new_state_dict



def A__  (			lowercase:     int					)				->		Union[str, Any]:
       return text_enc_dict


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

  parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
  parser.add_argument(
      '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
  )

  _lowercase							:      str      							=parser.parse_args()

  assert args.model_path is not None, "Must provide a model path!"

  assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

  # Path for safetensors
  _lowercase							:      Optional[Any]      							=osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
  _lowercase							:      List[Any]      							=osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
  _lowercase							:      Union[str, Any]      							=osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

  # Load models from safetensors if it exists, if it doesn't pytorch
  if osp.exists(unet_path):
    _lowercase							:      Optional[Any]      							=load_file(unet_path, device='''cpu''')
  else:
    _lowercase							:      Any      							=osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
    _lowercase							:      Optional[int]      							=torch.load(unet_path, map_location='''cpu''')

  if osp.exists(vae_path):
    _lowercase							:      List[Any]      							=load_file(vae_path, device='''cpu''')
  else:
    _lowercase							:      Optional[Any]      							=osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
    _lowercase							:      int      							=torch.load(vae_path, map_location='''cpu''')

  if osp.exists(text_enc_path):
    _lowercase							:      Union[str, Any]      							=load_file(text_enc_path, device='''cpu''')
  else:
    _lowercase							:      Optional[int]      							=osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
    _lowercase							:      Optional[int]      							=torch.load(text_enc_path, map_location='''cpu''')

  # Convert the UNet model
  _lowercase							:      Dict      							=convert_unet_state_dict(unet_state_dict)
  _lowercase							:      Optional[int]      							={'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

  # Convert the VAE model
  _lowercase							:      str      							=convert_vae_state_dict(vae_state_dict)
  _lowercase							:      Optional[Any]      							={'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

  # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
  _lowercase							:      Optional[Any]      							='''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

  if is_vaa_model:
    # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
    _lowercase							:      Dict      							={'''transformer.''' + k: v for k, v in text_enc_dict.items()}
    _lowercase							:      Optional[Any]      							=convert_text_enc_state_dict_vaa(text_enc_dict)
    _lowercase							:      List[Any]      							={'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
  else:
    _lowercase							:      Dict      							=convert_text_enc_state_dict(text_enc_dict)
    _lowercase							:      int      							={'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

  # Put together new checkpoint
  _lowercase							:      Optional[int]      							={**unet_state_dict, **vae_state_dict, **text_enc_dict}
  if args.half:
    _lowercase							:      str      							={k: v.half() for k, v in state_dict.items()}

  if args.use_safetensors:
    save_file(state_dict, args.checkpoint_path)
  else:
    _lowercase							:      Optional[int]      							={'''state_dict''': state_dict}
    torch.save(state_dict, args.checkpoint_path)







from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase							:      List[Any]      							=logging.get_logger(__name__)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  def __init__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					float      ,	**SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				int:
         A						:     Tuple         =feature_size
         A						:     int         =sampling_rate
         A						:     List[str]         =padding_value

         A						:     Tuple         =kwargs.pop('padding_side'      ,	'right'     )
         A						:     str         =kwargs.pop('return_attention_mask'      ,	SCREAMING_SNAKE_CASE__     )

         super().__init__(**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]      ,	SCREAMING_SNAKE_CASE__    :					Union[bool, str, PaddingStrategy] = True      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, TensorType]] = None      ,	)    ->				BatchFeature:
         # If we have a list of dicts, let's convert it in a dict of lists
         # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
         if isinstance(SCREAMING_SNAKE_CASE__      ,	(list, tuple)     ) and isinstance(processed_features[0]      ,	(dict, BatchFeature)     ):
                A						:     Tuple         ={
                    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
                }

         # The model's main input name, usually `input_values`, has be passed for padding
         if self.model_input_names[0] not in processed_features:
                raise ValueError(
                    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                    f' to this method that includes {self.model_input_names[0]}, but you provided'
                    f' {list(processed_features.keys()     )}'     )

         A						:     Dict         =processed_features[self.model_input_names[0]]
         A						:     int         =(
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(SCREAMING_SNAKE_CASE__     ) == 0:
                if return_attention_mask:
                       A						:     List[Any]         =[]
                return processed_features

         # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch

         A						:     List[str]         =required_input[0]
         if isinstance(SCREAMING_SNAKE_CASE__      ,	(list, tuple)     ):
                # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
                A						:     Any         =0
                while len(required_input[index]     ) == 0:
                       index += 1
                if index < len(SCREAMING_SNAKE_CASE__     ):
                       A						:     Dict         =required_input[index][0]

         if return_tensors is None:
                if is_tf_tensor(SCREAMING_SNAKE_CASE__     ):
                       A						:     List[Any]         ='tf'
                elif is_torch_tensor(SCREAMING_SNAKE_CASE__     ):
                       A						:     Optional[int]         ='pt'
                elif isinstance(SCREAMING_SNAKE_CASE__      ,	(int, float, list, tuple, np.ndarray)     ):
                       A						:     Union[str, Any]         ='np'
                else:
                       raise ValueError(
                           f'type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE__     )}. '
                           'Should be one of a python, numpy, pytorch or tensorflow object.'     )

         for key, value in processed_features.items():
                if isinstance(value[0]      ,	(int, float)     ):
                       A						:     int         =to_numpy(SCREAMING_SNAKE_CASE__     )
                else:
                       A						:     List[Any]         =[to_numpy(SCREAMING_SNAKE_CASE__     ) for v in value]

        # Convert padding_strategy in PaddingStrategy
         A						:     List[Any]         =self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE__      ,	max_length=SCREAMING_SNAKE_CASE__     )

         A						:     Optional[int]         =processed_features[self.model_input_names[0]]

         A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     )
         if not all(len(SCREAMING_SNAKE_CASE__     ) == batch_size for v in processed_features.values()     ):
                raise ValueError('Some items in the output dictionary have a different batch size than others.'     )

         A						:     Tuple         =[]
         for i in range(SCREAMING_SNAKE_CASE__     ):
                A						:     int         ={k: v[i] for k, v in processed_features.items()}
                # truncation
                A						:     List[Any]         =self._truncate(
                    SCREAMING_SNAKE_CASE__      ,	max_length=SCREAMING_SNAKE_CASE__      ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE__      ,	truncation=SCREAMING_SNAKE_CASE__      ,	)
                truncated_inputs.append(SCREAMING_SNAKE_CASE__     )

         if padding_strategy == PaddingStrategy.LONGEST:
                # make sure that `max_length` cannot be longer than the longest truncated length
                A						:     Any         =max(len(input_slice[self.model_input_names[0]]     ) for input_slice in truncated_inputs     )
                A						:     Optional[Any]         =PaddingStrategy.MAX_LENGTH

         A						:     List[Any]         ={}
         for i in range(SCREAMING_SNAKE_CASE__     ):
                # padding
                A						:     Optional[Any]         =self._pad(
                    truncated_inputs[i]      ,	max_length=SCREAMING_SNAKE_CASE__      ,	padding_strategy=SCREAMING_SNAKE_CASE__      ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE__      ,	return_attention_mask=SCREAMING_SNAKE_CASE__      ,	)

                for key, value in outputs.items():
                       if key not in batch_outputs:
                              A						:     Dict         =[]
                       if value.dtype is np.dtype(np.floataa     ):
                              A						:     Tuple         =value.astype(np.floataa     )
                       batch_outputs[key].append(SCREAMING_SNAKE_CASE__     )

         return BatchFeature(SCREAMING_SNAKE_CASE__      ,	tensor_type=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Union[Dict[str, np.ndarray], BatchFeature]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	)    ->				dict:
         A						:     Optional[int]         =processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
                A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     )

         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                A						:     Tuple         =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         A						:     int         =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE__     ) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
                A						:     str         =np.ones(len(SCREAMING_SNAKE_CASE__     )      ,	dtype=np.intaa     )

         if needs_to_be_padded:
                A						:     Union[str, Any]         =max_length - len(SCREAMING_SNAKE_CASE__     )
                if self.padding_side == "right":
                       if return_attention_mask:
                              A						:     Dict         =np.pad(
                                  processed_features['attention_mask']      ,	(0, difference)     )
                       A						:     str         =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                       A						:     Tuple         =np.pad(
                           SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'constant'      ,	constant_values=self.padding_value     )
                elif self.padding_side == "left":
                       if return_attention_mask:
                              A						:     List[Any]         =np.pad(
                                  processed_features['attention_mask']      ,	(difference, 0)     )
                       A						:     Union[str, Any]         =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                       A						:     Tuple         =np.pad(
                           SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'constant'      ,	constant_values=self.padding_value     )
                else:
                       raise ValueError('Invalid padding strategy:' + str(self.padding_side     )     )

         return processed_features







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Union[Dict[str, np.ndarray], BatchFeature]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	)    ->				Optional[Any]:
         if not truncation:
                return processed_features
         elif truncation and max_length is None:
                raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'     )

         A						:     Tuple         =processed_features[self.model_input_names[0]]

         # find `max_length` that fits `pad_to_multiple_of`
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                A						:     Any         =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     ) > max_length

         if needs_to_be_truncated:
                A						:     Union[str, Any]         =processed_features[self.model_input_names[0]][:max_length]
                if "attention_mask" in processed_features:
                       A						:     Dict         =processed_features['attention_mask'][:max_length]

         return processed_features







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any=False      ,	SCREAMING_SNAKE_CASE__    :					Dict=None     )    ->				Union[str, Any]:

         # Get padding strategy
         if padding is not False:
                if padding is True:
                       A						:     List[Any]         =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                elif not isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                       A						:     Tuple         =PaddingStrategy(SCREAMING_SNAKE_CASE__     )
                elif isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                       A						:     Optional[int]         =padding
         else:
                A						:     List[str]         =PaddingStrategy.DO_NOT_PAD

         # Set max length if needed
         if max_length is None:
                if padding_strategy == PaddingStrategy.MAX_LENGTH:
                       raise ValueError(
                           f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'     )

        # Test if we have a padding value
         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
                raise ValueError(
                    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'     )

         return padding_strategy






