





import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=1							)       ->   int:




    """simple docstring"""




    if n_shave_prefix_segments >= 0:
        return ".".join(path.split('.'							)[n_shave_prefix_segments:]							)
    else:
        return ".".join(path.split('.'							)[:n_shave_prefix_segments]							)





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=0							)       ->   Dict:




    """simple docstring"""




    _A    =       []
    for old_item in old_list:
        _A    =       old_item
        _A    =       new_item.replace('block.'       ,    'resnets.'							)
        _A    =       new_item.replace('conv_shorcut'       ,    'conv1'							)
        _A    =       new_item.replace('in_shortcut'       ,    'conv_shortcut'							)
        _A    =       new_item.replace('temb_proj'       ,    'time_emb_proj'							)

        _A    =       shave_segments(_SCREAMING_SNAKE_CASE       ,    n_shave_prefix_segments=_SCREAMING_SNAKE_CASE							)

        mapping.append({'old': old_item, 'new': new_item}							)

    return mapping





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=0       ,    _SCREAMING_SNAKE_CASE=False							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       []
    for old_item in old_list:
        _A    =       old_item

        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
            _A    =       new_item.replace('attn'       ,    'attentions'							)
        _A    =       new_item.replace('.k.'       ,    '.key.'							)
        _A    =       new_item.replace('.v.'       ,    '.value.'							)
        _A    =       new_item.replace('.q.'       ,    '.query.'							)

        _A    =       new_item.replace('proj_out'       ,    'proj_attn'							)
        _A    =       new_item.replace('norm'       ,    'group_norm'							)

        _A    =       shave_segments(_SCREAMING_SNAKE_CASE       ,    n_shave_prefix_segments=_SCREAMING_SNAKE_CASE							)
        mapping.append({'old': old_item, 'new': new_item}							)

    return mapping





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=None							)       ->   Any:




    """simple docstring"""




    assert isinstance(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							), "Paths should be a list of dicts containing 'old' and 'new' keys."

    if attention_paths_to_split is not None:
        if config is None:
            raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'							)

        for path, path_map in attention_paths_to_split.items():
            _A    =       old_checkpoint[path]
            _A    =       old_tensor.shape[0] // 3

            _A    =       (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

            _A    =       old_tensor.shape[0] // config.get('num_head_channels'       ,    1							) // 3

            _A    =       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
            _A    =       old_tensor.split(channels // num_heads       ,    dim=1							)

            _A    =       query.reshape(_SCREAMING_SNAKE_CASE							).squeeze()
            _A    =       key.reshape(_SCREAMING_SNAKE_CASE							).squeeze()
            _A    =       value.reshape(_SCREAMING_SNAKE_CASE							).squeeze()

    for path in paths:
        _A    =       path['new']

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        _A    =       new_path.replace('down.'       ,    'down_blocks.'							)
        _A    =       new_path.replace('up.'       ,    'up_blocks.'							)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                _A    =       new_path.replace(replacement['old']       ,    replacement['new']							)

        if "attentions" in new_path:
            _A    =       old_checkpoint[path['old']].squeeze()
        else:
            _A    =       old_checkpoint[path['old']]





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   str:




    """simple docstring"""




    _A    =       {}

    _A    =       checkpoint['temb.dense.0.weight']
    _A    =       checkpoint['temb.dense.0.bias']
    _A    =       checkpoint['temb.dense.1.weight']
    _A    =       checkpoint['temb.dense.1.bias']

    _A    =       checkpoint['norm_out.weight']
    _A    =       checkpoint['norm_out.bias']

    _A    =       checkpoint['conv_in.weight']
    _A    =       checkpoint['conv_in.bias']
    _A    =       checkpoint['conv_out.weight']
    _A    =       checkpoint['conv_out.bias']

    _A    =       len({'.'.join(layer.split('.'							)[:2]							) for layer in checkpoint if 'down' in layer}							)
    _A    =       {
        layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)
    }

    _A    =       len({'.'.join(layer.split('.'							)[:2]							) for layer in checkpoint if 'up' in layer}							)
    _A    =       {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)}

    for i in range(_SCREAMING_SNAKE_CASE							):
        _A    =       (i - 1) // (config['layers_per_block'] + 1)

        if any('downsample' in layer for layer in down_blocks[i]							):
            _A    =       checkpoint[
                F"down.{i}.downsample.op.weight"
            ]
            _A    =       checkpoint[F"down.{i}.downsample.op.bias"]
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

        if any('block' in layer for layer in down_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    2							).split('.'							)[:2]							) for layer in down_blocks[i] if 'block' in layer}							)
            _A    =       {
                layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block']							):
                    _A    =       renew_resnet_paths(blocks[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)

        if any('attn' in layer for layer in down_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    2							).split('.'							)[:2]							) for layer in down_blocks[i] if 'attn' in layer}							)
            _A    =       {
                layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_attn > 0:
                for j in range(config['layers_per_block']							):
                    _A    =       renew_attention_paths(attns[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    config=_SCREAMING_SNAKE_CASE							)

    _A    =       [key for key in checkpoint if 'mid.block_1' in key]
    _A    =       [key for key in checkpoint if 'mid.block_2' in key]
    _A    =       [key for key in checkpoint if 'mid.attn_1' in key]

    # Mid new 2
    _A    =       renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
    assign_to_checkpoint(
        _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,    )

    _A    =       renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
    assign_to_checkpoint(
        _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,    )

    _A    =       renew_attention_paths(_SCREAMING_SNAKE_CASE       ,    in_mid=_SCREAMING_SNAKE_CASE							)
    assign_to_checkpoint(
        _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,    )

    for i in range(_SCREAMING_SNAKE_CASE							):
        _A    =       num_up_blocks - 1 - i

        if any('upsample' in layer for layer in up_blocks[i]							):
            _A    =       checkpoint[
                F"up.{i}.upsample.conv.weight"
            ]
            _A    =       checkpoint[F"up.{i}.upsample.conv.bias"]

        if any('block' in layer for layer in up_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    2							).split('.'							)[:2]							) for layer in up_blocks[i] if 'block' in layer}							)
            _A    =       {
                layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block'] + 1							):
                    _A    =       {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                    _A    =       renew_resnet_paths(blocks[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[replace_indices]							)

        if any('attn' in layer for layer in up_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    2							).split('.'							)[:2]							) for layer in up_blocks[i] if 'attn' in layer}							)
            _A    =       {
                layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_attn > 0:
                for j in range(config['layers_per_block'] + 1							):
                    _A    =       {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                    _A    =       renew_attention_paths(attns[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[replace_indices]							)

    _A    =       {k.replace('mid_new_2'       ,    'mid_block'							): v for k, v in new_checkpoint.items()}
    return new_checkpoint





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       {}

    _A    =       checkpoint['encoder.norm_out.weight']
    _A    =       checkpoint['encoder.norm_out.bias']

    _A    =       checkpoint['encoder.conv_in.weight']
    _A    =       checkpoint['encoder.conv_in.bias']
    _A    =       checkpoint['encoder.conv_out.weight']
    _A    =       checkpoint['encoder.conv_out.bias']

    _A    =       checkpoint['decoder.norm_out.weight']
    _A    =       checkpoint['decoder.norm_out.bias']

    _A    =       checkpoint['decoder.conv_in.weight']
    _A    =       checkpoint['decoder.conv_in.bias']
    _A    =       checkpoint['decoder.conv_out.weight']
    _A    =       checkpoint['decoder.conv_out.bias']

    _A    =       len({'.'.join(layer.split('.'							)[:3]							) for layer in checkpoint if 'down' in layer}							)
    _A    =       {
        layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)
    }

    _A    =       len({'.'.join(layer.split('.'							)[:3]							) for layer in checkpoint if 'up' in layer}							)
    _A    =       {layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)}

    for i in range(_SCREAMING_SNAKE_CASE							):
        _A    =       (i - 1) // (config['layers_per_block'] + 1)

        if any('downsample' in layer for layer in down_blocks[i]							):
            _A    =       checkpoint[
                F"encoder.down.{i}.downsample.conv.weight"
            ]
            _A    =       checkpoint[
                F"encoder.down.{i}.downsample.conv.bias"
            ]

        if any('block' in layer for layer in down_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    3							).split('.'							)[:3]							) for layer in down_blocks[i] if 'block' in layer}							)
            _A    =       {
                layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
                for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block']							):
                    _A    =       renew_resnet_paths(blocks[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)

        if any('attn' in layer for layer in down_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    3							).split('.'							)[:3]							) for layer in down_blocks[i] if 'attn' in layer}							)
            _A    =       {
                layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
                for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_attn > 0:
                for j in range(config['layers_per_block']							):
                    _A    =       renew_attention_paths(attns[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    config=_SCREAMING_SNAKE_CASE							)

    _A    =       [key for key in checkpoint if 'mid.block_1' in key]
    _A    =       [key for key in checkpoint if 'mid.block_2' in key]
    _A    =       [key for key in checkpoint if 'mid.attn_1' in key]

    # Mid new 2
    _A    =       renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
    assign_to_checkpoint(
        _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,    )

    _A    =       renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
    assign_to_checkpoint(
        _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,    )

    _A    =       renew_attention_paths(_SCREAMING_SNAKE_CASE       ,    in_mid=_SCREAMING_SNAKE_CASE							)
    assign_to_checkpoint(
        _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,    )

    for i in range(_SCREAMING_SNAKE_CASE							):
        _A    =       num_up_blocks - 1 - i

        if any('upsample' in layer for layer in up_blocks[i]							):
            _A    =       checkpoint[
                F"decoder.up.{i}.upsample.conv.weight"
            ]
            _A    =       checkpoint[
                F"decoder.up.{i}.upsample.conv.bias"
            ]

        if any('block' in layer for layer in up_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    3							).split('.'							)[:3]							) for layer in up_blocks[i] if 'block' in layer}							)
            _A    =       {
                layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_blocks > 0:
                for j in range(config['layers_per_block'] + 1							):
                    _A    =       {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                    _A    =       renew_resnet_paths(blocks[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[replace_indices]							)

        if any('attn' in layer for layer in up_blocks[i]							):
            _A    =       len(
                {'.'.join(shave_segments(_SCREAMING_SNAKE_CASE       ,    3							).split('.'							)[:3]							) for layer in up_blocks[i] if 'attn' in layer}							)
            _A    =       {
                layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(_SCREAMING_SNAKE_CASE							)
            }

            if num_attn > 0:
                for j in range(config['layers_per_block'] + 1							):
                    _A    =       {'old': F"up_blocks.{i}", 'new': F"up_blocks.{block_id}"}
                    _A    =       renew_attention_paths(attns[j]							)
                    assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    additional_replacements=[replace_indices]							)

    _A    =       {k.replace('mid_new_2'       ,    'mid_block'							): v for k, v in new_checkpoint.items()}
    _A    =       checkpoint['quant_conv.weight']
    _A    =       checkpoint['quant_conv.bias']
    if "quantize.embedding.weight" in checkpoint:
        _A    =       checkpoint['quantize.embedding.weight']
    _A    =       checkpoint['post_quant_conv.weight']
    _A    =       checkpoint['post_quant_conv.bias']

    return new_checkpoint


if __name__ == "__main__":
 __A       : int						    = argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )

 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the architecture.",
 )

 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

 __A       : List[Any]						    = parser.parse_args()
 __A       : str						    = torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  __A       : int						    = json.loads(f.read())

 # unet case
 __A       : Optional[Any]						    = {key.split(".")[0] for key in checkpoint.keys()}
 if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  __A       : Dict						    = convert_vq_autoenc_checkpoint(checkpoint, config)
 else:
  __A       : List[Any]						    = convert_ddpm_checkpoint(checkpoint, config)

 if "ddpm" in config:
  del config["ddpm"]

 if config["_class_name"] == "VQModel":
  __A       : Optional[int]						    = VQModel(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 elif config["_class_name"] == "AutoencoderKL":
  __A       : Optional[Any]						    = AutoencoderKL(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 else:
  __A       : Union[str, Any]						    = UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  __A       : List[Any]						    = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

  __A       : Optional[Any]						    = DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)










import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__A       : Optional[int]						    = logging.get_logger(__name__)







@dataclass
class     lowerCamelCase:
     '''simple docstring'''


     __magic_name__        = 42
     __magic_name__        = 42
     __magic_name__        = None
     __magic_name__        = None




     def 					lowerCAmelCase__		(						self       ):
         return json.dumps(dataclasses.asdict(self       )							,    indent=2       ) + "\n"







@dataclass(frozen=__snake_case  )
class     lowerCamelCase:
     '''simple docstring'''


     __magic_name__        = 42
     __magic_name__        = None
     __magic_name__        = None
     __magic_name__        = None




     def 					lowerCAmelCase__		(						self       ):
         return json.dumps(dataclasses.asdict(self       )       ) + "\n"







class     lowerCamelCase:
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         raise NotImplementedError()




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         raise NotImplementedError()




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         raise NotImplementedError()




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         raise NotImplementedError()




     def 					lowerCAmelCase__		(						self       ):
         raise NotImplementedError()




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         if len(self.get_labels()       ) > 1:
             _A    =       self.get_labels()[int(example.label       )]
         return example




     @classmethod
     def 					lowerCAmelCase__		(						cls							,    snake_case_							,    snake_case_=None       ):
         with open(snake_case_							,    'r'							,    encoding='utf-8-sig'       ) as f:
             return list(csv.reader(snake_case_							,    delimiter='\t'							,    quotechar=snake_case_       )       )







class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     def __init__(						self							,    snake_case_=None							,    snake_case_=None							,    snake_case_="classification"							,    snake_case_=False       ):
         _A    =       [] if labels is None else labels
         _A    =       [] if examples is None else examples
         _A    =       mode
         _A    =       verbose




     def __len__(						self       ):
         return len(self.examples       )




     def __getitem__(						self							,    snake_case_       ):
         if isinstance(snake_case_							,    snake_case_       ):
             return SingleSentenceClassificationProcessor(labels=self.labels							,    examples=self.examples[idx]       )
         return self.examples[idx]




     @classmethod
     def 					lowerCAmelCase__		(						cls							,    snake_case_							,    snake_case_=""							,    snake_case_=0							,    snake_case_=1							,    snake_case_=None							,    snake_case_=False							,    **snake_case_       ):
         _A    =       cls(**snake_case_       )
         processor.add_examples_from_csv(
             snake_case_							,    split_name=snake_case_							,    column_label=snake_case_							,    column_text=snake_case_							,    column_id=snake_case_							,    skip_first_row=snake_case_							,    overwrite_labels=snake_case_							,    overwrite_examples=snake_case_							,    )
         return processor




     @classmethod
     def 					lowerCAmelCase__		(						cls							,    snake_case_							,    snake_case_=None							,    **snake_case_       ):
         _A    =       cls(**snake_case_       )
         processor.add_examples(snake_case_							,    labels=snake_case_       )
         return processor




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_=""							,    snake_case_=0							,    snake_case_=1							,    snake_case_=None							,    snake_case_=False							,    snake_case_=False							,    snake_case_=False							,    ):
         _A    =       self._read_tsv(snake_case_       )
         if skip_first_row:
             _A    =       lines[1:]
         _A    =       []
         _A    =       []
         _A    =       []
         for i, line in enumerate(snake_case_       ):
             texts.append(line[column_text]       )
             labels.append(line[column_label]       )
             if column_id is not None:
                 ids.append(line[column_id]       )
             else:
                 _A    =       F"{split_name}-{i}" if split_name else str(snake_case_       )
                 ids.append(snake_case_       )

         return self.add_examples(
             snake_case_							,    snake_case_							,    snake_case_							,    overwrite_labels=snake_case_							,    overwrite_examples=snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_=None							,    snake_case_=None							,    snake_case_=False							,    snake_case_=False       ):
         if labels is not None and len(snake_case_       ) != len(snake_case_       ):
             raise ValueError(
                 F"Text and labels have mismatched lengths {len(snake_case_       )} and {len(snake_case_       )}"       )
         if ids is not None and len(snake_case_       ) != len(snake_case_       ):
             raise ValueError(F"Text and ids have mismatched lengths {len(snake_case_       )} and {len(snake_case_       )}"       )
         if ids is None:
             _A    =       [None] * len(snake_case_       )
         if labels is None:
             _A    =       [None] * len(snake_case_       )
         _A    =       []
         _A    =       set()
         for text_or_text_and_label, label, guid in zip(snake_case_							,    snake_case_							,    snake_case_       ):
             if isinstance(snake_case_							,    (tuple, list)       ) and label is None:
                 _A, _A    =       text_or_text_and_label
             else:
                 _A    =       text_or_text_and_label
             added_labels.add(snake_case_       )
             examples.append(InputExample(guid=snake_case_							,    text_a=snake_case_							,    text_b=snake_case_							,    label=snake_case_       )       )

         # Update examples
         if overwrite_examples:
             _A    =       examples
         else:
             self.examples.extend(snake_case_       )

         # Update labels
         if overwrite_labels:
             _A    =       list(snake_case_       )
         else:
             _A    =       list(set(self.labels       ).union(snake_case_       )       )

         return self.examples




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_=None							,    snake_case_=False							,    snake_case_=0							,    snake_case_=True							,    snake_case_=None							,    ):
         if max_length is None:
             _A    =       tokenizer.max_len

         _A    =       {label: i for i, label in enumerate(self.labels       )}

         _A    =       []
         for ex_index, example in enumerate(self.examples       ):
             if ex_index % 1_0000 == 0:
                 logger.info(F"Tokenizing example {ex_index}"       )

             _A    =       tokenizer.encode(
                 example.text_a							,    add_special_tokens=snake_case_							,    max_length=min(snake_case_							,    tokenizer.max_len       )							,    )
             all_input_ids.append(snake_case_       )

         _A    =       max(len(snake_case_       ) for input_ids in all_input_ids       )

         _A    =       []
         for ex_index, (input_ids, example) in enumerate(zip(snake_case_							,    self.examples       )       ):
             if ex_index % 1_0000 == 0:
                 logger.info(F"Writing example {ex_index}/{len(self.examples       )}"       )
             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             _A    =       [1 if mask_padding_with_zero else 0] * len(snake_case_       )

             # Zero-pad up to the sequence length.
             _A    =       batch_length - len(snake_case_       )
             if pad_on_left:
                 _A    =       ([pad_token] * padding_length) + input_ids
                 _A    =       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
             else:
                 _A    =       input_ids + ([pad_token] * padding_length)
                 _A    =       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

             if len(snake_case_       ) != batch_length:
                 raise ValueError(F"Error with input length {len(snake_case_       )} vs {batch_length}"       )
             if len(snake_case_       ) != batch_length:
                 raise ValueError(F"Error with input length {len(snake_case_       )} vs {batch_length}"       )

             if self.mode == "classification":
                 _A    =       label_map[example.label]
             elif self.mode == "regression":
                 _A    =       float(example.label       )
             else:
                 raise ValueError(self.mode       )

             if ex_index < 5 and self.verbose:
                 logger.info('*** Example ***'       )
                 logger.info(F"guid: {example.guid}"       )
                 logger.info(F"input_ids: {' '.join([str(snake_case_       ) for x in input_ids]       )}"       )
                 logger.info(F"attention_mask: {' '.join([str(snake_case_       ) for x in attention_mask]       )}"       )
                 logger.info(F"label: {example.label} (id = {label})"       )

             features.append(InputFeatures(input_ids=snake_case_							,    attention_mask=snake_case_							,    label=snake_case_       )       )

         if return_tensors is None:
             return features
         elif return_tensors == "tf":
             if not is_tf_available():
                 raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'       )
             import tensorflow as tf

             def gen():
                 for ex in features:
                     yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

             _A    =       tf.data.Dataset.from_generator(
                 snake_case_							,    ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)							,    ({'input_ids': tf.TensorShape([None]       ), 'attention_mask': tf.TensorShape([None]       )}, tf.TensorShape([]       ))							,    )
             return dataset
         elif return_tensors == "pt":
             if not is_torch_available():
                 raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'       )
             import torch
             from torch.utils.data import TensorDataset

             _A    =       torch.tensor([f.input_ids for f in features]							,    dtype=torch.long       )
             _A    =       torch.tensor([f.attention_mask for f in features]							,    dtype=torch.long       )
             if self.mode == "classification":
                 _A    =       torch.tensor([f.label for f in features]							,    dtype=torch.long       )
             elif self.mode == "regression":
                 _A    =       torch.tensor([f.label for f in features]							,    dtype=torch.float       )

             _A    =       TensorDataset(snake_case_							,    snake_case_							,    snake_case_       )
             return dataset
         else:
             raise ValueError('return_tensors should be one of \'tf\' or \'pt\''       )





