
"""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()
SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Optional[Any]:







       """simple docstring"""
       UpperCamelCase					 =		SwinConfig.from_pretrained(
           "microsoft/swin-tiny-patch4-window7-224"		,      out_features=["stage1", "stage2", "stage3", "stage4"]				)
       UpperCamelCase					 =		MaskFormerConfig(backbone_config=UpperCAmelCase_				)

       UpperCamelCase					 =		"huggingface/label-files"
       if "ade20k-full" in model_name:
              # this should be ok
              UpperCamelCase					 =		8_47
              UpperCamelCase					 =		"maskformer-ade20k-full-id2label.json"
       elif "ade" in model_name:
              # this should be ok
              UpperCamelCase					 =		1_50
              UpperCamelCase					 =		"ade20k-id2label.json"
       elif "coco-stuff" in model_name:
              # this should be ok
              UpperCamelCase					 =		1_71
              UpperCamelCase					 =		"maskformer-coco-stuff-id2label.json"
       elif "coco" in model_name:
              # TODO
              UpperCamelCase					 =		1_33
              UpperCamelCase					 =		"coco-panoptic-id2label.json"
       elif "cityscapes" in model_name:
              # this should be ok
              UpperCamelCase					 =		19
              UpperCamelCase					 =		"cityscapes-id2label.json"
       elif "vistas" in model_name:
              # this should be ok
              UpperCamelCase					 =		65
              UpperCamelCase					 =		"mapillary-vistas-id2label.json"

       UpperCamelCase					 =		json.load(open(hf_hub_download(UpperCAmelCase_		,      UpperCAmelCase_		,      repo_type="dataset"				)		,      "r"				)				)
       UpperCamelCase					 =		{int(UpperCAmelCase_				): v for k, v in idalabel.items()}

       return config



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		[]
       # 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 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							Any:







       """simple docstring"""
       UpperCamelCase					 =		dct.pop(UpperCAmelCase_				)
       UpperCamelCase					 =		val



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Optional[int]:







       """simple docstring"""
       UpperCamelCase					 =		[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
       for i in range(len(backbone_config.depths				)				):
              UpperCamelCase					 =		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)
                     UpperCamelCase					 =		state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"				)
                     UpperCamelCase					 =		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
                     UpperCamelCase					 =		in_proj_weight[:dim, :]
                     UpperCamelCase					 =		in_proj_bias[: dim]
                     UpperCamelCase					 =		in_proj_weight[
                         dim : dim * 2, :
                     ]
                     UpperCamelCase					 =		in_proj_bias[
                         dim : dim * 2
                     ]
                     UpperCamelCase					 =		in_proj_weight[
                         -dim :, :
                     ]
                     UpperCamelCase					 =		in_proj_bias[-dim :]
                     # fmt: on



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Optional[int]:







       """simple docstring"""
       # fmt: off
       UpperCamelCase					 =		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)
              UpperCamelCase					 =		state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"				)
              UpperCamelCase					 =		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
              UpperCamelCase					 =		in_proj_weight[: hidden_size, :]
              UpperCamelCase					 =		in_proj_bias[:config.hidden_size]
              UpperCamelCase					 =		in_proj_weight[hidden_size : hidden_size * 2, :]
              UpperCamelCase					 =		in_proj_bias[hidden_size : hidden_size * 2]
              UpperCamelCase					 =		in_proj_weight[-hidden_size :, :]
              UpperCamelCase					 =		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)
              UpperCamelCase					 =		state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"				)
              UpperCamelCase					 =		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
              UpperCamelCase					 =		in_proj_weight[: hidden_size, :]
              UpperCamelCase					 =		in_proj_bias[:config.hidden_size]
              UpperCamelCase					 =		in_proj_weight[hidden_size : hidden_size * 2, :]
              UpperCamelCase					 =		in_proj_bias[hidden_size : hidden_size * 2]
              UpperCamelCase					 =		in_proj_weight[-hidden_size :, :]
              UpperCamelCase					 =		in_proj_bias[-hidden_size :]
       # fmt: on



def 	lowerCamelCase__			(							)->							torch.Tensor:







       """simple docstring"""
       UpperCamelCase					 =		"http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCamelCase					 =		Image.open(requests.get(UpperCAmelCase_		,      stream=UpperCAmelCase_				).raw				)
       return im



@torch.no_grad()
def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_ = False				)->							List[Any]:







       """simple docstring"""
       UpperCamelCase					 =		get_maskformer_config(UpperCAmelCase_				)

       # load original state_dict
       with open(UpperCAmelCase_		,      "rb"				) as f:
              UpperCamelCase					 =		pickle.load(UpperCAmelCase_				)
       UpperCamelCase					 =		data["model"]

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

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

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

       # load 🤗 model
       UpperCamelCase					 =		MaskFormerForInstanceSegmentation(UpperCAmelCase_				)
       model.eval()

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

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

       # verify results
       UpperCamelCase					 =		prepare_img()
       if "vistas" in model_name:
              UpperCamelCase					 =		65
       elif "cityscapes" in model_name:
              UpperCamelCase					 =		6_55_35
       else:
              UpperCamelCase					 =		2_55
       UpperCamelCase					 =		True if "ade" in model_name else False
       UpperCamelCase					 =		MaskFormerImageProcessor(ignore_index=UpperCAmelCase_		,      reduce_labels=UpperCAmelCase_				)

       UpperCamelCase					 =		image_processor(UpperCAmelCase_		,      return_tensors="pt"				)

       UpperCamelCase					 =		model(**UpperCAmelCase_				)

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

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

       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__":
      SCREAMING_SNAKE_CASE      =     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."""
      )

      SCREAMING_SNAKE_CASE      =     parser.parse_args()
      convert_maskformer_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )







"""simple docstring"""


import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE      =     logging.getLogger(__name__)



@dataclass(frozen=_lowerCAmelCase	)
class   __a  :
       UpperCamelCase_		:					str
       UpperCamelCase_		:					str
       UpperCamelCase_		:					Optional[str]   =			None
       UpperCamelCase_		:					Optional[str]   =			None
       UpperCamelCase_		:					Optional[str]   =			None





@dataclass(frozen=_lowerCAmelCase	)
class   __a  :
       UpperCamelCase_		:					List[int]
       UpperCamelCase_		:					Optional[List[int]]   =			None
       UpperCamelCase_		:					Optional[List[int]]   =			None
       UpperCamelCase_		:					Optional[Union[int, float]]   =			None
       UpperCamelCase_		:					Optional[int]   =			None


if is_torch_available():
      import torch
      from torch.utils.data import Dataset



      class   __a  (      _lowerCAmelCase	):
             UpperCamelCase_		:					List[InputFeatures]






             def __init__(  self :							Union[str, Any]       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							PreTrainedTokenizer       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Optional[int] = None       ,      UpperCAmelCase_ :							Optional[Any]=False       ,      UpperCAmelCase_ :							bool = False       ,      )->  Dict:







                    """simple docstring"""
                    UpperCamelCase					 =		hans_processors[task]()

                    UpperCamelCase					 =		os.path.join(
                        UpperCAmelCase_       ,      "cached_{}_{}_{}_{}".format(
                            "dev" if evaluate else "train"       ,      tokenizer.__class__.__name__       ,      str(UpperCAmelCase_			)       ,      UpperCAmelCase_       ,      )       ,      )
                    UpperCamelCase					 =		processor.get_labels()
                    if tokenizer.__class__ in (
                        RobertaTokenizer,
                        RobertaTokenizerFast,
                        XLMRobertaTokenizer,
                        BartTokenizer,
                        BartTokenizerFast,
                    ):
                           # HACK(label indices are swapped in RoBERTa pretrained model)
                           UpperCamelCase ,       UpperCamelCase					 =		label_list[2], label_list[1]
                    UpperCamelCase					 =		label_list

                    # Make sure only the first process in distributed training processes the dataset,
                    # and the others will use the cache.
                    UpperCamelCase					 =		cached_features_file + ".lock"
                    with FileLock(UpperCAmelCase_			):
                           if os.path.exists(UpperCAmelCase_			) and not overwrite_cache:
                                  logger.info(f"Loading features from cached file {cached_features_file}"			)
                                  UpperCamelCase					 =		torch.load(UpperCAmelCase_			)
                           else:
                                  logger.info(f"Creating features from dataset file at {data_dir}"			)

                                  UpperCamelCase					 =		(
                                      processor.get_dev_examples(UpperCAmelCase_			) if evaluate else processor.get_train_examples(UpperCAmelCase_			)
                                  )

                                  logger.info("Training examples: %s"       ,      len(UpperCAmelCase_			)			)
                                  UpperCamelCase					 =		hans_convert_examples_to_features(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
                                  logger.info("Saving features into cached file %s"       ,      UpperCAmelCase_			)
                                  torch.save(self.features       ,      UpperCAmelCase_			)






             def __len__(  self :							Optional[Any]			)->  List[Any]:







                    """simple docstring"""
                    return len(self.features			)






             def __getitem__(  self :							Optional[Any]       ,      UpperCAmelCase_ :							Any			)->  InputFeatures:







                    """simple docstring"""
                    return self.features[i]






             def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[Any]:







                    """simple docstring"""
                    return self.label_list


if is_tf_available():
      import tensorflow as tf



      class   __a  :
             UpperCamelCase_		:					List[InputFeatures]






             def __init__(  self :							int       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							PreTrainedTokenizer       ,      UpperCAmelCase_ :							str       ,      UpperCAmelCase_ :							Optional[int] = 128       ,      UpperCAmelCase_ :							Optional[Any]=False       ,      UpperCAmelCase_ :							bool = False       ,      )->  Union[str, Any]:







                    """simple docstring"""
                    UpperCamelCase					 =		hans_processors[task]()
                    UpperCamelCase					 =		processor.get_labels()
                    if tokenizer.__class__ in (
                        RobertaTokenizer,
                        RobertaTokenizerFast,
                        XLMRobertaTokenizer,
                        BartTokenizer,
                        BartTokenizerFast,
                    ):
                           # HACK(label indices are swapped in RoBERTa pretrained model)
                           UpperCamelCase ,       UpperCamelCase					 =		label_list[2], label_list[1]
                    UpperCamelCase					 =		label_list

                    UpperCamelCase					 =		processor.get_dev_examples(UpperCAmelCase_			) if evaluate else processor.get_train_examples(UpperCAmelCase_			)
                    UpperCamelCase					 =		hans_convert_examples_to_features(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)

                    def gen():
                           for ex_index, ex in tqdm.tqdm(enumerate(self.features			)       ,      desc="convert examples to features"			):
                                  if ex_index % 10_000 == 0:
                                         logger.info("Writing example %d of %d" % (ex_index, len(UpperCAmelCase_			))			)

                                  yield (
                                      {
                                          "example_id": 0,
                                          "input_ids": ex.input_ids,
                                          "attention_mask": ex.attention_mask,
                                          "token_type_ids": ex.token_type_ids,
                                      },
                                      ex.label,
                                  )

                    UpperCamelCase					 =		tf.data.Dataset.from_generator(
                        UpperCAmelCase_       ,      (
                            {
                                "example_id": tf.intaa,
                                "input_ids": tf.intaa,
                                "attention_mask": tf.intaa,
                                "token_type_ids": tf.intaa,
                            },
                            tf.intaa,
                        )       ,      (
                            {
                                "example_id": tf.TensorShape([]			),
                                "input_ids": tf.TensorShape([None, None]			),
                                "attention_mask": tf.TensorShape([None, None]			),
                                "token_type_ids": tf.TensorShape([None, None]			),
                            },
                            tf.TensorShape([]			),
                        )       ,      )






             def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Tuple:







                    """simple docstring"""
                    return self.dataset






             def __len__(  self :							List[Any]			)->  List[Any]:







                    """simple docstring"""
                    return len(self.features			)






             def __getitem__(  self :							Tuple       ,      UpperCAmelCase_ :							List[str]			)->  InputFeatures:







                    """simple docstring"""
                    return self.features[i]






             def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  List[str]:







                    """simple docstring"""
                    return self.label_list



class   __a  (      _lowerCAmelCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							Tuple			)->  Tuple:







              """simple docstring"""
              return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_       ,      "heuristics_train_set.txt"			)			)       ,      "train"			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							List[str]			)->  Dict:







              """simple docstring"""
              return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase_       ,      "heuristics_evaluation_set.txt"			)			)       ,      "dev"			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Optional[Any]:







              """simple docstring"""
              return ["contradiction", "entailment", "neutral"]






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							str			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		[]
              for i, line in enumerate(UpperCAmelCase_			):
                     if i == 0:
                            continue
                     UpperCamelCase					 =		"%s-%s" % (set_type, line[0])
                     UpperCamelCase					 =		line[5]
                     UpperCamelCase					 =		line[6]
                     UpperCamelCase					 =		line[7][2:] if line[7].startswith("ex"			) else line[7]
                     UpperCamelCase					 =		line[0]
                     examples.append(InputExample(guid=UpperCAmelCase_       ,      text_a=UpperCAmelCase_       ,      text_b=UpperCAmelCase_       ,      label=UpperCAmelCase_       ,      pairID=UpperCAmelCase_			)			)
              return examples





def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      )->							Union[str, Any]:







       """simple docstring"""
       UpperCamelCase					 =		{label: i for i, label in enumerate(UpperCAmelCase_				)}

       UpperCamelCase					 =		[]
       for ex_index, example in tqdm.tqdm(enumerate(UpperCAmelCase_				)		,      desc="convert examples to features"				):
              if ex_index % 1_00_00 == 0:
                     logger.info("Writing example %d" % (ex_index)				)

              UpperCamelCase					 =		tokenizer(
                  example.text_a		,      example.text_b		,      add_special_tokens=UpperCAmelCase_		,      max_length=UpperCAmelCase_		,      padding="max_length"		,      truncation=UpperCAmelCase_		,      return_overflowing_tokens=UpperCAmelCase_		,      )

              UpperCamelCase					 =		label_map[example.label] if example.label in label_map else 0

              UpperCamelCase					 =		int(example.pairID				)

              features.append(InputFeatures(**UpperCAmelCase_		,      label=UpperCAmelCase_		,      pairID=UpperCAmelCase_				)				)

       for i, example in enumerate(examples[:5]				):
              logger.info("*** Example ***"				)
              logger.info(F"guid: {example}"				)
              logger.info(F"features: {features[i]}"				)

       return features


SCREAMING_SNAKE_CASE      =     {
    """hans""": 3,
}

SCREAMING_SNAKE_CASE      =     {
    """hans""": HansProcessor,
}





