'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:     List[str]  =logging.get_logger(__name__)





def 	lowerCAmelCase_				(					snake_case_       : List[Any]			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   SwinConfig(
         embed_dim=1_92					,    depths=(2, 2, 18, 2)					,    num_heads=(6, 12, 24, 48)					,    window_size=12					,    out_features=["stage2", "stage3", "stage4"]					,    )

     UpperCAmelCase_           =   DetaConfig(
         backbone_config=snake_case_					,    num_queries=9_00					,    encoder_ffn_dim=20_48					,    decoder_ffn_dim=20_48					,    num_feature_levels=5					,    assign_first_stage=snake_case_					,    with_box_refine=snake_case_					,    two_stage=snake_case_					,    )

     # set labels
     UpperCAmelCase_           =   "huggingface/label-files"
     if "o365" in model_name:
          UpperCAmelCase_           =   3_66
          UpperCAmelCase_           =   "object365-id2label.json"
     else:
          UpperCAmelCase_           =   91
          UpperCAmelCase_           =   "coco-detection-id2label.json"

     UpperCAmelCase_           =   num_labels
     UpperCAmelCase_           =   json.load(open(cached_download(hf_hub_url(snake_case_					,    snake_case_					,    repo_type="dataset"			)			)					,    "r"			)			)
     UpperCAmelCase_           =   {int(snake_case_			): v for k, v in idalabel.items()}
     UpperCAmelCase_           =   idalabel
     UpperCAmelCase_           =   {v: k for k, v in idalabel.items()}

     return config





def 	lowerCAmelCase_				(					snake_case_       : int			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   []

     # stem
     # fmt: off
     rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")			)
     rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")			)
     rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")			)
     rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.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.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")			)

          if i < 3:
               rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")			)
               rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")			)

     rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")			)
     rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")			)
     rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")			)
     rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")			)
     rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")			)
     rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")			)

     # transformer encoder
     for i in range(config.encoder_layers			):
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")			)

     # transformer decoder
     for i in range(config.decoder_layers			):
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")			)
          rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")			)

     # fmt: on

     return rename_keys





def 	lowerCAmelCase_				(					snake_case_       : Tuple					,    snake_case_       : int					,    snake_case_       : Optional[int]			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   dct.pop(snake_case_			)
     UpperCAmelCase_           =   val





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Optional[Any]			)    ->      List[str]:


     '''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.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""			)
               UpperCAmelCase_           =   state_dict.pop(f"""backbone.0.body.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_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Dict			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   config.d_model
     for i in range(config.decoder_layers			):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase_           =   state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
          UpperCAmelCase_           =   state_dict.pop(f"""transformer.decoder.layers.{i}.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[: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:]





def 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_           =   Image.open(requests.get(snake_case_					,    stream=snake_case_			).raw			)

     return im





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : str					,    snake_case_       : str			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   get_deta_config(snake_case_			)

     # load original state dict
     if model_name == "deta-swin-large":
          UpperCAmelCase_           =   hf_hub_download(repo_id="nielsr/deta-checkpoints"					,    filename="adet_swin_ft.pth"			)
     elif model_name == "deta-swin-large-o365":
          UpperCAmelCase_           =   hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"					,    filename="deta_swin_pt_o365.pth"			)
     else:
          raise ValueError(f"""Model name {model_name} not supported"""			)

     UpperCAmelCase_           =   torch.load(snake_case_					,    map_location="cpu"			)["model"]

     # original state dict
     for name, param in state_dict.items():
          print(snake_case_					,    param.shape			)

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

     # fix some prefixes
     for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
               UpperCAmelCase_           =   state_dict.pop(snake_case_			)
               UpperCAmelCase_           =   val
          if "input_proj" in key:
               UpperCAmelCase_           =   state_dict.pop(snake_case_			)
               UpperCAmelCase_           =   val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               UpperCAmelCase_           =   state_dict.pop(snake_case_			)
               UpperCAmelCase_           =   val

    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_           =   DetaForObjectDetection(snake_case_			)
     model.load_state_dict(snake_case_			)
     model.eval()

     UpperCAmelCase_           =   "cuda" if torch.cuda.is_available() else "cpu"
     model.to(snake_case_			)

     # load image processor
     UpperCAmelCase_           =   DetaImageProcessor(format="coco_detection"			)

     # verify our conversion on image
     UpperCAmelCase_           =   prepare_img()
     UpperCAmelCase_           =   processor(images=snake_case_					,    return_tensors="pt"			)
     UpperCAmelCase_           =   encoding["pixel_values"]
     UpperCAmelCase_           =   model(pixel_values.to(snake_case_			)			)

     # verify logits
     print("Logits:"					,    outputs.logits[0, :3, :3]			)
     print("Boxes:"					,    outputs.pred_boxes[0, :3, :3]			)
     if model_name == "deta-swin-large":
          UpperCAmelCase_           =   torch.tensor(
              [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
          UpperCAmelCase_           =   torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
     elif model_name == "deta-swin-large-o365":
          UpperCAmelCase_           =   torch.tensor(
              [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
          UpperCAmelCase_           =   torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
     assert torch.allclose(outputs.logits[0, :3, :3]					,    expected_logits.to(snake_case_			)					,    atol=1E-4			)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,    expected_boxes.to(snake_case_			)					,    atol=1E-4			)
     print("Everything ok!"			)

     if pytorch_dump_folder_path:
          # Save model and processor
          logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""			)
          Path(snake_case_			).mkdir(exist_ok=snake_case_			)
          model.save_pretrained(snake_case_			)
          processor.save_pretrained(snake_case_			)

     # Push to hub
     if push_to_hub:
          print("Pushing model and processor to hub..."			)
          model.push_to_hub(f"""jozhang97/{model_name}"""			)
          processor.push_to_hub(f"""jozhang97/{model_name}"""			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Dict  =argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        type=str,
        default='deta-swin-large',
        choices=['deta-swin-large', 'deta-swin-large-o365'],
        help='Name of the model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        help='Path to the folder to output PyTorch model.',
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )
    SCREAMING_SNAKE_CASE_:     Optional[int]  =parser.parse_args()
    convert_deta_checkpoint(args.model_name, 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_:     str  =logging.getLogger(__name__)




@dataclass(frozen=UpperCamelCase__	)
class        __A      :
    a__       :						str
    a__       :						str
    a__       :						Optional[str]          =  None
    a__       :						Optional[str]          =  None
    a__       :						Optional[str]          =  None




@dataclass(frozen=UpperCamelCase__	)
class        __A      :
    a__       :						List[int]
    a__       :						Optional[List[int]]          =  None
    a__       :						Optional[List[int]]          =  None
    a__       :						Optional[Union[int, float]]          =  None
    a__       :						Optional[int]          =  None


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




    class        __A      ( UpperCamelCase__	):
        a__       :						List[InputFeatures]



        def __init__(self    :    Any   ,			__a    :    str   ,			__a    :    PreTrainedTokenizer   ,			__a    :    str   ,			__a    :    Optional[int] = None   ,			__a    :    Dict=False   ,			__a    :    bool = False   ,			):
             UpperCAmelCase_           =   hans_processors[task]()

             UpperCAmelCase_           =   os.path.join(
                 __a   ,			"cached_{}_{}_{}_{}".format(
                     "dev" if evaluate else "train"   ,			tokenizer.__class__.__name__   ,			str(__a )   ,			__a   ,			)   ,			)
             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(__a ):
                  if os.path.exists(__a ) and not overwrite_cache:
                       logger.info(f"""Loading features from cached file {cached_features_file}""" )
                       UpperCAmelCase_           =   torch.load(__a )
                  else:
                       logger.info(f"""Creating features from dataset file at {data_dir}""" )

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

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



        def __len__(self    :    List[Any] ):
             return len(self.features )



        def __getitem__(self    :    Any   ,			__a    :    Optional[Any] ):
             return self.features[i]






        def 					_lowercase						(self    :    Union[str, Any] ):
             return self.label_list


if is_tf_available():
    import tensorflow as tf




    class        __A      :
        a__       :						List[InputFeatures]



        def __init__(self    :    Union[str, Any]   ,			__a    :    str   ,			__a    :    PreTrainedTokenizer   ,			__a    :    str   ,			__a    :    Optional[int] = 128   ,			__a    :    Any=False   ,			__a    :    bool = False   ,			):
             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(__a ) if evaluate else processor.get_train_examples(__a )
             UpperCAmelCase_           =   hans_convert_examples_to_features(__a   ,			__a   ,			__a   ,			__a )

             def gen():
                  for ex_index, ex in tqdm.tqdm(enumerate(self.features )   ,			desc="convert examples to features" ):
                       if ex_index % 10000 == 0:
                            logger.info("Writing example %d of %d" % (ex_index, len(__a )) )

                       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(
                 __a   ,			(
                     {
                         "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 					_lowercase						(self    :    int ):
             return self.dataset



        def __len__(self    :    Any ):
             return len(self.features )



        def __getitem__(self    :    int   ,			__a    :    Union[str, Any] ):
             return self.features[i]






        def 					_lowercase						(self    :    int ):
             return self.label_list




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    Dict ):
         return self._create_examples(self._read_tsv(os.path.join(__a   ,			"heuristics_train_set.txt" ) )   ,			"train" )



    def 					_lowercase						(self    :    Any   ,			__a    :    List[Any] ):
         return self._create_examples(self._read_tsv(os.path.join(__a   ,			"heuristics_evaluation_set.txt" ) )   ,			"dev" )



    def 					_lowercase						(self    :    Any ):
         return ["contradiction", "entailment", "neutral"]






    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Optional[int]   ,			__a    :    Union[str, Any] ):
         UpperCAmelCase_           =   []
         for i, line in enumerate(__a ):
              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=__a   ,			text_a=__a   ,			text_b=__a   ,			label=__a   ,			pairID=__a ) )
         return examples





def 	lowerCAmelCase_				(					snake_case_       : List[InputExample]					,    snake_case_       : List[str]					,    snake_case_       : int					,    snake_case_       : PreTrainedTokenizer					,    )    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   {label: i for i, label in enumerate(snake_case_			)}

     UpperCAmelCase_           =   []
     for ex_index, example in tqdm.tqdm(enumerate(snake_case_			)					,    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=snake_case_					,    max_length=snake_case_					,    padding="max_length"					,    truncation=snake_case_					,    return_overflowing_tokens=snake_case_					,    )

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

          UpperCAmelCase_           =   int(example.pairID			)

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

     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_:     int  ={
    'hans': 3,
}






SCREAMING_SNAKE_CASE_:     Any  ={
    'hans': HansProcessor,
}




