


import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: Optional[int]					)     ->    str:
   lowerCamelCase_						:   List[str]		= XCLIPTextConfig()

   # derive patch size from model name
   lowerCamelCase_						:   Dict		= model_name.find("""patch"""					)
   lowerCamelCase_						:   Optional[Any]		= int(model_name[start_idx + len("""patch"""					) : start_idx + len("""patch"""					) + 2]					)
   lowerCamelCase_						:   List[str]		= XCLIPVisionConfig(patch_size=A__						,						num_frames=A__					)

   if "large" in model_name:
      lowerCamelCase_						:   Dict		= 768
      lowerCamelCase_						:   int		= 3072
      lowerCamelCase_						:   List[str]		= 12

      lowerCamelCase_						:   Optional[Any]		= 1024
      lowerCamelCase_						:   Any		= 4096
      lowerCamelCase_						:   List[str]		= 16
      lowerCamelCase_						:   Tuple		= 24
      lowerCamelCase_						:   str		= 768
      lowerCamelCase_						:   Dict		= 3072

   if model_name == "xclip-large-patch14-16-frames":
      lowerCamelCase_						:   Optional[Any]		= 336

   lowerCamelCase_						:   Optional[Any]		= XCLIPConfig.from_text_vision_configs(A__						,						A__					)

   if "large" in model_name:
      lowerCamelCase_						:   int		= 768

   return config


def    __lowerCamelCase   (      A__						: Dict					)     ->    List[Any]:
   # text encoder
   if name == "token_embedding.weight":
      lowerCamelCase_						:   Optional[Any]		= name.replace("""token_embedding.weight"""						,						"""text_model.embeddings.token_embedding.weight"""					)
   if name == "positional_embedding":
      lowerCamelCase_						:   Union[str, Any]		= name.replace("""positional_embedding"""						,						"""text_model.embeddings.position_embedding.weight"""					)
   if "ln_1" in name:
      lowerCamelCase_						:   Dict		= name.replace("""ln_1"""						,						"""layer_norm1"""					)
   if "ln_2" in name:
      lowerCamelCase_						:   List[str]		= name.replace("""ln_2"""						,						"""layer_norm2"""					)
   if "c_fc" in name:
      lowerCamelCase_						:   Optional[Any]		= name.replace("""c_fc"""						,						"""fc1"""					)
   if "c_proj" in name:
      lowerCamelCase_						:   Tuple		= name.replace("""c_proj"""						,						"""fc2"""					)
   if name.startswith("""transformer.resblocks"""					):
      lowerCamelCase_						:   List[str]		= name.replace("""transformer.resblocks"""						,						"""text_model.encoder.layers"""					)
   if "attn.out_proj" in name and "message" not in name:
      lowerCamelCase_						:   str		= name.replace("""attn.out_proj"""						,						"""self_attn.out_proj"""					)
   if "ln_final" in name:
      lowerCamelCase_						:   Optional[Any]		= name.replace("""ln_final"""						,						"""text_model.final_layer_norm"""					)
   # visual encoder
   if name == "visual.class_embedding":
      lowerCamelCase_						:   Tuple		= name.replace("""visual.class_embedding"""						,						"""vision_model.embeddings.class_embedding"""					)
   if name == "visual.positional_embedding":
      lowerCamelCase_						:   int		= name.replace("""visual.positional_embedding"""						,						"""vision_model.embeddings.position_embedding.weight"""					)
   if name.startswith("""visual.transformer.resblocks"""					):
      lowerCamelCase_						:   Any		= name.replace("""visual.transformer.resblocks"""						,						"""vision_model.encoder.layers"""					)
   if "visual.conv1" in name:
      lowerCamelCase_						:   List[Any]		= name.replace("""visual.conv1"""						,						"""vision_model.embeddings.patch_embedding"""					)
   if "visual.ln_pre" in name:
      lowerCamelCase_						:   Dict		= name.replace("""visual.ln_pre"""						,						"""vision_model.pre_layernorm"""					)
   if "visual.ln_post" in name:
      lowerCamelCase_						:   Optional[int]		= name.replace("""visual.ln_post"""						,						"""vision_model.post_layernorm"""					)
   if "visual.proj" in name:
      lowerCamelCase_						:   Optional[int]		= name.replace("""visual.proj"""						,						"""visual_projection.weight"""					)
   if "text_projection" in name:
      lowerCamelCase_						:   str		= name.replace("""text_projection"""						,						"""text_projection.weight"""					)
   # things on top
   if "prompts_visual_proj" in name:
      lowerCamelCase_						:   Union[str, Any]		= name.replace("""prompts_visual_proj"""						,						"""prompts_visual_projection"""					)
   if "prompts_visual_ln" in name:
      lowerCamelCase_						:   List[str]		= name.replace("""prompts_visual_ln"""						,						"""prompts_visual_layernorm"""					)
   # mit
   if name == "mit.positional_embedding":
      lowerCamelCase_						:   List[Any]		= name.replace("""positional"""						,						"""position"""					)
   if name.startswith("""mit.resblocks"""					):
      lowerCamelCase_						:   Any		= name.replace("""mit.resblocks"""						,						"""mit.encoder.layers"""					)
   # prompts generator
   if name.startswith("""prompts_generator.norm"""					):
      lowerCamelCase_						:   List[Any]		= name.replace("""prompts_generator.norm"""						,						"""prompts_generator.layernorm"""					)

   return name


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: Any					)     ->    Tuple:
   for key in orig_state_dict.copy().keys():
      lowerCamelCase_						:   str		= orig_state_dict.pop(A__					)

      if "attn.in_proj" in key:
         lowerCamelCase_						:   int		= key.split("""."""					)
         if key.startswith("""visual"""					):
            lowerCamelCase_						:   str		= key_split[3]
            lowerCamelCase_						:   str		= config.vision_config.hidden_size
            if "message_attn" in key:
               if "weight" in key:
                  lowerCamelCase_						:   Optional[int]		= val[
                      :dim, :
                  ]
                  lowerCamelCase_						:   int		= val[
                      dim : dim * 2, :
                  ]
                  lowerCamelCase_						:   List[str]		= val[
                      -dim:, :
                  ]
               else:
                  lowerCamelCase_						:   str		= val[
                      :dim
                  ]
                  lowerCamelCase_						:   Tuple		= val[
                      dim : dim * 2
                  ]
                  lowerCamelCase_						:   Union[str, Any]		= val[
                      -dim:
                  ]
            else:
               if "weight" in key:
                  lowerCamelCase_						:   Union[str, Any]		= val[
                      :dim, :
                  ]
                  lowerCamelCase_						:   List[Any]		= val[
                      dim : dim * 2, :
                  ]
                  lowerCamelCase_						:   Optional[int]		= val[
                      -dim:, :
                  ]
               else:
                  lowerCamelCase_						:   List[Any]		= val[:dim]
                  lowerCamelCase_						:   List[str]		= val[
                      dim : dim * 2
                  ]
                  lowerCamelCase_						:   List[str]		= val[-dim:]
         elif key.startswith("""mit"""					):
            lowerCamelCase_						:   Optional[Any]		= key_split[2]
            lowerCamelCase_						:   List[Any]		= config.vision_config.mit_hidden_size
            if "weight" in key:
               lowerCamelCase_						:   Optional[Any]		= val[:dim, :]
               lowerCamelCase_						:   Any		= val[dim : dim * 2, :]
               lowerCamelCase_						:   List[str]		= val[-dim:, :]
            else:
               lowerCamelCase_						:   str		= val[:dim]
               lowerCamelCase_						:   Tuple		= val[dim : dim * 2]
               lowerCamelCase_						:   Optional[Any]		= val[-dim:]
         else:
            lowerCamelCase_						:   Union[str, Any]		= key_split[2]
            lowerCamelCase_						:   Optional[Any]		= config.text_config.hidden_size
            if "weight" in key:
               lowerCamelCase_						:   Dict		= val[:dim, :]
               lowerCamelCase_						:   List[str]		= val[
                   dim : dim * 2, :
               ]
               lowerCamelCase_						:   Any		= val[-dim:, :]
            else:
               lowerCamelCase_						:   List[Any]		= val[:dim]
               lowerCamelCase_						:   Dict		= val[
                   dim : dim * 2
               ]
               lowerCamelCase_						:   List[str]		= val[-dim:]
      else:
         lowerCamelCase_						:   Optional[int]		= rename_key(A__					)
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            lowerCamelCase_						:   Tuple		= val.T
         lowerCamelCase_						:   Optional[Any]		= val

   return orig_state_dict


def    __lowerCamelCase   (      A__						: Optional[int]					)     ->    Dict:
   if num_frames == 8:
      lowerCamelCase_						:   Dict		= """eating_spaghetti_8_frames.npy"""
   elif num_frames == 16:
      lowerCamelCase_						:   Any		= """eating_spaghetti.npy"""
   elif num_frames == 32:
      lowerCamelCase_						:   Tuple		= """eating_spaghetti_32_frames.npy"""
   lowerCamelCase_						:   Tuple		= hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""						,						filename=A__						,						repo_type="""dataset"""						,						)
   lowerCamelCase_						:   Any		= np.load(A__					)
   return list(A__					)


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Tuple=None						,						A__						: Optional[Any]=False					)     ->    Dict:
   lowerCamelCase_						:   int		= {
       # fully supervised kinetics-400 checkpoints
       """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
       """xclip-base-patch32-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
       ),
       """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
       """xclip-base-patch16-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
       ),
       """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
       """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
       # fully supervised kinetics-600 checkpoints
       """xclip-base-patch16-kinetics-600""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
       ),
       """xclip-base-patch16-kinetics-600-16-frames""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
       ),
       """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
       # few shot
       """xclip-base-patch16-hmdb-2-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
       ),
       """xclip-base-patch16-hmdb-4-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
       ),
       """xclip-base-patch16-hmdb-8-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
       ),
       """xclip-base-patch16-hmdb-16-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
       ),
       """xclip-base-patch16-ucf-2-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
       ),
       """xclip-base-patch16-ucf-4-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
       ),
       """xclip-base-patch16-ucf-8-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
       ),
       """xclip-base-patch16-ucf-16-shot""": (
           """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
       ),
       # zero shot
       """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
   }

   lowerCamelCase_						:   Any		= model_to_url[model_name]
   lowerCamelCase_						:   Optional[int]		= 8
   if "16-frames" in model_name:
      lowerCamelCase_						:   Dict		= 16
   elif "shot" in model_name:
      lowerCamelCase_						:   Tuple		= 32

   lowerCamelCase_						:   Tuple		= get_xclip_config(A__						,						A__					)
   lowerCamelCase_						:   Union[str, Any]		= XCLIPModel(A__					)
   model.eval()

   if "drive" in checkpoint_url:
      lowerCamelCase_						:   Tuple		= """pytorch_model.bin"""
      gdown.cached_download(A__						,						A__						,						quiet=A__					)
      lowerCamelCase_						:   Any		= torch.load(A__						,						map_location="""cpu"""					)["""model"""]
   else:
      lowerCamelCase_						:   List[str]		= torch.hub.load_state_dict_from_url(A__					)["""model"""]

   lowerCamelCase_						:   List[Any]		= convert_state_dict(A__						,						A__					)

   lowerCamelCase_						:   Optional[Any]		= XCLIPModel(A__					)
   lowerCamelCase_,	lowerCamelCase_						:   str		= model.load_state_dict(A__						,						strict=A__					)
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   lowerCamelCase_						:   str		= 336 if model_name == """xclip-large-patch14-16-frames""" else 224
   lowerCamelCase_						:   Dict		= VideoMAEImageProcessor(size=A__					)
   lowerCamelCase_						:   Union[str, Any]		= CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""					)
   lowerCamelCase_						:   Any		= CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""					)
   lowerCamelCase_						:   Any		= XCLIPProcessor(image_processor=A__						,						tokenizer=A__					)

   lowerCamelCase_						:   Dict		= prepare_video(A__					)
   lowerCamelCase_						:   Union[str, Any]		= processor(
       text=["""playing sports""", """eating spaghetti""", """go shopping"""]						,						videos=A__						,						return_tensors="""pt"""						,						padding=A__					)

   print("""Shape of pixel values:"""						,						inputs.pixel_values.shape					)

   with torch.no_grad():
      lowerCamelCase_						:   Union[str, Any]		= model(**A__					)

   # Verify outputs
   lowerCamelCase_						:   Union[str, Any]		= outputs.logits_per_video
   lowerCamelCase_						:   Union[str, Any]		= logits_per_video.softmax(dim=1					)
   print("""Probs:"""						,						A__					)
   # kinetics-400
   if model_name == "xclip-base-patch32":
      lowerCamelCase_						:   Union[str, Any]		= torch.tensor([[0.0019, 0.9951, 0.0030]]					)
   elif model_name == "xclip-base-patch32-16-frames":
      lowerCamelCase_						:   Tuple		= torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]					)
   elif model_name == "xclip-base-patch16":
      lowerCamelCase_						:   List[str]		= torch.tensor([[0.0083, 0.9681, 0.0236]]					)
   elif model_name == "xclip-base-patch16-16-frames":
      lowerCamelCase_						:   Optional[Any]		= torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]					)
   elif model_name == "xclip-large-patch14":
      lowerCamelCase_						:   List[str]		= torch.tensor([[0.0062, 0.9864, 0.0075]]					)
   elif model_name == "xclip-large-patch14-16-frames":
      lowerCamelCase_						:   Dict		= torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]					)
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      lowerCamelCase_						:   Optional[Any]		= torch.tensor([[0.0555, 0.8914, 0.0531]]					)
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      lowerCamelCase_						:   Optional[int]		= torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]					)
   elif model_name == "xclip-large-patch14-kinetics-600":
      lowerCamelCase_						:   List[str]		= torch.tensor([[0.0036, 0.9920, 0.0045]]					)
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      lowerCamelCase_						:   Optional[int]		= torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]					)
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      lowerCamelCase_						:   Union[str, Any]		= torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]					)
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      lowerCamelCase_						:   Any		= torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]					)
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      lowerCamelCase_						:   Tuple		= torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]					)
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      lowerCamelCase_						:   str		= torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]					)
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      lowerCamelCase_						:   Optional[Any]		= torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]					)
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      lowerCamelCase_						:   Union[str, Any]		= torch.tensor([[0.0027, 0.9904, 0.0070]]					)
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      lowerCamelCase_						:   Optional[int]		= torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]					)
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      lowerCamelCase_						:   Tuple		= torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]					)
   else:
      raise ValueError(f'''Model name {model_name} not supported'''					)
   assert torch.allclose(A__						,						A__						,						atol=1e-3					)
   print("""Looks ok!"""					)

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(A__					)

   if push_to_hub:
      print("""Pushing model, processor and slow tokenizer files to the hub..."""					)
      model.push_to_hub(A__						,						organization="""nielsr"""					)
      processor.push_to_hub(A__						,						organization="""nielsr"""					)
      slow_tokenizer.push_to_hub(A__						,						organization="""nielsr"""					)


if __name__ == "__main__":
      snake_case__				:	List[Any]					       =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='xclip-base-patch32',
          type=str,
          help='Name of the model.',
      )
      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.'
      )

      snake_case__				:	Tuple					       =					parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__				:	str					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()						)}						)
       _a   							=							field(
           metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
       _a   							=							field(
           default=128						,       metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Overwrite the cached training and evaluation sets"}						)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          lowerCamelCase_						:   Optional[Any]		= self.task_name.lower()







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"train"
       _a   							=							"dev"
       _a   							=							"test"







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							42
       _a   							=							42
       _a   							=							42



       def __init__(						self				:				Any		,				__a				:				GlueDataTrainingArguments		,				__a				:				PreTrainedTokenizerBase		,				__a				:				Optional[int] = None		,				__a				:				Union[str, Split] = Split.train		,				__a				:				Optional[str] = None		,				)					->Optional[int]:
          warnings.warn(
              """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
              """library. You can have a look at this example script for pointers: """
              """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""		,				__a		,				)
          lowerCamelCase_						:   Optional[int]		= args
          lowerCamelCase_						:   Tuple		= glue_processors[args.task_name]()
          lowerCamelCase_						:   Optional[Any]		= glue_output_modes[args.task_name]
          if isinstance(__a		,				__a							):
             try:
                lowerCamelCase_						:   List[Any]		= Split[mode]
             except KeyError:
                raise KeyError("""mode is not a valid split name"""							)
        # Load data features from cache or dataset file
          lowerCamelCase_						:   List[Any]		= os.path.join(
              cache_dir if cache_dir is not None else args.data_dir		,				F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''		,				)
          lowerCamelCase_						:   Any		= self.processor.get_labels()
          if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
              "RobertaTokenizer",
              "RobertaTokenizerFast",
              "XLMRobertaTokenizer",
              "BartTokenizer",
              "BartTokenizerFast",
          ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase_,	lowerCamelCase_						:   int		= label_list[2], label_list[1]
          lowerCamelCase_						:   Any		= label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowerCamelCase_						:   List[Any]		= cached_features_file + """.lock"""
          with FileLock(__a							):
             if os.path.exists(__a							) and not args.overwrite_cache:
                lowerCamelCase_						:   str		= time.time()
                lowerCamelCase_						:   int		= torch.load(__a							)
                logger.info(
                    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''		,				time.time() - start							)
             else:
                logger.info(F'''Creating features from dataset file at {args.data_dir}'''							)

                if mode == Split.dev:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_dev_examples(args.data_dir							)
                elif mode == Split.test:
                   lowerCamelCase_						:   Tuple		= self.processor.get_test_examples(args.data_dir							)
                else:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_train_examples(args.data_dir							)
                if limit_length is not None:
                   lowerCamelCase_						:   Dict		= examples[:limit_length]
                lowerCamelCase_						:   Union[str, Any]		= glue_convert_examples_to_features(
                    __a		,				__a		,				max_length=args.max_seq_length		,				label_list=__a		,				output_mode=self.output_mode		,				)
                lowerCamelCase_						:   Optional[Any]		= time.time()
                torch.save(self.features		,				__a							)
                # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                logger.info(
                    F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''							)



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



       def __getitem__(						self				:				List[Any]		,				__a				:				Optional[int]							)					->InputFeatures:
          return self.features[i]




       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[int]:
          return self.label_list





