'''simple docstring'''
import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor







def 							__UpperCamelCase   (				__UpperCAmelCase     :				List[Any] )       ->  str:
  lowercase							:       int											=      TimesformerConfig()

  if "large" in model_name:
    lowercase							:       Optional[int]											=      96

  if "hr" in model_name:
    lowercase							:       Any											=      16
    lowercase							:       Optional[Any]											=      448

  lowercase							:       int											=      "huggingface/label-files"
  if "k400" in model_name:
    lowercase							:       List[Any]											=      400
    lowercase							:       int											=      "kinetics400-id2label.json"
  elif "k600" in model_name:
    lowercase							:       Dict											=      600
    lowercase							:       Optional[Any]											=      "kinetics600-id2label.json"
  elif "ssv2" in model_name:
    lowercase							:       Dict											=      174
    lowercase							:       Optional[Any]											=      "something-something-v2-id2label.json"
  else:
    raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.''' )
  lowercase							:       str											=      json.load(open(hf_hub_download(__UpperCAmelCase   ,      __UpperCAmelCase   ,      repo_type='''dataset''' )   ,      '''r''' ) )
  lowercase							:       List[Any]											=      {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
  lowercase							:       List[Any]											=      idalabel
  lowercase							:       Any											=      {v: k for k, v in idalabel.items()}

  return config







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Dict )       ->  List[str]:



  if "encoder." in name:
    lowercase							:       List[Any]											=      name.replace('''encoder.'''   ,      '''''' )
  if "cls_token" in name:
    lowercase							:       str											=      name.replace('''cls_token'''   ,      '''timesformer.embeddings.cls_token''' )
  if "pos_embed" in name:
    lowercase							:       Optional[Any]											=      name.replace('''pos_embed'''   ,      '''timesformer.embeddings.position_embeddings''' )
  if "time_embed" in name:
    lowercase							:       int											=      name.replace('''time_embed'''   ,      '''timesformer.embeddings.time_embeddings''' )
  if "patch_embed.proj" in name:
    lowercase							:       int											=      name.replace('''patch_embed.proj'''   ,      '''timesformer.embeddings.patch_embeddings.projection''' )
  if "patch_embed.norm" in name:
    lowercase							:       List[str]											=      name.replace('''patch_embed.norm'''   ,      '''timesformer.embeddings.norm''' )
  if "blocks" in name:
    lowercase							:       str											=      name.replace('''blocks'''   ,      '''timesformer.encoder.layer''' )
  if "attn.proj" in name:
    lowercase							:       Optional[Any]											=      name.replace('''attn.proj'''   ,      '''attention.output.dense''' )
  if "attn" in name and "bias" not in name and "temporal" not in name:
    lowercase							:       str											=      name.replace('''attn'''   ,      '''attention.self''' )
  if "attn" in name and "temporal" not in name:
    lowercase							:       Any											=      name.replace('''attn'''   ,      '''attention.attention''' )
  if "temporal_norm1" in name:
    lowercase							:       Optional[Any]											=      name.replace('''temporal_norm1'''   ,      '''temporal_layernorm''' )
  if "temporal_attn.proj" in name:
    lowercase							:       Dict											=      name.replace('''temporal_attn'''   ,      '''temporal_attention.output.dense''' )
  if "temporal_fc" in name:
    lowercase							:       Union[str, Any]											=      name.replace('''temporal_fc'''   ,      '''temporal_dense''' )
  if "norm1" in name and "temporal" not in name:
    lowercase							:       Any											=      name.replace('''norm1'''   ,      '''layernorm_before''' )
  if "norm2" in name:
    lowercase							:       int											=      name.replace('''norm2'''   ,      '''layernorm_after''' )
  if "mlp.fc1" in name:
    lowercase							:       int											=      name.replace('''mlp.fc1'''   ,      '''intermediate.dense''' )
  if "mlp.fc2" in name:
    lowercase							:       Optional[int]											=      name.replace('''mlp.fc2'''   ,      '''output.dense''' )
  if "norm.weight" in name and "fc" not in name and "temporal" not in name:
    lowercase							:       List[str]											=      name.replace('''norm.weight'''   ,      '''timesformer.layernorm.weight''' )
  if "norm.bias" in name and "fc" not in name and "temporal" not in name:
    lowercase							:       List[Any]											=      name.replace('''norm.bias'''   ,      '''timesformer.layernorm.bias''' )
  if "head" in name:
    lowercase							:       List[Any]											=      name.replace('''head'''   ,      '''classifier''' )

  return name







def 							__UpperCamelCase   (				__UpperCAmelCase     :				List[Any]   ,      __UpperCAmelCase     :				Any )       ->  int:



  for key in orig_state_dict.copy().keys():
    lowercase							:       int											=      orig_state_dict.pop(__UpperCAmelCase )

    if key.startswith('''model.''' ):
      lowercase							:       Optional[int]											=      key.replace('''model.'''   ,      '''''' )

    if "qkv" in key:
      lowercase							:       Tuple											=      key.split('''.''' )
      lowercase							:       Optional[Any]											=      int(key_split[1] )
      lowercase							:       Tuple											=      "timesformer.encoder.layer."
      if "temporal" in key:
        lowercase							:       str											=      ".temporal_attention.attention.qkv."
      else:
        lowercase							:       str											=      ".attention.attention.qkv."
      if "weight" in key:
        lowercase							:       str											=      val
      else:
        lowercase							:       Dict											=      val
    else:
      lowercase							:       int											=      val

  return orig_state_dict







def 							__UpperCamelCase   (				)       ->  List[str]:
  lowercase							:       Dict											=      hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''   ,      filename='''eating_spaghetti.npy'''   ,      repo_type='''dataset''' )
  lowercase							:       str											=      np.load(__UpperCAmelCase )
  return list(__UpperCAmelCase )







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Dict   ,      __UpperCAmelCase     :				Tuple   ,      __UpperCAmelCase     :				Union[str, Any] )       ->  str:
  lowercase							:       int											=      get_timesformer_config(__UpperCAmelCase )

  lowercase							:       Dict											=      TimesformerForVideoClassification(__UpperCAmelCase )

  # download original checkpoint, hosted on Google Drive
  lowercase							:       Dict											=      "pytorch_model.bin"
  gdown.cached_download(__UpperCAmelCase   ,      __UpperCAmelCase   ,      quiet=__UpperCAmelCase )
  lowercase							:       Tuple											=      torch.load(__UpperCAmelCase   ,      map_location='''cpu''' )
  if "model" in files:
    lowercase							:       Optional[Any]											=      files["model"]
  elif "module" in files:
    lowercase							:       Dict											=      files["module"]
  else:
    lowercase							:       int											=      files["model_state"]
  lowercase							:       List[Any]											=      convert_state_dict(__UpperCAmelCase   ,      __UpperCAmelCase )

  model.load_state_dict(__UpperCAmelCase )
  model.eval()

  # verify model on basic input
  lowercase							:       Any											=      VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,      image_std=[0.5, 0.5, 0.5] )
  lowercase							:       Optional[int]											=      prepare_video()
  lowercase							:       Tuple											=      image_processor(video[:8]   ,      return_tensors='''pt''' )

  lowercase							:       int											=      model(**__UpperCAmelCase )
  lowercase							:       Any											=      outputs.logits

  lowercase							:       List[str]											=      [
      # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
      "timesformer-base-finetuned-k400",
      "timesformer-large-finetuned-k400",
      "timesformer-hr-finetuned-k400",
      # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
      "timesformer-base-finetuned-k600",
      "timesformer-large-finetuned-k600",
      "timesformer-hr-finetuned-k600",
      # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
      "timesformer-base-finetuned-ssv2",
      "timesformer-large-finetuned-ssv2",
      "timesformer-hr-finetuned-ssv2",
  ]

  # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
  if model_name == "timesformer-base-finetuned-k400":
    lowercase							:       str											=      torch.Size([1, 400] )
    lowercase							:       Optional[Any]											=      torch.tensor([-0.3016, -0.7713, -0.4205] )
  elif model_name == "timesformer-base-finetuned-k600":
    lowercase							:       str											=      torch.Size([1, 600] )
    lowercase							:       str											=      torch.tensor([-0.7267, -0.7466, 3.2404] )
  elif model_name == "timesformer-base-finetuned-ssv2":
    lowercase							:       int											=      torch.Size([1, 174] )
    lowercase							:       Optional[Any]											=      torch.tensor([-0.9059, 0.6433, -3.1457] )
  elif model_name == "timesformer-large-finetuned-k400":
    lowercase							:       List[Any]											=      torch.Size([1, 400] )
    lowercase							:       Optional[int]											=      torch.tensor([0, 0, 0] )
  elif model_name == "timesformer-large-finetuned-k600":
    lowercase							:       List[str]											=      torch.Size([1, 600] )
    lowercase							:       Optional[Any]											=      torch.tensor([0, 0, 0] )
  elif model_name == "timesformer-large-finetuned-ssv2":
    lowercase							:       Union[str, Any]											=      torch.Size([1, 174] )
    lowercase							:       int											=      torch.tensor([0, 0, 0] )
  elif model_name == "timesformer-hr-finetuned-k400":
    lowercase							:       int											=      torch.Size([1, 400] )
    lowercase							:       str											=      torch.tensor([-0.9617, -3.7311, -3.7708] )
  elif model_name == "timesformer-hr-finetuned-k600":
    lowercase							:       Optional[Any]											=      torch.Size([1, 600] )
    lowercase							:       Optional[Any]											=      torch.tensor([2.5273, 0.7127, 1.8848] )
  elif model_name == "timesformer-hr-finetuned-ssv2":
    lowercase							:       Union[str, Any]											=      torch.Size([1, 174] )
    lowercase							:       str											=      torch.tensor([-3.6756, -0.7513, 0.7180] )
  else:
    raise ValueError(f"""Model name not supported. Should be one of {model_names}""" )

  # verify logits
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3]   ,      __UpperCAmelCase   ,      atol=1e-4 )
  print('''Logits ok!''' )

  if pytorch_dump_folder_path is not None:
    print(f"""Saving model and image processor to {pytorch_dump_folder_path}""" )
    image_processor.save_pretrained(__UpperCAmelCase )
    model.save_pretrained(__UpperCAmelCase )

  if push_to_hub:
    print('''Pushing to the hub...''' )
    model.push_to_hub(f"""fcakyon/{model_name}""" )


if __name__ == "__main__":
 _A		:			Optional[int]	      =argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
     type=str,
     help=(
         '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
         ''' download link.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''''',
     type=str,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', type=str, help='''Name of the model.''')
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )

 _A		:			Union[str, Any]	      =parser.parse_args()
 convert_timesformer_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
 )


'''simple docstring'''
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		:			Any	      =logging.get_logger(__name__)




@dataclass
class _lowerCAmelCase :
      UpperCAmelCase_  :	str
      UpperCAmelCase_  :	str
      UpperCAmelCase_  :	Optional[str]     =					None
      UpperCAmelCase_  :	Optional[str]     =					None







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





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







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





class _lowerCAmelCase :







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self    ):
        raise NotImplementedError()







      def     lowerCamelCase__			(     self     ,						lowercase    ):
        if len(self.get_labels()    ) > 1:
          lowercase							:       Tuple											=      self.get_labels()[int(example.label    )]
        return example







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





class _lowerCAmelCase (  snake_case				):







      def __init__(     self     ,						lowercase=None     ,						lowercase=None     ,						lowercase="classification"     ,						lowercase=False    ):
        lowercase							:       Tuple											=      [] if labels is None else labels
        lowercase							:       Optional[int]											=      [] if examples is None else examples
        lowercase							:       Tuple											=      mode
        lowercase							:       Any											=      verbose







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







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







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







      @classmethod
      def     lowerCamelCase__			(     cls     ,						lowercase     ,						lowercase=None     ,						**lowercase    ):
        lowercase							:       Dict											=      cls(**lowercase    )
        processor.add_examples(lowercase     ,						labels=lowercase    )
        return processor







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

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







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

        # Update examples
        if overwrite_examples:
          lowercase							:       List[str]											=      examples
        else:
          self.examples.extend(lowercase    )

        # Update labels
        if overwrite_labels:
          lowercase							:       Dict											=      list(lowercase    )
        else:
          lowercase							:       Tuple											=      list(set(self.labels    ).union(lowercase    )    )

        return self.examples







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase=None     ,						lowercase=False     ,						lowercase=0     ,						lowercase=True     ,						lowercase=None     ,						):
        if max_length is None:
          lowercase							:       Optional[Any]											=      tokenizer.max_len

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

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

          lowercase							:       Optional[int]											=      tokenizer.encode(
              example.text_a     ,						add_special_tokens=lowercase     ,						max_length=min(lowercase     ,						tokenizer.max_len    )     ,						)
          all_input_ids.append(lowercase    )

        lowercase							:       int											=      max(len(lowercase    ) for input_ids in all_input_ids    )

        lowercase							:       Dict											=      []
        for ex_index, (input_ids, example) in enumerate(zip(lowercase     ,						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.
          lowercase							:       List[Any]											=      [1 if mask_padding_with_zero else 0] * len(lowercase    )

          # Zero-pad up to the sequence length.
          lowercase							:       List[Any]											=      batch_length - len(lowercase    )
          if pad_on_left:
            lowercase							:       Optional[int]											=      ([pad_token] * padding_length) + input_ids
            lowercase							:       Any											=      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
          else:
            lowercase							:       int											=      input_ids + ([pad_token] * padding_length)
            lowercase							:       List[str]											=      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

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

          if self.mode == "classification":
            lowercase							:       Dict											=      label_map[example.label]
          elif self.mode == "regression":
            lowercase							:       Optional[int]											=      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(lowercase    ) for x in input_ids]    )}"""    )
            logger.info(F"""attention_mask: {" ".join([str(lowercase    ) for x in attention_mask]    )}"""    )
            logger.info(F"""label: {example.label} (id = {label})"""    )

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

        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)

          lowercase							:       Any											=      tf.data.Dataset.from_generator(
              lowercase     ,						({'''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

          lowercase							:       Any											=      torch.tensor([f.input_ids for f in features]     ,						dtype=torch.long    )
          lowercase							:       List[str]											=      torch.tensor([f.attention_mask for f in features]     ,						dtype=torch.long    )
          if self.mode == "classification":
            lowercase							:       int											=      torch.tensor([f.label for f in features]     ,						dtype=torch.long    )
          elif self.mode == "regression":
            lowercase							:       Tuple											=      torch.tensor([f.label for f in features]     ,						dtype=torch.float    )

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