




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       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  Optional[int]:
     a											=   TimesformerConfig()

     if "large" in model_name:
          a											=   96

     if "hr" in model_name:
          a											=   16
          a											=   4_48

     a											=   "huggingface/label-files"
     if "k400" in model_name:
          a											=   4_00
          a											=   "kinetics400-id2label.json"
     elif "k600" in model_name:
          a											=   6_00
          a											=   "kinetics600-id2label.json"
     elif "ssv2" in model_name:
          a											=   1_74
          a											=   "something-something-v2-id2label.json"
     else:
          raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'.")
     a											=   json.load(open(hf_hub_download(__UpperCamelCase	,      __UpperCamelCase	,      repo_type="dataset")	,      "r"))
     a											=   {int(__UpperCamelCase): v for k, v in idalabel.items()}
     a											=   idalabel
     a											=   {v: k for k, v in idalabel.items()}

     return config

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  int:
     if "encoder." in name:
          a											=   name.replace("encoder."	,      "")
     if "cls_token" in name:
          a											=   name.replace("cls_token"	,      "timesformer.embeddings.cls_token")
     if "pos_embed" in name:
          a											=   name.replace("pos_embed"	,      "timesformer.embeddings.position_embeddings")
     if "time_embed" in name:
          a											=   name.replace("time_embed"	,      "timesformer.embeddings.time_embeddings")
     if "patch_embed.proj" in name:
          a											=   name.replace("patch_embed.proj"	,      "timesformer.embeddings.patch_embeddings.projection")
     if "patch_embed.norm" in name:
          a											=   name.replace("patch_embed.norm"	,      "timesformer.embeddings.norm")
     if "blocks" in name:
          a											=   name.replace("blocks"	,      "timesformer.encoder.layer")
     if "attn.proj" in name:
          a											=   name.replace("attn.proj"	,      "attention.output.dense")
     if "attn" in name and "bias" not in name and "temporal" not in name:
          a											=   name.replace("attn"	,      "attention.self")
     if "attn" in name and "temporal" not in name:
          a											=   name.replace("attn"	,      "attention.attention")
     if "temporal_norm1" in name:
          a											=   name.replace("temporal_norm1"	,      "temporal_layernorm")
     if "temporal_attn.proj" in name:
          a											=   name.replace("temporal_attn"	,      "temporal_attention.output.dense")
     if "temporal_fc" in name:
          a											=   name.replace("temporal_fc"	,      "temporal_dense")
     if "norm1" in name and "temporal" not in name:
          a											=   name.replace("norm1"	,      "layernorm_before")
     if "norm2" in name:
          a											=   name.replace("norm2"	,      "layernorm_after")
     if "mlp.fc1" in name:
          a											=   name.replace("mlp.fc1"	,      "intermediate.dense")
     if "mlp.fc2" in name:
          a											=   name.replace("mlp.fc2"	,      "output.dense")
     if "norm.weight" in name and "fc" not in name and "temporal" not in name:
          a											=   name.replace("norm.weight"	,      "timesformer.layernorm.weight")
     if "norm.bias" in name and "fc" not in name and "temporal" not in name:
          a											=   name.replace("norm.bias"	,      "timesformer.layernorm.bias")
     if "head" in name:
          a											=   name.replace("head"	,      "classifier")

     return name

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  List[str]:
     for key in orig_state_dict.copy().keys():
          a											=   orig_state_dict.pop(__UpperCamelCase)

          if key.startswith("model."):
               a											=   key.replace("model."	,      "")

          if "qkv" in key:
               a											=   key.split(".")
               a											=   int(key_split[1])
               a											=   "timesformer.encoder.layer."
               if "temporal" in key:
                    a											=   ".temporal_attention.attention.qkv."
               else:
                    a											=   ".attention.attention.qkv."
               if "weight" in key:
                    a											=   val
               else:
                    a											=   val
          else:
               a											=   val

     return orig_state_dict

def       SCREAMING_SNAKE_CASE							(     )    ->  Union[str, Any]:
     a											=   hf_hub_download(
         repo_id="hf-internal-testing/spaghetti-video"	,      filename="eating_spaghetti.npy"	,      repo_type="dataset")
     a											=   np.load(__UpperCamelCase)
     return list(__UpperCamelCase)

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  str:
     a											=   get_timesformer_config(__UpperCamelCase)

     a											=   TimesformerForVideoClassification(__UpperCamelCase)

     # download original checkpoint, hosted on Google Drive
     a											=   "pytorch_model.bin"
     gdown.cached_download(__UpperCamelCase	,      __UpperCamelCase	,      quiet=__UpperCamelCase)
     a											=   torch.load(__UpperCamelCase	,      map_location="cpu")
     if "model" in files:
          a											=   files["model"]
     elif "module" in files:
          a											=   files["module"]
     else:
          a											=   files["model_state"]
     a											=   convert_state_dict(__UpperCamelCase	,      __UpperCamelCase)

     model.load_state_dict(__UpperCamelCase)
     model.eval()

     # verify model on basic input
     a											=   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,      image_std=[0.5, 0.5, 0.5])
     a											=   prepare_video()
     a											=   image_processor(video[:8]	,      return_tensors="pt")

     a											=   model(**__UpperCamelCase)
     a											=   outputs.logits

     a											=   [
         # 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":
          a											=   torch.Size([1, 4_00])
          a											=   torch.tensor([-0.3_016, -0.7_713, -0.4_205])
     elif model_name == "timesformer-base-finetuned-k600":
          a											=   torch.Size([1, 6_00])
          a											=   torch.tensor([-0.7_267, -0.7_466, 3.2_404])
     elif model_name == "timesformer-base-finetuned-ssv2":
          a											=   torch.Size([1, 1_74])
          a											=   torch.tensor([-0.9_059, 0.6_433, -3.1_457])
     elif model_name == "timesformer-large-finetuned-k400":
          a											=   torch.Size([1, 4_00])
          a											=   torch.tensor([0, 0, 0])
     elif model_name == "timesformer-large-finetuned-k600":
          a											=   torch.Size([1, 6_00])
          a											=   torch.tensor([0, 0, 0])
     elif model_name == "timesformer-large-finetuned-ssv2":
          a											=   torch.Size([1, 1_74])
          a											=   torch.tensor([0, 0, 0])
     elif model_name == "timesformer-hr-finetuned-k400":
          a											=   torch.Size([1, 4_00])
          a											=   torch.tensor([-0.9_617, -3.7_311, -3.7_708])
     elif model_name == "timesformer-hr-finetuned-k600":
          a											=   torch.Size([1, 6_00])
          a											=   torch.tensor([2.5_273, 0.7_127, 1.8_848])
     elif model_name == "timesformer-hr-finetuned-ssv2":
          a											=   torch.Size([1, 1_74])
          a											=   torch.tensor([-3.6_756, -0.7_513, 0.7_180])
     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__":
   lowercase__   :    Union[str, Any] 					=      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."
   )

   lowercase__   :    Dict 					=      parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__   :    str 					=      logging.get_logger(__name__)

lowercase__   :    List[str] 					=      {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}




class     a__ (      UpperCamelCase__   ):
     a	:    Union[str, Any]	      =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
     a	:    Optional[int]	      =			"""mobilebert"""







     def __init__(			self						,	A=30522						,	A=512						,	A=24						,	A=4						,	A=512						,	A="relu"						,	A=0.0						,	A=0.1						,	A=512						,	A=2						,	A=0.0_2						,	A=1e-12						,	A=0						,	A=128						,	A=True						,	A=True						,	A=128						,	A=False						,	A=True						,	A=4						,	A="no_norm"						,	A=True						,	A=None						,	**A						,	)						->					str:



          '''simple docstring'''



          super().__init__(pad_token_id=A						,	**A				)

          a											=   vocab_size
          a											=   hidden_size
          a											=   num_hidden_layers
          a											=   num_attention_heads
          a											=   hidden_act
          a											=   intermediate_size
          a											=   hidden_dropout_prob
          a											=   attention_probs_dropout_prob
          a											=   max_position_embeddings
          a											=   type_vocab_size
          a											=   initializer_range
          a											=   layer_norm_eps
          a											=   embedding_size
          a											=   trigram_input
          a											=   use_bottleneck
          a											=   intra_bottleneck_size
          a											=   use_bottleneck_attention
          a											=   key_query_shared_bottleneck
          a											=   num_feedforward_networks
          a											=   normalization_type
          a											=   classifier_activation

          if self.use_bottleneck:
               a											=   intra_bottleneck_size
          else:
               a											=   hidden_size

          a											=   classifier_dropout







class     a__ (      UpperCamelCase__   ):







     @property
     def     lowerCAmelCase_       (			self				)						->					Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          if self.task == "multiple-choice":
               a											=   {0: "batch", 1: "choice", 2: "sequence"}
          else:
               a											=   {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]				)

