




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),
              ]				)

