





"""simple docstring"""
import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A    )		->				int:
   # Initialise PyTorch model
   lowercase    :			int  =       FNetConfig.from_json_file(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )
   lowercase    :			Optional[int]  =       FNetForPreTraining(__A    )

   lowercase    :			List[str]  =       restore_checkpoint(__A ,__A    )
   lowercase    :			str  =       checkpoint_dict["""target"""]

   # Embeddings
   # Position IDs
   lowercase    :			Optional[Any]  =       fnet_pretraining_model.state_dict()

   lowercase    :			Optional[int]  =       state_dict["""fnet.embeddings.position_ids"""]
   lowercase    :			Any  =       {"""fnet.embeddings.position_ids""": position_ids}
   # Embedding Layers
   lowercase    :			Union[str, Any]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
   lowercase    :			Any  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]    )
   lowercase    :			Union[str, Any]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]    )
   lowercase    :			Dict  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]    ).T
   lowercase    :			Tuple  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]    )
   lowercase    :			Union[str, Any]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]    )
   lowercase    :			Optional[int]  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]    )

   # Encoder Layers
   for layer in range(config.num_hidden_layers    ):
      lowercase    :			List[Any]  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]    )
      lowercase    :			Tuple  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]    )

      lowercase    :			Dict  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]    ).T
      lowercase    :			Union[str, Any]  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]    )

      lowercase    :			List[Any]  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]    ).T
      lowercase    :			str  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]    )

      lowercase    :			Tuple  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]    )
      lowercase    :			Any  =       torch.tensor(
          pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]    )

   # Pooler Layers
   lowercase    :			Optional[Any]  =       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]    ).T
   lowercase    :			Optional[int]  =       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]    )

   # Masked LM Layers
   lowercase    :			List[Any]  =       torch.tensor(
       pretrained_model_params["""predictions_dense"""]["""kernel"""]    ).T
   lowercase    :			str  =       torch.tensor(
       pretrained_model_params["""predictions_dense"""]["""bias"""]    )
   lowercase    :			str  =       torch.tensor(
       pretrained_model_params["""predictions_layer_norm"""]["""scale"""]    )
   lowercase    :			Any  =       torch.tensor(
       pretrained_model_params["""predictions_layer_norm"""]["""bias"""]    )
   lowercase    :			Dict  =       torch.tensor(
       pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
   lowercase    :			List[str]  =       torch.tensor(
       pretrained_model_params["""predictions_output"""]["""output_bias"""]    )
   lowercase    :			Optional[Any]  =       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]    )

   # Seq Relationship Layers
   lowercase    :			int  =       torch.tensor(
       pretrained_model_params["""classification"""]["""output_kernel"""]    )
   lowercase    :			List[Any]  =       torch.tensor(
       pretrained_model_params["""classification"""]["""output_bias"""]    )

   # Load State Dict
   fnet_pretraining_model.load_state_dict(__A    )

   # Save PreTrained
   print(F'''Saving pretrained model to {save_path}'''    )
   fnet_pretraining_model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	int							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--fnet_config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained FNet model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
   lowerCAmelCase:	List[str]							      =parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)









"""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 			__snake_case						(      __A    )		->				Optional[int]:
   lowercase    :			int  =       TimesformerConfig()

   if "large" in model_name:
      lowercase    :			str  =       96

   if "hr" in model_name:
      lowercase    :			Tuple  =       16
      lowercase    :			Any  =       448

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

   return config




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

   return name




def 			__snake_case						(      __A ,__A    )		->				List[str]:
   for key in orig_state_dict.copy().keys():
      lowercase    :			str  =       orig_state_dict.pop(__A    )

      if key.startswith("""model."""    ):
         lowercase    :			Union[str, Any]  =       key.replace("""model.""" ,""""""    )

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

   return orig_state_dict




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




def 			__snake_case						(      __A ,__A ,__A ,__A    )		->				Union[str, Any]:
   lowercase    :			Union[str, Any]  =       get_timesformer_config(__A    )

   lowercase    :			Any  =       TimesformerForVideoClassification(__A    )

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

   model.load_state_dict(__A    )
   model.eval()

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

   lowercase    :			Dict  =       model(**__A    )
   lowercase    :			int  =       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    :			Optional[int]  =       torch.Size([1, 400]    )
      lowercase    :			Dict  =       torch.tensor([-0.30_16, -0.77_13, -0.42_05]    )
   elif model_name == "timesformer-base-finetuned-k600":
      lowercase    :			str  =       torch.Size([1, 600]    )
      lowercase    :			List[Any]  =       torch.tensor([-0.72_67, -0.74_66, 3.24_04]    )
   elif model_name == "timesformer-base-finetuned-ssv2":
      lowercase    :			Tuple  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       torch.tensor([-0.90_59, 0.64_33, -3.14_57]    )
   elif model_name == "timesformer-large-finetuned-k400":
      lowercase    :			str  =       torch.Size([1, 400]    )
      lowercase    :			List[str]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-k600":
      lowercase    :			List[Any]  =       torch.Size([1, 600]    )
      lowercase    :			str  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-ssv2":
      lowercase    :			Dict  =       torch.Size([1, 174]    )
      lowercase    :			List[Any]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-hr-finetuned-k400":
      lowercase    :			Tuple  =       torch.Size([1, 400]    )
      lowercase    :			Union[str, Any]  =       torch.tensor([-0.96_17, -3.73_11, -3.77_08]    )
   elif model_name == "timesformer-hr-finetuned-k600":
      lowercase    :			Union[str, Any]  =       torch.Size([1, 600]    )
      lowercase    :			Any  =       torch.tensor([2.52_73, 0.71_27, 1.88_48]    )
   elif model_name == "timesformer-hr-finetuned-ssv2":
      lowercase    :			int  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       torch.tensor([-3.67_56, -0.75_13, 0.71_80]    )
   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] ,__A ,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(__A    )
      model.save_pretrained(__A    )

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


if __name__ == "__main__":
   lowerCAmelCase:	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."
   )

   lowerCAmelCase:	int							      =parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )


