import argparse
import json

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

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)





def    __lowercase     (						lowerCamelCase					:     Any	):
 UpperCamelCase_					:						int =					VideoMAEConfig()

 set_architecture_configs(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__	)

 if "finetuned" not in model_name:
  UpperCamelCase_					:						Dict =					False

 if "finetuned" in model_name:
  UpperCamelCase_					:						Any =					"""huggingface/label-files"""
  if "kinetics" in model_name:
   UpperCamelCase_					:						Optional[int] =					400
   UpperCamelCase_					:						Any =					"""kinetics400-id2label.json"""
  elif "ssv2" in model_name:
   UpperCamelCase_					:						Dict =					174
   UpperCamelCase_					:						Tuple =					"""something-something-v2-id2label.json"""
  else:
   raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'	)
  UpperCamelCase_					:						Any =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  repo_type='dataset'	)  ,  'r'	)	)
  UpperCamelCase_					:						Any =					{int(SCREAMING_SNAKE_CASE__	): v for k, v in idalabel.items()}
  UpperCamelCase_					:						Any =					idalabel
  UpperCamelCase_					:						List[Any] =					{v: k for k, v in idalabel.items()}

 return config





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Optional[Any]	):
 if "small" in model_name:
  UpperCamelCase_					:						Optional[Any] =					384
  UpperCamelCase_					:						Dict =					1536
  UpperCamelCase_					:						Optional[Any] =					12
  UpperCamelCase_					:						Union[str, Any] =					16
  UpperCamelCase_					:						List[str] =					12
  UpperCamelCase_					:						int =					3
  UpperCamelCase_					:						List[Any] =					192
  UpperCamelCase_					:						Optional[Any] =					768
 elif "large" in model_name:
  UpperCamelCase_					:						int =					1024
  UpperCamelCase_					:						Optional[Any] =					4096
  UpperCamelCase_					:						Optional[Any] =					24
  UpperCamelCase_					:						List[str] =					16
  UpperCamelCase_					:						Optional[int] =					12
  UpperCamelCase_					:						Union[str, Any] =					8
  UpperCamelCase_					:						str =					512
  UpperCamelCase_					:						Union[str, Any] =					2048
 elif "huge" in model_name:
  UpperCamelCase_					:						Dict =					1280
  UpperCamelCase_					:						Union[str, Any] =					5120
  UpperCamelCase_					:						Optional[Any] =					32
  UpperCamelCase_					:						Optional[Any] =					16
  UpperCamelCase_					:						int =					12
  UpperCamelCase_					:						Dict =					8
  UpperCamelCase_					:						Dict =					640
  UpperCamelCase_					:						Dict =					2560
 elif "base" not in model_name:
  raise ValueError('Model name should include either \"small\", \"base\", \"large\", or \"huge\"'	)





def    __lowercase     (						lowerCamelCase					:     str	):
 if "encoder." in name:
  UpperCamelCase_					:						str =					name.replace('encoder.'  ,  ''	)
 if "cls_token" in name:
  UpperCamelCase_					:						Any =					name.replace('cls_token'  ,  'videomae.embeddings.cls_token'	)
 if "decoder_pos_embed" in name:
  UpperCamelCase_					:						Optional[int] =					name.replace('decoder_pos_embed'  ,  'decoder.decoder_pos_embed'	)
 if "pos_embed" in name and "decoder" not in name:
  UpperCamelCase_					:						Dict =					name.replace('pos_embed'  ,  'videomae.embeddings.position_embeddings'	)
 if "patch_embed.proj" in name:
  UpperCamelCase_					:						Tuple =					name.replace('patch_embed.proj'  ,  'videomae.embeddings.patch_embeddings.projection'	)
 if "patch_embed.norm" in name:
  UpperCamelCase_					:						List[str] =					name.replace('patch_embed.norm'  ,  'videomae.embeddings.norm'	)
 if "decoder.blocks" in name:
  UpperCamelCase_					:						List[str] =					name.replace('decoder.blocks'  ,  'decoder.decoder_layers'	)
 if "blocks" in name:
  UpperCamelCase_					:						List[str] =					name.replace('blocks'  ,  'videomae.encoder.layer'	)
 if "attn.proj" in name:
  UpperCamelCase_					:						Any =					name.replace('attn.proj'  ,  'attention.output.dense'	)
 if "attn" in name and "bias" not in name:
  UpperCamelCase_					:						Union[str, Any] =					name.replace('attn'  ,  'attention.self'	)
 if "attn" in name:
  UpperCamelCase_					:						str =					name.replace('attn'  ,  'attention.attention'	)
 if "norm1" in name:
  UpperCamelCase_					:						Tuple =					name.replace('norm1'  ,  'layernorm_before'	)
 if "norm2" in name:
  UpperCamelCase_					:						Any =					name.replace('norm2'  ,  'layernorm_after'	)
 if "mlp.fc1" in name:
  UpperCamelCase_					:						str =					name.replace('mlp.fc1'  ,  'intermediate.dense'	)
 if "mlp.fc2" in name:
  UpperCamelCase_					:						str =					name.replace('mlp.fc2'  ,  'output.dense'	)
 if "decoder_embed" in name:
  UpperCamelCase_					:						Dict =					name.replace('decoder_embed'  ,  'decoder.decoder_embed'	)
 if "decoder_norm" in name:
  UpperCamelCase_					:						Union[str, Any] =					name.replace('decoder_norm'  ,  'decoder.decoder_norm'	)
 if "decoder_pred" in name:
  UpperCamelCase_					:						str =					name.replace('decoder_pred'  ,  'decoder.decoder_pred'	)
 if "norm.weight" in name and "decoder" not in name and "fc" not in name:
  UpperCamelCase_					:						Optional[int] =					name.replace('norm.weight'  ,  'videomae.layernorm.weight'	)
 if "norm.bias" in name and "decoder" not in name and "fc" not in name:
  UpperCamelCase_					:						Any =					name.replace('norm.bias'  ,  'videomae.layernorm.bias'	)
 if "head" in name and "decoder" not in name:
  UpperCamelCase_					:						List[str] =					name.replace('head'  ,  'classifier'	)

 return name





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     Dict	):
 for key in orig_state_dict.copy().keys():
  UpperCamelCase_					:						Union[str, Any] =					orig_state_dict.pop(SCREAMING_SNAKE_CASE__	)

  if key.startswith('encoder.'	):
   UpperCamelCase_					:						Union[str, Any] =					key.replace('encoder.'  ,  ''	)

  if "qkv" in key:
   UpperCamelCase_					:						Tuple =					key.split('.'	)
   if key.startswith('decoder.blocks'	):
    UpperCamelCase_					:						Tuple =					config.decoder_hidden_size
    UpperCamelCase_					:						str =					int(key_split[2]	)
    UpperCamelCase_					:						Optional[Any] =					"""decoder.decoder_layers."""
    if "weight" in key:
     UpperCamelCase_					:						Optional[Any] =					val[:dim, :]
     UpperCamelCase_					:						List[str] =					val[dim : dim * 2, :]
     UpperCamelCase_					:						List[Any] =					val[-dim:, :]
   else:
    UpperCamelCase_					:						int =					config.hidden_size
    UpperCamelCase_					:						Optional[int] =					int(key_split[1]	)
    UpperCamelCase_					:						Optional[Any] =					"""videomae.encoder.layer."""
    if "weight" in key:
     UpperCamelCase_					:						Tuple =					val[:dim, :]
     UpperCamelCase_					:						Optional[Any] =					val[dim : dim * 2, :]
     UpperCamelCase_					:						Any =					val[-dim:, :]
  else:
   UpperCamelCase_					:						List[str] =					val

 return orig_state_dict





def    __lowercase     (						):
 UpperCamelCase_					:						str =					hf_hub_download(
     repo_id='hf-internal-testing/spaghetti-video'  ,  filename='eating_spaghetti.npy'  ,  repo_type='dataset'	)
 UpperCamelCase_					:						Dict =					np.load(SCREAMING_SNAKE_CASE__	)
 return list(SCREAMING_SNAKE_CASE__	)





def    __lowercase     (						lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						Optional[Any] =					get_videomae_config(SCREAMING_SNAKE_CASE__	)

 if "finetuned" in model_name:
  UpperCamelCase_					:						Tuple =					VideoMAEForVideoClassification(SCREAMING_SNAKE_CASE__	)
 else:
  UpperCamelCase_					:						Union[str, Any] =					VideoMAEForPreTraining(SCREAMING_SNAKE_CASE__	)

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

 model.load_state_dict(SCREAMING_SNAKE_CASE__	)
 model.eval()

 # verify model on basic input
 UpperCamelCase_					:						Union[str, Any] =					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,  image_std=[0.5, 0.5, 0.5]	)
 UpperCamelCase_					:						Union[str, Any] =					prepare_video()
 UpperCamelCase_					:						Any =					image_processor(SCREAMING_SNAKE_CASE__  ,  return_tensors='pt'	)

 if "finetuned" not in model_name:
  UpperCamelCase_					:						List[Any] =					hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'  ,  filename='bool_masked_pos.pt'	)
  UpperCamelCase_					:						Union[str, Any] =					torch.load(SCREAMING_SNAKE_CASE__	)

 UpperCamelCase_					:						Optional[int] =					model(**SCREAMING_SNAKE_CASE__	)
 UpperCamelCase_					:						str =					outputs.logits

 UpperCamelCase_					:						int =					[
     """videomae-small-finetuned-kinetics""",
     """videomae-small-finetuned-ssv2""",
     # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
     """videomae-base-short""",
     """videomae-base-short-finetuned-kinetics""",
     """videomae-base""",
     """videomae-base-finetuned-kinetics""",
     """videomae-large""",
     """videomae-large-finetuned-kinetics""",
     """videomae-huge-finetuned-kinetics""",
     # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
     """videomae-base-short-ssv2""",
     """videomae-base-short-finetuned-ssv2""",
     """videomae-base-ssv2""",
     """videomae-base-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 == "videomae-small-finetuned-kinetics":
  UpperCamelCase_					:						Tuple =					torch.Size([1, 400]	)
  UpperCamelCase_					:						Any =					torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]	)
 elif model_name == "videomae-small-finetuned-ssv2":
  UpperCamelCase_					:						int =					torch.Size([1, 174]	)
  UpperCamelCase_					:						List[Any] =					torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]	)
 elif model_name == "videomae-base":
  UpperCamelCase_					:						List[str] =					torch.Size([1, 1408, 1536]	)
  UpperCamelCase_					:						Tuple =					torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]	)
 elif model_name == "videomae-base-short":
  UpperCamelCase_					:						Tuple =					torch.Size([1, 1408, 1536]	)
  UpperCamelCase_					:						str =					torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]	)
  # we verified the loss both for normalized and unnormalized targets for this one
  UpperCamelCase_					:						List[str] =					torch.tensor([0.5_1_4_2]	) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]	)
 elif model_name == "videomae-large":
  UpperCamelCase_					:						Tuple =					torch.Size([1, 1408, 1536]	)
  UpperCamelCase_					:						int =					torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]	)
 elif model_name == "videomae-large-finetuned-kinetics":
  UpperCamelCase_					:						Optional[int] =					torch.Size([1, 400]	)
  UpperCamelCase_					:						Union[str, Any] =					torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]	)
 elif model_name == "videomae-huge-finetuned-kinetics":
  UpperCamelCase_					:						str =					torch.Size([1, 400]	)
  UpperCamelCase_					:						str =					torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]	)
 elif model_name == "videomae-base-short-finetuned-kinetics":
  UpperCamelCase_					:						Dict =					torch.Size([1, 400]	)
  UpperCamelCase_					:						Dict =					torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]	)
 elif model_name == "videomae-base-finetuned-kinetics":
  UpperCamelCase_					:						Tuple =					torch.Size([1, 400]	)
  UpperCamelCase_					:						str =					torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]	)
 elif model_name == "videomae-base-short-ssv2":
  UpperCamelCase_					:						Union[str, Any] =					torch.Size([1, 1408, 1536]	)
  UpperCamelCase_					:						Dict =					torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]	)
 elif model_name == "videomae-base-short-finetuned-ssv2":
  UpperCamelCase_					:						Optional[Any] =					torch.Size([1, 174]	)
  UpperCamelCase_					:						Optional[Any] =					torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]	)
 elif model_name == "videomae-base-ssv2":
  UpperCamelCase_					:						Union[str, Any] =					torch.Size([1, 1408, 1536]	)
  UpperCamelCase_					:						int =					torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]	)
 elif model_name == "videomae-base-finetuned-ssv2":
  UpperCamelCase_					:						Optional[Any] =					torch.Size([1, 174]	)
  UpperCamelCase_					:						Union[str, Any] =					torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]	)
 else:
  raise ValueError(F"Model name not supported. Should be one of {model_names}"	)

 # verify logits
 assert logits.shape == expected_shape
 if "finetuned" in model_name:
  assert torch.allclose(logits[0, :3]  ,  SCREAMING_SNAKE_CASE__  ,  atol=1e-4	)
 else:
  print('Logits:'  ,  logits[0, :3, :3]	)
  assert torch.allclose(logits[0, :3, :3]  ,  SCREAMING_SNAKE_CASE__  ,  atol=1e-4	)
 print('Logits ok!'	)

 # verify loss, if applicable
 if model_name == "videomae-base-short":
  UpperCamelCase_					:						Union[str, Any] =					outputs.loss
  assert torch.allclose(SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  atol=1e-4	)
  print('Loss 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(SCREAMING_SNAKE_CASE__	)
  model.save_pretrained(SCREAMING_SNAKE_CASE__	)

 if push_to_hub:
  print('Pushing to the hub...'	)
  model.push_to_hub(SCREAMING_SNAKE_CASE__  ,  organization='nielsr'	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_url',
       default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
       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='/Users/nielsrogge/Documents/VideoMAE/Test',
       type=str,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument('--model_name', default='videomae-base', 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_    					=       parser.parse_args()
   convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class __snake_case		(      lowerCAmelCase     ):
    _a		:						BigBirdConfig
    _a		:						jnp.dtype=				jnp.floataa
    _a		:						bool=				True
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().setup()
           lowercase							:     List[str]       	=      nn.Dense(5		,dtype=self.dtype      )
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      super().__call__(*snake_case		,**snake_case      )
           lowercase							:     Any       	=      self.cls(outputs[2]      )
           return outputs[:2] + (cls_out,)






class __snake_case		(      lowerCAmelCase     ):
    _a		:						List[Any]=				FlaxBigBirdForNaturalQuestionsModule







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       def cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				):
              lowercase							:     int       	=      logits.shape[-1]
              lowercase							:     Dict       	=      (labels[..., None] == jnp.arange(SCREAMING_SNAKE_CASE__				)[None]).astype("""f4"""				)
              lowercase							:     Any       	=      jax.nn.log_softmax(SCREAMING_SNAKE_CASE__					,	axis=-1				)
              lowercase							:     Optional[Any]       	=      -jnp.sum(labels * logits					,	axis=-1				)
              if reduction is not None:
                     lowercase							:     Any       	=      reduction(SCREAMING_SNAKE_CASE__				)
              return loss

       lowercase							:     Optional[Any]       	=      partial(SCREAMING_SNAKE_CASE__					,	reduction=jnp.mean				)
       lowercase							:     Optional[int]       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     int       	=      cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class __snake_case		:
    _a		:						str=				"google/bigbird-roberta-base"
    _a		:						int=				3000
    _a		:						int=				1_0500

    _a		:						int=				128
    _a		:						int=				3

    _a		:						int=				1
    _a		:						int=				5

    # tx_args
    _a		:						float=				3E-5
    _a		:						float=				0.0
    _a		:						int=				2_0000
    _a		:						float=				0.00_95

    _a		:						str=				"bigbird-roberta-natural-questions"
    _a		:						str=				"training-expt"
    _a		:						str=				"data/nq-training.jsonl"
    _a		:						str=				"data/nq-validation.jsonl"
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           os.makedirs(self.base_dir		,exist_ok=snake_case      )
           lowercase							:     Optional[int]       	=      os.path.join(self.base_dir		,self.save_dir      )
           lowercase							:     Optional[int]       	=      self.batch_size_per_device * jax.device_count()






@dataclass
class __snake_case		:
    _a		:						int
    _a		:						int=				4096  # no dynamic padding on TPUs
    def __call__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.collate_fn(snake_case      )
           lowercase							:     Union[str, Any]       	=      jax.tree_util.tree_map(snake_case		,snake_case      )
           return batch
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Union[str, Any]       	=      self.fetch_inputs(features["""input_ids"""]      )
           lowercase							:     Tuple       	=      {
               """input_ids""": jnp.array(snake_case		,dtype=jnp.intaa      ),
               """attention_mask""": jnp.array(snake_case		,dtype=jnp.intaa      ),
               """start_labels""": jnp.array(features["""start_token"""]		,dtype=jnp.intaa      ),
               """end_labels""": jnp.array(features["""end_token"""]		,dtype=jnp.intaa      ),
               """pooled_labels""": jnp.array(features["""category"""]		,dtype=jnp.intaa      ),
           }
           return batch
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      [self._fetch_inputs(snake_case      ) for ids in input_ids]
           return zip(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      [1 for _ in range(len(snake_case      )      )]
           while len(snake_case      ) < self.max_length:
                  input_ids.append(self.pad_id      )
                  attention_mask.append(0      )
           return input_ids, attention_mask







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				) ->    Any:
       if seed is not None:
              lowercase							:     Optional[int]       	=      dataset.shuffle(seed=SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				) // batch_size				):
              lowercase							:     Optional[Any]       	=      dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(SCREAMING_SNAKE_CASE__				)







@partial(jax.pmap					,	axis_name="""batch"""				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       def loss_fn(SCREAMING_SNAKE_CASE__				):
              lowercase							:     List[str]       	=      model_inputs.pop("""start_labels"""				)
              lowercase							:     Optional[int]       	=      model_inputs.pop("""end_labels"""				)
              lowercase							:     str       	=      model_inputs.pop("""pooled_labels"""				)

              lowercase							:     Union[str, Any]       	=      state.apply_fn(**SCREAMING_SNAKE_CASE__					,	params=SCREAMING_SNAKE_CASE__					,	dropout_rng=SCREAMING_SNAKE_CASE__					,	train=SCREAMING_SNAKE_CASE__				)
              lowercase	,			lowercase	,			lowercase							:     List[str]       	=      outputs

              return state.loss_fn(
                  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	)

       lowercase	,			lowercase							:     int       	=      jax.random.split(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      jax.value_and_grad(SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase							:     Union[str, Any]       	=      grad_fn(state.params				)
       lowercase							:     List[Any]       	=      jax.lax.pmean({"""loss""": loss}					,	axis_name="""batch"""				)
       lowercase							:     List[Any]       	=      jax.lax.pmean(SCREAMING_SNAKE_CASE__					,	"""batch"""				)

       lowercase							:     str       	=      state.apply_gradients(grads=SCREAMING_SNAKE_CASE__				)
       return state, metrics, new_drp_rng







@partial(jax.pmap					,	axis_name="""batch"""				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     int       	=      model_inputs.pop("""start_labels"""				)
       lowercase							:     Dict       	=      model_inputs.pop("""end_labels"""				)
       lowercase							:     Optional[Any]       	=      model_inputs.pop("""pooled_labels"""				)

       lowercase							:     Optional[int]       	=      state.apply_fn(**SCREAMING_SNAKE_CASE__					,	params=state.params					,	train=SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      outputs

       lowercase							:     Dict       	=      state.loss_fn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      jax.lax.pmean({"""loss""": loss}					,	axis_name="""batch"""				)
       return metrics

class __snake_case		(      train_state.TrainState     ):
    _a		:						Callable=				struct.field(pytree_node=lowerCAmelCase     )






@dataclass
class __snake_case		:
    _a		:						Args
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						wandb
    _a		:						Callable=				None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      model.params
           lowercase							:     Any       	=      TrainState.create(
               apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,loss_fn=snake_case		,)
           if ckpt_dir is not None:
                  lowercase	,			lowercase	,			lowercase	,			lowercase	,			lowercase							:     Tuple       	=      restore_checkpoint(snake_case		,snake_case      )
                  lowercase							:     List[str]       	=      {
                      """lr""": args.lr,
                      """init_lr""": args.init_lr,
                      """warmup_steps""": args.warmup_steps,
                      """num_train_steps""": num_train_steps,
                      """weight_decay""": args.weight_decay,
                  }
                  lowercase	,			lowercase							:     Tuple       	=      build_tx(**snake_case      )
                  lowercase							:     str       	=      train_state.TrainState(
                      step=snake_case		,apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,opt_state=snake_case		,)
                  lowercase							:     Any       	=      args
                  lowercase							:     Optional[Any]       	=      data_collator
                  lowercase							:     List[str]       	=      lr
                  lowercase							:     str       	=      params
           lowercase							:     Tuple       	=      jax_utils.replicate(snake_case      )
           return state
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.args
           lowercase							:     Optional[Any]       	=      len(snake_case      ) // args.batch_size

           lowercase							:     int       	=      jax.random.PRNGKey(0      )
           lowercase							:     List[str]       	=      jax.random.split(snake_case		,jax.device_count()      )
           for epoch in range(args.max_epochs      ):
                  lowercase							:     List[Any]       	=      jnp.array(0		,dtype=jnp.floataa      )
                  lowercase							:     List[str]       	=      get_batched_dataset(snake_case		,args.batch_size		,seed=snake_case      )
                  lowercase							:     int       	=      0
                  for batch in tqdm(snake_case		,total=snake_case		,desc=f"Running EPOCH-{epoch}"      ):
                         lowercase							:     Dict       	=      self.data_collator(snake_case      )
                         lowercase	,			lowercase	,			lowercase							:     Optional[int]       	=      self.train_step_fn(snake_case		,snake_case		,**snake_case      )
                         running_loss += jax_utils.unreplicate(metrics["""loss"""]      )
                         i += 1
                         if i % args.logging_steps == 0:
                                lowercase							:     Optional[Any]       	=      jax_utils.unreplicate(state.step      )
                                lowercase							:     List[str]       	=      running_loss.item() / i
                                lowercase							:     List[str]       	=      self.scheduler_fn(state_step - 1      )

                                lowercase							:     int       	=      self.evaluate(snake_case		,snake_case      )
                                lowercase							:     Tuple       	=      {
                                    """step""": state_step.item(),
                                    """eval_loss""": eval_loss.item(),
                                    """tr_loss""": tr_loss,
                                    """lr""": lr.item(),
                                }
                                tqdm.write(str(snake_case      )      )
                                self.logger.log(snake_case		,commit=snake_case      )

                         if i % args.save_steps == 0:
                                self.save_checkpoint(args.save_dir + f"-e{epoch}-s{i}"		,state=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      get_batched_dataset(snake_case		,self.args.batch_size      )
           lowercase							:     Any       	=      len(snake_case      ) // self.args.batch_size
           lowercase							:     List[Any]       	=      jnp.array(0		,dtype=jnp.floataa      )
           lowercase							:     Optional[int]       	=      0
           for batch in tqdm(snake_case		,total=snake_case		,desc="""Evaluating ... """      ):
                  lowercase							:     Tuple       	=      self.data_collator(snake_case      )
                  lowercase							:     Optional[int]       	=      self.val_step_fn(snake_case		,**snake_case      )
                  running_loss += jax_utils.unreplicate(metrics["""loss"""]      )
                  i += 1
           return running_loss / i
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      jax_utils.unreplicate(snake_case      )
           print(f"SAVING CHECKPOINT IN {save_dir}"		,end=""" ... """      )
           self.model_save_fn(snake_case		,params=state.params      )
           with open(os.path.join(snake_case		,"""opt_state.msgpack"""      )		,"""wb"""      ) as f:
                  f.write(to_bytes(state.opt_state      )      )
           joblib.dump(self.args		,os.path.join(snake_case		,"""args.joblib"""      )      )
           joblib.dump(self.data_collator		,os.path.join(snake_case		,"""data_collator.joblib"""      )      )
           with open(os.path.join(snake_case		,"""training_state.json"""      )		,"""w"""      ) as f:
                  json.dump({"""step""": state.step.item()}		,snake_case      )
           print("""DONE"""      )







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       print(f"RESTORING CHECKPOINT FROM {save_dir}"					,	end=""" ... """				)
       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""flax_model.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     str       	=      from_bytes(state.params					,	f.read()				)

       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""opt_state.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     Optional[int]       	=      from_bytes(state.opt_state					,	f.read()				)

       lowercase							:     Optional[Any]       	=      joblib.load(os.path.join(SCREAMING_SNAKE_CASE__					,	"""args.joblib"""				)				)
       lowercase							:     int       	=      joblib.load(os.path.join(SCREAMING_SNAKE_CASE__					,	"""data_collator.joblib"""				)				)

       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""training_state.json"""				)					,	"""r"""				) as f:
              lowercase							:     Tuple       	=      json.load(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      training_state["""step"""]

       print("""DONE"""				)
       return params, opt_state, step, args, data_collator







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     List[str]       	=      num_train_steps - warmup_steps
       lowercase							:     Dict       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=SCREAMING_SNAKE_CASE__					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=1e-7					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      optax.join_schedules(schedules=[warmup_fn, decay_fn]					,	boundaries=[warmup_steps]				)
       return lr







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       def weight_decay_mask(SCREAMING_SNAKE_CASE__				):
              lowercase							:     List[Any]       	=      traverse_util.flatten_dict(SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[Any]       	=      {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
              return traverse_util.unflatten_dict(SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      scheduler_fn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      optax.adamw(learning_rate=SCREAMING_SNAKE_CASE__					,	weight_decay=SCREAMING_SNAKE_CASE__					,	mask=SCREAMING_SNAKE_CASE__				)
       return tx, lr
