





import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   model.config

		SCREAMING_SNAKE_CASE__								=   DonutSwinConfig(
		    image_size=original_config.input_size       , patch_size=4       , depths=original_config.encoder_layer       , num_heads=[4, 8, 16, 32]       , window_size=original_config.window_size       , embed_dim=128       , )
		SCREAMING_SNAKE_CASE__								=   MBartConfig(
		    is_decoder=UpperCamelCase__       , is_encoder_decoder=UpperCamelCase__       , add_cross_attention=UpperCamelCase__       , decoder_layers=original_config.decoder_layer       , max_position_embeddings=original_config.max_position_embeddings       , vocab_size=len(
		        model.decoder.tokenizer     )       , scale_embedding=UpperCamelCase__       , add_final_layer_norm=UpperCamelCase__       , )

		return encoder_config, decoder_config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		if "encoder.model" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""encoder.model"""       , """encoder"""     )
		if "decoder.model" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""decoder.model"""       , """decoder"""     )
		if "patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""patch_embed.proj"""       , """embeddings.patch_embeddings.projection"""     )
		if "patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE__								=   name.replace("""patch_embed.norm"""       , """embeddings.norm"""     )
		if name.startswith("""encoder"""     ):
				if "layers" in name:
						SCREAMING_SNAKE_CASE__								=   """encoder.""" + name
				if "attn.proj" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""attn.proj"""       , """attention.output.dense"""     )
				if "attn" in name and "mask" not in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""attn"""       , """attention.self"""     )
				if "norm1" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""norm1"""       , """layernorm_before"""     )
				if "norm2" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""norm2"""       , """layernorm_after"""     )
				if "mlp.fc1" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""mlp.fc1"""       , """intermediate.dense"""     )
				if "mlp.fc2" in name:
						SCREAMING_SNAKE_CASE__								=   name.replace("""mlp.fc2"""       , """output.dense"""     )

				if name == "encoder.norm.weight":
						SCREAMING_SNAKE_CASE__								=   """encoder.layernorm.weight"""
				if name == "encoder.norm.bias":
						SCREAMING_SNAKE_CASE__								=   """encoder.layernorm.bias"""

		return name






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[int]     ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE__								=   orig_state_dict.pop(UpperCamelCase__     )

				if "qkv" in key:
						SCREAMING_SNAKE_CASE__								=   key.split("""."""     )
						SCREAMING_SNAKE_CASE__								=   int(key_split[3]     )
						SCREAMING_SNAKE_CASE__								=   int(key_split[5]     )
						SCREAMING_SNAKE_CASE__								=   model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								SCREAMING_SNAKE_CASE__								=   val[:dim, :]
								SCREAMING_SNAKE_CASE__								=   val[dim : dim * 2, :]
								SCREAMING_SNAKE_CASE__								=   val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE__								=   val[:dim]
								SCREAMING_SNAKE_CASE__								=   val[dim : dim * 2]
								SCREAMING_SNAKE_CASE__								=   val[-dim:]
				elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
						# HuggingFace implementation doesn't use attn_mask buffer
						# and model doesn't use final LayerNorms for the encoder
						pass
				else:
						SCREAMING_SNAKE_CASE__								=   val

		return orig_state_dict






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				int=None       , UpperCamelCase__:				str=False     ):
		# load original model
		SCREAMING_SNAKE_CASE__								=   DonutModel.from_pretrained(UpperCamelCase__     ).eval()

		# load HuggingFace model
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_configs(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   DonutSwinModel(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   MBartForCausalLM(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   VisionEncoderDecoderModel(encoder=UpperCamelCase__       , decoder=UpperCamelCase__     )
		model.eval()

		SCREAMING_SNAKE_CASE__								=   original_model.state_dict()
		SCREAMING_SNAKE_CASE__								=   convert_state_dict(UpperCamelCase__       , UpperCamelCase__     )
		model.load_state_dict(UpperCamelCase__     )

		# verify results on scanned document
		SCREAMING_SNAKE_CASE__								=   load_dataset("""hf-internal-testing/example-documents"""     )
		SCREAMING_SNAKE_CASE__								=   dataset["""test"""][0]["""image"""].convert("""RGB"""     )

		SCREAMING_SNAKE_CASE__								=   XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase__       , from_slow=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis       , size=original_model.config.input_size[::-1]     )
		SCREAMING_SNAKE_CASE__								=   DonutProcessor(UpperCamelCase__       , UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   processor(UpperCamelCase__       , return_tensors="""pt"""     ).pixel_values

		if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
				SCREAMING_SNAKE_CASE__								=   """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
				SCREAMING_SNAKE_CASE__								=   """When is the coffee break?"""
				SCREAMING_SNAKE_CASE__								=   task_prompt.replace("""{user_input}"""       , UpperCamelCase__     )
		elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
				SCREAMING_SNAKE_CASE__								=   """<s_rvlcdip>"""
		elif model_name in [
		    "naver-clova-ix/donut-base-finetuned-cord-v1",
		    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
		]:
				SCREAMING_SNAKE_CASE__								=   """<s_cord>"""
		elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
				SCREAMING_SNAKE_CASE__								=   """s_cord-v2>"""
		elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
				SCREAMING_SNAKE_CASE__								=   """<s_zhtrainticket>"""
		elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
				# use a random prompt
				SCREAMING_SNAKE_CASE__								=   """hello world"""
		else:
				raise ValueError("""Model name not supported"""     )
		SCREAMING_SNAKE_CASE__								=   original_model.decoder.tokenizer(UpperCamelCase__       , add_special_tokens=UpperCamelCase__       , return_tensors="""pt"""     )[
		    """input_ids"""
		]

		SCREAMING_SNAKE_CASE__								=   original_model.encoder.model.patch_embed(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   model.encoder.embeddings(UpperCamelCase__     )
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-3     )

		# verify encoder hidden states
		SCREAMING_SNAKE_CASE__								=   original_model.encoder(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   model.encoder(UpperCamelCase__     ).last_hidden_state
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-2     )

		# verify decoder hidden states
		SCREAMING_SNAKE_CASE__								=   original_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     ).logits
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__       , decoder_input_ids=UpperCamelCase__     ).logits
		assert torch.allclose(UpperCamelCase__       , UpperCamelCase__       , atol=1e-3     )
		print("""Looks ok!"""     )

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model and processor to {pytorch_dump_folder_path}'''     )
				model.save_pretrained(UpperCamelCase__     )
				processor.save_pretrained(UpperCamelCase__     )

		if push_to_hub:
				model.push_to_hub("""nielsr/""" + model_name.split("""/"""     )[-1]       , commit_message="""Update model"""     )
				processor.push_to_hub("""nielsr/""" + model_name.split("""/"""     )[-1]       , commit_message="""Update model"""     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='naver-clova-ix/donut-base-finetuned-docvqa',
	    required=False,
	    type=str,
	    help='Name of the original model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    required=False,
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether or not to push the converted model and processor to the 🤗 hub.',
	)

	_lowerCamelCase 					=      parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       jnp.floataa
							lowerCamelCase_							     =       True


							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									super().setup()
									SCREAMING_SNAKE_CASE__								=   nn.Dense(5				,					dtype=self.dtype		)


							def __call__(				self     :List[Any]				,					*__A     :int				,					**__A     :Optional[Any]		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   super().__call__(*__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   self.cls(outputs[2]		)
									return outputs[:2] + (cls_out,)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       FlaxBigBirdForNaturalQuestionsModule






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

		SCREAMING_SNAKE_CASE__								=   partial(UpperCamelCase__       , reduction=jnp.mean     )
		SCREAMING_SNAKE_CASE__								=   cross_entropy(UpperCamelCase__       , UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   cross_entropy(UpperCamelCase__       , UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   cross_entropy(UpperCamelCase__       , UpperCamelCase__     )
		return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       "google/bigbird-roberta-base"
							lowerCamelCase_							     =       30_00
							lowerCamelCase_							     =       1_05_00

							lowerCamelCase_							     =       1_28
							lowerCamelCase_							     =       3

							lowerCamelCase_							     =       1
							lowerCamelCase_							     =       5

							# tx_args
							lowerCamelCase_							     =       3e-5
							lowerCamelCase_							     =       0.0
							lowerCamelCase_							     =       2_00_00
							lowerCamelCase_							     =       0.0095

							lowerCamelCase_							     =       "bigbird-roberta-natural-questions"
							lowerCamelCase_							     =       "training-expt"
							lowerCamelCase_							     =       "data/nq-training.jsonl"
							lowerCamelCase_							     =       "data/nq-validation.jsonl"


							def   _snake_case	(				self     :str		) -> Optional[int]:



									"""simple docstring"""



									os.makedirs(self.base_dir				,					exist_ok=__A		)
									SCREAMING_SNAKE_CASE__								=   os.path.join(self.base_dir				,					self.save_dir		)
									SCREAMING_SNAKE_CASE__								=   self.batch_size_per_device * jax.device_count()




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       40_96  # no dynamic padding on TPUs


							def __call__(				self     :Optional[Any]				,					__A     :Optional[int]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.collate_fn(__A		)
									SCREAMING_SNAKE_CASE__								=   jax.tree_util.tree_map(__A				,					__A		)
									return batch


							def   _snake_case	(				self     :List[Any]				,					__A     :Union[str, Any]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.fetch_inputs(features["""input_ids"""]		)
									SCREAMING_SNAKE_CASE__								=   {
									    """input_ids""": jnp.array(__A				,					dtype=jnp.intaa		),
									    """attention_mask""": jnp.array(__A				,					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   _snake_case	(				self     :Tuple				,					__A     :list		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self._fetch_inputs(__A		) for ids in input_ids]
									return zip(*__A		)


							def   _snake_case	(				self     :List[str]				,					__A     :list		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [1 for _ in range(len(__A		)		)]
									while len(__A		) < self.max_length:
											input_ids.append(self.pad_id		)
											attention_mask.append(0		)
									return input_ids, attention_mask






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Optional[Any]=None     ):
		if seed is not None:
				SCREAMING_SNAKE_CASE__								=   dataset.shuffle(seed=UpperCamelCase__     )
		for i in range(len(UpperCamelCase__     ) // batch_size     ):
				SCREAMING_SNAKE_CASE__								=   dataset[i * batch_size : (i + 1) * batch_size]
				yield dict(UpperCamelCase__     )






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

				SCREAMING_SNAKE_CASE__								=   state.apply_fn(**UpperCamelCase__       , params=UpperCamelCase__       , dropout_rng=UpperCamelCase__       , train=UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   outputs

				return state.loss_fn(
				    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , )

		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   jax.random.split(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   jax.value_and_grad(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   grad_fn(state.params     )
		SCREAMING_SNAKE_CASE__								=   jax.lax.pmean({"""loss""": loss}       , axis_name="""batch"""     )
		SCREAMING_SNAKE_CASE__								=   jax.lax.pmean(UpperCamelCase__       , """batch"""     )

		SCREAMING_SNAKE_CASE__								=   state.apply_gradients(grads=UpperCamelCase__     )
		return state, metrics, new_drp_rng






@partial(jax.pmap       , axis_name="""batch"""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , **UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__								=   model_inputs.pop("""start_labels"""     )
		SCREAMING_SNAKE_CASE__								=   model_inputs.pop("""end_labels"""     )
		SCREAMING_SNAKE_CASE__								=   model_inputs.pop("""pooled_labels"""     )

		SCREAMING_SNAKE_CASE__								=   state.apply_fn(**UpperCamelCase__       , params=state.params       , train=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   outputs

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




class 	UpperCamelCase_							(  train_state.TrainState     ):
							lowerCamelCase_							     =       struct.field(pytree_node=UpperCamelCase__     )




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       None


							def   _snake_case	(				self     :List[Any]				,					__A     :str				,					__A     :str				,					__A     :str				,					__A     :Tuple=None		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   model.params
									SCREAMING_SNAKE_CASE__								=   TrainState.create(
									    apply_fn=model.__call__				,					params=__A				,					tx=__A				,					loss_fn=__A				,					)
									if ckpt_dir is not None:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   restore_checkpoint(__A				,					__A		)
											SCREAMING_SNAKE_CASE__								=   {
											    """lr""": args.lr,
											    """init_lr""": args.init_lr,
											    """warmup_steps""": args.warmup_steps,
											    """num_train_steps""": num_train_steps,
											    """weight_decay""": args.weight_decay,
											}
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   build_tx(**__A		)
											SCREAMING_SNAKE_CASE__								=   train_state.TrainState(
											    step=__A				,					apply_fn=model.__call__				,					params=__A				,					tx=__A				,					opt_state=__A				,					)
											SCREAMING_SNAKE_CASE__								=   args
											SCREAMING_SNAKE_CASE__								=   data_collator
											SCREAMING_SNAKE_CASE__								=   lr
											SCREAMING_SNAKE_CASE__								=   params
									SCREAMING_SNAKE_CASE__								=   jax_utils.replicate(__A		)
									return state


							def   _snake_case	(				self     :Optional[Any]				,					__A     :Optional[int]				,					__A     :int				,					__A     :int		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.args
									SCREAMING_SNAKE_CASE__								=   len(__A		) // args.batch_size

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

															SCREAMING_SNAKE_CASE__								=   self.evaluate(__A				,					__A		)
															SCREAMING_SNAKE_CASE__								=   {
															    """step""": state_step.item(),
															    """eval_loss""": eval_loss.item(),
															    """tr_loss""": tr_loss,
															    """lr""": lr.item(),
															}
															tqdm.write(str(__A		)		)
															self.logger.log(__A				,					commit=__A		)

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


							def   _snake_case	(				self     :List[str]				,					__A     :Dict				,					__A     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   get_batched_dataset(__A				,					self.args.batch_size		)
									SCREAMING_SNAKE_CASE__								=   len(__A		) // self.args.batch_size
									SCREAMING_SNAKE_CASE__								=   jnp.array(0				,					dtype=jnp.floataa		)
									SCREAMING_SNAKE_CASE__								=   0
									for batch in tqdm(__A				,					total=__A				,					desc="""Evaluating ... """		):
											SCREAMING_SNAKE_CASE__								=   self.data_collator(__A		)
											SCREAMING_SNAKE_CASE__								=   self.val_step_fn(__A				,					**__A		)
											running_loss += jax_utils.unreplicate(metrics["""loss"""]		)
											i += 1
									return running_loss / i


							def   _snake_case	(				self     :List[Any]				,					__A     :Any				,					__A     :Dict		) -> Union[str, Any]:



									"""simple docstring"""



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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[Any]     ):
		print(f'''RESTORING CHECKPOINT FROM {save_dir}'''       , end=""" ... """     )
		with open(os.path.join(UpperCamelCase__       , """flax_model.msgpack"""     )       , """rb"""     ) as f:
				SCREAMING_SNAKE_CASE__								=   from_bytes(state.params       , f.read()     )

		with open(os.path.join(UpperCamelCase__       , """opt_state.msgpack"""     )       , """rb"""     ) as f:
				SCREAMING_SNAKE_CASE__								=   from_bytes(state.opt_state       , f.read()     )

		SCREAMING_SNAKE_CASE__								=   joblib.load(os.path.join(UpperCamelCase__       , """args.joblib"""     )     )
		SCREAMING_SNAKE_CASE__								=   joblib.load(os.path.join(UpperCamelCase__       , """data_collator.joblib"""     )     )

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

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






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






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

		SCREAMING_SNAKE_CASE__								=   scheduler_fn(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   optax.adamw(learning_rate=UpperCamelCase__       , weight_decay=UpperCamelCase__       , mask=UpperCamelCase__     )
		return tx, lr