'''simple docstring'''
import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Any					,  _a						:						str					,  _a						:						List[Any] = 4_0_9_6      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						List[Any]		     =	key.shape[-3:]
    __UpperCamelCase						:						Optional[Any]		     =	value.shape[-1]
    __UpperCamelCase						:						List[str]		     =	min(lowercase_					,  lowercase_      )
    __UpperCamelCase						:						Optional[int]		     =	query / jnp.sqrt(lowercase_      )

    @functools.partial(jax.checkpoint					,  prevent_cse=lowercase_      )
    def summarize_chunk(_a						:						Optional[Any]					,  _a						:						Optional[int]					,  _a						:						Union[str, Any]      ):
        __UpperCamelCase						:						List[Any]		     =	jnp.einsum('...qhd,...khd->...qhk'					,  lowercase_					,  lowercase_					,  precision=lowercase_      )

        __UpperCamelCase						:						int		     =	jnp.max(lowercase_					,  axis=-1					,  keepdims=lowercase_      )
        __UpperCamelCase						:						List[str]		     =	jax.lax.stop_gradient(lowercase_      )
        __UpperCamelCase						:						Any		     =	jnp.exp(attn_weights - max_score      )

        __UpperCamelCase						:						List[str]		     =	jnp.einsum('...vhf,...qhv->...qhf'					,  lowercase_					,  lowercase_					,  precision=lowercase_      )
        __UpperCamelCase						:						int		     =	jnp.einsum('...qhk->...qh'					,  lowercase_      )

        return (exp_values, exp_weights.sum(axis=-1      ), max_score)

    def chunk_scanner(_a						:						Tuple      ):
        # julienne key array
        __UpperCamelCase						:						List[Any]		     =	jax.lax.dynamic_slice(
            operand=lowercase_					,  start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]					,  slice_sizes=list(key.shape[:-3]      ) + [key_chunk_size, num_heads, k_features]					,  )

        # julienne value array
        __UpperCamelCase						:						Tuple		     =	jax.lax.dynamic_slice(
            operand=lowercase_					,  start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]					,  slice_sizes=list(value.shape[:-3]      ) + [key_chunk_size, num_heads, v_features]					,  )

        return summarize_chunk(lowercase_					,  lowercase_					,  lowercase_      )

    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	jax.lax.map(f=lowercase_					,  xs=jnp.arange(0					,  lowercase_					,  lowercase_      )      )

    __UpperCamelCase						:						Optional[Any]		     =	jnp.max(lowercase_					,  axis=0					,  keepdims=lowercase_      )
    __UpperCamelCase						:						Union[str, Any]		     =	jnp.exp(chunk_max - global_max      )

    chunk_values *= jnp.expand_dims(lowercase_					,  axis=-1      )
    chunk_weights *= max_diffs

    __UpperCamelCase						:						int		     =	chunk_values.sum(axis=0      )
    __UpperCamelCase						:						str		     =	jnp.expand_dims(lowercase_					,  -1      ).sum(axis=0      )

    return all_values / all_weights







def 			_UpperCamelCase			(  _a						:						Any					,  _a						:						Any					,  _a						:						Optional[int]					,  _a						:						int=jax.lax.Precision.HIGHEST					,  _a						:						List[str] = 1_0_2_4					,  _a						:						str = 4_0_9_6      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	query.shape[-3:]

    def chunk_scanner(_a						:						Union[str, Any]					,  _a						:						Optional[Any]      ):
        # julienne query array
        __UpperCamelCase						:						List[Any]		     =	jax.lax.dynamic_slice(
            operand=lowercase_					,  start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]					,  slice_sizes=list(query.shape[:-3]      ) + [min(lowercase_					,  lowercase_      ), num_heads, q_features]					,  )

        return (
            chunk_idx + query_chunk_size,  # unused ignore it
            _query_chunk_attention(
                query=lowercase_					,  key=lowercase_					,  value=lowercase_					,  precision=lowercase_					,  key_chunk_size=lowercase_      ),
        )

    __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	jax.lax.scan(
        f=lowercase_					,  init=0					,  xs=lowercase_					,  length=math.ceil(num_q / query_chunk_size      )      )

    return jnp.concatenate(lowercase_					,  axis=-3      )  # fuse the chunked result back






class       __lowercase		(    nn.Module    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    8
     SCREAMING_SNAKE_CASE__		 =    64
     SCREAMING_SNAKE_CASE__		 =    0.0
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    jnp.floataa







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	self.dim_head * self.heads
         __UpperCamelCase						:						List[Any]		     =	self.dim_head**-0.5

         # Weights were exported with old names {to_q, to_k, to_v, to_out}
         __UpperCamelCase						:						Optional[int]		     =	nn.Dense(lowercase_  ,      use_bias=lowercase_  ,      dtype=self.dtype  ,      name='to_q'     )
         __UpperCamelCase						:						Dict		     =	nn.Dense(lowercase_  ,      use_bias=lowercase_  ,      dtype=self.dtype  ,      name='to_k'     )
         __UpperCamelCase						:						List[Any]		     =	nn.Dense(lowercase_  ,      use_bias=lowercase_  ,      dtype=self.dtype  ,      name='to_v'     )

         __UpperCamelCase						:						str		     =	nn.Dense(self.query_dim  ,      dtype=self.dtype  ,      name='to_out_0'     )
         __UpperCamelCase						:						List[Any]		     =	nn.Dropout(rate=self.dropout     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	tensor.shape
         __UpperCamelCase						:						int		     =	self.heads
         __UpperCamelCase						:						List[str]		     =	tensor.reshape(lowercase_  ,      lowercase_  ,      lowercase_  ,      dim // head_size     )
         __UpperCamelCase						:						Any		     =	jnp.transpose(lowercase_  ,      (0, 2, 1, 3)     )
         __UpperCamelCase						:						Any		     =	tensor.reshape(batch_size * head_size  ,      lowercase_  ,      dim // head_size     )
         return tensor







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Any		     =	tensor.shape
         __UpperCamelCase						:						Tuple		     =	self.heads
         __UpperCamelCase						:						Optional[Any]		     =	tensor.reshape(batch_size // head_size  ,      lowercase_  ,      lowercase_  ,      lowercase_     )
         __UpperCamelCase						:						List[Any]		     =	jnp.transpose(lowercase_  ,      (0, 2, 1, 3)     )
         __UpperCamelCase						:						Union[str, Any]		     =	tensor.reshape(batch_size // head_size  ,      lowercase_  ,      dim * head_size     )
         return tensor







     def __call__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=None  ,      _lowerCamelCase=True     ):
         __UpperCamelCase						:						Dict		     =	hidden_states if context is None else context

         __UpperCamelCase						:						Union[str, Any]		     =	self.query(lowercase_     )
         __UpperCamelCase						:						Dict		     =	self.key(lowercase_     )
         __UpperCamelCase						:						Tuple		     =	self.value(lowercase_     )

         __UpperCamelCase						:						Any		     =	self.reshape_heads_to_batch_dim(lowercase_     )
         __UpperCamelCase						:						List[Any]		     =	self.reshape_heads_to_batch_dim(lowercase_     )
         __UpperCamelCase						:						Optional[int]		     =	self.reshape_heads_to_batch_dim(lowercase_     )

         if self.use_memory_efficient_attention:
             __UpperCamelCase						:						str		     =	query_states.transpose(1  ,      0  ,      2     )
             __UpperCamelCase						:						Any		     =	key_states.transpose(1  ,      0  ,      2     )
             __UpperCamelCase						:						Union[str, Any]		     =	value_states.transpose(1  ,      0  ,      2     )

             # this if statement create a chunk size for each layer of the unet
             # the chunk size is equal to the query_length dimension of the deepest layer of the unet

             __UpperCamelCase						:						Any		     =	query_states.shape[-3]
             if flatten_latent_dim % 6_4 == 0:
                 __UpperCamelCase						:						Any		     =	int(flatten_latent_dim / 6_4     )
             elif flatten_latent_dim % 1_6 == 0:
                 __UpperCamelCase						:						Optional[Any]		     =	int(flatten_latent_dim / 1_6     )
             elif flatten_latent_dim % 4 == 0:
                 __UpperCamelCase						:						Union[str, Any]		     =	int(flatten_latent_dim / 4     )
             else:
                 __UpperCamelCase						:						List[str]		     =	int(lowercase_     )

             __UpperCamelCase						:						Union[str, Any]		     =	jax_memory_efficient_attention(
                 lowercase_  ,      lowercase_  ,      lowercase_  ,      query_chunk_size=lowercase_  ,      key_chunk_size=4_0_9_6 * 4     )

             __UpperCamelCase						:						List[Any]		     =	hidden_states.transpose(1  ,      0  ,      2     )
         else:
             # compute attentions
             __UpperCamelCase						:						str		     =	jnp.einsum('b i d, b j d->b i j'  ,      lowercase_  ,      lowercase_     )
             __UpperCamelCase						:						Tuple		     =	attention_scores * self.scale
             __UpperCamelCase						:						Dict		     =	nn.softmax(lowercase_  ,      axis=2     )

             # attend to values
             __UpperCamelCase						:						List[str]		     =	jnp.einsum('b i j, b j d -> b i d'  ,      lowercase_  ,      lowercase_     )

         __UpperCamelCase						:						Optional[Any]		     =	self.reshape_batch_dim_to_heads(lowercase_     )
         __UpperCamelCase						:						int		     =	self.proj_attn(lowercase_     )
         return self.dropout_layer(lowercase_  ,      deterministic=lowercase_     )






class       __lowercase		(    nn.Module    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    0.0
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    jnp.floataa
     SCREAMING_SNAKE_CASE__		 =    False







     def lowerCAmelCase  (		self     ):
         # self attention (or cross_attention if only_cross_attention is True)
         __UpperCamelCase						:						Dict		     =	FlaxAttention(
             self.dim  ,      self.n_heads  ,      self.d_head  ,      self.dropout  ,      self.use_memory_efficient_attention  ,      dtype=self.dtype     )
         # cross attention
         __UpperCamelCase						:						Dict		     =	FlaxAttention(
             self.dim  ,      self.n_heads  ,      self.d_head  ,      self.dropout  ,      self.use_memory_efficient_attention  ,      dtype=self.dtype     )
         __UpperCamelCase						:						str		     =	FlaxFeedForward(dim=self.dim  ,      dropout=self.dropout  ,      dtype=self.dtype     )
         __UpperCamelCase						:						Union[str, Any]		     =	nn.LayerNorm(epsilon=1E-5  ,      dtype=self.dtype     )
         __UpperCamelCase						:						Optional[int]		     =	nn.LayerNorm(epsilon=1E-5  ,      dtype=self.dtype     )
         __UpperCamelCase						:						List[Any]		     =	nn.LayerNorm(epsilon=1E-5  ,      dtype=self.dtype     )
         __UpperCamelCase						:						Tuple		     =	nn.Dropout(rate=self.dropout     )







     def __call__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=True     ):
         # self attention
         __UpperCamelCase						:						str		     =	hidden_states
         if self.only_cross_attention:
             __UpperCamelCase						:						int		     =	self.attna(self.norma(lowercase_     )  ,      lowercase_  ,      deterministic=lowercase_     )
         else:
             __UpperCamelCase						:						List[str]		     =	self.attna(self.norma(lowercase_     )  ,      deterministic=lowercase_     )
         __UpperCamelCase						:						Dict		     =	hidden_states + residual

         # cross attention
         __UpperCamelCase						:						List[Any]		     =	hidden_states
         __UpperCamelCase						:						List[Any]		     =	self.attna(self.norma(lowercase_     )  ,      lowercase_  ,      deterministic=lowercase_     )
         __UpperCamelCase						:						str		     =	hidden_states + residual

         # feed forward
         __UpperCamelCase						:						int		     =	hidden_states
         __UpperCamelCase						:						int		     =	self.ff(self.norma(lowercase_     )  ,      deterministic=lowercase_     )
         __UpperCamelCase						:						Optional[int]		     =	hidden_states + residual

         return self.dropout_layer(lowercase_  ,      deterministic=lowercase_     )






class       __lowercase		(    nn.Module    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    1
     SCREAMING_SNAKE_CASE__		 =    0.0
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    jnp.floataa
     SCREAMING_SNAKE_CASE__		 =    False







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	nn.GroupNorm(num_groups=3_2  ,      epsilon=1E-5     )

         __UpperCamelCase						:						Tuple		     =	self.n_heads * self.d_head
         if self.use_linear_projection:
             __UpperCamelCase						:						Optional[Any]		     =	nn.Dense(lowercase_  ,      dtype=self.dtype     )
         else:
             __UpperCamelCase						:						Any		     =	nn.Conv(
                 lowercase_  ,      kernel_size=(1, 1)  ,      strides=(1, 1)  ,      padding='VALID'  ,      dtype=self.dtype  ,      )

         __UpperCamelCase						:						Optional[Any]		     =	[
             FlaxBasicTransformerBlock(
                 lowercase_  ,      self.n_heads  ,      self.d_head  ,      dropout=self.dropout  ,      only_cross_attention=self.only_cross_attention  ,      dtype=self.dtype  ,      use_memory_efficient_attention=self.use_memory_efficient_attention  ,      )
             for _ in range(self.depth     )
         ]

         if self.use_linear_projection:
             __UpperCamelCase						:						List[str]		     =	nn.Dense(lowercase_  ,      dtype=self.dtype     )
         else:
             __UpperCamelCase						:						Optional[int]		     =	nn.Conv(
                 lowercase_  ,      kernel_size=(1, 1)  ,      strides=(1, 1)  ,      padding='VALID'  ,      dtype=self.dtype  ,      )

         __UpperCamelCase						:						List[str]		     =	nn.Dropout(rate=self.dropout     )







     def __call__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=True     ):
         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	hidden_states.shape
         __UpperCamelCase						:						Tuple		     =	hidden_states
         __UpperCamelCase						:						int		     =	self.norm(lowercase_     )
         if self.use_linear_projection:
             __UpperCamelCase						:						int		     =	hidden_states.reshape(lowercase_  ,      height * width  ,      lowercase_     )
             __UpperCamelCase						:						str		     =	self.proj_in(lowercase_     )
         else:
             __UpperCamelCase						:						List[Any]		     =	self.proj_in(lowercase_     )
             __UpperCamelCase						:						Any		     =	hidden_states.reshape(lowercase_  ,      height * width  ,      lowercase_     )

         for transformer_block in self.transformer_blocks:
             __UpperCamelCase						:						Tuple		     =	transformer_block(lowercase_  ,      lowercase_  ,      deterministic=lowercase_     )

         if self.use_linear_projection:
             __UpperCamelCase						:						List[str]		     =	self.proj_out(lowercase_     )
             __UpperCamelCase						:						Dict		     =	hidden_states.reshape(lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_     )
         else:
             __UpperCamelCase						:						List[str]		     =	hidden_states.reshape(lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_     )
             __UpperCamelCase						:						Any		     =	self.proj_out(lowercase_     )

         __UpperCamelCase						:						Tuple		     =	hidden_states + residual
         return self.dropout_layer(lowercase_  ,      deterministic=lowercase_     )






class       __lowercase		(    nn.Module    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    0.0
     SCREAMING_SNAKE_CASE__		 =    jnp.floataa







     def lowerCAmelCase  (		self     ):
         # The second linear layer needs to be called
         # net_2 for now to match the index of the Sequential layer
         __UpperCamelCase						:						List[str]		     =	FlaxGEGLU(self.dim  ,      self.dropout  ,      self.dtype     )
         __UpperCamelCase						:						Any		     =	nn.Dense(self.dim  ,      dtype=self.dtype     )







     def __call__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=True     ):
         __UpperCamelCase						:						int		     =	self.net_a(lowercase_  ,      deterministic=lowercase_     )
         __UpperCamelCase						:						Tuple		     =	self.net_a(lowercase_     )
         return hidden_states






class       __lowercase		(    nn.Module    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    42
     SCREAMING_SNAKE_CASE__		 =    0.0
     SCREAMING_SNAKE_CASE__		 =    jnp.floataa







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	self.dim * 4
         __UpperCamelCase						:						int		     =	nn.Dense(inner_dim * 2  ,      dtype=self.dtype     )
         __UpperCamelCase						:						List[Any]		     =	nn.Dropout(rate=self.dropout     )







     def __call__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=True     ):
         __UpperCamelCase						:						int		     =	self.proj(lowercase_     )
         __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	jnp.split(lowercase_  ,      2  ,      axis=2     )
         return self.dropout_layer(hidden_linear * nn.gelu(lowercase_     )  ,      deterministic=lowercase_     )








"""simple docstring"""


import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
				print("""#################### AVAILABLE DEVICES ####################""")
				print(jax.devices())
				print("""###########################################################""")

				# setup for wandb sweep
				snake_case_								=			Args()
				snake_case_								=			wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
				snake_case_								=			dict(logger.config)
				del wandb_args["batch_size"]
				snake_case_								=			replace(args, **wandb_args)
				snake_case_								=			args.base_dir + """-""" + wandb.run.id
				snake_case_								=			replace(args, base_dir=base_dir)
				print(args)

				snake_case_								=			load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
				snake_case_								=			load_dataset("""json""", data_files=args.val_data_path)["""train"""]

				# drop extra batch for now
				snake_case_								=			range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
				snake_case_								=			tr_dataset.shuffle().select(indices)
				snake_case_								=			range(len(val_dataset) - len(val_dataset) % args.batch_size)
				snake_case_								=			val_dataset.shuffle().select(indices)

				if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
								snake_case_								=			tr_dataset.shuffle().select(range(8_0000))
								snake_case_								=			val_dataset.shuffle().select(range(8000))

				print(tr_dataset)
				print(val_dataset)

				snake_case_								=			FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				snake_case_								=			BigBirdTokenizerFast.from_pretrained(args.model_id)
				snake_case_								=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

				snake_case_								=			{
				    """lr""": args.lr,
				    """init_lr""": args.init_lr,
				    """warmup_steps""": args.warmup_steps,
				    """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
				    """weight_decay""": args.weight_decay,
				}
				snake_case_	,  snake_case_								=			build_tx(**tx_args)

				snake_case_								=			Trainer(
				    args=args,
				    data_collator=data_collator,
				    model_save_fn=model.save_pretrained,
				    train_step_fn=train_step,
				    val_step_fn=val_step,
				    logger=logger,
				    scheduler_fn=lr,
				)

				snake_case_								=			None
				snake_case_								=			trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
				try:
								trainer.train(state, tr_dataset, val_dataset)
				except KeyboardInterrupt:
								print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

				print("""SAVING WEIGHTS IN `final-weights`""")
				snake_case_								=			jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





