'''simple docstring'''




import functools
import math

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




def 				lowerCamelCase      ( UpperCAmelCase__			:   Any , UpperCAmelCase__			:   str , UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   int = 4096					)     -> Tuple:
							lowercase_		,		lowercase_		,		lowercase_   :     Dict									=			key.shape[-3:]
							lowercase_   :     int									=			value.shape[-1]
							lowercase_   :     int									=			min(UpperCAmelCase__ , UpperCAmelCase__					)
							lowercase_   :     List[Any]									=			query / jnp.sqrt(UpperCAmelCase__					)

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

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

														lowercase_   :     Union[str, Any]									=			jnp.einsum("""...vhf,...qhv->...qhf""" , UpperCAmelCase__ , UpperCAmelCase__ , precision=UpperCAmelCase__					)
														lowercase_   :     Dict									=			jnp.einsum("""...qhk->...qh""" , UpperCAmelCase__					)

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

							def chunk_scanner(UpperCAmelCase__			:   Union[str, Any]					):
														# julienne key array
														lowercase_   :     str									=			jax.lax.dynamic_slice(
														    operand=UpperCAmelCase__ , 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
														lowercase_   :     Any									=			jax.lax.dynamic_slice(
														    operand=UpperCAmelCase__ , 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(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)

							lowercase_		,		lowercase_		,		lowercase_   :     List[Any]									=			jax.lax.map(f=UpperCAmelCase__ , xs=jnp.arange(0 , UpperCAmelCase__ , UpperCAmelCase__					)					)

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

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

							lowercase_   :     int									=			chunk_values.sum(axis=0					)
							lowercase_   :     List[Any]									=			jnp.expand_dims(UpperCAmelCase__ , -1					).sum(axis=0					)

							return all_values / all_weights




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   Any , UpperCAmelCase__			:   int=jax.lax.Precision.HIGHEST , UpperCAmelCase__			:   int = 1024 , UpperCAmelCase__			:   int = 4096					)     -> str:
							lowercase_		,		lowercase_		,		lowercase_   :     Optional[int]									=			query.shape[-3:]

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

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

							lowercase_		,		lowercase_   :     str									=			jax.lax.scan(
							    f=UpperCAmelCase__ , init=0 , xs=UpperCAmelCase__ , length=math.ceil(num_q / query_chunk_size					)					)

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





class        __magic_name__		(       nn.Module):
			UpperCamelCase__							=			42
			UpperCamelCase__							=			8
			UpperCamelCase__							=			64
			UpperCamelCase__							=			0.0
			UpperCamelCase__							=			False
			UpperCamelCase__							=			jnp.floataa







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     List[Any]									=			self.dim_head * self.heads
										lowercase_   :     str									=			self.dim_head**-0.5

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

										lowercase_   :     Any									=			nn.Dense(self.query_dim							,     dtype=self.dtype							,     name="""to_out_0"""       )
										lowercase_   :     Optional[Any]									=			nn.Dropout(rate=self.dropout       )







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







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







			def __call__(      self							:    Tuple							,     lowercase_							:    List[str]							,     lowercase_							:    Union[str, Any]=None							,     lowercase_							:    Union[str, Any]=True       ):
										lowercase_   :     int									=			hidden_states if context is None else context

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

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

										if self.use_memory_efficient_attention:
																	lowercase_   :     Dict									=			query_states.transpose(1							,     0							,     2       )
																	lowercase_   :     List[str]									=			key_states.transpose(1							,     0							,     2       )
																	lowercase_   :     int									=			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

																	lowercase_   :     Union[str, Any]									=			query_states.shape[-3]
																	if flatten_latent_dim % 64 == 0:
																								lowercase_   :     Union[str, Any]									=			int(flatten_latent_dim / 64       )
																	elif flatten_latent_dim % 16 == 0:
																								lowercase_   :     Any									=			int(flatten_latent_dim / 16       )
																	elif flatten_latent_dim % 4 == 0:
																								lowercase_   :     int									=			int(flatten_latent_dim / 4       )
																	else:
																								lowercase_   :     Dict									=			int(lowercase_       )

																	lowercase_   :     int									=			jax_memory_efficient_attention(
																	    lowercase_							,     lowercase_							,     lowercase_							,     query_chunk_size=lowercase_							,     key_chunk_size=4096 * 4       )

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

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

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





class        __magic_name__		(       nn.Module):
			UpperCamelCase__							=			42
			UpperCamelCase__							=			42
			UpperCamelCase__							=			42
			UpperCamelCase__							=			0.0
			UpperCamelCase__							=			False
			UpperCamelCase__							=			jnp.floataa
			UpperCamelCase__							=			False







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







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

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

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

										return self.dropout_layer(lowercase_							,     deterministic=lowercase_       )





class        __magic_name__		(       nn.Module):
			UpperCamelCase__							=			42
			UpperCamelCase__							=			42
			UpperCamelCase__							=			42
			UpperCamelCase__							=			1
			UpperCamelCase__							=			0.0
			UpperCamelCase__							=			False
			UpperCamelCase__							=			False
			UpperCamelCase__							=			jnp.floataa
			UpperCamelCase__							=			False







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Any									=			nn.GroupNorm(num_groups=32							,     epsilon=1E-5       )

										lowercase_   :     Optional[int]									=			self.n_heads * self.d_head
										if self.use_linear_projection:
																	lowercase_   :     Union[str, Any]									=			nn.Dense(lowercase_							,     dtype=self.dtype       )
										else:
																	lowercase_   :     Union[str, Any]									=			nn.Conv(
																	    lowercase_							,     kernel_size=(1, 1)							,     strides=(1, 1)							,     padding="""VALID"""							,     dtype=self.dtype							,     )

										lowercase_   :     Tuple									=			[
										    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:
																	lowercase_   :     Union[str, Any]									=			nn.Dense(lowercase_							,     dtype=self.dtype       )
										else:
																	lowercase_   :     Union[str, Any]									=			nn.Conv(
																	    lowercase_							,     kernel_size=(1, 1)							,     strides=(1, 1)							,     padding="""VALID"""							,     dtype=self.dtype							,     )

										lowercase_   :     Union[str, Any]									=			nn.Dropout(rate=self.dropout       )







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

										for transformer_block in self.transformer_blocks:
																	lowercase_   :     List[Any]									=			transformer_block(lowercase_							,     lowercase_							,     deterministic=lowercase_       )

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

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





class        __magic_name__		(       nn.Module):
			UpperCamelCase__							=			42
			UpperCamelCase__							=			0.0
			UpperCamelCase__							=			jnp.floataa







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







			def __call__(      self							:    List[str]							,     lowercase_							:    Optional[int]							,     lowercase_							:    Optional[int]=True       ):
										lowercase_   :     List[str]									=			self.net_a(lowercase_							,     deterministic=lowercase_       )
										lowercase_   :     List[str]									=			self.net_a(lowercase_       )
										return hidden_states





class        __magic_name__		(       nn.Module):
			UpperCamelCase__							=			42
			UpperCamelCase__							=			0.0
			UpperCamelCase__							=			jnp.floataa







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     Dict									=			self.dim * 4
										lowercase_   :     Optional[int]									=			nn.Dense(inner_dim * 2							,     dtype=self.dtype       )
										lowercase_   :     Optional[int]									=			nn.Dropout(rate=self.dropout       )







			def __call__(      self							:    List[str]							,     lowercase_							:    Tuple							,     lowercase_							:    int=True       ):
										lowercase_   :     int									=			self.proj(lowercase_       )
										lowercase_		,		lowercase_   :     Tuple									=			jnp.split(lowercase_							,     2							,     axis=2       )
										return self.dropout_layer(hidden_linear * nn.gelu(lowercase_       )							,     deterministic=lowercase_       )






'''simple docstring'''




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_lowercase		:					str													=		logging.getLogger(__name__)




def 				lowerCamelCase      ( )     -> Tuple:
							lowercase_   :     Any									=			argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""					)
							parser.add_argument(
							    """--dataset_name""" , type=UpperCAmelCase__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , )
							parser.add_argument(
							    """--dataset_config""" , type=UpperCAmelCase__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset."""					)
							parser.add_argument(
							    """--batch_size""" , type=UpperCAmelCase__ , default=1000 , help="""Batch size during training.""" , )
							parser.add_argument(
							    """--vocab_size""" , type=UpperCAmelCase__ , default=10048 , help="""Size of the desired vocabulary.""" , )
							parser.add_argument(
							    """--limit""" , default=UpperCAmelCase__ , type=UpperCAmelCase__ , help="""Limit the number of shards (used for debugging).""" , )
							parser.add_argument(
							    """--export_to_hub""" , action="""store_true""" , )

							lowercase_   :     List[Any]									=			parser.parse_args()
							return args




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> List[Any]:
							lowercase_   :     Any									=			datasets.load_dataset(args.dataset_name , args.dataset_config , split="""train"""					)

							if args.limit is not None:
														lowercase_   :     Optional[Any]									=			min(len(UpperCAmelCase__					) , args.limit					)
														lowercase_   :     Dict									=			dataset.select(range(UpperCAmelCase__					)					)
														logger.info(F'''Limiting the dataset to {args.limit} entries.'''					)

							def batch_iterator():
														for i in range(0 , len(UpperCAmelCase__					) , args.batch_size					):
																					yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
							lowercase_   :     Optional[int]									=			Tokenizer(Unigram()					)
							lowercase_   :     Optional[int]									=			normalizers.Sequence([normalizers.Replace("""``""" , """\""""					), normalizers.Replace("""''""" , """\""""					)]					)
							lowercase_   :     List[Any]									=			pre_tokenizers.Metaspace()

							# Prepare the trainer.
							lowercase_   :     str									=			UnigramTrainer(
							    unk_token="""<unk>""" , special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""] , vocab_size=args.vocab_size , )

							logger.info("""Training the tokenizer."""					)
							tokenizer.train_from_iterator(batch_iterator() , trainer=UpperCAmelCase__					)
							logger.info("""Tokenizer training complete!"""					)

							lowercase_   :     Dict									=			tokenizer.token_to_id("""[CLS]"""					)
							lowercase_   :     Any									=			tokenizer.token_to_id("""[SEP]"""					)
							lowercase_   :     Optional[Any]									=			processors.TemplateProcessing(
							    single="""[CLS]:0 $A:0 [SEP]:0""" , pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1""" , special_tokens=[
							        ("""[CLS]""", cls_token_id),
							        ("""[SEP]""", sep_token_id),
							    ] , )
							lowercase_   :     int									=			decoders.Metaspace()

							if args.export_to_hub:
														logger.info("""Exporting the trained tokenzier to Hub."""					)
														lowercase_   :     Any									=			AlbertTokenizerFast(tokenizer_object=UpperCAmelCase__					)
														new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


if __name__ == "__main__":
						_lowercase		:					Dict													=		parse_args()
						main(args)





