





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_		:				Optional[int]				=						object()

# For specifying empty leaf dict `{}`
UpperCamelCase_		:				List[Any]				=						object()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   tuple((re.compile(x + '$' ) for x in qs) )
				for i in range(len(__lowerCAmelCase ) - len(__lowerCAmelCase ) + 1 ):
								_snake_case :			Tuple  						=   [x.match(__lowerCAmelCase ) for x, y in zip(__lowerCAmelCase	,			ks[i:] )]
								if matches and all(__lowerCAmelCase ):
												return True
				return False

def       A__(		__lowerCAmelCase ):
				def replace(__lowerCAmelCase	,			__lowerCAmelCase ):
								for rule, replacement in rules:
												if _match(__lowerCAmelCase	,			__lowerCAmelCase ):
																return replacement
								return val

				return replace

def       A__(		):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('mp'	,			__lowerCAmelCase )),
				    (("transformer", "wte", "embedding"), P('mp'	,			__lowerCAmelCase )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCAmelCase	,			'mp' )),
				    (("attention", "out_proj", "kernel"), P('mp'	,			__lowerCAmelCase )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__lowerCAmelCase	,			'mp' )),
				    (("mlp", "c_fc", "bias"), P('mp' )),
				    (("mlp", "c_proj", "kernel"), P('mp'	,			__lowerCAmelCase )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]

def       A__(		__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   _get_partition_rules()
				_snake_case :			Optional[Any]  						=   _replacement_rules(__lowerCAmelCase )
				_snake_case :			str  						=   {k: _unmatched for k in flatten_dict(__lowerCAmelCase )}
				_snake_case :			str  						=   {k: replace(__lowerCAmelCase	,			__lowerCAmelCase ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__lowerCAmelCase ) )













import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						@register_to_config
						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Optional[int]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   False

										_snake_case :			Tuple  						=   nn.Dropout(p=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   TaConfig(
										    vocab_size=lowerCamelCase_ ,				d_model=lowerCamelCase_ ,				num_heads=lowerCamelCase_ ,				d_kv=lowerCamelCase_ ,				d_ff=lowerCamelCase_ ,				dropout_rate=lowerCamelCase_ ,				feed_forward_proj=lowerCamelCase_ ,				is_decoder=lowerCamelCase_ ,				is_encoder_decoder=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   nn.ModuleList()
										for lyr_num in range(lowerCamelCase_					):
														_snake_case :			Any  						=   TaBlock(lowerCamelCase_					)
														self.encoders.append(lowerCamelCase_					)

										_snake_case :			Tuple  						=   TaLayerNorm(lowerCamelCase_					)
										_snake_case :			List[str]  						=   nn.Dropout(p=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.token_embedder(lowerCamelCase_					)

										_snake_case :			List[Any]  						=   encoder_input_tokens.shape[1]
										_snake_case :			Any  						=   torch.arange(lowerCamelCase_ ,				device=encoder_input_tokens.device					)
										x += self.position_encoding(lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.dropout_pre(lowerCamelCase_					)

										# inverted the attention mask
										_snake_case :			Dict  						=   encoder_input_tokens.size()
										_snake_case :			Optional[int]  						=   self.get_extended_attention_mask(lowerCamelCase_ ,				lowerCamelCase_					)

										for lyr in self.encoders:
														_snake_case :			str  						=   lyr(lowerCamelCase_ ,				lowerCamelCase_					)[0]
										_snake_case :			Any  						=   self.layer_norm(lowerCamelCase_					)

										return self.dropout_post(lowerCamelCase_					), encoder_inputs_mask






