
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
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			object()

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, Any]							,						__UpperCamelCase :						int       ) ->       List[str]:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]       ) ->       Optional[int]:

				"""simple docstring"""







				def replace(__UpperCamelCase :						Any							,						__UpperCamelCase :						Tuple       ):
								for rule, replacement in rules:
												if _match(__UpperCamelCase							,						__UpperCamelCase       ):
																return replacement
								return val

				return replace

def 				SCREAMING_SNAKE_CASE     (      ) ->       Dict:

				"""simple docstring"""







				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('''mp'''							,						__UpperCamelCase       )),
				    (("transformer", "wte", "embedding"), P('''mp'''							,						__UpperCamelCase       )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__UpperCamelCase							,						'''mp'''       )),
				    (("attention", "out_proj", "kernel"), P('''mp'''							,						__UpperCamelCase       )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__UpperCamelCase							,						'''mp'''       )),
				    (("mlp", "c_fc", "bias"), P('''mp'''       )),
				    (("mlp", "c_proj", "kernel"), P('''mp'''							,						__UpperCamelCase       )),
				    (("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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Any:

				"""simple docstring"""







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

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids