
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A     :	Tuple    = logging.get_logger(__name__)

A     :	Dict    = {
    'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
    'RWKV/rwkv-4-430m-pile': 'https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json',
    'RWKV/rwkv-4-1b5-pile': 'https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json',
    'RWKV/rwkv-4-3b-pile': 'https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json',
    'RWKV/rwkv-4-7b-pile': 'https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json',
    'RWKV/rwkv-4-14b-pile': 'https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json',
    'RWKV/rwkv-raven-1b5': 'https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json',
    'RWKV/rwkv-raven-3b': 'https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json',
    'RWKV/rwkv-raven-7b': 'https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json',
    'RWKV/rwkv-raven-14b': 'https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json',
}







class 			__A( a			):
				snake_case_						   =    '''rwkv'''
				snake_case_						   =    {'''max_position_embeddings''': '''context_length'''}




				def __init__(     self		,     _snake_case=50_277		,     _snake_case=1_024		,     _snake_case=4_096		,     _snake_case=32		,     _snake_case=None		,     _snake_case=None		,     _snake_case=1E-5		,     _snake_case=0		,     _snake_case=0		,     _snake_case=6		,     _snake_case=False		,     _snake_case=True		,     **_snake_case		,     )    ->      List[str]:


								'''simple docstring'''




								__a										=    vocab_size
								__a										=    context_length
								__a										=    hidden_size
								__a										=    num_hidden_layers
								__a										=    attention_hidden_size if attention_hidden_size is not None else hidden_size
								__a										=    intermediate_size if intermediate_size is not None else 4 * hidden_size
								__a										=    layer_norm_epsilon
								__a										=    rescale_every
								__a										=    use_cache

								__a										=    bos_token_id
								__a										=    eos_token_id

								super().__init__(
								    tie_word_embeddings=_snake_case		,     bos_token_id=_snake_case		,     eos_token_id=_snake_case		,     **_snake_case				)


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									# For consistency across different places the DisjunctiveConstraint is called,
									# dc.token_ids is a list of integers. It is also initialized only by integers.

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [[1, 2, 4], [1, 2, 3, 4]]
									SCREAMING_SNAKE_CASE_: Any  		=       DisjunctiveConstraint(lowerCAmelCase__)
									self.assertTrue(isinstance(dc.token_ids   ,						lowerCAmelCase__))

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									# We can't have constraints that are complete subsets of another. This leads to a preverse
									# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
									# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
									# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
									# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2], [1, 2, 3, 4]]

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint(lowerCAmelCase__)  # fails here



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       [[1, 2, 3], [1, 2, 4]]

									SCREAMING_SNAKE_CASE_: Tuple  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       dc.update(1)
									SCREAMING_SNAKE_CASE_: Dict  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       dc.update(2)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(3)
									SCREAMING_SNAKE_CASE_: Tuple  		=       stepped is True and completed is True and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 3])




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

									SCREAMING_SNAKE_CASE_: List[Any]  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       dc.update(2)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(4)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2, 4])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dc.update(5)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 4, 5])

									dc.reset()

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.remaining() == 3)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(2)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.remaining() == 2)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       dc.update(5)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.remaining() == 0)
									self.assertTrue(dc.current_seq == [1, 2, 5])






