



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__snake_case     :Union[str, Any]			       =logging.get_logger(__name__)

__snake_case     :Dict[Optional[str], Type[Formatter]]			       ={}
__snake_case     :Dict[Optional[str], str]			       ={}
__snake_case     :Dict[Optional[str], Exception]			       ={}






def  lowerCamelCase_	(      lowerCAmelCase__	:	type			,	lowerCAmelCase__	:	Optional[str]			,	lowerCAmelCase__	:	Optional[List[str]] = None			,	)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    aliases if aliases is not None else []
							if format_type in _FORMAT_TYPES:
														logger.warning(
														    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
							A      						=    formatter_cls
							for alias in set(aliases + [format_type]				):
														if alias in _FORMAT_TYPES_ALIASES:
																					logger.warning(
																					    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
														A      						=    format_type






def  lowerCamelCase_	(      lowerCAmelCase__	:	Exception			,	lowerCAmelCase__	:	Optional[str]			,	lowerCAmelCase__	:	Optional[List[str]] = None				)       ->							Optional[int]:

							'''simple docstring'''






							A      						=    aliases if aliases is not None else []
							for alias in set(aliases + [format_type]				):
														A      						=    unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
				from .torch_formatter import TorchFormatter

				_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
				__snake_case     :int			       =ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
				_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
				from .tf_formatter import TFFormatter

				_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
				__snake_case     :List[str]			       =ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
				_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
				from .jax_formatter import JaxFormatter

				_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
				__snake_case     :Dict			       =ValueError('JAX needs to be installed to be able to return JAX arrays.')
				_register_unavailable_formatter(_jax_error, 'jax', aliases=[])






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[str]				)       ->							Optional[str]:

							'''simple docstring'''






							if format_type in _FORMAT_TYPES_ALIASES:
														return _FORMAT_TYPES_ALIASES[format_type]
							else:
														return format_type






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[str]			,	**lowerCAmelCase__	:	Any				)       ->							Formatter:

							'''simple docstring'''






							A      						=    get_format_type_from_alias(lowerCAmelCase__				)
							if format_type in _FORMAT_TYPES:
														return _FORMAT_TYPES[format_type](**lowerCAmelCase__				)
							if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
														raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
							else:
														raise ValueError(
														    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)




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


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :Tuple			       ={
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Any								=     'transfo-xl'
				A_   :     Dict								=     ['mems']
				A_   :     int								=     {
				    'n_token': 'vocab_size',
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}


				def __init__(							self      :  Optional[Any]						, __UpperCamelCase      :  str=267_735						, __UpperCamelCase      :  int=[20_000, 40_000, 200_000]						, __UpperCamelCase      :  List[str]=1_024						, __UpperCamelCase      :  Union[str, Any]=1_024						, __UpperCamelCase      :  Optional[Any]=16						, __UpperCamelCase      :  Dict=64						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  List[str]=False						, __UpperCamelCase      :  Optional[int]=18						, __UpperCamelCase      :  int=1_600						, __UpperCamelCase      :  Dict=1_000						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=0						, __UpperCamelCase      :  Any=-1						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  List[Any]=0.1						, __UpperCamelCase      :  str=0.0						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  int="normal"						, __UpperCamelCase      :  List[Any]=0.0_1						, __UpperCamelCase      :  str=0.0_1						, __UpperCamelCase      :  Any=0.0_2						, __UpperCamelCase      :  str=1e-5						, __UpperCamelCase      :  Optional[int]=0						, **__UpperCamelCase      :  Any						, )		->     int:
											A      						=    vocab_size
											A      						=    []
											self.cutoffs.extend(__UpperCamelCase       )
											if proj_share_all_but_first:
																		A      						=    [False] + [True] * len(self.cutoffs       )
											else:
																		A      						=    [False] + [False] * len(self.cutoffs       )
											A      						=    d_model
											A      						=    d_embed
											A      						=    d_head
											A      						=    d_inner
											A      						=    div_val
											A      						=    pre_lnorm
											A      						=    n_layer
											A      						=    n_head
											A      						=    mem_len
											A      						=    same_length
											A      						=    attn_type
											A      						=    clamp_len
											A      						=    sample_softmax
											A      						=    adaptive
											A      						=    dropout
											A      						=    dropatt
											A      						=    untie_r
											A      						=    init
											A      						=    init_range
											A      						=    proj_init_std
											A      						=    init_std
											A      						=    layer_norm_epsilon
											super().__init__(eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  Any       )		->     Optional[int]:
											# Message copied from Transformer-XL documentation
											logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )
											return -1


				@max_position_embeddings.setter
				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											# Message copied from Transformer-XL documentation
											raise NotImplementedError(
											    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )