
"""simple docstring"""


# 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


A_    							=					logging.get_logger(__name__)

A_    							=					{}
A_    							=					{}
A_    							=					{}






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__ = None	,):
			lowerCamelCase_					=  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__})"						)
			lowerCamelCase_					=  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})"						)
						lowerCamelCase_					=  format_type






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__ = None						):
			lowerCamelCase_					=  aliases if aliases is not None else []
			for alias in set(aliases + [format_type]						):
						lowerCamelCase_					=  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:
						A_    							=					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:
						A_    							=					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:
						A_    							=					ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
						_register_unavailable_formatter(_jax_error, """jax""", aliases=[])






def  lowercase						(	lowerCAmelCase__						):
			if format_type in _FORMAT_TYPES_ALIASES:
						return _FORMAT_TYPES_ALIASES[format_type]
			else:
						return format_type






def  lowercase						(	lowerCAmelCase__	,**lowerCAmelCase__						):
			lowerCamelCase_					=  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}'"						)










'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

							__UpperCAmelCase            =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



