




'''simple docstring'''




from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
     import timm


if is_torch_available():
     from torch import Tensor


class   __UpperCamelCase       ( lowerCamelCase__			,					lowerCamelCase__				):
       lowercase	:    Tuple      						='pixel_values'
       lowercase	:    Any      						=False
       lowercase	:    int      						=TimmBackboneConfig






       def __init__(							self,		lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







              requires_backends(self,		'''timm'''  )
              super().__init__(lowerCAmelCase  )
              lowerCamelCase_							=config

              if config.backbone is None:
                     raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

              if config.backbone not in timm.list_models():
                     raise ValueError(f'''backbone {config.backbone} is not supported by timm.'''  )

              if hasattr(lowerCAmelCase,		'''out_features'''  ) and config.out_features is not None:
                     raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

              lowerCamelCase_							=getattr(lowerCAmelCase,		'''use_pretrained_backbone''',		lowerCAmelCase  )
              if pretrained is None:
                     raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

              # We just take the final layer by default. This matches the default for the transformers models.
              lowerCamelCase_							=config.out_indices if getattr(lowerCAmelCase,		'''out_indices''',		lowerCAmelCase  ) is not None else (-1,)

              lowerCamelCase_							=timm.create_model(
                  config.backbone,		pretrained=lowerCAmelCase,		features_only=config.features_only,		in_chans=config.num_channels,		out_indices=lowerCAmelCase,		**lowerCAmelCase,		)
              # These are used to control the output of the model when called. If output_hidden_states is True, then
              # return_layers is modified to include all layers.
              lowerCamelCase_							=self._backbone.return_layers
              lowerCamelCase_							={layer['''module''']: str(lowerCAmelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
              super()._init_backbone(lowerCAmelCase  )






       @classmethod
       def      lowercase__     (							cls,		lowerCAmelCase,		*lowerCAmelCase,		**lowerCAmelCase  ):



              """simple docstring"""







              requires_backends(cls,		['''vision''', '''timm''']  )
              from ...models.timm_backbone import TimmBackboneConfig

              lowerCamelCase_							=kwargs.pop('''config''',		TimmBackboneConfig()  )

              lowerCamelCase_							=kwargs.pop('''use_timm_backbone''',		lowerCAmelCase  )
              if not use_timm:
                     raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

              lowerCamelCase_							=kwargs.pop('''num_channels''',		config.num_channels  )
              lowerCamelCase_							=kwargs.pop('''features_only''',		config.features_only  )
              lowerCamelCase_							=kwargs.pop('''use_pretrained_backbone''',		config.use_pretrained_backbone  )
              lowerCamelCase_							=kwargs.pop('''out_indices''',		config.out_indices  )
              lowerCamelCase_							=TimmBackboneConfig(
                  backbone=lowerCAmelCase,		num_channels=lowerCAmelCase,		features_only=lowerCAmelCase,		use_pretrained_backbone=lowerCAmelCase,		out_indices=lowerCAmelCase,		)
              return super()._from_config(lowerCAmelCase,		**lowerCAmelCase  )






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              pass






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase=None,		lowerCAmelCase=None,		lowerCAmelCase=None,		**lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=return_dict if return_dict is not None else self.config.use_return_dict
              lowerCamelCase_							=(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              lowerCamelCase_							=output_attentions if output_attentions is not None else self.config.output_attentions

              if output_attentions:
                     raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

              if output_hidden_states:
                     # We modify the return layers to include all the stages of the backbone
                     lowerCamelCase_							=self._all_layers
                     lowerCamelCase_							=self._backbone(lowerCAmelCase,		**lowerCAmelCase  )
                     lowerCamelCase_							=self._return_layers
                     lowerCamelCase_							=tuple(hidden_states[i] for i in self.out_indices  )
              else:
                     lowerCamelCase_							=self._backbone(lowerCAmelCase,		**lowerCAmelCase  )
                     lowerCamelCase_							=None

              lowerCamelCase_							=tuple(lowerCAmelCase  )
              lowerCamelCase_							=tuple(lowerCAmelCase  ) if hidden_states is not None else None

              if not return_dict:
                     lowerCamelCase_							=(feature_maps,)
                     if output_hidden_states:
                            lowerCamelCase_							=output + (hidden_states,)
                     return output

              return BackboneOutput(feature_maps=lowerCAmelCase,		hidden_states=lowerCAmelCase,		attentions=lowerCAmelCase  )







# 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_			  = logging.get_logger(__name__)

snake_case_			  = {}
snake_case_			  = {}
snake_case_			  = {}

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    type	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None	,     ):



     '''simple docstring'''





     lowercase__    :   Union[str, Any]      =       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__})"""			)
     lowercase__    :   Any      =       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})"""			)
          lowercase__    :   int      =       format_type

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Exception	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       aliases if aliases is not None else []
     for alias in set(aliases + [format_type]			):
          lowercase__    :   Any      =       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_			  = 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_			  = 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_			  = ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
       _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]	,     **SCREAMING_SNAKE_CASE_		:    Any			):



     '''simple docstring'''





     lowercase__    :   Tuple      =       get_format_type_from_alias(SCREAMING_SNAKE_CASE_			)
     if format_type in _FORMAT_TYPES:
          return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE_			)
     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}'"""			)

