
"""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 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''pixel_values'''
		__lowercase							:  Any						   =		False
		__lowercase							:  Optional[Any]						   =		TimmBackboneConfig




		def __init__(     self							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  """timm""")
				super().__init__(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = 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.""")

				__SCREAMING_SNAKE_CASE            = 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.
				__SCREAMING_SNAKE_CASE            = config.out_indices if getattr(lowerCAmelCase__							,  """out_indices"""							,  lowerCAmelCase__) is not None else (-1,)

				__SCREAMING_SNAKE_CASE            = 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.
				__SCREAMING_SNAKE_CASE            = self._backbone.return_layers
				__SCREAMING_SNAKE_CASE            = {layer["""module"""]: str(lowerCAmelCase__) for i, layer in enumerate(self._backbone.feature_info.info)}
				super()._init_backbone(lowerCAmelCase__)




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(cls							,  ["""vision""", """timm"""])
				from ...models.timm_backbone import TimmBackboneConfig

				__SCREAMING_SNAKE_CASE            = kwargs.pop("""config"""							,  TimmBackboneConfig())

				__SCREAMING_SNAKE_CASE            = kwargs.pop("""use_timm_backbone"""							,  lowerCAmelCase__)
				if not use_timm:
						raise ValueError("""use_timm_backbone must be True for timm backbones""")

				__SCREAMING_SNAKE_CASE            = kwargs.pop("""num_channels"""							,  config.num_channels)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""features_only"""							,  config.features_only)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""use_pretrained_backbone"""							,  config.use_pretrained_backbone)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""out_indices"""							,  config.out_indices)
				__SCREAMING_SNAKE_CASE            = TimmBackboneConfig(
				    backbone=lowerCAmelCase__							,  num_channels=lowerCAmelCase__							,  features_only=lowerCAmelCase__							,  use_pretrained_backbone=lowerCAmelCase__							,  out_indices=lowerCAmelCase__							,  )
				return super()._from_config(lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				pass




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = return_dict if return_dict is not None else self.config.use_return_dict
				__SCREAMING_SNAKE_CASE            = (
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				__SCREAMING_SNAKE_CASE            = 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
						__SCREAMING_SNAKE_CASE            = self._all_layers
						__SCREAMING_SNAKE_CASE            = self._backbone(lowerCAmelCase__							,  **lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = self._return_layers
						__SCREAMING_SNAKE_CASE            = tuple(hidden_states[i] for i in self.out_indices)
				else:
						__SCREAMING_SNAKE_CASE            = self._backbone(lowerCAmelCase__							,  **lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = None

				__SCREAMING_SNAKE_CASE            = tuple(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tuple(lowerCAmelCase__) if hidden_states is not None else None

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

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






"""simple docstring"""





import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs



@pytest.mark.parametrize(
    """kwargs, expected"""  ,				[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(UpperCamelCase_  ,				i + 1				) for i in range(10				)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0  ,				4				), range(4  ,				7				), range(7  ,				10				)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0  ,				1				), range(1  ,				2				), range(2  ,				3				)]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _distribute_shards(**UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""  ,				[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _split_gen_kwargs(UpperCamelCase_  ,				UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, expected"""  ,				[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		if expected is RuntimeError:
				with pytest.raises(UpperCamelCase_				):
						_number_of_shards_in_gen_kwargs(UpperCamelCase_				)
		else:
				__SCREAMING_SNAKE_CASE            = _number_of_shards_in_gen_kwargs(UpperCamelCase_				)
				assert out == expected




