






'''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       (       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"pixel_values"
			snake_case__	=					False
			snake_case__	=					TimmBackboneConfig






			def __init__(			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Tuple       )      ->      List[Any]:
							requires_backends(self	,    '''timm'''       )
							super().__init__(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   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(__SCREAMING_SNAKE_CASE	,    '''out_features'''       ) and config.out_features is not None:
											raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''       )

							a_		:	Optional[int]									=   getattr(__SCREAMING_SNAKE_CASE	,    '''use_pretrained_backbone'''	,    __SCREAMING_SNAKE_CASE       )
							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.
							a_		:	Union[str, Any]									=   config.out_indices if getattr(__SCREAMING_SNAKE_CASE	,    '''out_indices'''	,    __SCREAMING_SNAKE_CASE       ) is not None else (-1,)

							a_		:	List[Any]									=   timm.create_model(
							    config.backbone	,    pretrained=__SCREAMING_SNAKE_CASE	,    features_only=config.features_only	,    in_chans=config.num_channels	,    out_indices=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
							# 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.
							a_		:	Dict									=   self._backbone.return_layers
							a_		:	int									=   {layer['''module''']: str(__SCREAMING_SNAKE_CASE       ) for i, layer in enumerate(self._backbone.feature_info.info       )}
							super()._init_backbone(__SCREAMING_SNAKE_CASE       )






			@classmethod
			def       SCREAMING_SNAKE_CASE   (			cls   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Any	,    *__SCREAMING_SNAKE_CASE   :      int	,    **__SCREAMING_SNAKE_CASE   :      List[str]       )      ->      Optional[int]:
							requires_backends(cls	,    ['''vision''', '''timm''']       )
							from ...models.timm_backbone import TimmBackboneConfig

							a_		:	str									=   kwargs.pop('''config'''	,    TimmBackboneConfig()       )

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

							a_		:	List[str]									=   kwargs.pop('''num_channels'''	,    config.num_channels       )
							a_		:	Dict									=   kwargs.pop('''features_only'''	,    config.features_only       )
							a_		:	List[str]									=   kwargs.pop('''use_pretrained_backbone'''	,    config.use_pretrained_backbone       )
							a_		:	Dict									=   kwargs.pop('''out_indices'''	,    config.out_indices       )
							a_		:	Any									=   TimmBackboneConfig(
							    backbone=__SCREAMING_SNAKE_CASE	,    num_channels=__SCREAMING_SNAKE_CASE	,    features_only=__SCREAMING_SNAKE_CASE	,    use_pretrained_backbone=__SCREAMING_SNAKE_CASE	,    out_indices=__SCREAMING_SNAKE_CASE	,    )
							return super()._from_config(__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      int:
							pass







			def       SCREAMING_SNAKE_CASE   (			self   :      int	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      List[str]=None	,    __SCREAMING_SNAKE_CASE   :      List[str]=None	,    __SCREAMING_SNAKE_CASE   :      int=None	,    **__SCREAMING_SNAKE_CASE   :      Dict       )      ->      Union[BackboneOutput, Tuple[Tensor, ...]]:
							a_		:	Any									=   return_dict if return_dict is not None else self.config.use_return_dict
							a_		:	Optional[Any]									=   (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							a_		:	Tuple									=   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
											a_		:	int									=   self._all_layers
											a_		:	str									=   self._backbone(__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
											a_		:	List[str]									=   self._return_layers
											a_		:	Optional[int]									=   tuple(hidden_states[i] for i in self.out_indices       )
							else:
											a_		:	Optional[Any]									=   self._backbone(__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
											a_		:	str									=   None

							a_		:	List[str]									=   tuple(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   tuple(__SCREAMING_SNAKE_CASE       ) if hidden_states is not None else None

							if not return_dict:
											a_		:	Any									=   (feature_maps,)
											if output_hidden_states:
															a_		:	Optional[int]									=   output + (hidden_states,)
											return output

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












'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de




