



'''simple docstring'''




import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class      UpperCamelCase_					(				enum.Enum     ):
	lowercase		=					"timm"
	lowercase		=					"transformers"




def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase						) ->     str:





							if stage_names is None:
														raise ValueError("""Stage_names must be set for transformers backbones"""						)

							if out_features is not None:
														if not isinstance(lowerCAmelCase__      ,							(list,)						):
																					raise ValueError(F'''out_features must be a list {type(lowerCAmelCase__						)}'''						)
														if any(feat not in stage_names for feat in out_features						):
																					raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''						)

							if out_indices is not None:
														if not isinstance(lowerCAmelCase__      ,							(list, tuple)						):
																					raise ValueError(F'''out_indices must be a list or tuple, got {type(lowerCAmelCase__						)}'''						)
														if any(idx >= len(lowerCAmelCase__						) for idx in out_indices						):
																					raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""						)

							if out_features is not None and out_indices is not None:
														if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
																					raise ValueError("""out_features and out_indices should have the same length if both are set"""						)
														if out_features != [stage_names[idx] for idx in out_indices]:
																					raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							) ->     Optional[int]:





							if out_indices is None and out_features is None:
														UpperCAmelCase	:						Dict					=						[len(lowerCAmelCase__						) - 1]
														UpperCAmelCase	:						int					=						[stage_names[-1]]
							elif out_indices is None and out_features is not None:
														UpperCAmelCase	:						List[Any]					=						[stage_names.index(lowerCAmelCase__						) for layer in out_features]
							elif out_features is None and out_indices is not None:
														UpperCAmelCase	:						Dict					=						[stage_names[idx] for idx in out_indices]
							return out_features, out_indices



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							) ->     Tuple:
							verify_out_features_out_indices(out_features=lowerCAmelCase__      ,							out_indices=lowerCAmelCase__      ,							stage_names=lowerCAmelCase__						)
							UpperCAmelCase	:						str					=						_align_output_features_output_indices(
							    out_features=lowerCAmelCase__      ,							out_indices=lowerCAmelCase__      ,							stage_names=lowerCAmelCase__						)
							# Verify that the aligned out_features and out_indices are valid
							verify_out_features_out_indices(out_features=lowerCAmelCase__      ,							out_indices=lowerCAmelCase__      ,							stage_names=lowerCAmelCase__						)
							return output_features, output_indices

class      UpperCamelCase_					:
	lowercase		=					None





	def 					_lowercase(    self						,						A       )     ->     None:
								if getattr(self						,						"""_backbone"""						,						__UpperCAmelCase       ) is None:
															raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""       )

								# These will diagree with the defaults for the transformers models e.g. for resnet50
								# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
								# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
								UpperCAmelCase	:						Union[str, Any]					=						[stage["""module"""] for stage in self._backbone.feature_info.info]
								UpperCAmelCase	:						str					=						[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
								UpperCAmelCase	:						int					=						self._backbone.feature_info.out_indices
								UpperCAmelCase	:						List[Any]					=						self._backbone.feature_info.module_name()

								# We verify the out indices and out features are valid
								verify_out_features_out_indices(
								    out_features=__UpperCAmelCase						,						out_indices=__UpperCAmelCase						,						stage_names=self.stage_names       )
								UpperCAmelCase	:						str					=						out_features, out_indices





	def 					_lowercase(    self						,						A       )     ->     None:
								UpperCAmelCase	:						Tuple					=						getattr(__UpperCAmelCase						,						"""stage_names"""       )
								UpperCAmelCase	:						List[str]					=						getattr(__UpperCAmelCase						,						"""out_features"""						,						__UpperCAmelCase       )
								UpperCAmelCase	:						Optional[Any]					=						getattr(__UpperCAmelCase						,						"""out_indices"""						,						__UpperCAmelCase       )

								UpperCAmelCase	:						Dict					=						stage_names
								UpperCAmelCase	:						List[str]					=						get_aligned_output_features_output_indices(
								    out_features=__UpperCAmelCase						,						out_indices=__UpperCAmelCase						,						stage_names=__UpperCAmelCase       )
								# Number of channels for each stage. This is set in the transformer backbone model init
								UpperCAmelCase	:						Union[str, Any]					=						None





	def 					_lowercase(    self						,						A       )     ->     None:
								UpperCAmelCase	:						Union[str, Any]					=						config

								UpperCAmelCase	:						Tuple					=						getattr(__UpperCAmelCase						,						"""use_timm_backbone"""						,						__UpperCAmelCase       )
								UpperCAmelCase	:						str					=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

								if self.backbone_type == BackboneType.TIMM:
															self._init_timm_backbone(__UpperCAmelCase       )
								elif self.backbone_type == BackboneType.TRANSFORMERS:
															self._init_transformers_backbone(__UpperCAmelCase       )
								else:
															raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''       )





	@property
	def 					_lowercase(    self       )     ->     List[str]:
								return self._out_features





	@out_features.setter
	def 					_lowercase(    self						,						A       )     ->     str:
								UpperCAmelCase	:						List[str]					=						get_aligned_output_features_output_indices(
								    out_features=__UpperCAmelCase						,						out_indices=__UpperCAmelCase						,						stage_names=self.stage_names       )





	@property
	def 					_lowercase(    self       )     ->     int:
								return self._out_indices





	@out_indices.setter
	def 					_lowercase(    self						,						A       )     ->     int:
								UpperCAmelCase	:						Optional[Any]					=						get_aligned_output_features_output_indices(
								    out_features=__UpperCAmelCase						,						out_indices=__UpperCAmelCase						,						stage_names=self.stage_names       )





	@property
	def 					_lowercase(    self       )     ->     Union[str, Any]:
								return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names       )}





	@property
	def 					_lowercase(    self       )     ->     Any:
								return [self.out_feature_channels[name] for name in self.out_features]





	def 					_lowercase(    self						,						*A						,						**A       )     ->     List[str]:
								UpperCAmelCase	:						int					=						dict(inspect.signature(self.forward       ).parameters       )
								UpperCAmelCase	:						str					=						{k: v for k, v in kwargs.items() if k in signature}
								return self(*__UpperCAmelCase						,						**__UpperCAmelCase       )





	def 					_lowercase(    self						,						A						,						A = None						,						A = None						,						A = None						,						)     ->     Tuple:
								raise NotImplementedError("""This method should be implemented by the derived class."""       )






	def 					_lowercase(    self       )     ->     Optional[Any]:
								UpperCAmelCase	:						Tuple					=						super().to_dict()
								UpperCAmelCase	:						Optional[Any]					=						output.pop("""_out_features"""       )
								UpperCAmelCase	:						str					=						output.pop("""_out_indices"""       )
								return output




class      UpperCamelCase_					:





	@property
	def 					_lowercase(    self       )     ->     List[str]:
								return self._out_features





	@out_features.setter
	def 					_lowercase(    self						,						A       )     ->     Tuple:
								UpperCAmelCase	:						Union[str, Any]					=						get_aligned_output_features_output_indices(
								    out_features=__UpperCAmelCase						,						out_indices=__UpperCAmelCase						,						stage_names=self.stage_names       )





	@property
	def 					_lowercase(    self       )     ->     Union[str, Any]:
								return self._out_indices





	@out_indices.setter
	def 					_lowercase(    self						,						A       )     ->     Any:
								UpperCAmelCase	:						List[str]					=						get_aligned_output_features_output_indices(
								    out_features=__UpperCAmelCase						,						out_indices=__UpperCAmelCase						,						stage_names=self.stage_names       )






	def 					_lowercase(    self       )     ->     List[Any]:
								UpperCAmelCase	:						Optional[int]					=						super().to_dict()
								UpperCAmelCase	:						Any					=						output.pop("""_out_features"""       )
								UpperCAmelCase	:						Tuple					=						output.pop("""_out_indices"""       )
								return output






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase									=      {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					_UpperCamelCase									=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




