






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A	:			Dict         =logging.get_logger(__name__)

_A	:			Any         ={
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}

class _lowercase	(				_lowercase		):
							a				      =       """table-transformer"""
							a				      =       ["""past_key_values"""]
							a				      =       {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:				Dict			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				str=None			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				Any=100			,							UpperCamelCase__:				str=6			,							UpperCamelCase__:				Tuple=2_048			,							UpperCamelCase__:				Any=8			,							UpperCamelCase__:				List[str]=6			,							UpperCamelCase__:				Union[str, Any]=2_048			,							UpperCamelCase__:				Dict=8			,							UpperCamelCase__:				List[Any]=0.0			,							UpperCamelCase__:				Optional[int]=0.0			,							UpperCamelCase__:				Dict=True			,							UpperCamelCase__:				List[str]="relu"			,							UpperCamelCase__:				Optional[Any]=256			,							UpperCamelCase__:				Dict=0.1			,							UpperCamelCase__:				Optional[Any]=0.0			,							UpperCamelCase__:				str=0.0			,							UpperCamelCase__:				List[str]=0.02			,							UpperCamelCase__:				Union[str, Any]=1.0			,							UpperCamelCase__:				Dict=False			,							UpperCamelCase__:				Dict="sine"			,							UpperCamelCase__:				str="resnet50"			,							UpperCamelCase__:				List[str]=True			,							UpperCamelCase__:				Tuple=False			,							UpperCamelCase__:				List[str]=1			,							UpperCamelCase__:				Tuple=5			,							UpperCamelCase__:				Any=2			,							UpperCamelCase__:				Optional[int]=1			,							UpperCamelCase__:				Tuple=1			,							UpperCamelCase__:				List[Any]=5			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				int=0.1			,							**UpperCamelCase__:				Optional[int]			,							):
														if backbone_config is not None and use_timm_backbone:
																					raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""     )

														if not use_timm_backbone:
																					if backbone_config is None:
																												logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""     )
																												lowerCamelCase__					:	str         =       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]     )
																					elif isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																												lowerCamelCase__					:	Optional[int]         =       backbone_config.get("""model_type"""     )
																												lowerCamelCase__					:	Tuple         =       CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__					:	str         =       config_class.from_dict(UpperCamelCase__     )
																					# set timm attributes to None
																					lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       None, None, None

														lowerCamelCase__					:	Union[str, Any]         =       use_timm_backbone
														lowerCamelCase__					:	Optional[Any]         =       backbone_config
														lowerCamelCase__					:	Any         =       num_channels
														lowerCamelCase__					:	Tuple         =       num_queries
														lowerCamelCase__					:	Dict         =       d_model
														lowerCamelCase__					:	List[Any]         =       encoder_ffn_dim
														lowerCamelCase__					:	int         =       encoder_layers
														lowerCamelCase__					:	Union[str, Any]         =       encoder_attention_heads
														lowerCamelCase__					:	Dict         =       decoder_ffn_dim
														lowerCamelCase__					:	List[Any]         =       decoder_layers
														lowerCamelCase__					:	Any         =       decoder_attention_heads
														lowerCamelCase__					:	Any         =       dropout
														lowerCamelCase__					:	List[Any]         =       attention_dropout
														lowerCamelCase__					:	List[str]         =       activation_dropout
														lowerCamelCase__					:	List[Any]         =       activation_function
														lowerCamelCase__					:	Tuple         =       init_std
														lowerCamelCase__					:	List[str]         =       init_xavier_std
														lowerCamelCase__					:	Optional[int]         =       encoder_layerdrop
														lowerCamelCase__					:	Any         =       decoder_layerdrop
														lowerCamelCase__					:	int         =       encoder_layers
														lowerCamelCase__					:	Tuple         =       auxiliary_loss
														lowerCamelCase__					:	Tuple         =       position_embedding_type
														lowerCamelCase__					:	List[Any]         =       backbone
														lowerCamelCase__					:	Optional[Any]         =       use_pretrained_backbone
														lowerCamelCase__					:	Any         =       dilation
														# Hungarian matcher
														lowerCamelCase__					:	Dict         =       class_cost
														lowerCamelCase__					:	Union[str, Any]         =       bbox_cost
														lowerCamelCase__					:	Any         =       giou_cost
														# Loss coefficients
														lowerCamelCase__					:	Any         =       mask_loss_coefficient
														lowerCamelCase__					:	Dict         =       dice_loss_coefficient
														lowerCamelCase__					:	Union[str, Any]         =       bbox_loss_coefficient
														lowerCamelCase__					:	List[Any]         =       giou_loss_coefficient
														lowerCamelCase__					:	List[str]         =       eos_coefficient
														super().__init__(is_encoder_decoder=UpperCamelCase__			,							**UpperCamelCase__     )




							@property
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return self.encoder_attention_heads




							@property
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														return self.d_model



class _lowercase	(				_lowercase		):
							a				      =       version.parse("""1.11"""		)




							@property
							def 		lowerCamelCase_				(							self:				Any     ):
														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														        ("""pixel_mask""", {0: """batch"""}),
														    ]     )




							@property
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														return 1e-5




							@property
							def 		lowerCamelCase_				(							self:				Tuple     ):
														return 12








import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


UpperCAmelCase						:   Any			  =     logging.get_logger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      None:
					__A	:						int       =				nn.ModuleList([src_layers[i] for i in layers_to_copy]  )
					assert len(a  ) == len(a  ), F"""{len(a  )} != {len(a  )}"""
					dest_layers.load_state_dict(layers_to_copy.state_dict()  )


UpperCAmelCase						:   List[Any]			  =     {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
UpperCAmelCase						:   Optional[int]			  =     {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Dict:
					try:
										__A	:						int       =				LAYERS_TO_COPY[n_teacher][n_student]
										return val
					except KeyError:
										if n_student != n_teacher:
															warnings.warn(
															    F"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
															    F""" {n_student}"""  )
										return list(range(a  )  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      List[int]:
					if n_student > n_teacher:
										raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"""  )
					elif n_teacher == n_student:
										return list(range(a  )  )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def  _SCREAMING_SNAKE_CASE				(   a					,						a = "student"					,						a = None					,						a = None					,						a=False					,						a=None					,						a=None					,						**a					,						)      ->      Tuple[PreTrainedModel, List[int], List[int]]:
					__A	:						List[str]       =				'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
					assert (e is not None) or (d is not None), _msg
					if isinstance(a					,						a  ):
										AutoTokenizer.from_pretrained(a  ).save_pretrained(a  )  # purely for convenience
										__A	:						Optional[int]       =				AutoModelForSeqaSeqLM.from_pretrained(a  ).eval()
					else:
										assert isinstance(a					,						a  ), F"""teacher must be a model or string got type {type(a  )}"""
					__A	:						int       =				teacher.config.to_diff_dict()

					try:
										__A    ,				__A	:						List[Any]       =				teacher.config.encoder_layers, teacher.config.decoder_layers
										if e is None:
															__A	:						str       =				teacher_e
										if d is None:
															__A	:						List[Any]       =				teacher_d
										init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}  )
					except AttributeError:  # T5
										if hasattr(teacher.config					,						'num_encoder_layers'  ):
															__A    ,				__A	:						List[Any]       =				teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
										else:
															__A    ,				__A	:						Optional[int]       =				teacher.config.num_layers, teacher.config.num_decoder_layers
										if e is None:
															__A	:						int       =				teacher_e
										if d is None:
															__A	:						Optional[Any]       =				teacher_d
										if hasattr(teacher.config					,						'num_encoder_layers'  ):
															init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d}  )
										else:
															init_kwargs.update({'num_layers': e, 'num_decoder_layers': d}  )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
					init_kwargs.update(a  )

					# Copy weights
					__A	:						Dict       =				teacher.config_class(**a  )
					__A	:						int       =				AutoModelForSeqaSeqLM.from_config(a  )
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					__A	:						Any       =				student.load_state_dict(teacher.state_dict()					,						strict=a  )
					assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

					if copy_first_teacher_layers:  # Our copying is done. We just log and save
										__A    ,				__A	:						Optional[int]       =				list(range(a  )  ), list(range(a  )  )
										logger.info(
										    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
										    F""" {save_path}"""  )
										student.save_pretrained(a  )
										return student, e_layers_to_copy, d_layers_to_copy

					# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
					if e_layers_to_copy is None:
										__A	:						List[int]       =				pick_layers_to_copy(a					,						a  )
					if d_layers_to_copy is None:
										__A	:						List[int]       =				pick_layers_to_copy(a					,						a  )

					try:
										if hasattr(
										    a					,						'prophetnet'  ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers					,						student.prophetnet.encoder.layers					,						a  )
															copy_layers(teacher.prophetnet.decoder.layers					,						student.prophetnet.decoder.layers					,						a  )
										else:
															copy_layers(teacher.model.encoder.layers					,						student.model.encoder.layers					,						a  )
															copy_layers(teacher.model.decoder.layers					,						student.model.decoder.layers					,						a  )
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block					,						student.encoder.block					,						a  )
										copy_layers(teacher.decoder.block					,						student.decoder.block					,						a  )
					logger.info(
					    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""  )
					__A	:						Optional[int]       =				{
					    'teacher_type': teacher.config.model_type,
					    'copied_encoder_layers': e_layers_to_copy,
					    'copied_decoder_layers': d_layers_to_copy,
					}
					student.save_pretrained(a  )
					# Save information about copying for easier reproducibility

					return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
				fire.Fire(create_student_by_copying_alternating_layers)




