import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


a		=logging.get_logger(__name__)

# General docstring
a		="""PoolFormerConfig"""

# Base docstring
a		="""sail/poolformer_s12"""
a		=[1, 512, 7, 7]

# Image classification docstring
a		="""sail/poolformer_s12"""
a		="""tabby, tabby cat"""

a		=[
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = False      )  ->					Optional[int]:
	if drop_prob == 0.0 or not training:
		return input
	__lowerCamelCase    :    Optional[int] 	=			1 - drop_prob
	__lowerCamelCase    :    List[str] 	=			(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
	__lowerCamelCase    :    Union[str, Any] 	=			keep_prob + torch.rand(lowerCamelCase__     ,			dtype=input.dtype     ,			device=input.device      )
	random_tensor.floor_()  # binarize
	__lowerCamelCase    :    int 	=			input.div(lowerCamelCase__      ) * random_tensor
	return output






class 	A_		(	nn.Module    ):
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[float] = None):
								super().__init__()
								__lowerCamelCase    :    Union[str, Any] 	=			drop_prob
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								return drop_path(SCREAMING_SNAKE_CASE__       ,self.drop_prob       ,self.training)


							def    lowerCAmelCase					(		self		:      List[Any]):
								return "p={}".format(self.drop_prob)






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int=None):
								super().__init__()
								__lowerCamelCase    :    List[Any] 	=			patch_size if isinstance(SCREAMING_SNAKE_CASE__       ,collections.abc.Iterable) else (patch_size, patch_size)
								__lowerCamelCase    :    Union[str, Any] 	=			stride if isinstance(SCREAMING_SNAKE_CASE__       ,collections.abc.Iterable) else (stride, stride)
								__lowerCamelCase    :    Optional[Any] 	=			padding if isinstance(SCREAMING_SNAKE_CASE__       ,collections.abc.Iterable) else (padding, padding)

								__lowerCamelCase    :    List[str] 	=			nn.Convad(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,kernel_size=SCREAMING_SNAKE_CASE__       ,stride=SCREAMING_SNAKE_CASE__       ,padding=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			norm_layer(SCREAMING_SNAKE_CASE__) if norm_layer else nn.Identity()


							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    Any 	=			self.projection(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			self.norm(SCREAMING_SNAKE_CASE__)
								return embeddings






class 	A_		(	nn.GroupNorm    ):
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,**SCREAMING_SNAKE_CASE__		:      Optional[int]):
								super().__init__(1       ,SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)






class 	A_		(	nn.Module    ):
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int):
								super().__init__()
								__lowerCamelCase    :    Any 	=			nn.AvgPoolad(SCREAMING_SNAKE_CASE__       ,stride=1       ,padding=pool_size // 2       ,count_include_pad=SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								return self.pool(SCREAMING_SNAKE_CASE__) - hidden_states






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								super().__init__()
								__lowerCamelCase    :    Optional[Any] 	=			nn.Convad(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,1)
								__lowerCamelCase    :    Dict 	=			nn.Convad(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,1)
								__lowerCamelCase    :    List[str] 	=			PoolFormerDropPath(SCREAMING_SNAKE_CASE__)
								if isinstance(config.hidden_act       ,SCREAMING_SNAKE_CASE__):
									__lowerCamelCase    :    Optional[Any] 	=			ACTaFN[config.hidden_act]
								else:
									__lowerCamelCase    :    str 	=			config.hidden_act


							def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    Optional[int] 	=			self.conva(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			self.act_fn(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			self.drop(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Any 	=			self.conva(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			self.drop(SCREAMING_SNAKE_CASE__)

								return hidden_states






class 	A_		(	nn.Module    ):
							def __init__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Any):
								super().__init__()
								__lowerCamelCase    :    Optional[int] 	=			PoolFormerPooling(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			PoolFormerOutput(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			PoolFormerGroupNorm(SCREAMING_SNAKE_CASE__)

								# Useful for training neural nets
								__lowerCamelCase    :    Any 	=			PoolFormerDropPath(SCREAMING_SNAKE_CASE__) if drop_path > 0.0 else nn.Identity()
								__lowerCamelCase    :    Optional[int] 	=			config.use_layer_scale
								if config.use_layer_scale:
									__lowerCamelCase    :    Union[str, Any] 	=			nn.Parameter(
									    config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__))       ,requires_grad=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Union[str, Any] 	=			nn.Parameter(
									    config.layer_scale_init_value * torch.ones((SCREAMING_SNAKE_CASE__))       ,requires_grad=SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								if self.use_layer_scale:
									__lowerCamelCase    :    int 	=			self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    int 	=			self.layer_scale_a.unsqueeze(-1).unsqueeze(-1) * pooling_output
									# First residual connection
									__lowerCamelCase    :    Dict 	=			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    str 	=			()

									__lowerCamelCase    :    Optional[Any] 	=			self.output(self.after_norm(SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    Tuple 	=			self.layer_scale_a.unsqueeze(-1).unsqueeze(-1) * layer_output
									# Second residual connection
									__lowerCamelCase    :    Tuple 	=			hidden_states + self.drop_path(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Tuple 	=			(output,) + outputs
									return outputs

								else:
									__lowerCamelCase    :    Optional[Any] 	=			self.drop_path(self.pooling(self.before_norm(SCREAMING_SNAKE_CASE__)))
									# First residual connection
									__lowerCamelCase    :    List[str] 	=			pooling_output + hidden_states
									__lowerCamelCase    :    List[str] 	=			()

									# Second residual connection inside the PoolFormerOutput block
									__lowerCamelCase    :    Tuple 	=			self.drop_path(self.output(self.after_norm(SCREAMING_SNAKE_CASE__)))
									__lowerCamelCase    :    Dict 	=			hidden_states + layer_output

									__lowerCamelCase    :    Dict 	=			(output,) + outputs
									return outputs






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								super().__init__()
								__lowerCamelCase    :    List[str] 	=			config
								# stochastic depth decay rule
								__lowerCamelCase    :    Union[str, Any] 	=			[x.item() for x in torch.linspace(0       ,config.drop_path_rate       ,sum(config.depths))]

								# patch embeddings
								__lowerCamelCase    :    Tuple 	=			[]
								for i in range(config.num_encoder_blocks):
									embeddings.append(
									    PoolFormerEmbeddings(
									        patch_size=config.patch_sizes[i]       ,stride=config.strides[i]       ,padding=config.padding[i]       ,num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]       ,hidden_size=config.hidden_sizes[i]       ,))
								__lowerCamelCase    :    str 	=			nn.ModuleList(SCREAMING_SNAKE_CASE__)

								# Transformer blocks
								__lowerCamelCase    :    Optional[int] 	=			[]
								__lowerCamelCase    :    List[str] 	=			0
								for i in range(config.num_encoder_blocks):
									# each block consists of layers
									__lowerCamelCase    :    int 	=			[]
									if i != 0:
										cur += config.depths[i - 1]
									for j in range(config.depths[i]):
										layers.append(
										    PoolFormerLayer(
										        SCREAMING_SNAKE_CASE__       ,num_channels=config.hidden_sizes[i]       ,pool_size=config.pool_size       ,hidden_size=config.hidden_sizes[i]       ,intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio)       ,drop_path=dpr[cur + j]       ,))
									blocks.append(nn.ModuleList(SCREAMING_SNAKE_CASE__))

								__lowerCamelCase    :    str 	=			nn.ModuleList(SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=False       ,SCREAMING_SNAKE_CASE__		:      Any=True):
								__lowerCamelCase    :    Optional[int] 	=			() if output_hidden_states else None

								__lowerCamelCase    :    List[Any] 	=			pixel_values
								for idx, layers in enumerate(zip(self.patch_embeddings       ,self.block)):
									__lowerCamelCase    :    Optional[Any] 	=			layers
									# Get patch embeddings from hidden_states
									__lowerCamelCase    :    Tuple 	=			embedding_layer(SCREAMING_SNAKE_CASE__)
									# Send the embeddings through the blocks
									for _, blk in enumerate(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase    :    Optional[Any] 	=			blk(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    str 	=			layer_outputs[0]

									if output_hidden_states:
										__lowerCamelCase    :    Any 	=			all_hidden_states + (hidden_states,)

								if not return_dict:
									return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)

								return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE__       ,hidden_states=SCREAMING_SNAKE_CASE__)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict										=				PoolFormerConfig
							_UpperCAmelCase   :  List[Any]										=				'''poolformer'''
							_UpperCAmelCase   :  Union[str, Any]										=				'''pixel_values'''
							_UpperCAmelCase   :  Any										=				True
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								if isinstance(SCREAMING_SNAKE_CASE__       ,(nn.Linear, nn.Convad)):
									module.weight.data.normal_(mean=0.0       ,std=self.config.initializer_range)
									if module.bias is not None:
										module.bias.data.zero_()
								elif isinstance(SCREAMING_SNAKE_CASE__       ,nn.LayerNorm):
									module.bias.data.zero_()
									module.weight.data.fill_(1.0)


							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=False):
								if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
									__lowerCamelCase    :    Any 	=			value
a		=r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

a		=r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""






@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''      ,							SCREAMING_SNAKE_CASE      ,							)
class 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								super().__init__(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			config

								__lowerCamelCase    :    Dict 	=			PoolFormerEncoder(SCREAMING_SNAKE_CASE__)

								# Initialize weights and apply final processing
								self.post_init()
							def    lowerCAmelCase					(		self		:      List[str]):
								return self.embeddings.patch_embeddings


							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC       ,output_type=SCREAMING_SNAKE_CASE__       ,config_class=_CONFIG_FOR_DOC       ,modality='vision'       ,expected_output=_EXPECTED_OUTPUT_SHAPE       ,)
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.FloatTensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,):
								__lowerCamelCase    :    Tuple 	=			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								__lowerCamelCase    :    Optional[int] 	=			return_dict if return_dict is not None else self.config.use_return_dict

								if pixel_values is None:
									raise ValueError('You have to specify pixel_values')

								__lowerCamelCase    :    Tuple 	=			self.encoder(
								    SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__       ,return_dict=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Any 	=			encoder_outputs[0]

								if not return_dict:
									return (sequence_output, None) + encoder_outputs[1:]

								return BaseModelOutputWithNoAttention(
								    last_hidden_state=SCREAMING_SNAKE_CASE__       ,hidden_states=encoder_outputs.hidden_states       ,)






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								super().__init__()
								__lowerCamelCase    :    Tuple 	=			nn.Linear(config.hidden_size       ,config.hidden_size)


							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								__lowerCamelCase    :    Any 	=			self.dense(SCREAMING_SNAKE_CASE__)
								return output






@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''      ,							SCREAMING_SNAKE_CASE      ,							)
class 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								super().__init__(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			config.num_labels
								__lowerCamelCase    :    Optional[int] 	=			PoolFormerModel(SCREAMING_SNAKE_CASE__)

								# Final norm
								__lowerCamelCase    :    int 	=			PoolFormerGroupNorm(config.hidden_sizes[-1])
								# Classifier head
								__lowerCamelCase    :    Any 	=			(
								    nn.Linear(config.hidden_sizes[-1]       ,config.num_labels) if config.num_labels > 0 else nn.Identity()
								)

								# Initialize weights and apply final processing
								self.post_init()


							@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,output_type=SCREAMING_SNAKE_CASE__       ,config_class=_CONFIG_FOR_DOC       ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.FloatTensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.LongTensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,):
								__lowerCamelCase    :    Dict 	=			return_dict if return_dict is not None else self.config.use_return_dict

								__lowerCamelCase    :    Optional[int] 	=			self.poolformer(
								    SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__       ,return_dict=SCREAMING_SNAKE_CASE__       ,)

								__lowerCamelCase    :    int 	=			outputs[0]

								__lowerCamelCase    :    Tuple 	=			self.classifier(self.norm(SCREAMING_SNAKE_CASE__).mean([-2, -1]))

								__lowerCamelCase    :    Union[str, Any] 	=			None
								if labels is not None:
									if self.config.problem_type is None:
										if self.num_labels == 1:
											__lowerCamelCase    :    List[str] 	=			'regression'
										elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											__lowerCamelCase    :    List[Any] 	=			'single_label_classification'
										else:
											__lowerCamelCase    :    List[Any] 	=			'multi_label_classification'

									if self.config.problem_type == "regression":
										__lowerCamelCase    :    Optional[int] 	=			MSELoss()
										if self.num_labels == 1:
											__lowerCamelCase    :    Optional[Any] 	=			loss_fct(logits.squeeze()       ,labels.squeeze())
										else:
											__lowerCamelCase    :    str 	=			loss_fct(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									elif self.config.problem_type == "single_label_classification":
										__lowerCamelCase    :    Optional[Any] 	=			CrossEntropyLoss()
										__lowerCamelCase    :    Any 	=			loss_fct(logits.view(-1       ,self.num_labels)       ,labels.view(-1))
									elif self.config.problem_type == "multi_label_classification":
										__lowerCamelCase    :    Dict 	=			BCEWithLogitsLoss()
										__lowerCamelCase    :    Dict 	=			loss_fct(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

								if not return_dict:
									__lowerCamelCase    :    Optional[int] 	=			(logits,) + outputs[2:]
									return ((loss,) + output) if loss is not None else output

								return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__       ,logits=SCREAMING_SNAKE_CASE__       ,hidden_states=outputs.hidden_states)







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


a		=logging.get_logger(__name__)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    Tuple 	=			nn.ModuleList([src_layers[i] for i in layers_to_copy]      )
	assert len(lowerCamelCase__      ) == len(lowerCamelCase__      ), F"{len(lowerCamelCase__      )} != {len(lowerCamelCase__      )}"
	dest_layers.load_state_dict(layers_to_copy.state_dict()      )


a		={
    # 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))},
}
a		={
    # 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__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	try:
		__lowerCamelCase    :    List[str] 	=			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(lowerCamelCase__      )      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					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(lowerCamelCase__      )      )
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = "student"     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = None     ,			lowerCamelCase__=False     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			**lowerCamelCase__     ,			)  ->					Tuple[PreTrainedModel, List[int], List[int]]:
	__lowerCamelCase    :    int 	=			'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(lowerCamelCase__     ,			lowerCamelCase__      ):
		AutoTokenizer.from_pretrained(lowerCamelCase__      ).save_pretrained(lowerCamelCase__      )  # purely for convenience
		__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__      ).eval()
	else:
		assert isinstance(lowerCamelCase__     ,			lowerCamelCase__      ), F"teacher must be a model or string got type {type(lowerCamelCase__      )}"
	__lowerCamelCase    :    str 	=			teacher.config.to_diff_dict()

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

	# Copy weights
	__lowerCamelCase    :    str 	=			teacher.config_class(**lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_config(lowerCamelCase__      )
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	__lowerCamelCase    :    Tuple 	=			student.load_state_dict(teacher.state_dict()     ,			strict=lowerCamelCase__      )
	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
		__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			list(range(lowerCamelCase__      )      ), list(range(lowerCamelCase__      )      )
		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(lowerCamelCase__      )
		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:
		__lowerCamelCase    :    List[int] 	=			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__      )
	if d_layers_to_copy is None:
		__lowerCamelCase    :    List[int] 	=			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__      )

	try:
		if hasattr(
		    lowerCamelCase__     ,			'prophetnet'      ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
			copy_layers(teacher.prophetnet.encoder.layers     ,			student.prophetnet.encoder.layers     ,			lowerCamelCase__      )
			copy_layers(teacher.prophetnet.decoder.layers     ,			student.prophetnet.decoder.layers     ,			lowerCamelCase__      )
		else:
			copy_layers(teacher.model.encoder.layers     ,			student.model.encoder.layers     ,			lowerCamelCase__      )
			copy_layers(teacher.model.decoder.layers     ,			student.model.decoder.layers     ,			lowerCamelCase__      )
	except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
		copy_layers(teacher.encoder.block     ,			student.encoder.block     ,			lowerCamelCase__      )
		copy_layers(teacher.decoder.block     ,			student.decoder.block     ,			lowerCamelCase__      )
	logger.info(
	    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"      )
	__lowerCamelCase    :    Dict 	=			{
	    'teacher_type': teacher.config.model_type,
	    'copied_encoder_layers': e_layers_to_copy,
	    'copied_decoder_layers': d_layers_to_copy,
	}
	student.save_pretrained(lowerCamelCase__      )
	# 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)




