'''simple docstring'''





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


lowerCAmelCase_	:		Tuple			=     logging.get_logger(__name__)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			None:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						nn.ModuleList([src_layers[i] for i in layers_to_copy]					)
						assert len(UpperCAmelCase					) == len(UpperCAmelCase					), f'''{len(UpperCAmelCase					)} != {len(UpperCAmelCase					)}'''
						dest_layers.load_state_dict(layers_to_copy.state_dict()					)


lowerCAmelCase_	:		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))},
}
lowerCAmelCase_	:		List[str]			=     {
    # 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 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Any:




						'''simple docstring'''
						try:
												_UpperCamelCase			:			Any   			=						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(UpperCAmelCase					)					)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			List[int]:




						'''simple docstring'''
						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(UpperCAmelCase					)					)
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase = "student"     ,UpperCAmelCase = None     ,UpperCAmelCase = None     ,UpperCAmelCase=False     ,UpperCAmelCase=None     ,UpperCAmelCase=None     ,**UpperCAmelCase     ,)     ->			Tuple[PreTrainedModel, List[int], List[int]]:




						'''simple docstring'''
						_UpperCamelCase			:			List[Any]   			=						"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(UpperCAmelCase     ,UpperCAmelCase					):
												AutoTokenizer.from_pretrained(UpperCAmelCase					).save_pretrained(UpperCAmelCase					)  # purely for convenience
												_UpperCamelCase			:			Tuple   			=						AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase					).eval()
						else:
												assert isinstance(UpperCAmelCase     ,UpperCAmelCase					), f'''teacher must be a model or string got type {type(UpperCAmelCase					)}'''
						_UpperCamelCase			:			Optional[int]   			=						teacher.config.to_diff_dict()

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

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

						try:
												if hasattr(
												    UpperCAmelCase     ,"prophetnet"					):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
																		copy_layers(teacher.prophetnet.encoder.layers     ,student.prophetnet.encoder.layers     ,UpperCAmelCase					)
																		copy_layers(teacher.prophetnet.decoder.layers     ,student.prophetnet.decoder.layers     ,UpperCAmelCase					)
												else:
																		copy_layers(teacher.model.encoder.layers     ,student.model.encoder.layers     ,UpperCAmelCase					)
																		copy_layers(teacher.model.decoder.layers     ,student.model.decoder.layers     ,UpperCAmelCase					)
						except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
												copy_layers(teacher.encoder.block     ,student.encoder.block     ,UpperCAmelCase					)
												copy_layers(teacher.decoder.block     ,student.decoder.block     ,UpperCAmelCase					)
						logger.info(
						    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''					)
						_UpperCamelCase			:			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(UpperCAmelCase					)
						# 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)



'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase_	:		int			=     logging.get_logger(__name__)

lowerCAmelCase_	:		Any			=     {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''gptj'''
				UpperCAmelCase__							=							{
				    '''max_position_embeddings''': '''n_positions''',
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						Union[str, Any]=50_400       ,		lowercase__					:						Union[str, Any]=2_048       ,		lowercase__					:						Tuple=4_096       ,		lowercase__					:						List[str]=28       ,		lowercase__					:						Optional[int]=16       ,		lowercase__					:						str=64       ,		lowercase__					:						Any=None       ,		lowercase__					:						Any="gelu_new"       ,		lowercase__					:						Union[str, Any]=0.0       ,		lowercase__					:						Optional[Any]=0.0       ,		lowercase__					:						Any=0.0       ,		lowercase__					:						Tuple=1e-5       ,		lowercase__					:						Any=0.0_2       ,		lowercase__					:						int=True       ,		lowercase__					:						int=50_256       ,		lowercase__					:						Any=50_256       ,		lowercase__					:						Tuple=False       ,		**lowercase__					:						str       ,		)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						vocab_size
										_UpperCamelCase			:			List[str]   			=						n_positions
										_UpperCamelCase			:			Union[str, Any]   			=						n_embd
										_UpperCamelCase			:			Union[str, Any]   			=						n_layer
										_UpperCamelCase			:			Optional[Any]   			=						n_head
										_UpperCamelCase			:			Dict   			=						n_inner
										_UpperCamelCase			:			Optional[Any]   			=						rotary_dim
										_UpperCamelCase			:			Tuple   			=						activation_function
										_UpperCamelCase			:			List[Any]   			=						resid_pdrop
										_UpperCamelCase			:			Any   			=						embd_pdrop
										_UpperCamelCase			:			Optional[Any]   			=						attn_pdrop
										_UpperCamelCase			:			Optional[Any]   			=						layer_norm_epsilon
										_UpperCamelCase			:			Union[str, Any]   			=						initializer_range
										_UpperCamelCase			:			Optional[int]   			=						use_cache

										_UpperCamelCase			:			str   			=						bos_token_id
										_UpperCamelCase			:			Any   			=						eos_token_id

										super().__init__(
										    bos_token_id=lowercase__       ,		eos_token_id=lowercase__       ,		tie_word_embeddings=lowercase__       ,		**lowercase__					)





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Optional[int]       ,		lowercase__					:						PretrainedConfig       ,		lowercase__					:						str = "default"       ,		lowercase__					:						List[PatchingSpec] = None       ,		lowercase__					:						bool = False       ,		)							->Union[str, Any]:


										'''simple docstring'''




										super().__init__(lowercase__       ,		task=lowercase__       ,		patching_specs=lowercase__       ,		use_past=lowercase__					)
										if not getattr(self._config       ,		"pad_token_id"       ,		lowercase__					):
																# TODO: how to do that better?
																_UpperCamelCase			:			Optional[int]   			=						0





				@property
				def  snake_case__				(						self					:						List[str]					)							->Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
										if self.use_past:
																self.fill_with_past_key_values_(lowercase__       ,		direction="inputs"					)
																_UpperCamelCase			:			str   			=						{0: "batch", 1: "past_sequence + sequence"}
										else:
																_UpperCamelCase			:			Optional[Any]   			=						{0: "batch", 1: "sequence"}

										return common_inputs





				@property
				def  snake_case__				(						self					:						int					)							->int:


										'''simple docstring'''




										return self._config.n_layer





				@property
				def  snake_case__				(						self					:						Dict					)							->int:


										'''simple docstring'''




										return self._config.n_head





				def  snake_case__				(						self					:						int       ,		lowercase__					:						PreTrainedTokenizer       ,		lowercase__					:						int = -1       ,		lowercase__					:						int = -1       ,		lowercase__					:						bool = False       ,		lowercase__					:						Optional[TensorType] = None       ,		)							->Mapping[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						super(lowercase__       ,		self					).generate_dummy_inputs(
										    lowercase__       ,		batch_size=lowercase__       ,		seq_length=lowercase__       ,		is_pair=lowercase__       ,		framework=lowercase__					)

										# We need to order the input in the way they appears in the forward()
										_UpperCamelCase			:			Tuple   			=						OrderedDict({"input_ids": common_inputs["input_ids"]}					)

										# Need to add the past_keys
										if self.use_past:
																if not is_torch_available():
																						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
																else:
																						import torch

																						_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						_UpperCamelCase			:			Optional[int]   			=						seqlen + 2
																						_UpperCamelCase			:			str   			=						(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						_UpperCamelCase			:			Dict   			=						[
																						    (torch.zeros(lowercase__					), torch.zeros(lowercase__					)) for _ in range(self.num_layers					)
																						]

										_UpperCamelCase			:			str   			=						common_inputs["attention_mask"]
										if self.use_past:
																_UpperCamelCase			:			int   			=						ordered_inputs["attention_mask"].dtype
																_UpperCamelCase			:			Optional[int]   			=						torch.cat(
																    [ordered_inputs["attention_mask"], torch.ones(lowercase__       ,		lowercase__       ,		dtype=lowercase__					)]       ,		dim=1					)

										return ordered_inputs







				@property
				def  snake_case__				(						self					:						Tuple					)							->int:


										'''simple docstring'''




										return 13


