

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

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


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"imagegpt"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{
						    "hidden_size": "n_embd",
						    "max_position_embeddings": "n_positions",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}


						def __init__(       self						,					A_=512 + 1						,					A_=32 * 32						,					A_=512						,					A_=24						,					A_=8						,					A_=None						,					A_="quick_gelu"						,					A_=0.1						,					A_=0.1						,					A_=0.1						,					A_=1e-5						,					A_=0.02						,					A_=True						,					A_=True						,					A_=False						,					A_=False						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    vocab_size
										_UpperCAmelCase       :		int 		=    n_positions
										_UpperCAmelCase       :		List[Any] 		=    n_embd
										_UpperCAmelCase       :		List[Any] 		=    n_layer
										_UpperCAmelCase       :		List[Any] 		=    n_head
										_UpperCAmelCase       :		List[Any] 		=    n_inner
										_UpperCAmelCase       :		List[Any] 		=    activation_function
										_UpperCAmelCase       :		int 		=    resid_pdrop
										_UpperCAmelCase       :		Optional[int] 		=    embd_pdrop
										_UpperCAmelCase       :		str 		=    attn_pdrop
										_UpperCAmelCase       :		List[str] 		=    layer_norm_epsilon
										_UpperCAmelCase       :		Tuple 		=    initializer_range
										_UpperCAmelCase       :		int 		=    scale_attn_weights
										_UpperCAmelCase       :		str 		=    use_cache
										_UpperCAmelCase       :		Optional[Any] 		=    scale_attn_by_inverse_layer_idx
										_UpperCAmelCase       :		Dict 		=    reorder_and_upcast_attn
										_UpperCAmelCase       :		Tuple 		=    tie_word_embeddings

										super().__init__(tie_word_embeddings=A_						,					**A_	)







class 				a  (	UpperCAmelCase				):


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "sequence"}),
										    ]	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = 1						,					A_ = -1						,					A_ = False						,					A_ = None						,					A_ = 3						,					A_ = 32						,					A_ = 32						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self._generate_dummy_images(A_						,					A_						,					A_						,					A_	)
										_UpperCAmelCase       :		Optional[int] 		=    dict(preprocessor(images=A_						,					return_tensors=A_	)	)

										return inputs





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  List[int] )   ->       None:
				_UpperCAmelCase       :		str 		=    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() )


SCREAMING_SNAKE_CASE_        =      {
    # 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))},
}
SCREAMING_SNAKE_CASE_        =      {
    # 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:  Any  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				try:
								_UpperCAmelCase       :		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(lowerCAmelCase ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       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:  Union[str, PreTrainedModel]  ,      lowerCAmelCase:  Union[str, Path] = "student"  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  str=False  ,      lowerCAmelCase:  Union[str, Any]=None  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  str  ,      )   ->       Tuple[PreTrainedModel, List[int], List[int]]:
				_UpperCAmelCase       :		Dict 		=    "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
								_UpperCAmelCase       :		Dict 		=    AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
				else:
								assert isinstance(lowerCAmelCase  ,      lowerCAmelCase ), F'teacher must be a model or string got type {type(lowerCAmelCase )}'
				_UpperCAmelCase       :		List[str] 		=    teacher.config.to_diff_dict()

				try:
								_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												_UpperCAmelCase       :		int 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[Any] 		=    teacher_d
								init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
				except AttributeError:  # T5
								if hasattr(teacher.config  ,      "num_encoder_layers" ):
												_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												_UpperCAmelCase       :		List[Any] 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[int] 		=    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
				_UpperCAmelCase       :		Any 		=    teacher.config_class(**lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				_UpperCAmelCase       :		List[Any] 		=    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
								_UpperCAmelCase		,							_UpperCAmelCase       :		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:
								_UpperCAmelCase       :		List[int] 		=    pick_layers_to_copy(lowerCAmelCase  ,      lowerCAmelCase )
				if d_layers_to_copy is None:
								_UpperCAmelCase       :		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}' )
				_UpperCAmelCase       :		str 		=    {
				    "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)


