





'''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       		=      logging.get_logger(__name__)






def      __A						(							a_						:  nn.ModuleList      ,a_						:  nn.ModuleList      ,a_						:  List[int]	):
		lowerCAmelCase     :    List[Any]						=   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()	)


lowerCAmelCase       		=      {
    # 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       		=      {
    # 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						(							a_						:  Union[str, Any]      ,a_						:  Tuple	):
		try:
				lowerCAmelCase     :    Tuple						=   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      __A						(							a_						:  int      ,a_						:  List[Any]	):
		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      __A						(							a_						:  Union[str, PreTrainedModel]      ,a_						:  Union[str, Path] = "student"      ,a_						:  Union[int, None] = None      ,a_						:  Union[int, None] = None      ,a_						:  List[str]=False      ,a_						:  Union[str, Any]=None      ,a_						:  int=None      ,**a_						:  List[str]      ,):
		lowerCAmelCase     :    Union[str, 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(a_      ,a_	):
				AutoTokenizer.from_pretrained(a_	).save_pretrained(a_	)  # purely for convenience
				lowerCAmelCase     :    List[Any]						=   AutoModelForSeqaSeqLM.from_pretrained(a_	).eval()
		else:
				assert isinstance(a_      ,a_	), f'''teacher must be a model or string got type {type(a_	)}'''
		lowerCAmelCase     :    int						=   teacher.config.to_diff_dict()

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

		# Copy weights
		lowerCAmelCase     :    Tuple						=   teacher.config_class(**a_	)
		lowerCAmelCase     :    List[Any]						=   AutoModelForSeqaSeqLM.from_config(a_	)
		# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
		lowerCAmelCase     :    Union[str, 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
				lowerCAmelCase	,		lowerCAmelCase     :    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:
				lowerCAmelCase     :    List[int]						=   pick_layers_to_copy(a_      ,a_	)
		if d_layers_to_copy is None:
				lowerCAmelCase     :    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}'''	)
		lowerCAmelCase     :    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)










'''simple docstring'''






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					lowerCAmelCase       		=      argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)
					parser.add_argument(
					    """--original_config_file""",
					    type=str,
					    required=True,
					    help="""The YAML config file corresponding to the original architecture.""",
					)
					parser.add_argument(
					    """--num_in_channels""",
					    default=None,
					    type=int,
					    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
					)
					parser.add_argument(
					    """--image_size""",
					    default=5_12,
					    type=int,
					    help=(
					        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
					        """ Base. Use 768 for Stable Diffusion v2."""
					    ),
					)
					parser.add_argument(
					    """--extract_ema""",
					    action="""store_true""",
					    help=(
					        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
					        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
					        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
					    ),
					)
					parser.add_argument(
					    """--upcast_attention""",
					    action="""store_true""",
					    help=(
					        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
					        """ diffusion 2.1."""
					    ),
					)
					parser.add_argument(
					    """--from_safetensors""",
					    action="""store_true""",
					    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
					)
					parser.add_argument(
					    """--to_safetensors""",
					    action="""store_true""",
					    help="""Whether to store pipeline in safetensors format or not.""",
					)
					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")






					def      __A						(							a_						:  List[str]	):
							if string == "True":
									return True
							elif string == "False":
									return False
							else:
									raise ValueError(f'''could not parse string as bool {string}'''	)

					parser.add_argument(
					    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
					)

					parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

					lowerCAmelCase       		=      parser.parse_args()

					lowerCAmelCase       		=      download_controlnet_from_original_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    extract_ema=args.extract_ema,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    use_linear_projection=args.use_linear_projection,
					    cross_attention_dim=args.cross_attention_dim,
					)

					controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



