
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    lowerCamelCase__				(				__lowerCAmelCase  :  nn.ModuleList    ,							__lowerCAmelCase  :  nn.ModuleList    ,							__lowerCAmelCase  :  List[int]				):




			"""simple docstring"""


			lowerCAmelCase_						=	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    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


			try:
						lowerCAmelCase_						=	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    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  List[Any]				):




			"""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(__lowerCAmelCase				)				)
			elif n_student == 1:
						return [n_teacher - 1]
			else:
						return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, PreTrainedModel]    ,							__lowerCAmelCase  :  Union[str, Path] = "student"    ,							__lowerCAmelCase  :  Union[int, None] = None    ,							__lowerCAmelCase  :  Union[int, None] = None    ,							__lowerCAmelCase  :  Optional[Any]=False    ,							__lowerCAmelCase  :  List[str]=None    ,							__lowerCAmelCase  :  List[Any]=None    ,							**__lowerCAmelCase  :  Optional[Any]    ,							):




			"""simple docstring"""


			lowerCAmelCase_						=	"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_						=	AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase				).eval()
			else:
						assert isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				), F"""teacher must be a model or string got type {type(__lowerCAmelCase				)}"""
			lowerCAmelCase_						=	teacher.config.to_diff_dict()

			try:
						lowerCAmelCase_	,  lowerCAmelCase_						=	teacher.config.encoder_layers, teacher.config.decoder_layers
						if e is None:
									lowerCAmelCase_						=	teacher_e
						if d is None:
									lowerCAmelCase_						=	teacher_d
						init_kwargs.update({"encoder_layers": e, "decoder_layers": d}				)
			except AttributeError:  # T5
						if hasattr(teacher.config    ,							"num_encoder_layers"				):
									lowerCAmelCase_	,  lowerCAmelCase_						=	teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
						else:
									lowerCAmelCase_	,  lowerCAmelCase_						=	teacher.config.num_layers, teacher.config.num_decoder_layers
						if e is None:
									lowerCAmelCase_						=	teacher_e
						if d is None:
									lowerCAmelCase_						=	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_						=	teacher.config_class(**__lowerCAmelCase				)
			lowerCAmelCase_						=	AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase				)
			# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
			lowerCAmelCase_						=	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_						=	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_						=	pick_layers_to_copy(__lowerCAmelCase    ,							__lowerCAmelCase				)
			if d_layers_to_copy is None:
						lowerCAmelCase_						=	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_						=	{
			    "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)





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_A   =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_A   =     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"





class 	_lowerCAmelCase					(	unittest.TestCase       ):




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir				,				"schedulers/"     )     )
							lowerCAmelCase_						=	self.diffusers_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase				,				"src/diffusers/schedulers/scheduling_ddpm.py"     )				,				os.path.join(self.diffusers_dir				,				"schedulers/scheduling_ddpm.py"     )				,				)




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	"src/diffusers"
							shutil.rmtree(self.diffusers_dir     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
										lowerCAmelCase_						=	comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							lowerCAmelCase_						=	black.Mode(target_versions={black.TargetVersion.PYaa}				,				line_length=119     )
							lowerCAmelCase_						=	black.format_str(_UpperCamelCase				,				mode=_UpperCamelCase     )
							lowerCAmelCase_						=	os.path.join(self.diffusers_dir				,				"new_code.py"     )
							with open(_UpperCamelCase				,				"w"				,				newline="\n"     ) as f:
										f.write(_UpperCamelCase     )
							if overwrite_result is None:
										self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase     )     ) == 0     )
							else:
										check_copies.is_copy_consistent(f.name				,				overwrite=_UpperCamelCase     )
										with open(_UpperCamelCase				,				"r"     ) as f:
													self.assertTrue(f.read()				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"     )
							self.assertEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      Tuple:
							# Base copy consistency
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"				,				"DDPMSchedulerOutput"				,				REFERENCE_CODE + "\n"				,				)

							# With no empty line at the end
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"				,				"DDPMSchedulerOutput"				,				_UpperCamelCase				,				)

							# Copy consistency with rename
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"				,				"TestSchedulerOutput"				,				re.sub("DDPM"				,				"Test"				,				_UpperCamelCase     )				,				)

							# Copy consistency with a really long name
							lowerCAmelCase_						=	"TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
							self.check_copy_consistency(
							    f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""				,				f"""{long_class_name}SchedulerOutput"""				,				re.sub("Bert"				,				_UpperCamelCase				,				_UpperCamelCase     )				,				)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"				,				"TestSchedulerOutput"				,				_UpperCamelCase				,				overwrite_result=re.sub("DDPM"				,				"Test"				,				_UpperCamelCase     )				,				)



