






"""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       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	A_		:    int						     = nn.ModuleList([src_layers[i] for i in layers_to_copy]       )
	assert len(snake_case__       ) == len(snake_case__       ), F"""{len(snake_case__       )} != {len(snake_case__       )}"""
	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       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	try:
		A_		:    List[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(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	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(snake_case__       )       )
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def       __UpperCamelCase						(		snake_case__ , snake_case__ = "student" , snake_case__ = None , snake_case__ = None , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ):
	A_		:    Optional[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(snake_case__ , snake_case__       ):
		AutoTokenizer.from_pretrained(snake_case__       ).save_pretrained(snake_case__       )  # purely for convenience
		A_		:    Union[str, Any]						     = AutoModelForSeqaSeqLM.from_pretrained(snake_case__       ).eval()
	else:
		assert isinstance(snake_case__ , snake_case__       ), F"""teacher must be a model or string got type {type(snake_case__       )}"""
	A_		:    Dict						     = teacher.config.to_diff_dict()

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

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

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

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					(UniPCMultistepScheduler,)
	_A							:					Optional[Any] 			=					(("""num_inference_steps""", 25),)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		A_		:    str						     = {
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """solver_order""": 2,
		    """solver_type""": """bh2""",
		}

		config.update(**lowerCAmelCase_				)
		return config





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Tuple						     = dict(self.forward_default_kwargs				)
		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    str						     = self.dummy_sample
		A_		:    Dict						     = 0.1 * sample
		A_		:    int						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    List[Any]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Dict						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)
			# copy over dummy past residuals
			A_		:    Tuple						     = dummy_past_residuals[: scheduler.config.solver_order]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_				)
				A_		:    Any						     = scheduler_class.from_pretrained(lowerCAmelCase_				)
				new_scheduler.set_timesteps(lowerCAmelCase_				)
				# copy over dummy past residuals
				A_		:    Any						     = dummy_past_residuals[: new_scheduler.config.solver_order]

			A_     ,  A_		:    Optional[int]						     = sample, sample
			for t in range(lowerCAmelCase_  ,  time_step + scheduler.config.solver_order + 1				):
				A_		:    Any						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
				A_		:    Dict						     = new_scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

				assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Optional[int]						     = dict(self.forward_default_kwargs				)
		A_		:    int						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    Optional[Any]						     = self.dummy_sample
		A_		:    List[Any]						     = 0.1 * sample
		A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    str						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)

			# copy over dummy past residuals (must be after setting timesteps)
			A_		:    Union[str, Any]						     = dummy_past_residuals[: scheduler.config.solver_order]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_				)
				A_		:    Tuple						     = scheduler_class.from_pretrained(lowerCAmelCase_				)
				# copy over dummy past residuals
				new_scheduler.set_timesteps(lowerCAmelCase_				)

				# copy over dummy past residual (must be after setting timesteps)
				A_		:    Optional[int]						     = dummy_past_residuals[: new_scheduler.config.solver_order]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    str						     = new_scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

			assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"





	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if scheduler is None:
			A_		:    int						     = self.scheduler_classes[0]
			A_		:    List[str]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Any						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    int						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(**lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Optional[int]						     = 10
		A_		:    Optional[Any]						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Optional[int]						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		return sample





	def  lowerCamelCase(self				):
		A_		:    Any						     = dict(self.forward_default_kwargs				)

		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    List[Any]						     = scheduler_class(**lowerCAmelCase_				)

			A_		:    int						     = self.dummy_sample
			A_		:    str						     = 0.1 * sample

			if num_inference_steps is not None and hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				scheduler.set_timesteps(lowerCAmelCase_				)
			elif num_inference_steps is not None and not hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				A_		:    Tuple						     = num_inference_steps

			# copy over dummy past residuals (must be done after set_timesteps)
			A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]
			A_		:    Dict						     = dummy_past_residuals[: scheduler.config.solver_order]

			A_		:    str						     = scheduler.timesteps[5]
			A_		:    Any						     = scheduler.timesteps[6]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    Dict						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

			self.assertEqual(output_a.shape  ,  sample.shape				)
			self.assertEqual(output_a.shape  ,  output_a.shape				)





	def  lowerCamelCase(self				):
		# make sure that iterating over schedulers with same config names gives same results
		# for defaults
		A_		:    Tuple						     = UniPCMultistepScheduler(**self.get_scheduler_config()				)
		A_		:    List[str]						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    int						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3

		A_		:    List[str]						     = DPMSolverSinglestepScheduler.from_config(scheduler.config				)
		A_		:    List[str]						     = DEISMultistepScheduler.from_config(scheduler.config				)
		A_		:    Optional[int]						     = DPMSolverMultistepScheduler.from_config(scheduler.config				)
		A_		:    str						     = UniPCMultistepScheduler.from_config(scheduler.config				)

		A_		:    Dict						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    List[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		for timesteps in [25, 50, 100, 999, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self.check_over_configs(thresholding=lowerCAmelCase_				)
		for order in [1, 2, 3]:
			for solver_type in ["bh1", "bh2"]:
				for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "sample"]:
						self.check_over_configs(
						    thresholding=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  sample_max_value=lowerCAmelCase_  ,  solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  )





	def  lowerCamelCase(self				):
		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		for solver_type in ["bh1", "bh2"]:
			for order in [1, 2, 3]:
				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					A_		:    Union[str, Any]						     = self.full_loop(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					assert not torch.isnan(lowerCAmelCase_				).any(), "Samples have nan numbers"





	def  lowerCamelCase(self				):
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
			self.check_over_forward(num_inference_steps=lowerCAmelCase_  ,  time_step=0				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.full_loop()
		A_		:    Optional[int]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    str						     = self.full_loop(prediction_type="""v_prediction"""				)
		A_		:    Optional[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.1014				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(thresholding=lowerCAmelCase_  ,  dynamic_thresholding_ratio=0				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Any						     = 10
		A_		:    int						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter.half()
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Dict						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		assert sample.dtype == torch.floataa





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		for scheduler_class in self.scheduler_classes:
			A_		:    Tuple						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    int						     = scheduler_class(**lowerCAmelCase_				)

			scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
			assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps

