

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a		=logging.get_logger(__name__)  # pylint: disable=invalid-name

a		="""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=8      )  ->					Tuple:
	__lowerCamelCase    :    str 	=			height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	__lowerCamelCase    :    Optional[int] 	=			width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      UNetaDConditionModel       ,SCREAMING_SNAKE_CASE__		:      DDPMScheduler       ,SCREAMING_SNAKE_CASE__		:      VQModel       ,):
								super().__init__()

								self.register_modules(
								    unet=SCREAMING_SNAKE_CASE__       ,scheduler=SCREAMING_SNAKE_CASE__       ,movq=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Tuple 	=			2 ** (len(self.movq.config.block_out_channels) - 1)
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      List[str]):
								if latents is None:
									__lowerCamelCase    :    List[str] 	=			randn_tensor(SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,device=SCREAMING_SNAKE_CASE__       ,dtype=SCREAMING_SNAKE_CASE__)
								else:
									if latents.shape != shape:
										raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
									__lowerCamelCase    :    Dict 	=			latents.to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    str 	=			latents * scheduler.init_noise_sigma
								return latents
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      int=0):
								if is_accelerate_available():
									from accelerate import cpu_offload
								else:
									raise ImportError('Please install accelerate via `pip install accelerate`')

								__lowerCamelCase    :    Dict 	=			torch.device(F"cuda:{gpu_id}")

								__lowerCamelCase    :    List[str] 	=			[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
										cpu_offload(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=0):
								if is_accelerate_available() and is_accelerate_version('>='       ,'0.17.0.dev0'):
									from accelerate import cpu_offload_with_hook
								else:
									raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.')

								__lowerCamelCase    :    Tuple 	=			torch.device(F"cuda:{gpu_id}")

								if self.device.type != "cpu":
									self.to('cpu'       ,silence_dtype_warnings=SCREAMING_SNAKE_CASE__)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

								__lowerCamelCase    :    int 	=			None
								for cpu_offloaded_model in [self.unet, self.movq]:
									__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			cpu_offload_with_hook(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,prev_module_hook=SCREAMING_SNAKE_CASE__)

								# We'll offload the last model manually.
								__lowerCamelCase    :    str 	=			hook
							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def    lowerCAmelCase					(		self		:      Tuple):
								if not hasattr(self.unet       ,'_hf_hook'):
									return self.device
								for module in self.unet.modules():
									if (
									    hasattr(SCREAMING_SNAKE_CASE__       ,'_hf_hook')
									    and hasattr(module._hf_hook       ,'execution_device')
									    and module._hf_hook.execution_device is not None
									):
										return torch.device(module._hf_hook.execution_device)
								return self.device


							@torch.no_grad()
							@replace_example_docstring(SCREAMING_SNAKE_CASE__)
							def __call__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Union[torch.FloatTensor, List[torch.FloatTensor]]       ,SCREAMING_SNAKE_CASE__		:      Union[torch.FloatTensor, List[torch.FloatTensor]]       ,SCREAMING_SNAKE_CASE__		:      int = 5_1_2       ,SCREAMING_SNAKE_CASE__		:      int = 5_1_2       ,SCREAMING_SNAKE_CASE__		:      int = 1_0_0       ,SCREAMING_SNAKE_CASE__		:      float = 4.0       ,SCREAMING_SNAKE_CASE__		:      int = 1       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.FloatTensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[str] = "pil"       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,):
								__lowerCamelCase    :    List[Any] 	=			self._execution_device

								__lowerCamelCase    :    Union[str, Any] 	=			guidance_scale > 1.0

								if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
									__lowerCamelCase    :    str 	=			torch.cat(SCREAMING_SNAKE_CASE__       ,dim=0)
								__lowerCamelCase    :    Tuple 	=			image_embeds.shape[0] * num_images_per_prompt
								if isinstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__):
									__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(SCREAMING_SNAKE_CASE__       ,dim=0)

								if do_classifier_free_guidance:
									__lowerCamelCase    :    str 	=			image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__       ,dim=0)
									__lowerCamelCase    :    List[str] 	=			negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__       ,dim=0)

								__lowerCamelCase    :    Optional[int] 	=			torch.cat([negative_image_embeds, image_embeds]       ,dim=0).to(dtype=self.unet.dtype       ,device=SCREAMING_SNAKE_CASE__)

								self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       ,device=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			self.scheduler.timesteps

								__lowerCamelCase    :    List[Any] 	=			self.unet.config.in_channels

								__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			downscale_height_and_width(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,self.movq_scale_factor)

								# create initial latent
								__lowerCamelCase    :    Optional[Any] 	=			self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)       ,image_embeds.dtype       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,self.scheduler       ,)

								for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__)):
									# expand the latents if we are doing classifier free guidance
									__lowerCamelCase    :    Optional[Any] 	=			torch.cat([latents] * 2) if do_classifier_free_guidance else latents

									__lowerCamelCase    :    Optional[int] 	=			{'image_embeds': image_embeds}
									__lowerCamelCase    :    Optional[int] 	=			self.unet(
									    sample=SCREAMING_SNAKE_CASE__       ,timestep=SCREAMING_SNAKE_CASE__       ,encoder_hidden_states=SCREAMING_SNAKE_CASE__       ,added_cond_kwargs=SCREAMING_SNAKE_CASE__       ,return_dict=SCREAMING_SNAKE_CASE__       ,)[0]

									if do_classifier_free_guidance:
										__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			noise_pred.split(latents.shape[1]       ,dim=1)
										__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			noise_pred.chunk(2)
										__lowerCamelCase	,			__lowerCamelCase    :    int 	=			variance_pred.chunk(2)
										__lowerCamelCase    :    Any 	=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
										__lowerCamelCase    :    Optional[Any] 	=			torch.cat([noise_pred, variance_pred_text]       ,dim=1)

									if not (
									    hasattr(self.scheduler.config       ,'variance_type')
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
										__lowerCamelCase	,			__lowerCamelCase    :    int 	=			noise_pred.split(latents.shape[1]       ,dim=1)

									# compute the previous noisy sample x_t -> x_t-1
									__lowerCamelCase    :    List[Any] 	=			self.scheduler.step(
									    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,)[0]
								# post-processing
								__lowerCamelCase    :    str 	=			self.movq.decode(SCREAMING_SNAKE_CASE__       ,force_not_quantize=SCREAMING_SNAKE_CASE__)['sample']

								if output_type not in ["pt", "np", "pil"]:
									raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")

								if output_type in ["np", "pil"]:
									__lowerCamelCase    :    Union[str, Any] 	=			image * 0.5 + 0.5
									__lowerCamelCase    :    str 	=			image.clamp(0       ,1)
									__lowerCamelCase    :    str 	=			image.cpu().permute(0       ,2       ,3       ,1).float().numpy()

								if output_type == "pil":
									__lowerCamelCase    :    Union[str, Any] 	=			self.numpy_to_pil(SCREAMING_SNAKE_CASE__)

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__)







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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    Tuple 	=			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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	try:
		__lowerCamelCase    :    List[str] 	=			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__     ,			lowerCamelCase__      )  ->					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__     ,			lowerCamelCase__ = "student"     ,			lowerCamelCase__ = None     ,			lowerCamelCase__ = None     ,			lowerCamelCase__=False     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			**lowerCamelCase__     ,			)  ->					Tuple[PreTrainedModel, List[int], List[int]]:
	__lowerCamelCase    :    int 	=			'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    :    Union[str, Any] 	=			AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__      ).eval()
	else:
		assert isinstance(lowerCamelCase__     ,			lowerCamelCase__      ), F"teacher must be a model or string got type {type(lowerCamelCase__      )}"
	__lowerCamelCase    :    str 	=			teacher.config.to_diff_dict()

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




