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


_lowercase							:      List[str]      							=logging.get_logger(__name__)



def A__  (			lowercase:     nn.ModuleList,		lowercase:     nn.ModuleList,		lowercase:     List[int]					)				->		None:
       A						:     Dict         =nn.ModuleList([src_layers[i] for i in layers_to_copy]					)
       assert len(lowercase					) == len(lowercase					), F'{len(lowercase					)} != {len(lowercase					)}'
       dest_layers.load_state_dict(layers_to_copy.state_dict()					)


_lowercase							:      Union[str, Any]      							={
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_lowercase							:      Optional[int]      							={
    # 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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}



def A__  (			lowercase:     Any,		lowercase:     Union[str, Any]					)				->		int:
       try:
              A						:     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(lowercase					)					)



def A__  (			lowercase:     Optional[int],		lowercase:     str					)				->		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(lowercase					)					)
       elif n_student == 1:
              return [n_teacher - 1]
       else:
              return LAYERS_TO_SUPERVISE[n_teacher][n_student]



def A__  (			lowercase:     Union[str, PreTrainedModel],		lowercase:     Union[str, Path] = "student",		lowercase:     Union[int, None] = None,		lowercase:     Union[int, None] = None,		lowercase:     Optional[Any]=False,		lowercase:     Optional[Any]=None,		lowercase:     List[str]=None,		**lowercase:     List[str],		)				->		Tuple[PreTrainedModel, List[int], List[int]]:
       A						:     Optional[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(lowercase,		lowercase					):
              AutoTokenizer.from_pretrained(lowercase					).save_pretrained(lowercase					)  # purely for convenience
              A						:     int         =AutoModelForSeqaSeqLM.from_pretrained(lowercase					).eval()
       else:
              assert isinstance(lowercase,		lowercase					), F'teacher must be a model or string got type {type(lowercase					)}'
       A						:     Any         =teacher.config.to_diff_dict()

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

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

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

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				DDIMPipeline
  lowercase		:     int            =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowercase		:     Optional[Any]            =				PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  lowercase		:     Optional[Any]            =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  lowercase		:     Union[str, Any]            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         torch.manual_seed(0     )
         A						:     str         =UNetaDModel(
             block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,	up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,	)
         A						:     Optional[int]         =DDIMScheduler()
         A						:     Optional[Any]         ={'unet': unet, 'scheduler': scheduler}
         return components







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0     )    ->				Any:
         if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                A						:     List[Any]         =torch.manual_seed(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Union[str, Any]         =torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         ={
             'batch_size': 1,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         A						:     Union[str, Any]         ='cpu'

         A						:     Tuple         =self.get_dummy_components()
         A						:     Union[str, Any]         =self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
         pipe.to(SCREAMING_SNAKE_CASE__     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     str         =self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
         A						:     str         =pipe(**SCREAMING_SNAKE_CASE__     ).images
         A						:     Optional[Any]         =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape      ,	(1, 32, 32, 3)     )
         A						:     Optional[Any]         =np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]     )
         A						:     str         =np.abs(image_slice.flatten() - expected_slice     ).max()
         self.assertLessEqual(SCREAMING_SNAKE_CASE__      ,	1e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         super().test_save_load_local(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         super().test_save_load_optional_components(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Any         ='google/ddpm-cifar10-32'

         A						:     Optional[int]         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =DDIMScheduler()

         A						:     int         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddim.to(SCREAMING_SNAKE_CASE__     )
         ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =torch.manual_seed(0     )
         A						:     Optional[Any]         =ddim(generator=SCREAMING_SNAKE_CASE__      ,	eta=0.0      ,	output_type='numpy'     ).images

         A						:     str         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A						:     Tuple         =np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     Optional[int]         ='google/ddpm-ema-bedroom-256'

         A						:     str         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     str         =DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__     )

         A						:     Tuple         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddpm.to(SCREAMING_SNAKE_CASE__     )
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Any         =torch.manual_seed(0     )
         A						:     Optional[int]         =ddpm(generator=SCREAMING_SNAKE_CASE__      ,	output_type='numpy'     ).images

         A						:     List[Any]         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         A						:     Optional[int]         =np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2






