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			: Dict							   =		logging.get_logger(__name__)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       nn.ModuleList ,     __UpperCamelCase    :       nn.ModuleList ,     __UpperCamelCase    :       List[int]	)							->					None:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      nn.ModuleList([src_layers[i] for i in layers_to_copy]	)
  assert len(__UpperCamelCase	) == len(__UpperCamelCase	), f"""{len(__UpperCamelCase	)} != {len(__UpperCamelCase	)}"""
  dest_layers.load_state_dict(layers_to_copy.state_dict()	)


__lowerCamelCase			: Any							   =		{
    # 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			: List[str]							   =		{
    # 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  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       List[Any]	)							->					int:







  """simple docstring"""





  try:
    SCREAMING_SNAKE_CASE__										=      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(__UpperCamelCase	)	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       Any	)							->					List[int]:







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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, PreTrainedModel] ,     __UpperCamelCase    :       Union[str, Path] = "student" ,     __UpperCamelCase    :       Union[int, None] = None ,     __UpperCamelCase    :       Union[int, None] = None ,     __UpperCamelCase    :       List[Any]=False ,     __UpperCamelCase    :       Optional[int]=None ,     __UpperCamelCase    :       str=None ,     **__UpperCamelCase    :       Union[str, Any] ,     )							->					Tuple[PreTrainedModel, List[int], List[int]]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      """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(__UpperCamelCase ,     __UpperCamelCase	):
    AutoTokenizer.from_pretrained(__UpperCamelCase	).save_pretrained(__UpperCamelCase	)  # purely for convenience
    SCREAMING_SNAKE_CASE__										=      AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase	).eval()
  else:
    assert isinstance(__UpperCamelCase ,     __UpperCamelCase	), f"""teacher must be a model or string got type {type(__UpperCamelCase	)}"""
  SCREAMING_SNAKE_CASE__										=      teacher.config.to_diff_dict()

  try:
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      teacher.config.encoder_layers, teacher.config.decoder_layers
    if e is None:
      SCREAMING_SNAKE_CASE__										=      teacher_e
    if d is None:
      SCREAMING_SNAKE_CASE__										=      teacher_d
    init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d}	)
  except AttributeError:  # T5
    if hasattr(teacher.config ,     """num_encoder_layers"""	):
      SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
    else:
      SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      teacher.config.num_layers, teacher.config.num_decoder_layers
    if e is None:
      SCREAMING_SNAKE_CASE__										=      teacher_e
    if d is None:
      SCREAMING_SNAKE_CASE__										=      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(__UpperCamelCase	)

  # Copy weights
  SCREAMING_SNAKE_CASE__										=      teacher.config_class(**__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      AutoModelForSeqaSeqLM.from_config(__UpperCamelCase	)
  # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
  SCREAMING_SNAKE_CASE__										=      student.load_state_dict(teacher.state_dict() ,     strict=__UpperCamelCase	)
  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
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      list(range(__UpperCamelCase	)	), list(range(__UpperCamelCase	)	)
    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(__UpperCamelCase	)
    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:
    SCREAMING_SNAKE_CASE__										=      pick_layers_to_copy(__UpperCamelCase ,     __UpperCamelCase	)
  if d_layers_to_copy is None:
    SCREAMING_SNAKE_CASE__										=      pick_layers_to_copy(__UpperCamelCase ,     __UpperCamelCase	)

  try:
    if hasattr(
        __UpperCamelCase ,     """prophetnet"""	):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
      copy_layers(teacher.prophetnet.encoder.layers ,     student.prophetnet.encoder.layers ,     __UpperCamelCase	)
      copy_layers(teacher.prophetnet.decoder.layers ,     student.prophetnet.decoder.layers ,     __UpperCamelCase	)
    else:
      copy_layers(teacher.model.encoder.layers ,     student.model.encoder.layers ,     __UpperCamelCase	)
      copy_layers(teacher.model.decoder.layers ,     student.model.decoder.layers ,     __UpperCamelCase	)
  except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
    copy_layers(teacher.encoder.block ,     student.encoder.block ,     __UpperCamelCase	)
    copy_layers(teacher.decoder.block ,     student.decoder.block ,     __UpperCamelCase	)
  logger.info(
      f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""	)
  SCREAMING_SNAKE_CASE__										=      {
      """teacher_type""": teacher.config.model_type,
      """copied_encoder_layers""": e_layers_to_copy,
      """copied_decoder_layers""": d_layers_to_copy,
  }
  student.save_pretrained(__UpperCamelCase	)
  # 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 gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							KandinskyVaaImgaImgPipeline
   lowerCAmelCase_														=							["image_embeds", "negative_image_embeds", "image"]
   lowerCAmelCase_														=							[
       "image_embeds",
       "negative_image_embeds",
       "image",
   ]
   lowerCAmelCase_														=							[
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCAmelCase_														=							False




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     return self.time_input_dim




   @property
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return self.time_input_dim * 4




   @property
   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     return 1_00




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     torch.manual_seed(0      )

     SCREAMING_SNAKE_CASE__										=      {
         """in_channels""": 4,
         # Out channels is double in channels because predicts mean and variance
         """out_channels""": 8,
         """addition_embed_type""": """image""",
         """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
         """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
         """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
         """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
         """layers_per_block""": 1,
         """encoder_hid_dim""": self.text_embedder_hidden_size,
         """encoder_hid_dim_type""": """image_proj""",
         """cross_attention_dim""": self.cross_attention_dim,
         """attention_head_dim""": 4,
         """resnet_time_scale_shift""": """scale_shift""",
         """class_embed_type""": None,
     }

     SCREAMING_SNAKE_CASE__										=      UNetaDConditionModel(**_lowercase      )
     return model




   @property
   def 	__a						(    self			:				str      ):



     """simple docstring"""






     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      VQModel(**self.dummy_movq_kwargs      )
     return model




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.dummy_unet
     SCREAMING_SNAKE_CASE__										=      self.dummy_movq

     SCREAMING_SNAKE_CASE__										=      {
         """num_train_timesteps""": 10_00,
         """beta_schedule""": """linear""",
         """beta_start""": 0.0_00_85,
         """beta_end""": 0.0_12,
         """clip_sample""": False,
         """set_alpha_to_one""": False,
         """steps_offset""": 0,
         """prediction_type""": """epsilon""",
         """thresholding""": False,
     }

     SCREAMING_SNAKE_CASE__										=      DDIMScheduler(**_lowercase      )

     SCREAMING_SNAKE_CASE__										=      {
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }

     return components




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Tuple=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(seed + 1      )      ).to(
         _lowercase      )
     # create init_image
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, 3, 64, 64)       ,	rng=random.Random(_lowercase      )      ).to(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_lowercase      )      ).convert("""RGB"""      ).resize((2_56, 2_56)      )

     if str(_lowercase      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_lowercase      ).manual_seed(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      {
         """image""": init_image,
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """num_inference_steps""": 10,
         """guidance_scale""": 7.0,
         """strength""": 0.2,
         """output_type""": """np""",
     }
     return inputs




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """cpu"""

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_lowercase      )

     pipe.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      pipe(**self.get_dummy_inputs(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      output.images

     SCREAMING_SNAKE_CASE__										=      pipe(
         **self.get_dummy_inputs(_lowercase      )       ,	return_dict=_lowercase       ,	)[0]

     SCREAMING_SNAKE_CASE__										=      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__										=      image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__										=      np.array(
         [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]      )
     assert (
         np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
     ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
     ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







@slow
@require_torch_gpu
class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

     SCREAMING_SNAKE_CASE__										=      load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
     SCREAMING_SNAKE_CASE__										=      """A red cartoon frog, 4k"""

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior"""       ,	torch_dtype=torch.floataa      )
     pipe_prior.to(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaImgaImgPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipeline.to(_lowercase      )

     pipeline.set_progress_bar_config(disable=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cpu"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      pipe_prior(
         _lowercase       ,	generator=_lowercase       ,	num_inference_steps=5       ,	negative_prompt=""""""       ,	).to_tuple()

     SCREAMING_SNAKE_CASE__										=      pipeline(
         image=_lowercase       ,	image_embeds=_lowercase       ,	negative_image_embeds=_lowercase       ,	generator=_lowercase       ,	num_inference_steps=1_00       ,	height=7_68       ,	width=7_68       ,	strength=0.2       ,	output_type="""np"""       ,	)

     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (7_68, 7_68, 3)

     assert_mean_pixel_difference(_lowercase       ,	_lowercase      )
