


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


UpperCAmelCase      =   logging.get_logger(__name__)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  None:




      """simple docstring"""

      snake_case_	=				nn.ModuleList([src_layers[i] for i in layers_to_copy] )
      assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE ), f'''{len(SCREAMING_SNAKE_CASE )} != {len(SCREAMING_SNAKE_CASE )}'''
      dest_layers.load_state_dict(layers_to_copy.state_dict() )


UpperCAmelCase      =   {
    # 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))},
}
UpperCAmelCase      =   {
    # 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   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      try:
            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(SCREAMING_SNAKE_CASE ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  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(SCREAMING_SNAKE_CASE ) )
      elif n_student == 1:
            return [n_teacher - 1]
      else:
            return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = "student" , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , )->  Tuple[PreTrainedModel, List[int], List[int]]:




      """simple docstring"""

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

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

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

      try:
            if hasattr(
                SCREAMING_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 , SCREAMING_SNAKE_CASE )
                  copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , SCREAMING_SNAKE_CASE )
            else:
                  copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , SCREAMING_SNAKE_CASE )
                  copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , SCREAMING_SNAKE_CASE )
      except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
            copy_layers(teacher.encoder.block , student.encoder.block , SCREAMING_SNAKE_CASE )
            copy_layers(teacher.decoder.block , student.decoder.block , SCREAMING_SNAKE_CASE )
      logger.info(
          f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
      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(SCREAMING_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)



import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       super().__init__()
       snake_case_	=				value_function
       snake_case_	=				unet
       snake_case_	=				scheduler
       snake_case_	=				env
       snake_case_	=				env.get_dataset()
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].mean()
             except:  # noqa: E722
                   pass
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].std()
             except:  # noqa: E722
                   pass
       snake_case_	=				env.observation_space.shape[0]
       snake_case_	=				env.action_space.shape[0]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return (x_in - self.means[key]) / self.stds[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return x_in * self.stds[key] + self.means[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       if type(_UpperCAmelCase ) is dict:
             return {k: self.to_torch(_UpperCAmelCase ) for k, v in x_in.items()}
       elif torch.is_tensor(_UpperCAmelCase ):
             return x_in.to(self.unet.device )
       return torch.tensor(_UpperCAmelCase		,				device=self.unet.device )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				x.shape[0]
       snake_case_	=				None
       for i in tqdm.tqdm(self.scheduler.timesteps ):
             # create batch of timesteps to pass into model
             snake_case_	=				torch.full((batch_size,)		,				_UpperCAmelCase		,				device=self.unet.device		,				dtype=torch.long )
             for _ in range(_UpperCAmelCase ):
                   with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         snake_case_	=				self.value_function(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample
                         snake_case_	=				torch.autograd.grad([y.sum()]		,				[x] )[0]

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         snake_case_	=				torch.exp(0.5 * posterior_variance )
                         snake_case_	=				model_std * grad

                   snake_case_	=				0
                   snake_case_	=				x.detach()
                   snake_case_	=				x + scale * grad
                   snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

             snake_case_	=				self.unet(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample.permute(0		,				2		,				1 )

             # TODO: verify deprecation of this kwarg
             snake_case_	=				self.scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				predict_epsilon=_UpperCAmelCase )['''prev_sample''']

             # apply conditions to the trajectory (set the initial state)
             snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
             snake_case_	=				self.to_torch(_UpperCAmelCase )
       return x, y





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

       snake_case_	=				{0: self.to_torch(_UpperCAmelCase )}
       snake_case_	=				(batch_size, planning_horizon, self.state_dim + self.action_dim)

       # generate initial noise and apply our conditions (to make the trajectories start at current state)
       snake_case_	=				randn_tensor(_UpperCAmelCase		,				device=self.unet.device )
       snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
       snake_case_	=				self.to_torch(_UpperCAmelCase )

       # run the diffusion process
       snake_case_	,   snake_case_	=				self.run_diffusion(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       # sort output trajectories by value
       snake_case_	=				y.argsort(0		,				descending=_UpperCAmelCase ).squeeze()
       snake_case_	=				x[sorted_idx]
       snake_case_	=				sorted_values[:, :, : self.action_dim]
       snake_case_	=				actions.detach().cpu().numpy()
       snake_case_	=				self.de_normalize(_UpperCAmelCase		,				key='''actions''' )

       # select the action with the highest value
       if y is not None:
             snake_case_	=				0
       else:
             # if we didn't run value guiding, select a random action
             snake_case_	=				np.random.randint(0		,				_UpperCAmelCase )

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions