
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    lowerCamelCase__				(				__lowerCAmelCase  :  List[str]    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	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    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			try:
						lowerCAmelCase_						=	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    lowerCamelCase__				(				__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  Dict				):




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






def    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  List[str] = "student"    ,							__lowerCAmelCase  :  List[Any] = None    ,							__lowerCAmelCase  :  int = None    ,							__lowerCAmelCase  :  Optional[int]=False    ,							__lowerCAmelCase  :  Tuple=None    ,							__lowerCAmelCase  :  Optional[int]=None    ,							**__lowerCAmelCase  :  List[str]    ,							):




			"""simple docstring"""


			lowerCAmelCase_						=	"""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_						=	AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase				).eval()
			else:
						assert isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				), F"""teacher must be a model or string got type {type(__lowerCAmelCase				)}"""
			lowerCAmelCase_						=	teacher.config.to_diff_dict()

			try:
						lowerCAmelCase_						=	teacher.config.encoder_layers, teacher.config.decoder_layers
						if e is None:
									lowerCAmelCase_						=	teacher_e
						if d is None:
									lowerCAmelCase_						=	teacher_d
						init_kwargs.update({"encoder_layers": e, "decoder_layers": d}				)
			except AttributeError:  # T5
						if hasattr(teacher.config    ,							"num_encoder_layers"				):
									lowerCAmelCase_						=	teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
						else:
									lowerCAmelCase_						=	teacher.config.num_layers, teacher.config.num_decoder_layers
						if e is None:
									lowerCAmelCase_						=	teacher_e
						if d is None:
									lowerCAmelCase_						=	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_						=	teacher.config_class(**__lowerCAmelCase				)
			lowerCAmelCase_						=	AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase				)
			# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
			lowerCAmelCase_						=	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_						=	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_						=	pick_layers_to_copy(__lowerCAmelCase    ,							__lowerCAmelCase				)
			if d_layers_to_copy is None:
						lowerCAmelCase_						=	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_						=	{
			    """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)





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    List[Any]           =			"""gpt_neo"""
      _UpperCAmelCase				:    Union[str, Any]           =			["""past_key_values"""]
      _UpperCAmelCase				:    List[Any]           =			{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
      def __init__(      self    ,  __magic_name__=5_0_2_5_7    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_4    ,  __magic_name__=[[["global", "local"], 1_2]]    ,  __magic_name__=1_6    ,  __magic_name__=None    ,  __magic_name__=2_5_6    ,  __magic_name__="gelu_new"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=1e-5    ,  __magic_name__=0.02    ,  __magic_name__=True    ,  __magic_name__=5_0_2_5_6    ,  __magic_name__=5_0_2_5_6    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	List[Any]							=      vocab_size
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Optional[int]							=      num_layers
       lowerCamelCase		:	str							=      num_heads
       lowerCamelCase		:	Optional[Any]							=      intermediate_size
       lowerCamelCase		:	List[Any]							=      window_size
       lowerCamelCase		:	int							=      activation_function
       lowerCamelCase		:	Union[str, Any]							=      resid_dropout
       lowerCamelCase		:	List[Any]							=      embed_dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Dict							=      classifier_dropout
       lowerCamelCase		:	Any							=      layer_norm_epsilon
       lowerCamelCase		:	Dict							=      initializer_range
       lowerCamelCase		:	Dict							=      use_cache

       lowerCamelCase		:	Optional[Any]							=      bos_token_id
       lowerCamelCase		:	int							=      eos_token_id

       lowerCamelCase		:	List[Any]							=      attention_types
       lowerCamelCase		:	Optional[Any]							=      self.expand_attention_types_params(__magic_name__      )

       if len(self.attention_layers      ) != self.num_layers:
        raise ValueError(
            """Configuration for convolutional module is incorrect. """
            """It is required that `len(config.attention_layers)` == `config.num_layers` """
            F'''but is `len(config.attention_layers) = {len(self.attention_layers      )}`, '''
            F'''`config.num_layers = {self.num_layers}`. '''
            """`config.attention_layers` is prepared using `config.attention_types`. """
            """Please verify the value of `config.attention_types` argument."""      )

       super().__init__(bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )






      @staticmethod
      def 						UpperCamelCase__							(      __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      []
       for item in attention_types:
        for _ in range(item[1]      ):
         attentions.extend(item[0]      )
       return attentions





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	Any							=      input.size()
 lowerCamelCase		:	List[Any]							=      len(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      shape[dimension]

 lowerCamelCase		:	Optional[int]							=      torch.arange(0,	lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	Dict							=      torch.div(sizedim - size,	lowerCamelCase,	rounding_mode="""floor"""							) + 1
 lowerCamelCase		:	int							=      torch.arange(lowerCamelCase							) + low_indices[:min_length][:, None]

 lowerCamelCase		:	str							=      [slice(lowerCamelCase							)] * rank
 lowerCamelCase		:	List[str]							=      indices
 lowerCamelCase		:	Dict							=      input[s]

 lowerCamelCase		:	Any							=      list(range(0,	rank + 1							)							)
 perm.append(perm.pop(dimension + 1							)							)

 return sliced.permute(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	List[Any]							=      torch.arange(1,	lowerCamelCase							)
 lowerCamelCase		:	Optional[int]							=      torch.remainder(lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      remainders == 0
 lowerCamelCase		:	List[Any]							=      candidates[divisor_indices]
 lowerCamelCase		:	Optional[Any]							=      torch.max(lowerCamelCase							)
 return largest_divisor, torch.div(lowerCamelCase,	lowerCamelCase,	rounding_mode="""floor"""							)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
       if self.use_past:
        self.fill_with_past_key_values_(__magic_name__    ,  direction="""inputs"""      )
        lowerCamelCase		:	int							=      {0: """batch""", 1: """past_sequence + sequence"""}
       else:
        lowerCamelCase		:	Tuple							=      {0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def 						UpperCamelCase__							(      self      ):
       return self._config.num_heads
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = -1    ,  __magic_name__ = -1    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  ):
       lowerCamelCase		:	Optional[int]							=      super(__magic_name__    ,  self      ).generate_dummy_inputs(
           __magic_name__    ,  batch_size=__magic_name__    ,  seq_length=__magic_name__    ,  is_pair=__magic_name__    ,  framework=__magic_name__      )

       # We need to order the input in the way they appears in the forward()
       lowerCamelCase		:	int							=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
        else:
         import torch

         lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         lowerCamelCase		:	Optional[int]							=      seqlen + 2
         lowerCamelCase		:	List[Any]							=      (
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowerCamelCase		:	str							=      [
             (torch.zeros(__magic_name__      ), torch.zeros(__magic_name__      )) for _ in range(self.num_layers      )
         ]

       lowerCamelCase		:	Tuple							=      common_inputs["""attention_mask"""]
       if self.use_past:
        lowerCamelCase		:	str							=      ordered_inputs["""attention_mask"""].dtype
        lowerCamelCase		:	Any							=      torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(__magic_name__    ,  __magic_name__    ,  dtype=__magic_name__      )]    ,  dim=1      )

       return ordered_inputs






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_3




