
import os
import sys
import unittest


lowercase_ 				=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowercase_ 				=			os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
lowercase_ 				=			os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")





class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		get_test_to_tester_mapping(_snake_case )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		get_test_to_tester_mapping(_snake_case )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{'''BertModelTest''': '''BertModelTester'''}

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{
           '''BlipModelTest''': '''BlipModelTester''',
           '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
           '''BlipTextModelTest''': '''BlipTextModelTester''',
           '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
           '''BlipVQAModelTest''': '''BlipVQAModelTester''',
           '''BlipVisionModelTest''': '''BlipVisionModelTester''',
       }

       self.assertEqual(get_test_info.to_json(_snake_case )     ,						_snake_case )
       self.assertEqual(get_test_info.to_json(_snake_case )     ,						_snake_case )



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		get_model_to_test_mapping(_snake_case )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		get_model_to_test_mapping(_snake_case )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{
           '''BertForMaskedLM''': ['''BertModelTest'''],
           '''BertForMultipleChoice''': ['''BertModelTest'''],
           '''BertForNextSentencePrediction''': ['''BertModelTest'''],
           '''BertForPreTraining''': ['''BertModelTest'''],
           '''BertForQuestionAnswering''': ['''BertModelTest'''],
           '''BertForSequenceClassification''': ['''BertModelTest'''],
           '''BertForTokenClassification''': ['''BertModelTest'''],
           '''BertLMHeadModel''': ['''BertModelTest'''],
           '''BertModel''': ['''BertModelTest'''],
       }

       __SCREAMING_SNAKE_CASE      :	Tuple					=		{
           '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
           '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
           '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
           '''BlipModel''': ['''BlipModelTest'''],
           '''BlipTextModel''': ['''BlipTextModelTest'''],
           '''BlipVisionModel''': ['''BlipVisionModelTest'''],
       }

       self.assertEqual(get_test_info.to_json(_snake_case )     ,						_snake_case )
       self.assertEqual(get_test_info.to_json(_snake_case )     ,						_snake_case )




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		get_model_to_tester_mapping(_snake_case )
       __SCREAMING_SNAKE_CASE      :	int					=		get_model_to_tester_mapping(_snake_case )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		{
           '''BertForMaskedLM''': ['''BertModelTester'''],
           '''BertForMultipleChoice''': ['''BertModelTester'''],
           '''BertForNextSentencePrediction''': ['''BertModelTester'''],
           '''BertForPreTraining''': ['''BertModelTester'''],
           '''BertForQuestionAnswering''': ['''BertModelTester'''],
           '''BertForSequenceClassification''': ['''BertModelTester'''],
           '''BertForTokenClassification''': ['''BertModelTester'''],
           '''BertLMHeadModel''': ['''BertModelTester'''],
           '''BertModel''': ['''BertModelTester'''],
       }

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{
           '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
           '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
           '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
           '''BlipModel''': ['''BlipModelTester'''],
           '''BlipTextModel''': ['''BlipTextModelTester'''],
           '''BlipVisionModel''': ['''BlipVisionModelTester'''],
       }

       self.assertEqual(get_test_info.to_json(_snake_case )     ,						_snake_case )
       self.assertEqual(get_test_info.to_json(_snake_case )     ,						_snake_case )







"""simple docstring"""
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     :						List[Any]						       =  logging.get_logger(__name__)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    nn.ModuleList  ,     _UpperCAmelCase					:    List[int]			):
 lowerCAmelCase    				= 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()			)


__UpperCamelCase     :						Optional[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))},
}
__UpperCamelCase     :						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]},
    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[int]  ,     _UpperCAmelCase					:    List[Any]			):
 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(_UpperCAmelCase			)			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Dict			):
 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[str]=False  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     _UpperCAmelCase					:    Optional[Any]=None  ,     **_UpperCAmelCase					:    str  ,     ):
 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(_UpperCAmelCase  ,     _UpperCAmelCase			):
  AutoTokenizer.from_pretrained(_UpperCAmelCase			).save_pretrained(_UpperCAmelCase			)  # purely for convenience
  lowerCAmelCase    				= AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase			).eval()
 else:
  assert isinstance(_UpperCAmelCase  ,     _UpperCAmelCase			), F'teacher must be a model or string got type {type(_UpperCAmelCase			)}'
 lowerCAmelCase    				= teacher.config.to_diff_dict()

 try:
  lowerCAmelCase				,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				,lowerCAmelCase    				= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
  else:
   lowerCAmelCase				,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(_UpperCAmelCase			)

 # Copy weights
 lowerCAmelCase    				= teacher.config_class(**_UpperCAmelCase			)
 lowerCAmelCase    				= AutoModelForSeqaSeqLM.from_config(_UpperCAmelCase			)
 # 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=_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
  lowerCAmelCase				,lowerCAmelCase    				= 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:
  lowerCAmelCase    				= pick_layers_to_copy(_UpperCAmelCase  ,     _UpperCAmelCase			)
 if d_layers_to_copy is None:
  lowerCAmelCase    				= 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}'			)
 lowerCAmelCase    				= {
     '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)





