





'''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


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       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()   )


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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Optional[Any]:

   '''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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase = "student"				,						__UpperCAmelCase = None				,						__UpperCAmelCase = None				,						__UpperCAmelCase=False				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						**__UpperCAmelCase				,						)    ->       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)












'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import RegNetForImageClassification, RegNetModel
    from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class __a			:





   def __init__(       self					:      Any					,lowerCamelCase					:      Any					,lowerCamelCase					:      int=3					,lowerCamelCase					:      Optional[int]=32					,lowerCamelCase					:      Any=3					,lowerCamelCase					:      Dict=10					,lowerCamelCase					:      Union[str, Any]=[10, 20, 30, 40]					,lowerCamelCase					:      Any=[1, 1, 2, 1]					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      Dict="relu"					,lowerCamelCase					:      str=3					,lowerCamelCase					:      Union[str, Any]=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							embeddings_size
      __SCREAMING_SNAKE_CASE   				=							hidden_sizes
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							scope
      __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_labels     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      return config, pixel_values, labels





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return RegNetConfig(
          num_channels=self.num_channels					,embeddings_size=self.embeddings_size					,hidden_sizes=self.hidden_sizes					,depths=self.depths					,hidden_act=self.hidden_act					,num_labels=self.num_labels					,)





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      int					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape					,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,)





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class __a			( _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			List[str]						   =						(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   __UpperCamelCase :			Optional[int]						   =						(
       {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   __UpperCamelCase :			Dict						   =						False
   __UpperCamelCase :			str						   =						False
   __UpperCamelCase :			Any						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,has_text_modality=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return





   @unittest.skip(reason="""RegNet does not use inputs_embeds"""     )
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""RegNet does not support input and output embeddings"""     )
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							inspect.signature(model.forward     )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __SCREAMING_SNAKE_CASE   				=							[*signature.parameters.keys()]

         __SCREAMING_SNAKE_CASE   				=							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							model_class(config=lowerCamelCase     )
         for name, module in model.named_modules():
            if isinstance(lowerCamelCase					,(nn.BatchNormad, nn.GroupNorm)     ):
               self.assertTrue(
                   torch.all(module.weight == 1     )					,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,)
               self.assertTrue(
                   torch.all(module.bias == 0     )					,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""					,)





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      def check_hidden_states_output(lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[int]     ):
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         model.to(lowerCamelCase     )
         model.eval()

         with torch.no_grad():
            __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(lowerCamelCase					,lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         __SCREAMING_SNAKE_CASE   				=							self.model_tester.num_stages
         self.assertEqual(len(lowerCamelCase     )					,expected_num_stages + 1     )

         # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]     )					,[self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,)

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							["""basic""", """bottleneck"""]
      for model_class in self.all_model_classes:
         for layer_type in layers_type:
            __SCREAMING_SNAKE_CASE   				=							layer_type
            __SCREAMING_SNAKE_CASE   				=							True
            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __SCREAMING_SNAKE_CASE   				=							True

            check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase     )





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							RegNetModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )





def     __magic_name__							(				)    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image







@require_torch
@require_vision
class __a			( unittest.TestCase      ):





   @cached_property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
          if is_vision_available()
          else None
      )





   @slow
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.default_image_processor
      __SCREAMING_SNAKE_CASE   				=							prepare_img()
      __SCREAMING_SNAKE_CASE   				=							image_processor(images=lowerCamelCase					,return_tensors="""pt"""     ).to(lowerCamelCase     )

      # forward pass
      with torch.no_grad():
         __SCREAMING_SNAKE_CASE   				=							model(**lowerCamelCase     )

      # verify the logits
      __SCREAMING_SNAKE_CASE   				=							torch.Size((1, 1000)     )
      self.assertEqual(outputs.logits.shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([-0.4_180, -1.5_051, -3.4_836]     ).to(lowerCamelCase     )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]					,lowerCamelCase					,atol=1E-4     )     )





