





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__   : Tuple					  = logging.get_logger(__name__)




def    A_(			A				,      A				,      A   ):
 UpperCAmelCase_								=     nn.ModuleList([src_layers[i] for i in layers_to_copy]   )
 assert len(A   ) == len(A   ), f"""{len(A   )} != {len(A   )}"""
 dest_layers.load_state_dict(layers_to_copy.state_dict()   )


UpperCamelCase__   : str					  = {
    # 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__   : Any					  = {
    # 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    A_(			A				,      A   ):
 try:
  UpperCAmelCase_								=     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(A   )   )




def    A_(			A				,      A   ):
 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(A   )   )
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]




def    A_(			A				,      A = "student"				,      A = None				,      A = None				,      A=False				,      A=None				,      A=None				,      **A				,      ):
 UpperCAmelCase_								=     """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(A				,      A   ):
  AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )  # purely for convenience
  UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(A   ).eval()
 else:
  assert isinstance(A				,      A   ), f"""teacher must be a model or string got type {type(A   )}"""
 UpperCAmelCase_								=     teacher.config.to_diff_dict()

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

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

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

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

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 ASTForAudioClassification, ASTModel
				from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)


if is_torchaudio_available():
				import torchaudio

				from transformers import ASTFeatureExtractor







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Optional[int] ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Optional[int]=13 ,					__lowercase    :  Union[str, Any]=2 ,					__lowercase    :  Union[str, Any]=24 ,					__lowercase    :  Tuple=16 ,					__lowercase    :  Any=True ,					__lowercase    :  List[str]=True ,					__lowercase    :  List[Any]=32 ,					__lowercase    :  Tuple=5 ,					__lowercase    :  List[str]=4 ,					__lowercase    :  Optional[Any]=37 ,					__lowercase    :  Tuple="gelu" ,					__lowercase    :  Any=0.1 ,					__lowercase    :  Optional[int]=0.1 ,					__lowercase    :  Tuple=10 ,					__lowercase    :  List[str]=0.02 ,					__lowercase    :  str=None ,					__lowercase    :  Any=2 ,					__lowercase    :  Optional[int]=2 ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     parent
						UpperCAmelCase_								=     batch_size
						UpperCAmelCase_								=     patch_size
						UpperCAmelCase_								=     max_length
						UpperCAmelCase_								=     num_mel_bins
						UpperCAmelCase_								=     is_training
						UpperCAmelCase_								=     use_labels
						UpperCAmelCase_								=     hidden_size
						UpperCAmelCase_								=     num_hidden_layers
						UpperCAmelCase_								=     num_attention_heads
						UpperCAmelCase_								=     intermediate_size
						UpperCAmelCase_								=     hidden_act
						UpperCAmelCase_								=     hidden_dropout_prob
						UpperCAmelCase_								=     attention_probs_dropout_prob
						UpperCAmelCase_								=     type_sequence_label_size
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     scope
						UpperCAmelCase_								=     frequency_stride
						UpperCAmelCase_								=     time_stride

						# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
						UpperCAmelCase_								=     (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
						UpperCAmelCase_								=     (self.max_length - self.patch_size) // self.time_stride + 1
						UpperCAmelCase_								=     frequency_out_dimension * time_out_dimension
						UpperCAmelCase_								=     num_patches + 2





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]		)

						UpperCAmelCase_								=     None
						if self.use_labels:
							UpperCAmelCase_								=     ids_tensor([self.batch_size] ,					self.type_sequence_label_size		)

						UpperCAmelCase_								=     self.get_config()

						return config, input_values, labels





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						return ASTConfig(
						    patch_size=self.patch_size ,					max_length=self.max_length ,					num_mel_bins=self.num_mel_bins ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					is_decoder=__lowercase ,					initializer_range=self.initializer_range ,					frequency_stride=self.frequency_stride ,					time_stride=self.time_stride ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  int ,					__lowercase    :  Dict ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     ASTModel(config=__lowercase		)
						model.to(__lowercase		)
						model.eval()
						UpperCAmelCase_								=     model(__lowercase		)
						self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_
						)						,	(
						    UpperCAmelCase_
						)						,	(
						    UpperCAmelCase_
						)						,	
						)								=     config_and_inputs
						UpperCAmelCase_								=     {"""input_values""": input_values}
						return config, inputs_dict







@require_torch
class   _UpperCamelCase  (       A_						,       A_						,       unittest.TestCase  ):







					'''simple docstring'''

					lowerCamelCase     :     Any			    =       (
					    (
					        ASTModel,
					        ASTForAudioClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCamelCase     :     Optional[Any]			    =       (
					    {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
					    if is_torch_available()
					    else {}
					)
					lowerCamelCase     :     Union[str, Any]			    =       False
					lowerCamelCase     :     Tuple			    =       False
					lowerCamelCase     :     List[str]			    =       False
					lowerCamelCase     :     List[Any]			    =       False





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  List[str] ,					__lowercase    :  int ,					__lowercase    :  str ,					__lowercase    :  Dict		):






						'''simple docstring'''







						if pipeline_test_casse_name == "AudioClassificationPipelineTests":
							return True

						return False





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     ASTModelTester(self		)
						UpperCAmelCase_								=     ConfigTester(self ,					config_class=__lowercase ,					has_text_modality=__lowercase ,					hidden_size=37		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						self.config_tester.run_common_tests()





					@unittest.skip(reason="""AST does not use inputs_embeds"""		)
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						pass





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase_								=     model_class(__lowercase		)
							self.assertIsInstance(model.get_input_embeddings() ,					(nn.Module)		)
							UpperCAmelCase_								=     model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__lowercase ,					nn.Linear		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase_								=     ["""input_values"""]
							self.assertListEqual(arg_names[:1] ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase		)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_								=     ASTModel.from_pretrained(__lowercase		)
							self.assertIsNotNone(__lowercase		)




def    A_(			):
	UpperCAmelCase_								=     hf_hub_download(
	    repo_id="""nielsr/audio-spectogram-transformer-checkpoint"""				,      filename="""sample_audio.flac"""				,      repo_type="""dataset"""   )

	UpperCAmelCase_						,	UpperCAmelCase_								=     torchaudio.load(A   )

	return audio, sampling_rate







@require_torch
@require_torchaudio
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					@cached_property
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						return (
						    ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593"""		)
						    if is_torchaudio_available()
						    else None
						)





					@slow
					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.default_feature_extractor
						UpperCAmelCase_								=     ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593"""		).to(__lowercase		)

						UpperCAmelCase_								=     self.default_feature_extractor
						UpperCAmelCase_						,	UpperCAmelCase_								=     prepare_audio()
						UpperCAmelCase_								=     audio.squeeze().numpy()
						UpperCAmelCase_								=     feature_extractor(__lowercase ,					sampling_rate=__lowercase ,					return_tensors="""pt"""		).to(__lowercase		)

						# forward pass
						with torch.no_grad():
							UpperCAmelCase_								=     model(**__lowercase		)

						# verify the logits
						UpperCAmelCase_								=     torch.Size((1, 5_27)		)
						self.assertEqual(outputs.logits.shape ,					__lowercase		)

						UpperCAmelCase_								=     torch.tensor([-0.8760, -7.0042, -8.6602]		).to(__lowercase		)

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


