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


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							nn.ModuleList      , UpperCAmelCase__     :							nn.ModuleList      , UpperCAmelCase__     :							List[int]  ):
					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()  )


_lowerCamelCase					:   List[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))},
}
_lowerCamelCase					:   List[str]							=		{
    # 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							(UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							Optional[int]  ):
					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 	__lowerCamelCase							(UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							int  ):
					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 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, PreTrainedModel]      , UpperCAmelCase__     :							Union[str, Path] = "student"      , UpperCAmelCase__     :							Union[int, None] = None      , UpperCAmelCase__     :							Union[int, None] = None      , UpperCAmelCase__     :							Optional[int]=False      , UpperCAmelCase__     :							Tuple=None      , UpperCAmelCase__     :							Any=None      , **UpperCAmelCase__     :							str      , ):
					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)





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					List[Any]=7	,							_UpperCamelCase   :					Any=3	,							_UpperCamelCase   :					str=18	,							_UpperCamelCase   :					Tuple=30	,							_UpperCamelCase   :					Optional[int]=400	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Optional[Any]=None	,							_UpperCamelCase   :					int=True	,							_UpperCamelCase   :					Optional[int]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					List[str]=[0.5, 0.5, 0.5]	,							)					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 18, "width": 18}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std





							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Any								=							DPTImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DPTImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					List[Any]      )					->				List[str]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Dict      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"height": 18, "width": 18}      )

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict	,							size=42      )
												self.assertEqual(image_processor.size	,							{"height": 42, "width": 42}      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							Image.Image      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)




							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							np.ndarray      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)





							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							torch.Tensor      )

												# Test not batched input
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values
												self.assertEqual(
												    encoded_images.shape	,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,							)




