




from random import shuffle

import tensorflow as tf
from numpy import array







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    int(lowercase_						)
       assert noofclusters < len(lowercase_						)

       # Find out the dimensionality
       A__       =    len(vectors[0]						)

       # Will help select random centroids from among the available vectors
       A__       =    list(range(len(lowercase_						)						)						)
       shuffle(lowercase_						)

       # GRAPH OF COMPUTATION
       # We initialize a new graph and set it as the default during each run
       # of this algorithm. This ensures that as this function is called
       # multiple times, the default graph doesn't keep getting crowded with
       # unused ops and Variables from previous function calls.

       A__       =    tf.Graph()

       with graph.as_default():
              # SESSION OF COMPUTATION

              A__       =    tf.Session()

              ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

              ##First lets ensure we have a Variable vector for each centroid,
              ##initialized to one of the vectors from the available data points
              A__       =    [
                  tf.Variable(vectors[vector_indices[i]]						) for i in range(lowercase_						)
              ]
              ##These nodes will assign the centroid Variables the appropriate
              ##values
              A__       =    tf.placeholder('''float64''' ,				[dim]						)
              A__       =    []
              for centroid in centroids:
                     cent_assigns.append(tf.assign(lowercase_ ,				lowercase_						)						)

              ##Variables for cluster assignments of individual vectors(initialized
              ##to 0 at first)
              A__       =    [tf.Variable(0						) for i in range(len(lowercase_						)						)]
              ##These nodes will assign an assignment Variable the appropriate
              ##value
              A__       =    tf.placeholder('''int32'''						)
              A__       =    []
              for assignment in assignments:
                     cluster_assigns.append(tf.assign(lowercase_ ,				lowercase_						)						)

              ##Now lets construct the node that will compute the mean
              # The placeholder for the input
              A__       =    tf.placeholder('''float''' ,				[None, dim]						)
              # The Node/op takes the input and computes a mean along the 0th
              # dimension, i.e. the list of input vectors
              A__       =    tf.reduce_mean(lowercase_ ,				0						)

              ##Node for computing Euclidean distances
              # Placeholders for input
              A__       =    tf.placeholder('''float''' ,				[dim]						)
              A__       =    tf.placeholder('''float''' ,				[dim]						)
              A__       =    tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ ,				lowercase_						) ,				2						)						)						)

              ##This node will figure out which cluster to assign a vector to,
              ##based on Euclidean distances of the vector from the centroids.
              # Placeholder for input
              A__       =    tf.placeholder('''float''' ,				[noofclusters]						)
              A__       =    tf.argmin(lowercase_ ,				0						)

              ##INITIALIZING STATE VARIABLES

              ##This will help initialization of all Variables defined with respect
              ##to the graph. The Variable-initializer should be defined after
              ##all the Variables have been constructed, so that each of them
              ##will be included in the initialization.
              A__       =    tf.initialize_all_variables()

              # Initialize all variables
              sess.run(lowercase_						)

              ##CLUSTERING ITERATIONS

              # Now perform the Expectation-Maximization steps of K-Means clustering
              # iterations. To keep things simple, we will only do a set number of
              # iterations, instead of using a Stopping Criterion.
              A__       =    100
              for _ in range(lowercase_						):
                     ##EXPECTATION STEP
                     ##Based on the centroid locations till last iteration, compute
                     ##the _expected_ centroid assignments.
                     # Iterate over each vector
                     for vector_n in range(len(lowercase_						)						):
                            A__       =    vectors[vector_n]
                            # Compute Euclidean distance between this vector and each
                            # centroid. Remember that this list cannot be named
                            #'centroid_distances', since that is the input to the
                            # cluster assignment node.
                            A__       =    [
                                sess.run(lowercase_ ,				feed_dict={va: vect, va: sess.run(lowercase_						)}						)
                                for centroid in centroids
                            ]
                            # Now use the cluster assignment node, with the distances
                            # as the input
                            A__       =    sess.run(
                                lowercase_ ,				feed_dict={centroid_distances: distances}						)
                            # Now assign the value to the appropriate state variable
                            sess.run(
                                cluster_assigns[vector_n] ,				feed_dict={assignment_value: assignment}						)

                     ##MAXIMIZATION STEP
                     # Based on the expected state computed from the Expectation Step,
                     # compute the locations of the centroids so as to maximize the
                     # overall objective of minimizing within-cluster Sum-of-Squares
                     for cluster_n in range(lowercase_						):
                            # Collect all the vectors assigned to this cluster
                            A__       =    [
                                vectors[i]
                                for i in range(len(lowercase_						)						)
                                if sess.run(assignments[i]						) == cluster_n
                            ]
                            # Compute new centroid location
                            A__       =    sess.run(
                                lowercase_ ,				feed_dict={mean_input: array(lowercase_						)}						)
                            # Assign value to appropriate variable
                            sess.run(
                                cent_assigns[cluster_n] ,				feed_dict={centroid_value: new_location}						)

        # Return centroids and assignments
              A__       =    sess.run(lowercase_						)
              A__       =    sess.run(lowercase_						)
              return centroids, assignments










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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  None:





						"""simple docstring"""



						_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						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(__lowerCAmelCase	)	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  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(__lowerCAmelCase	)	)
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase = "student" ,		__lowerCAmelCase = None ,		__lowerCAmelCase = None ,		__lowerCAmelCase=False ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		**__lowerCAmelCase ,		)->  Tuple[PreTrainedModel, List[int], List[int]]:





						"""simple docstring"""



						_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(__lowerCAmelCase ,		__lowerCAmelCase	):
												AutoTokenizer.from_pretrained(__lowerCAmelCase	).save_pretrained(__lowerCAmelCase	)  # purely for convenience
												_UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase	).eval()
						else:
												assert isinstance(__lowerCAmelCase ,		__lowerCAmelCase	), F"""teacher must be a model or string got type {type(__lowerCAmelCase	)}"""
						_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(__lowerCAmelCase	)

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