




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__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				nn.ModuleList      ,      SCREAMING_SNAKE_CASE_							:				nn.ModuleList      ,      SCREAMING_SNAKE_CASE_							:				List[int]     ):
					__lowerCAmelCase				   =       nn.ModuleList([src_layers[i] for i in layers_to_copy]     )
					assert len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     ), F"""{len(SCREAMING_SNAKE_CASE_     )} != {len(SCREAMING_SNAKE_CASE_     )}"""
					dest_layers.load_state_dict(layers_to_copy.state_dict()     )


UpperCamelCase__    			=			{
    # 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__    			=			{
    # 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      (						SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[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(SCREAMING_SNAKE_CASE_     )     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					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(SCREAMING_SNAKE_CASE_     )     )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, PreTrainedModel]      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Path] = "student"      ,      SCREAMING_SNAKE_CASE_							:				Union[int, None] = None      ,      SCREAMING_SNAKE_CASE_							:				Union[int, None] = None      ,      SCREAMING_SNAKE_CASE_							:				Dict=False      ,      SCREAMING_SNAKE_CASE_							:				int=None      ,      SCREAMING_SNAKE_CASE_							:				List[str]=None      ,      **SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      ):
					__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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
										AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     ).save_pretrained(SCREAMING_SNAKE_CASE_     )  # purely for convenience
										__lowerCAmelCase				   =       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_     ).eval()
					else:
										assert isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ), F"""teacher must be a model or string got type {type(SCREAMING_SNAKE_CASE_     )}"""
					__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(SCREAMING_SNAKE_CASE_     )

					# Copy weights
					__lowerCAmelCase				   =       teacher.config_class(**SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       AutoModelForSeqaSeqLM.from_config(SCREAMING_SNAKE_CASE_     )
					# 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=SCREAMING_SNAKE_CASE_     )
					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(SCREAMING_SNAKE_CASE_     )     ), list(range(SCREAMING_SNAKE_CASE_     )     )
										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(SCREAMING_SNAKE_CASE_     )
										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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					if d_layers_to_copy is None:
										__lowerCAmelCase				   =       pick_layers_to_copy(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

					try:
										if hasattr(
										    SCREAMING_SNAKE_CASE_      ,      "prophetnet"     ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers      ,      student.prophetnet.encoder.layers      ,      SCREAMING_SNAKE_CASE_     )
															copy_layers(teacher.prophetnet.decoder.layers      ,      student.prophetnet.decoder.layers      ,      SCREAMING_SNAKE_CASE_     )
										else:
															copy_layers(teacher.model.encoder.layers      ,      student.model.encoder.layers      ,      SCREAMING_SNAKE_CASE_     )
															copy_layers(teacher.model.decoder.layers      ,      student.model.decoder.layers      ,      SCREAMING_SNAKE_CASE_     )
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block      ,      student.encoder.block      ,      SCREAMING_SNAKE_CASE_     )
										copy_layers(teacher.decoder.block      ,      student.decoder.block      ,      SCREAMING_SNAKE_CASE_     )
					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(SCREAMING_SNAKE_CASE_     )
					# 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)










from random import randint
from tempfile import TemporaryFile

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__lowerCAmelCase				   =       0
					if start < end:
										__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       a[end]
										__lowerCAmelCase				   =       a[pivot]
										__lowerCAmelCase				   =       temp

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       _in_place_partition(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      p - 1     )
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE_      ,      p + 1      ,      SCREAMING_SNAKE_CASE_     )
					return count







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       0
					__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       a[pivot]
					__lowerCAmelCase				   =       temp
					__lowerCAmelCase				   =       start - 1
					for index in range(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															__lowerCAmelCase				   =       new_pivot_index + 1
															__lowerCAmelCase				   =       a[new_pivot_index]
															__lowerCAmelCase				   =       a[index]
															__lowerCAmelCase				   =       temp

					__lowerCAmelCase				   =       a[new_pivot_index + 1]
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       temp
					return new_pivot_index + 1, count


UpperCamelCase__    			=			TemporaryFile()
UpperCamelCase__    			=			100  # 1000 elements are to be sorted


UpperCamelCase__			,	UpperCamelCase__    			=			0, 1  # mean and standard deviation
UpperCamelCase__    			=			np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
UpperCamelCase__    			=			np.load(outfile)
UpperCamelCase__    			=			len(M) - 1
UpperCamelCase__    			=			_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)


print(z)




