
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				=			logging.get_logger(__name__)


def      UpperCamelCase   (       __lowerCamelCase	:					nn.ModuleList							,   __lowerCamelCase	:					nn.ModuleList							,   __lowerCamelCase	:					List[int]       ):
       snake_case			:  int     							= 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()       )


__lowerCamelCase				=			{
    # 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				=			{
    # 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      UpperCamelCase   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					Union[str, Any]       ):
       try:
              snake_case			:  Dict     							= 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      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					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(__lowerCamelCase       )       )
       elif n_student == 1:
              return [n_teacher - 1]
       else:
              return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, PreTrainedModel]							,   __lowerCamelCase	:					Union[str, Path] = "student"							,   __lowerCamelCase	:					Union[int, None] = None							,   __lowerCamelCase	:					Union[int, None] = None							,   __lowerCamelCase	:					Dict=False							,   __lowerCamelCase	:					List[Any]=None							,   __lowerCamelCase	:					Tuple=None							,   **__lowerCamelCase	:					Optional[int]							,   ):
       snake_case			:  Any     							= "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
              snake_case			:  int     							= AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase       ).eval()
       else:
              assert isinstance(__lowerCamelCase							,   __lowerCamelCase       ), f"""teacher must be a model or string got type {type(__lowerCamelCase       )}"""
       snake_case			:  Dict     							= teacher.config.to_diff_dict()

       try:
              snake_case							,   snake_case			:  str     							= teacher.config.encoder_layers, teacher.config.decoder_layers
              if e is None:
                     snake_case			:  Dict     							= teacher_e
              if d is None:
                     snake_case			:  Optional[Any]     							= teacher_d
              init_kwargs.update({"encoder_layers": e, "decoder_layers": d}       )
       except AttributeError:  # T5
              if hasattr(teacher.config							,   "num_encoder_layers"       ):
                     snake_case							,   snake_case			:  int     							= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
              else:
                     snake_case							,   snake_case			:  Any     							= teacher.config.num_layers, teacher.config.num_decoder_layers
              if e is None:
                     snake_case			:  int     							= teacher_e
              if d is None:
                     snake_case			:  str     							= 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
       snake_case			:  List[str]     							= teacher.config_class(**__lowerCamelCase       )
       snake_case			:  Dict     							= AutoModelForSeqaSeqLM.from_config(__lowerCamelCase       )
       # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
       snake_case			:  Optional[Any]     							= 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
              snake_case							,   snake_case			:  int     							= 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:
              snake_case			:  List[int]     							= pick_layers_to_copy(__lowerCamelCase							,   __lowerCamelCase       )
       if d_layers_to_copy is None:
              snake_case			:  List[int]     							= 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}"""       )
       snake_case			:  Any     							= {
           "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)







import qiskit


def      UpperCamelCase   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					int       ):
       snake_case			:  int     							= qiskit.Aer.get_backend("aer_simulator"       )

       # Create a Quantum Circuit acting on the q register
       snake_case			:  Dict     							= qiskit.QuantumCircuit(__lowerCamelCase							,   __lowerCamelCase       )

       # Apply X (NOT) Gate to Qubits 0 & 1
       circuit.x(0       )
       circuit.x(1       )

       # Map the quantum measurement to the classical bits
       circuit.measure([0, 1]							,   [0, 1]       )

       # Execute the circuit on the qasm simulator
       snake_case			:  Dict     							= qiskit.execute(__lowerCamelCase							,   __lowerCamelCase							,   shots=1000       )

       # Return the histogram data of the results of the experiment.
       return job.result().get_counts(__lowerCamelCase       )


if __name__ == "__main__":
   __lowerCamelCase				=			single_qubit_measure(2, 2)
   print(F'Total count for various states are: {counts}')





