
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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    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()							)


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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):


	try:
		lowercase       =    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_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):


	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_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = "student" ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE = None ,							__SCREAMING_SNAKE_CASE=False ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE=None ,							**__SCREAMING_SNAKE_CASE ,							):
	lowercase       =    '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
		lowercase       =    AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase							).eval()
	else:
		assert isinstance(__lowerCAmelCase ,							__lowerCAmelCase							), F'''teacher must be a model or string got type {type(__lowerCAmelCase							)}'''
	lowercase       =    teacher.config.to_diff_dict()

	try:
		lowercase  ,					lowercase       =    teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			lowercase       =    teacher_e
		if d is None:
			lowercase       =    teacher_d
		init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}							)
	except AttributeError:  # T5
		if hasattr(teacher.config ,							'num_encoder_layers'							):
			lowercase  ,					lowercase       =    teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			lowercase  ,					lowercase       =    teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			lowercase       =    teacher_e
		if d is None:
			lowercase       =    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
	lowercase       =    teacher.config_class(**__lowerCAmelCase							)
	lowercase       =    AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase							)
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	lowercase       =    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
		lowercase  ,					lowercase       =    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:
		lowercase       =    pick_layers_to_copy(__lowerCAmelCase ,							__lowerCAmelCase							)
	if d_layers_to_copy is None:
		lowercase       =    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}'''							)
	lowercase       =    {
	    '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)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


_A   =     {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextTokenizer"]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextFeatureExtractor"]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFSpeech2TextForConditionalGeneration",
			    "TFSpeech2TextModel",
			    "TFSpeech2TextPreTrainedModel",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Speech2TextForConditionalGeneration",
			    "Speech2TextModel",
			    "Speech2TextPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



