

import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MPNetForMaskedLM,
					    MPNetForMultipleChoice,
					    MPNetForQuestionAnswering,
					    MPNetForSequenceClassification,
					    MPNetForTokenClassification,
					    MPNetModel,
					)

class 				a  :


						def __init__(       self						,					A_						,					A_=13						,					A_=7						,					A_=True						,					A_=True						,					A_=False						,					A_=True						,					A_=99						,					A_=64						,					A_=5						,					A_=4						,					A_=64						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=16						,					A_=2						,					A_=0.02						,					A_=3						,					A_=4						,					A_=None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    parent
										_UpperCAmelCase       :		str 		=    batch_size
										_UpperCAmelCase       :		List[Any] 		=    seq_length
										_UpperCAmelCase       :		Optional[Any] 		=    is_training
										_UpperCAmelCase       :		Optional[int] 		=    use_input_mask
										_UpperCAmelCase       :		Union[str, Any] 		=    use_token_type_ids
										_UpperCAmelCase       :		Optional[int] 		=    use_labels
										_UpperCAmelCase       :		str 		=    vocab_size
										_UpperCAmelCase       :		Any 		=    hidden_size
										_UpperCAmelCase       :		Optional[Any] 		=    num_hidden_layers
										_UpperCAmelCase       :		List[str] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    intermediate_size
										_UpperCAmelCase       :		Optional[int] 		=    hidden_act
										_UpperCAmelCase       :		Optional[int] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		str 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		List[str] 		=    max_position_embeddings
										_UpperCAmelCase       :		Any 		=    type_vocab_size
										_UpperCAmelCase       :		str 		=    type_sequence_label_size
										_UpperCAmelCase       :		int 		=    initializer_range
										_UpperCAmelCase       :		List[Any] 		=    num_labels
										_UpperCAmelCase       :		Tuple 		=    num_choices
										_UpperCAmelCase       :		Optional[int] 		=    scope


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return MPNetConfig.from_pretrained("microsoft/mpnet-base"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										_UpperCAmelCase       :		Optional[int] 		=    None
										if self.use_input_mask:
														_UpperCAmelCase       :		int 		=    random_attention_mask([self.batch_size, self.seq_length]	)

										_UpperCAmelCase       :		List[str] 		=    None
										_UpperCAmelCase       :		List[Any] 		=    None
										_UpperCAmelCase       :		Optional[int] 		=    None
										if self.use_labels:
														_UpperCAmelCase       :		Dict 		=    ids_tensor([self.batch_size]						,					self.type_sequence_label_size	)
														_UpperCAmelCase       :		Any 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels	)
														_UpperCAmelCase       :		str 		=    ids_tensor([self.batch_size]						,					self.num_choices	)

										_UpperCAmelCase       :		Optional[int] 		=    self.get_config()
										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return MPNetConfig(
										    vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					num_hidden_layers=self.num_hidden_layers						,					num_attention_heads=self.num_attention_heads						,					intermediate_size=self.intermediate_size						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					max_position_embeddings=self.max_position_embeddings						,					initializer_range=self.initializer_range						,					)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    MPNetModel(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		str 		=    model(A_						,					A_	)
										_UpperCAmelCase       :		List[Any] 		=    model(A_	)
										self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)	)
										self.parent.assertEqual(result.pooler_output.shape						,					(self.batch_size, self.hidden_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    MPNetForQuestionAnswering(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Dict 		=    model(
										    A_						,					attention_mask=A_						,					start_positions=A_						,					end_positions=A_						,					)
										self.parent.assertEqual(result.start_logits.shape						,					(self.batch_size, self.seq_length)	)
										self.parent.assertEqual(result.end_logits.shape						,					(self.batch_size, self.seq_length)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.num_labels
										_UpperCAmelCase       :		str 		=    MPNetForSequenceClassification(A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Optional[Any] 		=    model(A_						,					attention_mask=A_						,					labels=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.num_choices
										_UpperCAmelCase       :		List[str] 		=    MPNetForMultipleChoice(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Any 		=    input_ids.unsqueeze(1	).expand(-1						,					self.num_choices						,					-1	).contiguous()
										_UpperCAmelCase       :		Tuple 		=    input_mask.unsqueeze(1	).expand(-1						,					self.num_choices						,					-1	).contiguous()
										_UpperCAmelCase       :		Any 		=    model(
										    A_						,					attention_mask=A_						,					labels=A_						,					)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_choices)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.num_labels
										_UpperCAmelCase       :		Dict 		=    MPNetForTokenClassification(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Tuple 		=    model(A_						,					attention_mask=A_						,					labels=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.prepare_config_and_inputs()
										((_UpperCAmelCase)		,							(_UpperCAmelCase)		,							(_UpperCAmelCase)		,							(_UpperCAmelCase)		,							(_UpperCAmelCase)		,							(_UpperCAmelCase))       :		Optional[Any] 		=    config_and_inputs
										_UpperCAmelCase       :		List[str] 		=    {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_torch
class 				a  (	UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(
						    (
						        MPNetForMaskedLM,
						        MPNetForMultipleChoice,
						        MPNetForQuestionAnswering,
						        MPNetForSequenceClassification,
						        MPNetForTokenClassification,
						        MPNetModel,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowercase    						=			(
						    {
						        "feature-extraction": MPNetModel,
						        "fill-mask": MPNetForMaskedLM,
						        "question-answering": MPNetForQuestionAnswering,
						        "text-classification": MPNetForSequenceClassification,
						        "token-classification": MPNetForTokenClassification,
						        "zero-shot": MPNetForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowercase    						=			False
						_lowercase    						=			True


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    MPNetModelTester(self	)
										_UpperCAmelCase       :		Tuple 		=    ConfigTester(self						,					config_class=A_						,					hidden_size=37	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_model(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_sequence_classification(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_multiple_choice(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_token_classification(*A_	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_question_answering(*A_	)







@require_torch
class 				a  (	unittest.TestCase				):


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    MPNetModel.from_pretrained("microsoft/mpnet-base"	)
										_UpperCAmelCase       :		str 		=    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]	)
										_UpperCAmelCase       :		Optional[Any] 		=    model(A_	)[0]
										_UpperCAmelCase       :		Any 		=    torch.Size((1, 11, 768)	)
										self.assertEqual(output.shape						,					A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    torch.tensor(
										    [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]]	)
										# compare the actual values for a slice.
										self.assertTrue(torch.allclose(output[:, :3, :3]						,					A_						,					atol=1e-4	)	)





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  List[int] )   ->       None:
				_UpperCAmelCase       :		str 		=    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() )


SCREAMING_SNAKE_CASE_        =      {
    # 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))},
}
SCREAMING_SNAKE_CASE_        =      {
    # 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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				try:
								_UpperCAmelCase       :		int 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       List[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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, PreTrainedModel]  ,      lowerCAmelCase:  Union[str, Path] = "student"  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  str=False  ,      lowerCAmelCase:  Union[str, Any]=None  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  str  ,      )   ->       Tuple[PreTrainedModel, List[int], List[int]]:
				_UpperCAmelCase       :		Dict 		=    "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       :		Dict 		=    AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
				else:
								assert isinstance(lowerCAmelCase  ,      lowerCAmelCase ), F'teacher must be a model or string got type {type(lowerCAmelCase )}'
				_UpperCAmelCase       :		List[str] 		=    teacher.config.to_diff_dict()

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


