

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE_        =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE_        =      '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    tempfile.mkdtemp()
										os.makedirs(os.path.join(self.transformer_dir						,					"models/bert/"	)	)
										_UpperCAmelCase       :		Any 		=    self.transformer_dir
										shutil.copy(
										    os.path.join(A_						,					"src/transformers/models/bert/modeling_bert.py"	)						,					os.path.join(self.transformer_dir						,					"models/bert/modeling_bert.py"	)						,					)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    "src/transformers"
										shutil.rmtree(self.transformer_dir	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_=None	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    comment + f'\nclass {class_name}(nn.Module):\n' + class_code
										if overwrite_result is not None:
														_UpperCAmelCase       :		Optional[Any] 		=    comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
										_UpperCAmelCase       :		Dict 		=    black.Mode(target_versions={black.TargetVersion.PYaa}						,					line_length=119	)
										_UpperCAmelCase       :		Dict 		=    black.format_str(A_						,					mode=A_	)
										_UpperCAmelCase       :		List[str] 		=    os.path.join(self.transformer_dir						,					"new_code.py"	)
										with open(A_						,					"w"						,					newline="\n"	) as f:
														f.write(A_	)
										if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(A_	)	) == 0	)
										else:
														check_copies.is_copy_consistent(f.name						,					overwrite=A_	)
														with open(A_						,					"r"	) as f:
																		self.assertTrue(f.read()						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"	)
										self.assertEqual(A_						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,					"BertLMPredictionHead"						,					REFERENCE_CODE + "\n"						,					)

										# With no empty line at the end
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"						,					"BertLMPredictionHead"						,					A_						,					)

										# Copy consistency with rename
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,					"TestModelLMPredictionHead"						,					re.sub("Bert"						,					"TestModel"						,					A_	)						,					)

										# Copy consistency with a really long name
										_UpperCAmelCase       :		Optional[int] 		=    "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
										self.check_copy_consistency(
										    f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'						,					f'{long_class_name}LMPredictionHead'						,					re.sub("Bert"						,					A_						,					A_	)						,					)

										# Copy consistency with overwrite
										self.check_copy_consistency(
										    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"						,					"TestModelLMPredictionHead"						,					A_						,					overwrite_result=re.sub("Bert"						,					"TestModel"						,					A_	)						,					)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    check_copies.LOCALIZED_READMES["README_zh-hans.md"]

										_UpperCAmelCase       :		Dict 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
										    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
										    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
										    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
										    " Luong, Quoc V. Le, Christopher D. Manning."
										)
										_UpperCAmelCase       :		Dict 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										_UpperCAmelCase       :		Union[str, Any] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
										    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
										    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
										    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
										    " method has been applied to compress GPT2 into"
										    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
										    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
										    " Multilingual BERT into"
										    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
										    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
										    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
										    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
										    " Christopher D. Manning 发布。\n"
										)

										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    check_copies.convert_to_localized_md(
										    A_						,					A_						,					localized_readme["format_model_list"]	)

										self.assertFalse(A_	)
										self.assertEqual(A_						,					A_	)

										_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    check_copies.convert_to_localized_md(
										    A_						,					A_						,					localized_readme["format_model_list"]	)

										# Check whether the number of models is equal to README.md after conversion.
										self.assertTrue(A_	)

										_UpperCAmelCase       :		List[str] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
										    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
										    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
										    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
										)
										_UpperCAmelCase       :		Optional[int] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
										    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)
										_UpperCAmelCase       :		Optional[Any] 		=    (
										    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
										    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
										    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
										    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
										)

										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    check_copies.convert_to_localized_md(
										    A_						,					A_						,					localized_readme["format_model_list"]	)

										# Check if the model link is synchronized.
										self.assertEqual(A_						,					A_	)





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)


