


"""simple docstring"""




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     lowercase			(SCREAMING_SNAKE_CASE_		:				nn.ModuleList ,			SCREAMING_SNAKE_CASE_		:				nn.ModuleList ,			SCREAMING_SNAKE_CASE_		:				List[int]  )   ->						None:
						SCREAMING_SNAKE_CASE													=					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     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						List[str]:
						try:
												SCREAMING_SNAKE_CASE													=					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     lowercase			(SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				Union[str, 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(SCREAMING_SNAKE_CASE_  )  )
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def     lowercase			(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_		:				int=False ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None ,			**SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			)   ->						Tuple[PreTrainedModel, List[int], List[int]]:
						SCREAMING_SNAKE_CASE													=					'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
												SCREAMING_SNAKE_CASE													=					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_  )}'
						SCREAMING_SNAKE_CASE													=					teacher.config.to_diff_dict()

						try:
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					teacher.config.encoder_layers, teacher.config.decoder_layers
												if e is None:
																		SCREAMING_SNAKE_CASE													=					teacher_e
												if d is None:
																		SCREAMING_SNAKE_CASE													=					teacher_d
												init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}  )
						except AttributeError:  # T5
												if hasattr(teacher.config ,			'num_encoder_layers'  ):
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
												else:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					teacher.config.num_layers, teacher.config.num_decoder_layers
												if e is None:
																		SCREAMING_SNAKE_CASE													=					teacher_e
												if d is None:
																		SCREAMING_SNAKE_CASE													=					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
						SCREAMING_SNAKE_CASE													=					teacher.config_class(**SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					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.
						SCREAMING_SNAKE_CASE													=					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
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					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:
												SCREAMING_SNAKE_CASE													=					pick_layers_to_copy(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						if d_layers_to_copy is None:
												SCREAMING_SNAKE_CASE													=					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}'  )
						SCREAMING_SNAKE_CASE													=					{
						    '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)






"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__UpperCamelCase						=      logging.get_logger(__name__)

__UpperCamelCase						=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase						=      [
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

__UpperCamelCase						=      {
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
__UpperCamelCase						=      {f'''funnel-transformer/{name}''': 512 for name in _model_names}
__UpperCamelCase						=      {f'''funnel-transformer/{name}''': {'''do_lower_case''': True} for name in _model_names}
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     int								=     VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_						:     List[str]								=     PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     PRETRAINED_INIT_CONFIGURATION
		SCREAMING_SNAKE_CASE_						:     str								=     FunnelTokenizer
		SCREAMING_SNAKE_CASE_						:     Dict								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_						:     int								=     2







		def __init__(	self						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True						,       lowerCAmelCase__="<unk>"						,       lowerCAmelCase__="<sep>"						,       lowerCAmelCase__="<pad>"						,       lowerCAmelCase__="<cls>"						,       lowerCAmelCase__="<mask>"						,       lowerCAmelCase__="<s>"						,       lowerCAmelCase__="</s>"						,       lowerCAmelCase__=True						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__="##"						,       **lowerCAmelCase__						,       )     ->							Tuple:
								super().__init__(
								    lowerCAmelCase__						,       tokenizer_file=lowerCAmelCase__						,       do_lower_case=lowerCAmelCase__						,       unk_token=lowerCAmelCase__						,       sep_token=lowerCAmelCase__						,       pad_token=lowerCAmelCase__						,       cls_token=lowerCAmelCase__						,       mask_token=lowerCAmelCase__						,       bos_token=lowerCAmelCase__						,       eos_token=lowerCAmelCase__						,       clean_text=lowerCAmelCase__						,       tokenize_chinese_chars=lowerCAmelCase__						,       strip_accents=lowerCAmelCase__						,       wordpieces_prefix=lowerCAmelCase__						,       **lowerCAmelCase__						,       )

								SCREAMING_SNAKE_CASE													=					json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get('lowercase'						,       lowerCAmelCase__				) != do_lower_case
								    or normalizer_state.get('strip_accents'						,       lowerCAmelCase__				) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'						,       lowerCAmelCase__				) != tokenize_chinese_chars
								):
														SCREAMING_SNAKE_CASE													=					getattr(lowerCAmelCase__						,       normalizer_state.pop('type'				)				)
														SCREAMING_SNAKE_CASE													=					do_lower_case
														SCREAMING_SNAKE_CASE													=					strip_accents
														SCREAMING_SNAKE_CASE													=					tokenize_chinese_chars
														SCREAMING_SNAKE_CASE													=					normalizer_class(**lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					do_lower_case







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=None				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

								return output







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[int]:
								SCREAMING_SNAKE_CASE													=					[self.sep_token_id]
								SCREAMING_SNAKE_CASE													=					[self.cls_token_id]
								if token_ids_a is None:
														return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0]
								return len(cls				) * [self.cls_token_type_id] + len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							Tuple[str]:
								SCREAMING_SNAKE_CASE													=					self._tokenizer.model.save(lowerCAmelCase__						,       name=lowerCAmelCase__				)
								return tuple(lowerCAmelCase__				)


