






"""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_   : int   			= logging.get_logger(__name__)



def 					_A			(__a    ,				__a    ,				__a							) ->       None:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		nn.ModuleList([src_layers[i] for i in layers_to_copy]							)
    assert len(__a							) == len(__a							), f'{len(__a							)} != {len(__a							)}'
    dest_layers.load_state_dict(layers_to_copy.state_dict()							)


UpperCAmelCase_   : Dict   			= {
    # 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_   : List[str]   			= {
    # 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 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    try:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		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(__a							)							)



def 					_A			(__a    ,				__a							) ->       List[int]:
    """simple docstring"""






    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(__a							)							)
    elif n_student == 1:
        return [n_teacher - 1]
    else:
        return LAYERS_TO_SUPERVISE[n_teacher][n_student]



def 					_A			(__a    ,				__a = "student"    ,				__a = None    ,				__a = None    ,				__a=False    ,				__a=None    ,				__a=None    ,				**__a    ,				) ->       Tuple[PreTrainedModel, List[int], List[int]]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		'''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(__a    ,				__a							):
        AutoTokenizer.from_pretrained(__a							).save_pretrained(__a							)  # purely for convenience
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		AutoModelForSeqaSeqLM.from_pretrained(__a							).eval()
    else:
        assert isinstance(__a    ,				__a							), f'teacher must be a model or string got type {type(__a							)}'
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		teacher.config.to_diff_dict()

    try:
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		teacher.config.encoder_layers, teacher.config.decoder_layers
        if e is None:
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		teacher_e
        if d is None:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		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_      :  Optional[int]    						=		teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
        else:
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		teacher.config.num_layers, teacher.config.num_decoder_layers
        if e is None:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		teacher_e
        if d is None:
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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(__a							)

    # Copy weights
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		teacher.config_class(**__a							)
    SCREAMING_SNAKE_CASE_      :  str    						=		AutoModelForSeqaSeqLM.from_config(__a							)
    # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		student.load_state_dict(teacher.state_dict()    ,				strict=__a							)
    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_      :  Tuple    						=		list(range(__a							)							), list(range(__a							)							)
        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(__a							)
        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_      :  List[int]    						=		pick_layers_to_copy(__a    ,				__a							)
    if d_layers_to_copy is None:
        SCREAMING_SNAKE_CASE_      :  List[int]    						=		pick_layers_to_copy(__a    ,				__a							)

    try:
        if hasattr(
            __a    ,				'''prophetnet'''							):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
            copy_layers(teacher.prophetnet.encoder.layers    ,				student.prophetnet.encoder.layers    ,				__a							)
            copy_layers(teacher.prophetnet.decoder.layers    ,				student.prophetnet.decoder.layers    ,				__a							)
        else:
            copy_layers(teacher.model.encoder.layers    ,				student.model.encoder.layers    ,				__a							)
            copy_layers(teacher.model.decoder.layers    ,				student.model.decoder.layers    ,				__a							)
    except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
        copy_layers(teacher.encoder.block    ,				student.encoder.block    ,				__a							)
        copy_layers(teacher.decoder.block    ,				student.decoder.block    ,				__a							)
    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_      :  Optional[Any]    						=		{
        '''teacher_type''': teacher.config.model_type,
        '''copied_encoder_layers''': e_layers_to_copy,
        '''copied_decoder_layers''': d_layers_to_copy,
    }
    student.save_pretrained(__a							)
    # 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 argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCAmelCase_   : Optional[int]   			= """src/transformers"""
UpperCAmelCase_   : Tuple   			= """docs/source/en"""
UpperCAmelCase_   : Optional[Any]   			= """."""



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    with open(__a    ,				'''r'''    ,				encoding='''utf-8'''    ,				newline='''\n'''							) as f:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		f.readlines()
    # Find the start prompt.
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
    while not lines[start_index].startswith(__a							):
        start_index += 1
    start_index += 1

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		start_index
    while not lines[end_index].startswith(__a							):
        end_index += 1
    end_index -= 1

    while len(lines[start_index]							) <= 1:
        start_index += 1
    while len(lines[end_index]							) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index]							), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
UpperCAmelCase_   : Optional[Any]   			= """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
UpperCAmelCase_   : int   			= re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
UpperCAmelCase_   : Dict   			= re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCAmelCase_   : int   			= re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_   : Optional[int]   			= direct_transformers_import(TRANSFORMERS_PATH)



def 					_A			(__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''    ,				__a							)
    return [m.group(0							) for m in matches]



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		2 if text == '''✅''' or text == '''❌''' else len(__a							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		(width - text_length) // 2
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent



def 					_A			() ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{name: config.replace('''Config'''    ,				''''''							) for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		collections.defaultdict(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		collections.defaultdict(__a							)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(__a							):
        SCREAMING_SNAKE_CASE_      :  Any    						=		None
        if attr_name.endswith('''Tokenizer'''							):
            SCREAMING_SNAKE_CASE_      :  Dict    						=		slow_tokenizers
            SCREAMING_SNAKE_CASE_      :  Dict    						=		attr_name[:-9]
        elif attr_name.endswith('''TokenizerFast'''							):
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		fast_tokenizers
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attr_name[:-13]
        elif _re_tf_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  int    						=		tf_models
            SCREAMING_SNAKE_CASE_      :  Dict    						=		_re_tf_models.match(__a							).groups()[0]
        elif _re_flax_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  Any    						=		flax_models
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		_re_flax_models.match(__a							).groups()[0]
        elif _re_pt_models.match(__a							) is not None:
            SCREAMING_SNAKE_CASE_      :  str    						=		pt_models
            SCREAMING_SNAKE_CASE_      :  int    						=		_re_pt_models.match(__a							).groups()[0]

        if lookup_dict is not None:
            while len(__a							) > 0:
                if attr_name in model_name_to_prefix.values():
                    SCREAMING_SNAKE_CASE_      :  List[str]    						=		True
                    break
                # Try again after removing the last word in the name
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		''''''.join(camel_case_split(__a							)[:-1]							)

    # Let's build that table!
    SCREAMING_SNAKE_CASE_      :  Any    						=		list(model_name_to_config.keys()							)
    model_names.sort(key=str.lower							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
    # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		[len(__a							) + 2 for c in columns]
    SCREAMING_SNAKE_CASE_      :  str    						=		max([len(__a							) for name in model_names]							) + 2

    # Build the table per se
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''|''' + '''|'''.join([_center_text(__a    ,				__a							) for c, w in zip(__a    ,				__a							)]							) + '''|\n'''
    # Use ":-----:" format to center-aligned table cell texts
    table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]							) + "|\n"

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{True: '''✅''', False: '''❌'''}
    for name in model_names:
        SCREAMING_SNAKE_CASE_      :  str    						=		model_name_to_prefix[name]
        SCREAMING_SNAKE_CASE_      :  int    						=		[
            name,
            check[slow_tokenizers[prefix]],
            check[fast_tokenizers[prefix]],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(__a    ,				__a							) for l, w in zip(__a    ,				__a							)]							) + "|\n"
    return table



def 					_A			(__a=False							) ->       List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		_find_text_in_file(
        filename=os.path.join(__a    ,				'''index.md'''							)    ,				start_prompt='''<!--This table is updated automatically from the auto modules'''    ,				end_prompt='''<!-- End table-->'''    ,				)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(__a    ,				'''index.md'''							)    ,				'''w'''    ,				encoding='''utf-8'''    ,				newline='''\n'''							) as f:
                f.writelines(lines[:start_index] + [new_table] + lines[end_index:]							)
        else:
            raise ValueError(
                '''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'''							)


if __name__ == "__main__":
  UpperCAmelCase_   : Union[str, Any]   			= argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  UpperCAmelCase_   : Any   			= parser.parse_args()

  check_model_table(args.fix_and_overwrite)


