

'''simple docstring'''



import collections
import importlib.util
import os
import re
from pathlib import Path


_UpperCamelCase						:  str							       =				"src/transformers"


# Matches is_xxx_available()
_UpperCamelCase						:  Optional[int]							       =				re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
_UpperCamelCase						:  str							       =				re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_UpperCamelCase						:  Dict							       =				re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
_UpperCamelCase						:  str							       =				re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
_UpperCamelCase						:  Dict							       =				re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_UpperCamelCase						:  List[Any]							       =				re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
_UpperCamelCase						:  Tuple							       =				re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
_UpperCamelCase						:  Dict							       =				re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
_UpperCamelCase						:  int							       =				re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
_UpperCamelCase						:  Optional[Any]							       =				re.compile(r"^\s*try:")
# Catches a line with else:
_UpperCamelCase						:  int							       =				re.compile(r"^\s*else:")


def       snake_case   (	snake_case      : str			) ->  Tuple:


    """simple docstring"""


    if _re_test_backend.search(snake_case			) is None:
        return None
    lowerCAmelCase 						=							[b[0] for b in _re_backend.findall(snake_case			)]
    backends.sort()
    return "_and_".join(snake_case			)


def       snake_case   (	snake_case      : Any			) ->  Dict:


    """simple docstring"""


    with open(snake_case					,		'r'					,		encoding='utf-8'					,		newline='\n'			) as f:
        lowerCAmelCase 						=							f.readlines()

    lowerCAmelCase 						=							0
    while line_index < len(snake_case			) and not lines[line_index].startswith('_import_structure = {'			):
        line_index += 1

    # If this is a traditional init, just return.
    if line_index >= len(snake_case			):
        return None

    # First grab the objects without a specific backend in _import_structure
    lowerCAmelCase 						=							[]
    while not lines[line_index].startswith('if TYPE_CHECKING'			) and find_backend(lines[line_index]			) is None:
        lowerCAmelCase 						=							lines[line_index]
        # If we have everything on a single line, let's deal with it.
        if _re_one_line_import_struct.search(snake_case			):
            lowerCAmelCase 						=							_re_one_line_import_struct.search(snake_case			).groups()[0]
            lowerCAmelCase 						=							re.findall('\[([^\]]+)\]'					,		snake_case			)
            for imp in imports:
                objects.extend([obj[1:-1] for obj in imp.split(', '			)]			)
            line_index += 1
            continue
        lowerCAmelCase 						=							_re_import_struct_key_value.search(snake_case			)
        if single_line_import_search is not None:
            lowerCAmelCase 						=							[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '			) if len(snake_case			) > 0]
            objects.extend(snake_case			)
        elif line.startswith(' ' * 8 + '"'			):
            objects.append(line[9:-3]			)
        line_index += 1

    lowerCAmelCase 						=							{'none': objects}
    # Let's continue with backend-specific objects in _import_structure
    while not lines[line_index].startswith('if TYPE_CHECKING'			):
        # If the line is an if not is_backend_available, we grab all objects associated.
        lowerCAmelCase 						=							find_backend(lines[line_index]			)
        # Check if the backend declaration is inside a try block:
        if _re_try.search(lines[line_index - 1]			) is None:
            lowerCAmelCase 						=							None

        if backend is not None:
            line_index += 1

            # Scroll until we hit the else block of try-except-else
            while _re_else.search(lines[line_index]			) is None:
                line_index += 1

            line_index += 1

            lowerCAmelCase 						=							[]
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]			) <= 1 or lines[line_index].startswith(' ' * 4			):
                lowerCAmelCase 						=							lines[line_index]
                if _re_import_struct_add_one.search(snake_case			) is not None:
                    objects.append(_re_import_struct_add_one.search(snake_case			).groups()[0]			)
                elif _re_import_struct_add_many.search(snake_case			) is not None:
                    lowerCAmelCase 						=							_re_import_struct_add_many.search(snake_case			).groups()[0].split(', '			)
                    lowerCAmelCase 						=							[obj[1:-1] for obj in imports if len(snake_case			) > 0]
                    objects.extend(snake_case			)
                elif _re_between_brackets.search(snake_case			) is not None:
                    lowerCAmelCase 						=							_re_between_brackets.search(snake_case			).groups()[0].split(', '			)
                    lowerCAmelCase 						=							[obj[1:-1] for obj in imports if len(snake_case			) > 0]
                    objects.extend(snake_case			)
                elif _re_quote_object.search(snake_case			) is not None:
                    objects.append(_re_quote_object.search(snake_case			).groups()[0]			)
                elif line.startswith(' ' * 8 + '"'			):
                    objects.append(line[9:-3]			)
                elif line.startswith(' ' * 12 + '"'			):
                    objects.append(line[13:-3]			)
                line_index += 1

            lowerCAmelCase 						=							objects
        else:
            line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
    lowerCAmelCase 						=							[]
    while (
        line_index < len(snake_case			)
        and find_backend(lines[line_index]			) is None
        and not lines[line_index].startswith('else'			)
    ):
        lowerCAmelCase 						=							lines[line_index]
        lowerCAmelCase 						=							_re_import.search(snake_case			)
        if single_line_import_search is not None:
            objects.extend(single_line_import_search.groups()[0].split(', '			)			)
        elif line.startswith(' ' * 8			):
            objects.append(line[8:-2]			)
        line_index += 1

    lowerCAmelCase 						=							{'none': objects}
    # Let's continue with backend-specific objects
    while line_index < len(snake_case			):
        # If the line is an if is_backend_available, we grab all objects associated.
        lowerCAmelCase 						=							find_backend(lines[line_index]			)
        # Check if the backend declaration is inside a try block:
        if _re_try.search(lines[line_index - 1]			) is None:
            lowerCAmelCase 						=							None

        if backend is not None:
            line_index += 1

            # Scroll until we hit the else block of try-except-else
            while _re_else.search(lines[line_index]			) is None:
                line_index += 1

            line_index += 1

            lowerCAmelCase 						=							[]
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]			) <= 1 or lines[line_index].startswith(' ' * 8			):
                lowerCAmelCase 						=							lines[line_index]
                lowerCAmelCase 						=							_re_import.search(snake_case			)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(', '			)			)
                elif line.startswith(' ' * 12			):
                    objects.append(line[12:-2]			)
                line_index += 1

            lowerCAmelCase 						=							objects
        else:
            line_index += 1

    return import_dict_objects, type_hint_objects


def       snake_case   (	snake_case      : Union[str, Any]					,		snake_case      : str			) ->  str:


    """simple docstring"""


    def find_duplicates(snake_case      : Tuple			):
        return [k for k, v in collections.Counter(snake_case			).items() if v > 1]

    if list(import_dict_objects.keys()			) != list(type_hint_objects.keys()			):
        return ["Both sides of the init do not have the same backends!"]

    lowerCAmelCase 						=							[]
    for key in import_dict_objects.keys():
        lowerCAmelCase 						=							find_duplicates(import_dict_objects[key]			)
        if duplicate_imports:
            errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}'			)
        lowerCAmelCase 						=							find_duplicates(type_hint_objects[key]			)
        if duplicate_type_hints:
            errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'			)

        if sorted(set(import_dict_objects[key]			)			) != sorted(set(type_hint_objects[key]			)			):
            lowerCAmelCase 						=							'base imports' if key == 'none' else F'{key} backend'
            errors.append(F'Differences for {name}:'			)
            for a in type_hint_objects[key]:
                if a not in import_dict_objects[key]:
                    errors.append(F'  {a} in TYPE_HINT but not in _import_structure.'			)
            for a in import_dict_objects[key]:
                if a not in type_hint_objects[key]:
                    errors.append(F'  {a} in _import_structure but not in TYPE_HINT.'			)
    return errors


def       snake_case   (	) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							[]
    for root, _, files in os.walk(snake_case			):
        if "__init__.py" in files:
            lowerCAmelCase 						=							os.path.join(snake_case					,		'__init__.py'			)
            lowerCAmelCase 						=							parse_init(snake_case			)
            if objects is not None:
                lowerCAmelCase 						=							analyze_results(*snake_case			)
                if len(snake_case			) > 0:
                    lowerCAmelCase 						=							F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
                    failures.append('\n'.join(snake_case			)			)
    if len(snake_case			) > 0:
        raise ValueError('\n\n'.join(snake_case			)			)


def       snake_case   (	) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							[]
    for path, directories, files in os.walk(snake_case			):
        for folder in directories:
            # Ignore private modules
            if folder.startswith('_'			):
                directories.remove(snake_case			)
                continue
            # Ignore leftovers from branches (empty folders apart from pycache)
            if len(list((Path(snake_case			) / folder).glob('*.py'			)			)			) == 0:
                continue
            lowerCAmelCase 						=							str((Path(snake_case			) / folder).relative_to(snake_case			)			)
            lowerCAmelCase 						=							short_path.replace(os.path.sep					,		'.'			)
            submodules.append(snake_case			)
        for fname in files:
            if fname == "__init__.py":
                continue
            lowerCAmelCase 						=							str((Path(snake_case			) / fname).relative_to(snake_case			)			)
            lowerCAmelCase 						=							short_path.replace('.py'					,		''			).replace(os.path.sep					,		'.'			)
            if len(submodule.split('.'			)			) == 1:
                submodules.append(snake_case			)
    return submodules


_UpperCamelCase						:  List[str]							       =				[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]


def       snake_case   (	) ->  Tuple:


    """simple docstring"""


    lowerCAmelCase 						=							importlib.util.spec_from_file_location(
        'transformers'					,		os.path.join(snake_case					,		'__init__.py'			)					,		submodule_search_locations=[PATH_TO_TRANSFORMERS]					,		)
    lowerCAmelCase 						=							spec.loader.load_module()

    lowerCAmelCase 						=							[
        module
        for module in get_transformers_submodules()
        if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
    ]
    if len(snake_case			) > 0:
        lowerCAmelCase 						=							'\n'.join(F'- {module}' for module in module_not_registered			)
        raise ValueError(
            'The following submodules are not properly registered in the main init of Transformers:\n'
            F'{list_of_modules}\n'
            'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'			)


if __name__ == "__main__":
     check_all_inits()
     check_submodules()








'''simple docstring'''



import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
_UpperCamelCase						:  Optional[Any]							       =				logging.getLogger(__name__)


_UpperCamelCase						:  List[Any]							       =				"Hello world! cécé herlolip"


_UpperCamelCase						:  int							       =				namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def       snake_case   (	snake_case      : int					,		snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							BertAbsConfig(
        temp_dir='.'					,		finetune_bert=snake_case					,		large=snake_case					,		share_emb=snake_case					,		use_bert_emb=snake_case					,		encoder='bert'					,		max_pos=512					,		enc_layers=6					,		enc_hidden_size=512					,		enc_heads=8					,		enc_ff_size=512					,		enc_dropout=0.2					,		dec_layers=6					,		dec_hidden_size=768					,		dec_heads=8					,		dec_ff_size=2048					,		dec_dropout=0.2					,		)
    lowerCAmelCase 						=							torch.load(snake_case					,		lambda snake_case					,		snake_case			: storage			)
    lowerCAmelCase 						=							AbsSummarizer(snake_case					,		torch.device('cpu'			)					,		snake_case			)
    original.eval()

    lowerCAmelCase 						=							BertAbsSummarizer(snake_case					,		torch.device('cpu'			)			)
    new_model.eval()

    # -------------------
    # Convert the weights
    # -------------------

    logging.info('convert the model'			)
    new_model.bert.load_state_dict(original.bert.state_dict()			)
    new_model.decoder.load_state_dict(original.decoder.state_dict()			)
    new_model.generator.load_state_dict(original.generator.state_dict()			)

    # ----------------------------------
    # Make sure the outpus are identical
    # ----------------------------------

    logging.info('Make sure that the models\' outputs are identical'			)
    lowerCAmelCase 						=							BertTokenizer.from_pretrained('bert-base-uncased'			)

    # prepare the model inputs
    lowerCAmelCase 						=							tokenizer.encode('This is sample éàalj\'-.'			)
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							torch.tensor(snake_case			).unsqueeze(0			)
    lowerCAmelCase 						=							tokenizer.encode('This is sample 3 éàalj\'-.'			)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							torch.tensor(snake_case			).unsqueeze(0			)

    # failsafe to make sure the weights reset does not affect the
    # loaded weights.
    assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight			)			) == 0

    # forward pass
    lowerCAmelCase 						=							encoder_input_ids
    lowerCAmelCase 						=							decoder_input_ids
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							None
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							lowerCAmelCase 						=							None
    lowerCAmelCase 						=							None

    # The original model does not apply the geneator layer immediatly but rather in
    # the beam search (where it combines softmax + linear layer). Since we already
    # apply the softmax in our generation process we only apply the linear layer here.
    # We make sure that the outputs of the full stack are identical
    lowerCAmelCase 						=							original(snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							original.generator(snake_case			)

    lowerCAmelCase 						=							new_model(
        snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							new_model.generator(snake_case			)

    lowerCAmelCase 						=							torch.max(torch.abs(output_converted_model - output_original_model			)			).item()
    print('Maximum absolute difference beween weights: {:.2f}'.format(snake_case			)			)
    lowerCAmelCase 						=							torch.max(torch.abs(output_converted_generator - output_original_generator			)			).item()
    print('Maximum absolute difference beween weights: {:.2f}'.format(snake_case			)			)

    lowerCAmelCase 						=							torch.allclose(snake_case					,		snake_case					,		atol=1e-3			)
    if are_identical:
        logging.info('all weights are equal up to 1e-3'			)
    else:
        raise ValueError('the weights are different. The new model is likely different from the original one.'			)

    # The model has been saved with torch.save(model) and this is bound to the exact
    # directory structure. We save the state_dict instead.
    logging.info('saving the model\'s state dictionary'			)
    torch.save(
        new_model.state_dict()					,		'./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'			)


if __name__ == "__main__":
     _UpperCamelCase						:  List[str]							       =				argparse.ArgumentParser()
     parser.add_argument(
         "--bertabs_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path the official PyTorch dump.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default=None,
         type=str,
         required=True,
         help="Path to the output PyTorch model.",
     )
     _UpperCamelCase						:  Optional[int]							       =				parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )





