
"""simple docstring"""







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase__   					=		"""src/diffusers"""

# Matches is_xxx_available()
lowerCamelCase__   					=		re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
lowerCamelCase__   					=		re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


lowerCamelCase__   					=		"""
{0} = None
"""

lowerCamelCase__   					=		"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


lowerCamelCase__   					=		"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						List[str]									=      _re_backend.findall(_UpperCamelCase		)
	if len(_UpperCamelCase		) == 0:
		return None

	return "_and_".join(_UpperCamelCase		)
def     __lowerCAmelCase				():
	with open(os.path.join(_UpperCamelCase       ,  '__init__.py'		)       ,  'r'       ,  encoding='utf-8'       ,  newline='\n'		) as f:
		__lowerCAmelCase			:						Union[str, Any]									=      f.readlines()

	# Get to the point we do the actual imports for type checking
	__lowerCAmelCase			:						List[Any]									=      0
	__lowerCAmelCase			:						Dict									=      {}
	# Go through the end of the file
	while line_index < len(_UpperCamelCase		):
		# If the line contains is_backend_available, we grab all objects associated with the `else` block
		__lowerCAmelCase			:						Tuple									=      find_backend(lines[line_index]		)
		if backend is not None:
			while not lines[line_index].startswith('else:'		):
				line_index += 1
			line_index += 1
			__lowerCAmelCase			:						Any									=      []
			# Until we unindent, add backend objects to the list
			while line_index < len(_UpperCamelCase		) and len(lines[line_index]		) > 1:
				__lowerCAmelCase			:						Optional[int]									=      lines[line_index]
				__lowerCAmelCase			:						Optional[int]									=      _re_single_line_import.search(_UpperCamelCase		)
				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

			if len(_UpperCamelCase		) > 0:
				__lowerCAmelCase			:						List[Any]									=      objects
		else:
			line_index += 1

	return backend_specific_objects
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase		):
	if name.isupper():
		return DUMMY_CONSTANT.format(_UpperCamelCase		)
	elif name.islower():
		return DUMMY_FUNCTION.format(_UpperCamelCase       ,  _UpperCamelCase		)
	else:
		return DUMMY_CLASS.format(_UpperCamelCase       ,  _UpperCamelCase		)
def     __lowerCAmelCase				(_UpperCamelCase=None		):
	if backend_specific_objects is None:
		__lowerCAmelCase			:						Dict									=      read_init()
	# For special correspondence backend to module name as used in the function requires_modulename
	__lowerCAmelCase			:						Any									=      {}

	for backend, objects in backend_specific_objects.items():
		__lowerCAmelCase			:						List[Any]									=      '[' + ', '.join(F"\"{b}\"" for b in backend.split('_and_'		)		) + ']'
		__lowerCAmelCase			:						Optional[int]									=      '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
		dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
		dummy_file += "\n".join([create_dummy_object(_UpperCamelCase       ,  _UpperCamelCase		) for o in objects]		)
		__lowerCAmelCase			:						Optional[Any]									=      dummy_file

	return dummy_files
def     __lowerCAmelCase				(_UpperCamelCase=False		):
	__lowerCAmelCase			:						str									=      create_dummy_files()
	# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
	__lowerCAmelCase			:						Optional[Any]									=      {'torch': 'pt'}

	# Locate actual dummy modules and read their content.
	__lowerCAmelCase			:						Dict									=      os.path.join(_UpperCamelCase       ,  'utils'		)
	__lowerCAmelCase			:						Dict									=      {
	    backend: os.path.join(_UpperCamelCase       ,  F"dummy_{short_names.get(_UpperCamelCase       ,  _UpperCamelCase		)}_objects.py"		)
	    for backend in dummy_files.keys()
	}

	__lowerCAmelCase			:						str									=      {}
	for backend, file_path in dummy_file_paths.items():
		if os.path.isfile(_UpperCamelCase		):
			with open(_UpperCamelCase       ,  'r'       ,  encoding='utf-8'       ,  newline='\n'		) as f:
				__lowerCAmelCase			:						Union[str, Any]									=      f.read()
		else:
			__lowerCAmelCase			:						List[str]									=      ''

	for backend in dummy_files.keys():
		if dummy_files[backend] != actual_dummies[backend]:
			if overwrite:
				print(
				    F"Updating diffusers.utils.dummy_{short_names.get(_UpperCamelCase       ,  _UpperCamelCase		)}_objects.py as the main "
				    '__init__ has new objects.'		)
				with open(dummy_file_paths[backend]       ,  'w'       ,  encoding='utf-8'       ,  newline='\n'		) as f:
					f.write(dummy_files[backend]		)
			else:
				raise ValueError(
				    'The main __init__ has objects that are not present in '
				    F"diffusers.utils.dummy_{short_names.get(_UpperCamelCase       ,  _UpperCamelCase		)}_objects.py. Run `make fix-copies` "
				    'to fix this.'		)


if __name__ == "__main__":
							lowerCamelCase__   					=		argparse.ArgumentParser()
							parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
							lowerCamelCase__   					=		parser.parse_args()

							check_dummies(args.fix_and_overwrite)






'''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)
lowercase_					= logging.getLogger(__name__)


lowercase_					= """Hello world! cécé herlolip"""


lowercase_					= 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 lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			BertAbsConfig(
        temp_dir="""."""					,       finetune_bert=__lowerCamelCase					,       large=__lowerCamelCase					,       share_emb=__lowerCamelCase					,       use_bert_emb=__lowerCamelCase					,       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					,       )
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       lambda __lowerCamelCase					,       __lowerCamelCase    : storage    )
    _SCREAMING_SNAKE_CASE									=			AbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )					,       __lowerCamelCase    )
    original.eval()

    _SCREAMING_SNAKE_CASE									=			BertAbsSummarizer(__lowerCamelCase					,       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"""    )
    _SCREAMING_SNAKE_CASE									=			BertTokenizer.from_pretrained("""bert-base-uncased"""    )

    # prepare the model inputs
    _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""This is sample éàalj'-."""    )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase    ))    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor(__lowerCamelCase    ).unsqueeze(0    )
    _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""This is sample 3 éàalj'-."""    )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase    ))    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor(__lowerCamelCase    ).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
    _SCREAMING_SNAKE_CASE									=			encoder_input_ids
    _SCREAMING_SNAKE_CASE									=			decoder_input_ids
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			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
    _SCREAMING_SNAKE_CASE									=			original(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			original.generator(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			new_model(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			new_model.generator(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			torch.allclose(__lowerCamelCase					,       __lowerCamelCase					,       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__":
      lowercase_					= 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.""",
      )
      lowercase_					= parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )



