

"""simple docstring"""




import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


A_							:			Union[str, Any]											= logging.get_logger(__name__)

A_							:			Optional[Any]											= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A_							:			Any											= {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

A_							:			int											= {
    "Salesforce/codegen-350M-mono": 2_048,
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Tuple							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		str							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		List[str]							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		int							=							['input_ids', 'attention_mask']
			lowerCamelCase__   :		Optional[int]							=							CodeGenTokenizer

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						Optional[int]=None    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						int=None    ,				__UpperCAmelCase				:						Optional[Any]="<|endoftext|>"    ,				__UpperCAmelCase				:						List[str]="<|endoftext|>"    ,				__UpperCAmelCase				:						Optional[Any]="<|endoftext|>"    ,				__UpperCAmelCase				:						List[str]=False    ,				**__UpperCAmelCase				:						Optional[int]    ,				)   ->    Any:
						super().__init__(
						    __UpperCAmelCase    ,				__UpperCAmelCase    ,				tokenizer_file=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				add_prefix_space=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

						if kwargs.pop("""add_bos_token"""    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      kwargs.pop("""name_or_path"""    ,				""""""       )
									raise ValueError(
									    """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
									    """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
									    F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
									    F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
									    """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
									    """ so that the fast tokenizer works correctly."""       )

						SCREAMING_SNAKE_CASE__								=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
						if pre_tok_state.get("""add_prefix_space"""    ,				__UpperCAmelCase       ) != add_prefix_space:
									SCREAMING_SNAKE_CASE__								=      getattr(__UpperCAmelCase    ,				pre_tok_state.pop("""type"""       )       )
									SCREAMING_SNAKE_CASE__								=      add_prefix_space
									SCREAMING_SNAKE_CASE__								=      pre_tok_class(**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      add_prefix_space

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				*__UpperCAmelCase				:						Optional[int]    ,				**__UpperCAmelCase				:						Optional[int]       )   ->    BatchEncoding:
						SCREAMING_SNAKE_CASE__								=      kwargs.get("""is_split_into_words"""    ,				__UpperCAmelCase       )
						assert self.add_prefix_space or not is_split_into_words, (
						    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				*__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    BatchEncoding:
						SCREAMING_SNAKE_CASE__								=      kwargs.get("""is_split_into_words"""    ,				__UpperCAmelCase       )

						assert self.add_prefix_space or not is_split_into_words, (
						    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						SCREAMING_SNAKE_CASE__								=      self._tokenizer.model.save(__UpperCAmelCase    ,				name=__UpperCAmelCase       )
						return tuple(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						Optional[List[str]] = None    ,				**__UpperCAmelCase				:						Tuple    ,				)   ->    str:
						SCREAMING_SNAKE_CASE__								=      super().decode(
						    token_ids=__UpperCAmelCase    ,				skip_special_tokens=__UpperCAmelCase    ,				clean_up_tokenization_spaces=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

						if truncate_before_pattern is not None and len(__UpperCAmelCase       ) > 0:
									SCREAMING_SNAKE_CASE__								=      self.truncate(__UpperCAmelCase    ,				__UpperCAmelCase       )

						return decoded_text





			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						List[Any]       )   ->    List[str]:
						def find_re(__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						int       ):
									SCREAMING_SNAKE_CASE__								=      pattern.search(__UpperCAmelCase    ,				__UpperCAmelCase       )
									return m.start() if m else -1

						SCREAMING_SNAKE_CASE__								=      [re.compile(__UpperCAmelCase    ,				re.MULTILINE       ) for pattern in truncate_before_pattern]

						SCREAMING_SNAKE_CASE__								=      list(re.finditer("""^print"""    ,				__UpperCAmelCase    ,				re.MULTILINE       )       )

						if len(__UpperCAmelCase       ) > 1:
									SCREAMING_SNAKE_CASE__								=      completion[: prints[1].start()]

						SCREAMING_SNAKE_CASE__								=      list(re.finditer("""^def"""    ,				__UpperCAmelCase    ,				re.MULTILINE       )       )

						if len(__UpperCAmelCase       ) > 1:
									SCREAMING_SNAKE_CASE__								=      completion[: defs[1].start()]

						SCREAMING_SNAKE_CASE__								=      0

						SCREAMING_SNAKE_CASE__								=      [
						    pos for pos in [find_re(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       ) for terminal in terminals] if pos != -1
						]

						if len(__UpperCAmelCase       ) > 0:
									return completion[: min(__UpperCAmelCase       )]
						else:
									return completion





"""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)
A_							:			Tuple											= logging.getLogger(__name__)


A_							:			Tuple											= "Hello world! cécé herlolip"


A_							:			Dict											= 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 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      BertAbsConfig(
			    temp_dir="""."""		,				finetune_bert=snake_case__		,				large=snake_case__		,				share_emb=snake_case__		,				use_bert_emb=snake_case__		,				encoder="""bert"""		,				max_pos=5_12		,				enc_layers=6		,				enc_hidden_size=5_12		,				enc_heads=8		,				enc_ff_size=5_12		,				enc_dropout=0.2		,				dec_layers=6		,				dec_hidden_size=7_68		,				dec_heads=8		,				dec_ff_size=20_48		,				dec_dropout=0.2		,				)
			SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__		,				lambda snake_case__		,				snake_case__    : storage    )
			SCREAMING_SNAKE_CASE__								=      AbsSummarizer(snake_case__		,				torch.device("""cpu"""    )		,				snake_case__    )
			original.eval()

			SCREAMING_SNAKE_CASE__								=      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"""    )
			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] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample 3 éàalj'-."""    )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      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
			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(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      original.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      new_model(
			    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      new_model.generator(snake_case__    )

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

			SCREAMING_SNAKE_CASE__								=      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__":
						A_							:			Dict											= 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.",
						)
						A_							:			Tuple											= parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


