
"""simple docstring"""




import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_							:			Optional[Any]											= logging.get_logger(__name__)


A_							:			Dict											= {
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

A_							:			Any											= {
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

A_							:			int											= "</w>"
A_							:			Dict											= "@@ "

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      set()
			SCREAMING_SNAKE_CASE__								=      word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)    )
						SCREAMING_SNAKE_CASE__								=      char
			return pairs


# Speech2Text2 has no max input length
A_							:			List[str]											= {"facebook/s2t-wav2vec2-large-en-de": 1_024}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		Optional[Any]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		Any							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		Dict							=							['input_ids', 'attention_mask']

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						List[Any]="<s>"    ,				__UpperCAmelCase				:						Optional[Any]="<pad>"    ,				__UpperCAmelCase				:						List[Any]="</s>"    ,				__UpperCAmelCase				:						Optional[Any]="<unk>"    ,				__UpperCAmelCase				:						str=False    ,				__UpperCAmelCase				:						int=None    ,				**__UpperCAmelCase				:						Any    ,				)   ->    Optional[Any]:
						super().__init__(
						    unk_token=__UpperCAmelCase    ,				bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				do_lower_case=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

						SCREAMING_SNAKE_CASE__								=      do_lower_case

						with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as vocab_handle:
									SCREAMING_SNAKE_CASE__								=      json.load(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.encoder.items()}

						if merges_file is None:
									logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""       )

									SCREAMING_SNAKE_CASE__								=      None
									SCREAMING_SNAKE_CASE__								=      None
						else:
									with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as merges_handle:
												SCREAMING_SNAKE_CASE__								=      merges_handle.read().split("""\n"""       )[:-1]

									SCREAMING_SNAKE_CASE__								=      [tuple(merge.split()[:2]       ) for merge in merges]
									SCREAMING_SNAKE_CASE__								=      dict(zip(__UpperCAmelCase    ,				range(len(__UpperCAmelCase       )       )       )       )
									SCREAMING_SNAKE_CASE__								=      {}

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    int:
						return len(self.decoder       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						return dict(self.encoder    ,				**self.added_tokens_encoder       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      tuple(token[:-1]       ) + (token[-1] + BPE_TOKEN_MERGES,)
						if token in self.cache:
									return self.cache[token]
						SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )

						if not pairs:
									return token

						while True:
									SCREAMING_SNAKE_CASE__								=      min(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase       : self.bpe_ranks.get(__UpperCAmelCase    ,				float("""inf"""       )       )       )
									if bigram not in self.bpe_ranks:
												break
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      bigram
									SCREAMING_SNAKE_CASE__								=      []
									SCREAMING_SNAKE_CASE__								=      0
									while i < len(__UpperCAmelCase       ):
												try:
															SCREAMING_SNAKE_CASE__								=      word.index(__UpperCAmelCase    ,				__UpperCAmelCase       )
												except ValueError:
															new_word.extend(word[i:]       )
															break
												else:
															new_word.extend(word[i:j]       )
															SCREAMING_SNAKE_CASE__								=      j

												if word[i] == first and i < len(__UpperCAmelCase       ) - 1 and word[i + 1] == second:
															new_word.append(first + second       )
															i += 2
												else:
															new_word.append(word[i]       )
															i += 1
									SCREAMING_SNAKE_CASE__								=      tuple(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      new_word
									if len(__UpperCAmelCase       ) == 1:
												break
									else:
												SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )
						if word == "\n  " + BPE_TOKEN_MERGES:
									SCREAMING_SNAKE_CASE__								=      """\n""" + BPE_TOKEN_MERGES

						if word.endswith(__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      word.replace(__UpperCAmelCase    ,				""""""       )

						SCREAMING_SNAKE_CASE__								=      word.replace(""" """    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      word
						return word

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[int]       )   ->    Any:

						if self.bpe_ranks is None:
									raise ValueError(
									    """This tokenizer was instantiated without a `merges.txt` file, so"""
									    """ that it can only be used for decoding, not for encoding."""
									    """Make sure to provide `merges.txt` file at instantiation to enable """
									    """encoding."""       )

						if self.do_lower_case:
									SCREAMING_SNAKE_CASE__								=      text.lower()

						SCREAMING_SNAKE_CASE__								=      text.split()

						SCREAMING_SNAKE_CASE__								=      []
						for token in text:
									if token:
												split_tokens.extend(list(self.bpe(__UpperCAmelCase       ).split(""" """       )       )       )

						return split_tokens

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						str       )   ->    int:
						return self.encoder.get(__UpperCAmelCase    ,				self.encoder.get(self.unk_token       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						int       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.decoder.get(__UpperCAmelCase    ,				self.unk_token       )
						return result

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )

						# make sure @@ tokens are concatenated
						SCREAMING_SNAKE_CASE__								=      """""".join(string.split(__UpperCAmelCase       )       )

						return string





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						if not os.path.isdir(__UpperCAmelCase       ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
									return
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

						with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as f:
									f.write(json.dumps(self.encoder    ,				indent=2    ,				sort_keys=__UpperCAmelCase    ,				ensure_ascii=__UpperCAmelCase       ) + """\n"""       )

						SCREAMING_SNAKE_CASE__								=      0
						if self.bpe_ranks is None:
									return (vocab_file,)

						with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,				key=lambda __UpperCAmelCase       : kv[1]       ):
												if index != token_index:
															logger.warning(
															    F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
															    """ Please check that the tokenizer is not corrupted!"""       )
															SCREAMING_SNAKE_CASE__								=      token_index
												writer.write(""" """.join(__UpperCAmelCase       ) + """\n"""       )
												index += 1

						return (vocab_file, merges_file)





"""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,
						)


