





'''simple docstring'''


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  a_  (							SCREAMING_SNAKE_CASE__      ,		unittest.TestCase				):
     __A						=     LongformerTokenizer
     __A						=     True
     __A						=     LongformerTokenizerFast
     __A						=     True





     def 				lowercase__ (					self						:   List[str]		):





        """simple docstring"""




        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase_     :Optional[Any]            =					[
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        lowercase_     :Optional[Any]            =					dict(zip(snake_case__      , range(len(snake_case__		)		)		)		)
        lowercase_     :str            =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        lowercase_     :str            =					{"unk_token": "<unk>"}

        lowercase_     :Optional[Any]            =					os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["vocab_file"]		)
        lowercase_     :Optional[Any]            =					os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["merges_file"]		)
        with open(self.vocab_file      , "w"      , encoding="utf-8"		) as fp:
           fp.write(json.dumps(snake_case__		) + "\n"		)
        with open(self.merges_file      , "w"      , encoding="utf-8"		) as fp:
           fp.write("\n".join(snake_case__		)		)





     def 				lowercase__ (					self						:   Union[str, Any]      , **lowercase						:   List[str]		):





        """simple docstring"""




        kwargs.update(self.special_tokens_map		)
        return self.tokenizer_class.from_pretrained(self.tmpdirname      , **snake_case__		)





     def 				lowercase__ (					self						:   List[Any]      , **lowercase						:   List[Any]		):





        """simple docstring"""




        kwargs.update(self.special_tokens_map		)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      , **snake_case__		)





     def 				lowercase__ (					self						:   List[Any]      , lowercase						:   str		):





        """simple docstring"""




        lowercase_     :Dict            =					"lower newer"
        lowercase_     :str            =					"lower newer"
        return input_text, output_text





     def 				lowercase__ (					self						:   Tuple		):





        """simple docstring"""




        lowercase_     :Dict            =					self.tokenizer_class(self.vocab_file      , self.merges_file      , **self.special_tokens_map		)
        lowercase_     :List[Any]            =					"lower newer"
        lowercase_     :Optional[Any]            =					["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
        lowercase_     :Optional[int]            =					tokenizer.tokenize(snake_case__		)  # , add_prefix_space=True)
        self.assertListEqual(snake_case__      , snake_case__		)

        lowercase_     :Tuple            =					tokens + [tokenizer.unk_token]
        lowercase_     :str            =					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__		)      , snake_case__		)





     def 				lowercase__ (					self						:   Optional[int]		):





        """simple docstring"""




        lowercase_     :Union[str, Any]            =					self.get_tokenizer()

        self.assertListEqual(tokenizer.encode("Hello world!"      , add_special_tokens=snake_case__		)      , [0, 31_414, 232, 328, 2]		)
        self.assertListEqual(
            tokenizer.encode("Hello world! cécé herlolip 418"      , add_special_tokens=snake_case__		)      , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]      , )





     @slow
     def 				lowercase__ (					self						:   Any		):





        """simple docstring"""




        lowercase_     :List[Any]            =					self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"		)

        lowercase_     :List[str]            =					tokenizer.encode("sequence builders"      , add_special_tokens=snake_case__		)
        lowercase_     :str            =					tokenizer.encode("multi-sequence build"      , add_special_tokens=snake_case__		)

        lowercase_     :List[str]            =					tokenizer.encode(
            "sequence builders"      , add_special_tokens=snake_case__      , add_prefix_space=snake_case__		)
        lowercase_     :Any            =					tokenizer.encode(
            "sequence builders"      , "multi-sequence build"      , add_special_tokens=snake_case__      , add_prefix_space=snake_case__		)

        lowercase_     :List[str]            =					tokenizer.build_inputs_with_special_tokens(snake_case__		)
        lowercase_     :Union[str, Any]            =					tokenizer.build_inputs_with_special_tokens(snake_case__      , snake_case__		)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode





     def 				lowercase__ (					self						:   List[Any]		):





        """simple docstring"""




        lowercase_     :Optional[Any]            =					self.get_tokenizer()

        lowercase_     :Tuple            =					"Encode this sequence."
        lowercase_     :List[Any]            =					tokenizer.byte_encoder[" ".encode("utf-8"		)[0]]

        # Testing encoder arguments
        lowercase_     :Union[str, Any]            =					tokenizer.encode(snake_case__      , add_special_tokens=snake_case__      , add_prefix_space=snake_case__		)
        lowercase_     :Union[str, Any]            =					tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
        self.assertNotEqual(snake_case__      , snake_case__		)

        lowercase_     :Optional[int]            =					tokenizer.encode(snake_case__      , add_special_tokens=snake_case__      , add_prefix_space=snake_case__		)
        lowercase_     :Union[str, Any]            =					tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
        self.assertEqual(snake_case__      , snake_case__		)

        tokenizer.add_special_tokens({"bos_token": "<s>"}		)
        lowercase_     :List[Any]            =					tokenizer.encode(snake_case__      , add_special_tokens=snake_case__		)
        lowercase_     :List[str]            =					tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
        self.assertNotEqual(snake_case__      , snake_case__		)

        # Testing spaces after special tokens
        lowercase_     :List[Any]            =					"<mask>"
        tokenizer.add_special_tokens(
            {"mask_token": AddedToken(snake_case__      , lstrip=snake_case__      , rstrip=snake_case__		)}		)  # mask token has a left space
        lowercase_     :Dict            =					tokenizer.convert_tokens_to_ids(snake_case__		)

        lowercase_     :Tuple            =					"Encode <mask> sequence"
        lowercase_     :str            =					"Encode <mask>sequence"

        lowercase_     :int            =					tokenizer.encode(snake_case__		)
        lowercase_     :Optional[Any]            =					encoded.index(snake_case__		)
        lowercase_     :Optional[int]            =					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
        self.assertEqual(snake_case__      , snake_case__		)

        lowercase_     :List[Any]            =					tokenizer.encode(snake_case__		)
        lowercase_     :Optional[Any]            =					encoded.index(snake_case__		)
        lowercase_     :List[str]            =					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
        self.assertNotEqual(snake_case__      , snake_case__		)





     def 				lowercase__ (					self						:   Optional[int]		):





        """simple docstring"""




        pass





     def 				lowercase__ (					self						:   Optional[int]		):





        """simple docstring"""




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
              lowercase_     :Optional[int]            =					self.rust_tokenizer_class.from_pretrained(snake_case__      , **snake_case__		)
              lowercase_     :Optional[int]            =					self.tokenizer_class.from_pretrained(snake_case__      , **snake_case__		)
              lowercase_     :str            =					"A, <mask> AllenNLP sentence."
              lowercase_     :Optional[int]            =					tokenizer_r.encode_plus(snake_case__      , add_special_tokens=snake_case__      , return_token_type_ids=snake_case__		)
              lowercase_     :Optional[Any]            =					tokenizer_p.encode_plus(snake_case__      , add_special_tokens=snake_case__      , return_token_type_ids=snake_case__		)

              # token_type_ids should put 0 everywhere
              self.assertEqual(sum(tokens_r["token_type_ids"]		)      , sum(tokens_p["token_type_ids"]		)		)

              # attention_mask should put 1 everywhere, so sum over length should be 1
              self.assertEqual(
                  sum(tokens_r["attention_mask"]		) / len(tokens_r["attention_mask"]		)      , sum(tokens_p["attention_mask"]		) / len(tokens_p["attention_mask"]		)      , )

              lowercase_     :Optional[int]            =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
              lowercase_     :int            =					tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]		)

              # Rust correctly handles the space before the mask while python doesnt
              self.assertSequenceEqual(tokens_p["input_ids"]      , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
              self.assertSequenceEqual(tokens_r["input_ids"]      , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)

              self.assertSequenceEqual(
                  snake_case__      , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)
              self.assertSequenceEqual(
                  snake_case__      , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)





     def 				lowercase__ (					self						:   int		):





        """simple docstring"""




        for trim_offsets, add_prefix_space in itertools.product([True, False]      , repeat=2		):
           lowercase_     :List[Any]            =					self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)

           lowercase_     :Dict            =					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
           lowercase_     :Any            =					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

           self.assertEqual(pre_tokenizer_state["add_prefix_space"]      , snake_case__		)

           self.assertEqual(post_processor_state["add_prefix_space"]      , snake_case__		)
           self.assertEqual(post_processor_state["trim_offsets"]      , snake_case__		)







     def 				lowercase__ (					self						:   Optional[int]		):





        """simple docstring"""




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
              lowercase_     :str            =					"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
              lowercase_     :List[str]            =					F'{text_of_1_token} {text_of_1_token}'

              lowercase_     :Optional[Any]            =					self.rust_tokenizer_class.from_pretrained(
                  snake_case__      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)
              lowercase_     :Tuple            =					tokenizer_r(snake_case__      , return_offsets_mapping=snake_case__      , add_special_tokens=snake_case__		)
              self.assertEqual(encoding.offset_mapping[0]      , (0, len(snake_case__		))		)
              self.assertEqual(
                  encoding.offset_mapping[1]      , (len(snake_case__		) + 1, len(snake_case__		) + 1 + len(snake_case__		))      , )

              lowercase_     :List[Any]            =					self.rust_tokenizer_class.from_pretrained(
                  snake_case__      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)
              lowercase_     :int            =					tokenizer_r(snake_case__      , return_offsets_mapping=snake_case__      , add_special_tokens=snake_case__		)
              self.assertEqual(encoding.offset_mapping[0]      , (0, len(snake_case__		))		)
              self.assertEqual(
                  encoding.offset_mapping[1]      , (len(snake_case__		) + 1, len(snake_case__		) + 1 + len(snake_case__		))      , )

              lowercase_     :Optional[Any]            =					self.rust_tokenizer_class.from_pretrained(
                  snake_case__      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)
              lowercase_     :Any            =					tokenizer_r(snake_case__      , return_offsets_mapping=snake_case__      , add_special_tokens=snake_case__		)
              self.assertEqual(encoding.offset_mapping[0]      , (0, len(snake_case__		))		)
              self.assertEqual(
                  encoding.offset_mapping[1]      , (len(snake_case__		), len(snake_case__		) + 1 + len(snake_case__		))      , )

              lowercase_     :List[Any]            =					self.rust_tokenizer_class.from_pretrained(
                  snake_case__      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)
              lowercase_     :Optional[int]            =					tokenizer_r(snake_case__      , return_offsets_mapping=snake_case__      , add_special_tokens=snake_case__		)
              self.assertEqual(encoding.offset_mapping[0]      , (0, len(snake_case__		))		)
              self.assertEqual(
                  encoding.offset_mapping[1]      , (len(snake_case__		), len(snake_case__		) + 1 + len(snake_case__		))      , )

              lowercase_     :List[Any]            =					F' {text}'

              # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
              #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
              # )
              # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
              # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
              # self.assertEqual(
              #     encoding.offset_mapping[1],
              #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
              # )

              lowercase_     :Union[str, Any]            =					self.rust_tokenizer_class.from_pretrained(
                  snake_case__      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)
              lowercase_     :Any            =					tokenizer_r(snake_case__      , return_offsets_mapping=snake_case__      , add_special_tokens=snake_case__		)
              self.assertEqual(encoding.offset_mapping[0]      , (1, 1 + len(snake_case__		))		)
              self.assertEqual(
                  encoding.offset_mapping[1]      , (1 + len(snake_case__		) + 1, 1 + len(snake_case__		) + 1 + len(snake_case__		))      , )

              lowercase_     :Optional[int]            =					self.rust_tokenizer_class.from_pretrained(
                  snake_case__      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)
              lowercase_     :Tuple            =					tokenizer_r(snake_case__      , return_offsets_mapping=snake_case__      , add_special_tokens=snake_case__		)
              self.assertEqual(encoding.offset_mapping[0]      , (0, 1 + len(snake_case__		))		)
              self.assertEqual(
                  encoding.offset_mapping[1]      , (1 + len(snake_case__		), 1 + len(snake_case__		) + 1 + len(snake_case__		))      , )

              lowercase_     :Tuple            =					self.rust_tokenizer_class.from_pretrained(
                  snake_case__      , use_fast=snake_case__      , add_prefix_space=snake_case__      , trim_offsets=snake_case__		)
              lowercase_     :Tuple            =					tokenizer_r(snake_case__      , return_offsets_mapping=snake_case__      , add_special_tokens=snake_case__		)
              self.assertEqual(encoding.offset_mapping[0]      , (0, 1 + len(snake_case__		))		)
              self.assertEqual(
                  encoding.offset_mapping[1]      , (1 + len(snake_case__		), 1 + len(snake_case__		) + 1 + len(snake_case__		))      , )








import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE:     Optional[int]									= [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]


def 					_a							(					lowerCAmelCase )->	str:
		for pegasus_name, hf_name in PATTERNS:
				SCREAMING_SNAKE_CASE_					      =							k.replace(lowerCAmelCase    ,						lowerCAmelCase )
		return k


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	PegasusForConditionalGeneration:
		SCREAMING_SNAKE_CASE_					      =							DEFAULTS.copy()
		cfg_kwargs.update(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusConfig(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							PegasusForConditionalGeneration(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.model.state_dict()
		SCREAMING_SNAKE_CASE_					      =							{}
		for k, v in tf_weights.items():
				SCREAMING_SNAKE_CASE_					      =							rename_state_dict_key(lowerCAmelCase )
				if new_k not in sd:
						raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})''' )

				if "dense" in k or "proj" in new_k:
						SCREAMING_SNAKE_CASE_					      =							v.T
				SCREAMING_SNAKE_CASE_					      =							torch.tensor(lowerCAmelCase    ,						dtype=sd[new_k].dtype )
				assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
		# make sure embedding.padding_idx is respected
		SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							mapping['shared.weight']
		SCREAMING_SNAKE_CASE_					      =							{k: torch.zeros_like(lowerCAmelCase ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
		mapping.update(**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							torch_model.model.load_state_dict(lowerCAmelCase    ,						strict=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							[
		    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
		]
		assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
		assert extra == [], F'''no matches found for the following tf keys {extra}'''
		return torch_model


def 					_a							(					lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000" )->	Dict:
		SCREAMING_SNAKE_CASE_					      =							tf.train.list_variables(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							{}
		SCREAMING_SNAKE_CASE_					      =							['Adafactor', 'global_step']
		for name, shape in tqdm(lowerCAmelCase    ,						desc='converting tf checkpoint to dict' ):
				SCREAMING_SNAKE_CASE_					      =							any(pat in name for pat in ignore_name )
				if skip_key:
						continue
				SCREAMING_SNAKE_CASE_					      =							tf.train.load_variable(lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							array
		return tf_weights


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[Any]:
		# save tokenizer first
		SCREAMING_SNAKE_CASE_					      =							Path(lowerCAmelCase ).parent.name
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
		SCREAMING_SNAKE_CASE_					      =							PegasusTokenizer.from_pretrained('sshleifer/pegasus'    ,						model_max_length=lowerCAmelCase )
		assert tok.model_max_length == desired_max_model_length
		tok.save_pretrained(lowerCAmelCase )

		# convert model
		SCREAMING_SNAKE_CASE_					      =							get_tf_weights_as_numpy(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							task_specific_params[F'''summarization_{dataset}''']
		if dataset == "large":
				SCREAMING_SNAKE_CASE_					      =							task_specific_params
		SCREAMING_SNAKE_CASE_					      =							convert_pegasus(lowerCAmelCase    ,						lowerCAmelCase )
		torch_model.save_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							torch_model.state_dict()
		sd.pop('model.decoder.embed_positions.weight' )
		sd.pop('model.encoder.embed_positions.weight' )
		torch.save(lowerCAmelCase    ,						Path(lowerCAmelCase ) / 'pytorch_model.bin' )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
						parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
						SCREAMING_SNAKE_CASE:     int									= parser.parse_args()
						if args.save_dir is None:
												SCREAMING_SNAKE_CASE:     List[Any]									= Path(args.tf_ckpt_path).parent.name
												SCREAMING_SNAKE_CASE:     Tuple									= os.path.join('''pegasus''', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)