






'''simple docstring'''





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


_a    :   Any        =			[
    # 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 				snake_case__							(							UpperCAmelCase							:  List[str]       ):
    for pegasus_name, hf_name in PATTERNS:
        lowerCAmelCase__   :List[Any]      							=				k.replace(UpperCAmelCase    ,			UpperCAmelCase       )
    return k


def 				snake_case__							(							UpperCAmelCase							:  dict    ,			UpperCAmelCase							:  dict       ):
    lowerCAmelCase__   :Tuple      							=				DEFAULTS.copy()
    cfg_kwargs.update(UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				PegasusConfig(**UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				PegasusForConditionalGeneration(UpperCAmelCase       )
    lowerCAmelCase__   :List[str]      							=				torch_model.model.state_dict()
    lowerCAmelCase__   :Union[str, Any]      							=				{}
    for k, v in tf_weights.items():
        lowerCAmelCase__   :Optional[Any]      							=				rename_state_dict_key(UpperCAmelCase       )
        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:
            lowerCAmelCase__   :Tuple      							=				v.T
        lowerCAmelCase__   :int      							=				torch.tensor(UpperCAmelCase    ,			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
    lowerCAmelCase__   :List[Any]      							=				torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]       )
    lowerCAmelCase__   :List[Any]      							=				mapping["shared.weight"]
    lowerCAmelCase__   :int      							=				mapping["shared.weight"]
    lowerCAmelCase__   :List[str]      							=				{k: torch.zeros_like(UpperCAmelCase       ) for k, v in sd.items() if k.endswith("bias"       ) and k not in mapping}
    mapping.update(**UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				torch_model.model.load_state_dict(UpperCAmelCase    ,			strict=UpperCAmelCase       )
    lowerCAmelCase__   :Optional[Any]      							=				[
        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 				snake_case__							(							UpperCAmelCase							:  str="./ckpt/aeslc/model.ckpt-32000"       ):
    lowerCAmelCase__   :Optional[Any]      							=				tf.train.list_variables(UpperCAmelCase       )
    lowerCAmelCase__   :Optional[Any]      							=				{}
    lowerCAmelCase__   :str      							=				["Adafactor", "global_step"]
    for name, shape in tqdm(UpperCAmelCase    ,			desc="converting tf checkpoint to dict"       ):
        lowerCAmelCase__   :Tuple      							=				any(pat in name for pat in ignore_name       )
        if skip_key:
            continue
        lowerCAmelCase__   :Tuple      							=				tf.train.load_variable(UpperCAmelCase    ,			UpperCAmelCase       )
        lowerCAmelCase__   :int      							=				array
    return tf_weights


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  str       ):
    # save tokenizer first
    lowerCAmelCase__   :Optional[Any]      							=				Path(UpperCAmelCase       ).parent.name
    lowerCAmelCase__   :List[str]      							=				task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
    lowerCAmelCase__   :Union[str, Any]      							=				PegasusTokenizer.from_pretrained("sshleifer/pegasus"    ,			model_max_length=UpperCAmelCase       )
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(UpperCAmelCase       )

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


if __name__ == "__main__":
 _a    :   Tuple        =			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.""")
 _a    :   Tuple        =			parser.parse_args()
 if args.save_dir is None:
  _a    :   Optional[Any]        =			Path(args.tf_ckpt_path).parent.name
  _a    :   Tuple        =			os.path.join("""pegasus""", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)











import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_a    :   List[Any]        =			get_tests_dir("""fixtures/test_sentencepiece.model""")
_a    :   Dict        =			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_a    :   Optional[Any]        =			"""pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    CamembertTokenizer
      A          =    CamembertTokenizerFast
      A          =    True
      A          =    True
      def 			snake_case_      (   self		):



          '''simple docstring'''
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowerCAmelCase__   :List[str]      							=				CamembertTokenizer(_lowerCAmelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				"<pad>"
          lowerCAmelCase__   :Union[str, Any]      							=				1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase		)    ,	_lowerCAmelCase		)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase		)    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				list(self.get_tokenizer().get_vocab().keys()		)

          self.assertEqual(vocab_keys[0]    ,	"<s>NOTUSED"		)
          self.assertEqual(vocab_keys[1]    ,	"<pad>"		)
          self.assertEqual(vocab_keys[-1]    ,	"<mask>"		)
          self.assertEqual(len(_lowerCAmelCase		)    ,	1_004		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.assertEqual(self.get_tokenizer().vocab_size    ,	1_005		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				CamembertTokenizer(_lowerCAmelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
          lowerCAmelCase__   :Tuple      							=				CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

          lowerCAmelCase__   :List[str]      							=				"I was born in 92000, and this is falsé."

          lowerCAmelCase__   :Any      							=				tokenizer.encode(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				rust_tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          lowerCAmelCase__   :Union[str, Any]      							=				tokenizer.convert_ids_to_tokens(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				rust_tokenizer.tokenize(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          if not self.test_rust_tokenizer:
              return

          lowerCAmelCase__   :Optional[Any]      							=				self.get_tokenizer()
          lowerCAmelCase__   :int      							=				self.get_rust_tokenizer()

          lowerCAmelCase__   :List[str]      							=				"I was born in 92000, and this is falsé."

          lowerCAmelCase__   :int      							=				tokenizer.tokenize(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				rust_tokenizer.tokenize(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Union[str, Any]      							=				tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Dict      							=				self.get_rust_tokenizer()
          lowerCAmelCase__   :Tuple      							=				tokenizer.encode(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # fmt: off
          lowerCAmelCase__   :str      							=				{"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          lowerCAmelCase__   :Tuple      							=				[
              "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
              "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
              "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
              "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
              "telles que la traduction et la synthèse de texte.",
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=_lowerCAmelCase    ,	model_name="camembert-base"    ,	revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"    ,	sequences=_lowerCAmelCase    ,	)



