


import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_   :				Tuple         = '▁'

UpperCAmelCase_   :				Optional[int]         = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Union[str, Any]       =		BigBirdTokenizer
      snake_case__       :  int       =		BigBirdTokenizerFast
      snake_case__       :  Optional[Any]       =		True
      snake_case__       :  List[str]       =		True
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Optional[Any]:
             super().setUp()

             a_      :	int							=						self.tokenizer_class(SCREAMING_SNAKE_CASE__  ,							keep_accents=SCREAMING_SNAKE_CASE__  )
             tokenizer.save_pretrained(self.tmpdirname  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             a_      :	str							=						'<s>'
             a_      :	int							=						1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__  )  ,							SCREAMING_SNAKE_CASE__  )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__  )  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             a_      :	Tuple							=						list(self.get_tokenizer().get_vocab().keys()  )

             self.assertEqual(vocab_keys[0]  ,							'<unk>'  )
             self.assertEqual(vocab_keys[1]  ,							'<s>'  )
             self.assertEqual(vocab_keys[-1]  ,							'[MASK]'  )
             self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,							1_0_0_4  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             self.assertEqual(self.get_tokenizer().vocab_size  ,							1_0_0_0  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             if not self.test_rust_tokenizer:
                    return

             a_      :	str							=						self.get_tokenizer()
             a_      :	List[Any]							=						self.get_rust_tokenizer()

             a_      :	List[Any]							=						'I was born in 92000, and this is falsé.'

             a_      :	Optional[Any]							=						tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						tokenizer.encode(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  )
             a_      :	int							=						rust_tokenizer.encode(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Any							=						self.get_rust_tokenizer()
             a_      :	str							=						tokenizer.encode(SCREAMING_SNAKE_CASE__  )
             a_      :	Union[str, Any]							=						rust_tokenizer.encode(SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    List[str]:
             a_      :	Union[str, Any]							=						BigBirdTokenizer(SCREAMING_SNAKE_CASE__  ,							keep_accents=SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						tokenizer.tokenize('This is a test'  )
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							['▁This', '▁is', '▁a', '▁t', 'est']  )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )  ,							[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]  ,							)

             a_      :	int							=						tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__  ,							[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]  ,							)
             a_      :	Tuple							=						tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__  ,							[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]  ,							)

             a_      :	int							=						tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  )
             self.assertListEqual(
                 SCREAMING_SNAKE_CASE__  ,							[
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]  ,							)
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Dict:
             a_      :	str							=						'Hello World!'
             a_      :	Optional[Any]							=						[6_5, 1_8_5_3_6, 2_2_6_0, 1_0_1, 6_6]

             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__  )  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    str:
             a_      :	List[Any]							=						(
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             # fmt: off
             a_      :	Optional[int]							=						[6_5, 8_7_1, 4_1_9, 3_5_8, 9_4_6, 9_9_1, 2_5_2_1, 4_5_2, 3_5_8, 1_3_5_7, 3_8_7, 7_7_5_1, 3_5_3_6, 1_1_2, 9_8_5, 4_5_6, 1_2_6, 8_6_5, 9_3_8, 5_4_0_0, 5_7_3_4, 4_5_8, 1_3_6_8, 4_6_7, 7_8_6, 2_4_6_2, 5_2_4_6, 1_1_5_9, 6_3_3, 8_6_5, 4_5_1_9, 4_5_7, 5_8_2, 8_5_2, 2_5_5_7, 4_2_7, 9_1_6, 5_0_8, 4_0_5, 3_4_3_2_4, 4_9_7, 3_9_1, 4_0_8, 1_1_3_4_2, 1_2_4_4, 3_8_5, 1_0_0, 9_3_8, 9_8_5, 4_5_6, 5_7_4, 3_6_2, 1_2_5_9_7, 3_2_0_0, 3_1_2_9, 1_1_7_2, 6_6]  # noqa: E231
             # fmt: on
             self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__  )  )
      @require_torch
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             import torch

             from transformers import BigBirdConfig, BigBirdModel

             # Build sequence
             a_      :	Optional[int]							=						list(self.big_tokenizer.get_vocab().keys()  )[:1_0]
             a_      :	Union[str, Any]							=						' '.join(SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.big_tokenizer.batch_encode_plus(
                 [sequence + ' ' + sequence]  ,							return_tensors='pt'  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						BigBirdConfig(attention_type='original_full'  )
             a_      :	int							=						BigBirdModel(SCREAMING_SNAKE_CASE__  )

             assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

             with torch.no_grad():
                    model(**SCREAMING_SNAKE_CASE__  )
                    model(**SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Dict:
             a_      :	Dict							=						BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'  )
             a_      :	List[str]							=						tokenizer.decode(tokenizer('Paris is the [MASK].'  ).input_ids  )

             self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[Any]:
             # fmt: off
             a_      :	Optional[Any]							=						{'input_ids': [[6_5, 3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4, 6_6], [6_5, 4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6_5, 4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=SCREAMING_SNAKE_CASE__  ,							model_name='google/bigbird-roberta-base'  ,							revision='215c99f1600e06f83acce68422f2035b2b5c3510'  ,							)






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


UpperCAmelCase_   :				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  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

       a_      :	int							=						DEFAULTS.copy()
       cfg_kwargs.update(__A       )
       a_      :	List[str]							=						PegasusConfig(**__A       )
       a_      :	List[Any]							=						PegasusForConditionalGeneration(__A       )
       a_      :	List[str]							=						torch_model.model.state_dict()
       a_      :	Optional[int]							=						{}
       for k, v in tf_weights.items():
              a_      :	str							=						rename_state_dict_key(__A       )
              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:
                     a_      :	Union[str, Any]							=						v.T
              a_      :	List[str]							=						torch.tensor(__A						,					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
       a_      :	int							=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
       a_      :	Optional[Any]							=						mapping['shared.weight']
       a_      :	Any							=						mapping['shared.weight']
       a_      :	Optional[int]							=						{k: torch.zeros_like(__A       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
       mapping.update(**__A       )
       a_							,	a_      :	Optional[Any]							=						torch_model.model.load_state_dict(__A						,					strict=__A       )
       a_      :	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  SCREAMING_SNAKE_CASE_		(    __A				:    Dict="./ckpt/aeslc/model.ckpt-32000"       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						tf.train.list_variables(__A       )
       a_      :	Optional[Any]							=						{}
       a_      :	int							=						['Adafactor', 'global_step']
       for name, shape in tqdm(__A						,					desc='converting tf checkpoint to dict'       ):
              a_      :	Optional[int]							=						any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              a_      :	List[str]							=						tf.train.load_variable(__A						,					__A       )
              a_      :	Optional[Any]							=						array
       return tf_weights







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Any							=						Path(__A       ).parent.name
       a_      :	Dict							=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
       a_      :	Union[str, Any]							=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,					model_max_length=__A       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__A       )

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


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = 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.')
   UpperCAmelCase_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


