


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						Any    =    BartTokenizer
    UpperCAmelCase__		:						Optional[int]    =    BartTokenizerFast
    UpperCAmelCase__		:						List[Any]    =    True
    UpperCAmelCase__		:						int    =    filter_roberta_detectors






    def _a   (     self       )    ->			str:
         super().setUp()
         __UpperCamelCase       				=[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         __UpperCamelCase       				=dict(zip(A_   ,  range(len(A_       )       )       )       )
         __UpperCamelCase       				=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __UpperCamelCase       				={'unk_token': '<unk>'}

         __UpperCamelCase       				=os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES['vocab_file']       )
         __UpperCamelCase       				=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(A_       ) + '\n'       )
         with open(self.merges_file   ,  'w'   ,  encoding='utf-8'       ) as fp:
              fp.write('\n'.join(A_       )       )






    def _a   (     self   ,  **A_       )    ->			List[Any]:
         kwargs.update(self.special_tokens_map       )
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,  **A_       )






    def _a   (     self   ,  **A_       )    ->			Tuple:
         kwargs.update(self.special_tokens_map       )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,  **A_       )






    def _a   (     self   ,  A_       )    ->			Optional[int]:
         return "lower newer", "lower newer"






    @cached_property
    def _a   (     self       )    ->			Dict:
         return BartTokenizer.from_pretrained('facebook/bart-large'       )






    @cached_property
    def _a   (     self       )    ->			Optional[Any]:
         return BartTokenizerFast.from_pretrained('facebook/bart-large'       )






    @require_torch
    def _a   (     self       )    ->			int:
         __UpperCamelCase       				=['A long paragraph for summarization.', 'Another paragraph for summarization.']
         __UpperCamelCase       				=[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase       				=tokenizer(A_   ,  max_length=len(A_       )   ,  padding=A_   ,  return_tensors='pt'       )
              self.assertIsInstance(A_   ,  A_       )

              self.assertEqual((2, 9)   ,  batch.input_ids.shape       )
              self.assertEqual((2, 9)   ,  batch.attention_mask.shape       )
              __UpperCamelCase       				=batch.input_ids.tolist()[0]
              self.assertListEqual(A_   ,  A_       )
              # Test that special tokens are reset






    @require_torch
    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=['A long paragraph for summarization.', 'Another paragraph for summarization.']
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase       				=tokenizer(A_   ,  padding=A_   ,  return_tensors='pt'       )
              # check if input_ids are returned and no labels
              self.assertIn('input_ids'   ,  A_       )
              self.assertIn('attention_mask'   ,  A_       )
              self.assertNotIn('labels'   ,  A_       )
              self.assertNotIn('decoder_attention_mask'   ,  A_       )






    @require_torch
    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=[
             'Summary of the text.',
             'Another summary.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase       				=tokenizer(text_target=A_   ,  max_length=32   ,  padding='max_length'   ,  return_tensors='pt'       )
              self.assertEqual(32   ,  targets['input_ids'].shape[1]       )






    @require_torch
    def _a   (     self       )    ->			Union[str, Any]:
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase       				=tokenizer(
                  ['I am a small frog' * 1024, 'I am a small frog']   ,  padding=A_   ,  truncation=A_   ,  return_tensors='pt'       )
              self.assertIsInstance(A_   ,  A_       )
              self.assertEqual(batch.input_ids.shape   ,  (2, 1024)       )






    @require_torch
    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				=['A long paragraph for summarization.']
         __UpperCamelCase       				=[
             'Summary of the text.',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase       				=tokenizer(A_   ,  return_tensors='pt'       )
              __UpperCamelCase       				=tokenizer(text_target=A_   ,  return_tensors='pt'       )
              __UpperCamelCase       				=inputs['input_ids']
              __UpperCamelCase       				=targets['input_ids']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )






    def _a   (     self       )    ->			List[str]:
         pass






    def _a   (     self       )    ->			int:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
                   __UpperCamelCase       				=self.rust_tokenizer_class.from_pretrained(A_   ,  **A_       )
                   __UpperCamelCase       				=self.tokenizer_class.from_pretrained(A_   ,  **A_       )
                   __UpperCamelCase       				='A, <mask> AllenNLP sentence.'
                   __UpperCamelCase       				=tokenizer_r.encode_plus(A_   ,  add_special_tokens=A_   ,  return_token_type_ids=A_       )
                   __UpperCamelCase       				=tokenizer_p.encode_plus(A_   ,  add_special_tokens=A_   ,  return_token_type_ids=A_       )

                   # 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']       )   ,  )

                   __UpperCamelCase       				=tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
                   __UpperCamelCase       				=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, 50264, 3823, 487, 21992, 3645, 4, 2]       )
                   self.assertSequenceEqual(tokens_r['input_ids']   ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )

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









import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





