





'''simple docstring'''




import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer


class 			SCREAMING_SNAKE_CASE   (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	tempfile.mkdtemp()
          __A						:			Tuple								=	5

          # Realm tok
          __A						:			Union[str, Any]								=	[
              '[UNK]',
              '[CLS]',
              '[SEP]',
              '[PAD]',
              '[MASK]',
              'test',
              'question',
              'this',
              'is',
              'the',
              'first',
              'second',
              'third',
              'fourth',
              'fifth',
              'record',
              'want',
              '##want',
              '##ed',
              'wa',
              'un',
              'runn',
              '##ing',
              ',',
              'low',
              'lowest',
          ]
          __A						:			Optional[Any]								=	os.path.join(self.tmpdirname						,     'realm_tokenizer')
          os.makedirs(_UpperCAmelCase						,     exist_ok=_UpperCAmelCase)
          __A						:			Dict								=	os.path.join(_UpperCAmelCase						,     VOCAB_FILES_NAMES['vocab_file'])
          with open(self.vocab_file						,     'w'						,     encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

          __A						:			Dict								=	os.path.join(self.tmpdirname						,     'realm_block_records')
          os.makedirs(_UpperCAmelCase						,     exist_ok=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,     'realm_tokenizer'))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	RealmConfig(num_block_records=self.num_block_records)
          return config


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	Dataset.from_dict(
              {
                  'id': ['0', '1'],
                  'question': ['foo', 'bar'],
                  'answers': [['Foo', 'Bar'], ['Bar']],
              })
          return dataset


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	np.array(
              [
                  B'This is the first record',
                  B'This is the second record',
                  B'This is the third record',
                  B'This is the fourth record',
                  B'This is the fifth record',
                  B'This is a longer longer longer record',
              ]						,     dtype=_UpperCAmelCase						,     )
          return block_records


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	RealmRetriever(
              block_records=self.get_dummy_block_records()						,     tokenizer=self.get_tokenizer()						,     )
          return retriever


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	self.get_config()
          __A						:			Tuple								=	self.get_dummy_retriever()
          __A						:			Union[str, Any]								=	retriever.tokenizer

          __A						:			int								=	np.array([0, 3]						,     dtype='long')
          __A						:			Union[str, Any]								=	tokenizer(['Test question']).input_ids
          __A						:			List[Any]								=	tokenizer(
              ['the fourth']						,     add_special_tokens=_UpperCAmelCase						,     return_token_type_ids=_UpperCAmelCase						,     return_attention_mask=_UpperCAmelCase						,     ).input_ids
          __A						:			int								=	config.reader_seq_len

          __A						:			Any								=	retriever(
              _UpperCAmelCase						,     _UpperCAmelCase						,     answer_ids=_UpperCAmelCase						,     max_length=_UpperCAmelCase						,     return_tensors='np')

          self.assertEqual(len(_UpperCAmelCase)						,     2)
          self.assertEqual(len(_UpperCAmelCase)						,     2)
          self.assertEqual(len(_UpperCAmelCase)						,     2)
          self.assertEqual(concat_inputs.input_ids.shape						,     (2, 10))
          self.assertEqual(concat_inputs.attention_mask.shape						,     (2, 10))
          self.assertEqual(concat_inputs.token_type_ids.shape						,     (2, 10))
          self.assertEqual(concat_inputs.special_tokens_mask.shape						,     (2, 10))
          self.assertEqual(
              tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0])						,     ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']						,     )
          self.assertEqual(
              tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1])						,     ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']						,     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.get_config()
          __A						:			str								=	self.get_dummy_retriever()
          __A						:			int								=	retriever.tokenizer

          __A						:			Any								=	np.array([0, 3, 5]						,     dtype='long')
          __A						:			Tuple								=	tokenizer(['Test question']).input_ids
          __A						:			Dict								=	tokenizer(
              ['the fourth', 'longer longer']						,     add_special_tokens=_UpperCAmelCase						,     return_token_type_ids=_UpperCAmelCase						,     return_attention_mask=_UpperCAmelCase						,     ).input_ids
          __A						:			int								=	config.reader_seq_len

          __A						:			Any								=	retriever(
              _UpperCAmelCase						,     _UpperCAmelCase						,     answer_ids=_UpperCAmelCase						,     max_length=_UpperCAmelCase						,     return_tensors='np')

          self.assertEqual([False, True, True]						,     _UpperCAmelCase)
          self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]						,     _UpperCAmelCase)
          self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	self.get_dummy_retriever()
          retriever.save_pretrained(os.path.join(self.tmpdirname						,     'realm_block_records'))

          # Test local path
          __A						:			Union[str, Any]								=	retriever.from_pretrained(os.path.join(self.tmpdirname						,     'realm_block_records'))
          self.assertEqual(retriever.block_records[0]						,     B'This is the first record')

          # Test mocked remote path
          with patch('transformers.models.realm.retrieval_realm.hf_hub_download') as mock_hf_hub_download:
               __A						:			List[str]								=	os.path.join(
                   os.path.join(self.tmpdirname						,     'realm_block_records')						,     _REALM_BLOCK_RECORDS_FILENAME)
               __A						:			Dict								=	RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa')

          self.assertEqual(retriever.block_records[0]						,     B'This is the first record')






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)