




'''simple docstring'''






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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
      import faiss






@require_faiss
class        lowercase_      (						lowerCAmelCase_							):





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Tuple	      =    tempfile.mkdtemp()
           snake_case__       :   Dict	      =    8

           # DPR tok
           snake_case__       :   Any	      =    [
               '[UNK]',
               '[CLS]',
               '[SEP]',
               '[PAD]',
               '[MASK]',
               'want',
               '##want',
               '##ed',
               'wa',
               'un',
               'runn',
               '##ing',
               ',',
               'low',
               'lowest',
           ]
           snake_case__       :   Optional[int]	      =    os.path.join(self.tmpdirname     ,							'dpr_tokenizer'			)
           os.makedirs(__lowerCamelCase     ,							exist_ok=__lowerCamelCase			)
           snake_case__       :   Tuple	      =    os.path.join(__lowerCamelCase     ,							DPR_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]			)			)

           # BART tok
           snake_case__       :   Dict	      =    [
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           snake_case__       :   str	      =    dict(zip(__lowerCamelCase     ,							range(len(__lowerCamelCase			)			)			)			)
           snake_case__       :   Union[str, Any]	      =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           snake_case__       :   Any	      =    {'unk_token': '<unk>'}

           snake_case__       :   List[str]	      =    os.path.join(self.tmpdirname     ,							'bart_tokenizer'			)
           os.makedirs(__lowerCamelCase     ,							exist_ok=__lowerCamelCase			)
           snake_case__       :   Dict	      =    os.path.join(__lowerCamelCase     ,							BART_VOCAB_FILES_NAMES['vocab_file']			)
           snake_case__       :   int	      =    os.path.join(__lowerCamelCase     ,							BART_VOCAB_FILES_NAMES['merges_file']			)
           with open(self.vocab_file     ,							'w'     ,							encoding='utf-8'			) as fp:
                 fp.write(json.dumps(__lowerCamelCase			) + '\n'			)
           with open(self.merges_file     ,							'w'     ,							encoding='utf-8'			) as fp:
                 fp.write('\n'.join(__lowerCamelCase			)			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'dpr_tokenizer'			)			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'dpr_tokenizer'			)			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'bart_tokenizer'			)			)





     def 			_lowerCAmelCase      (     self     :					int			):
           shutil.rmtree(self.tmpdirname			)





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   int	      =    Dataset.from_dict(
               {
                   'id': ['0', '1'],
                   'text': ['foo', 'bar'],
                   'title': ['Foo', 'Bar'],
                   'embeddings': [np.ones(self.retrieval_vector_size			), 2 * np.ones(self.retrieval_vector_size			)],
               }			)
           dataset.add_faiss_index('embeddings'     ,							string_factory='Flat'     ,							metric_type=faiss.METRIC_INNER_PRODUCT			)
           return dataset





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   List[str]	      =    self.get_dummy_dataset()
           snake_case__       :   Dict	      =    RagConfig(
               retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							)
           with patch('transformers.models.rag.retrieval_rag.load_dataset'			) as mock_load_dataset:
                 snake_case__       :   Optional[Any]	      =    dataset
                 snake_case__       :   List[Any]	      =    RagRetriever(
                     __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							)
           return retriever





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					bool			):
           snake_case__       :   Union[str, Any]	      =    self.get_dummy_dataset()
           snake_case__       :   Dict	      =    RagConfig(
               retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							index_name='custom'     ,							)
           if from_disk:
                 snake_case__       :   List[Any]	      =    os.path.join(self.tmpdirname     ,							'dataset'			)
                 snake_case__       :   List[str]	      =    os.path.join(self.tmpdirname     ,							'index.faiss'			)
                 dataset.get_index('embeddings'			).save(os.path.join(self.tmpdirname     ,							'index.faiss'			)			)
                 dataset.drop_index('embeddings'			)
                 dataset.save_to_disk(os.path.join(self.tmpdirname     ,							'dataset'			)			)
                 del dataset
                 snake_case__       :   Optional[int]	      =    RagRetriever(
                     __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							)
           else:
                 snake_case__       :   Union[str, Any]	      =    RagRetriever(
                     __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							index=CustomHFIndex(config.retrieval_vector_size     ,							__lowerCamelCase			)     ,							)
           return retriever





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Dict	      =    Dataset.from_dict(
               {
                   'id': ['0', '1'],
                   'text': ['foo', 'bar'],
                   'title': ['Foo', 'Bar'],
                   'embeddings': [np.ones(self.retrieval_vector_size + 1			), 2 * np.ones(self.retrieval_vector_size + 1			)],
               }			)
           dataset.add_faiss_index('embeddings'     ,							string_factory='Flat'     ,							metric_type=faiss.METRIC_INNER_PRODUCT			)

           snake_case__       :   List[str]	      =    os.path.join(self.tmpdirname     ,							'hf_bert_base.hnswSQ8_correct_phi_128.c_index'			)
           dataset.save_faiss_index('embeddings'     ,							index_file_name + '.index.dpr'			)
           pickle.dump(dataset['id']     ,							open(index_file_name + '.index_meta.dpr'     ,							'wb'			)			)

           snake_case__       :   Optional[int]	      =    os.path.join(self.tmpdirname     ,							'psgs_w100.tsv.pkl'			)
           snake_case__       :   Optional[Any]	      =    {sample['id']: [sample['text'], sample['title']] for sample in dataset}
           pickle.dump(__lowerCamelCase     ,							open(__lowerCamelCase     ,							'wb'			)			)

           snake_case__       :   Union[str, Any]	      =    RagConfig(
               retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							index_name='legacy'     ,							index_path=self.tmpdirname     ,							)
           snake_case__       :   List[str]	      =    RagRetriever(
               __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()			)
           return retriever





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   List[str]	      =    1
           snake_case__       :   int	      =    self.get_dummy_canonical_hf_index_retriever()
           snake_case__       :   Tuple	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   int	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['embeddings', 'id', 'text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['id']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['id'][0]     ,							'1'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['id'][0]     ,							'0'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   str	      =    self.get_dummy_canonical_hf_index_retriever()
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 with patch('transformers.models.rag.retrieval_rag.load_dataset'			) as mock_load_dataset:
                       snake_case__       :   Union[str, Any]	      =    self.get_dummy_dataset()
                       retriever.save_pretrained(__lowerCamelCase			)
                       snake_case__       :   List[Any]	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                       self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                       snake_case__       :   str	      =    np.array(
                           [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                       snake_case__       :   Tuple	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                       self.assertTrue(out is not None			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Any	      =    1
           snake_case__       :   Tuple	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           snake_case__       :   Dict	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   List[str]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['embeddings', 'id', 'text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['id']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['id'][0]     ,							'1'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['id'][0]     ,							'0'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   str	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase			)
                 snake_case__       :   int	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                 self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Tuple	      =    np.array(
                     [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                 snake_case__       :   List[str]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                 self.assertTrue(out is not None			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Any	      =    1
           snake_case__       :   Optional[int]	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           snake_case__       :   str	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   List[Any]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['embeddings', 'id', 'text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['id']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['id'][0]     ,							'1'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['id'][0]     ,							'0'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Optional[int]	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase			)
                 snake_case__       :   int	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                 self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Optional[int]	      =    np.array(
                     [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                 snake_case__       :   List[Any]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                 self.assertTrue(out is not None			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Tuple	      =    1
           snake_case__       :   Tuple	      =    self.get_dummy_legacy_index_retriever()
           snake_case__       :   str	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   List[str]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=__lowerCamelCase			)
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertEqual(len(__lowerCamelCase			)     ,							2			)
           self.assertEqual(sorted(doc_dicts[0]			)     ,							['text', 'title']			)
           self.assertEqual(len(doc_dicts[0]['text']			)     ,							__lowerCamelCase			)
           self.assertEqual(doc_dicts[0]['text'][0]     ,							'bar'			)  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['text'][0]     ,							'foo'			)  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()     ,							[[1], [0]]			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Any	      =    self.get_dummy_legacy_index_retriever()
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(__lowerCamelCase			)
                 snake_case__       :   Dict	      =    RagRetriever.from_pretrained(__lowerCamelCase			)
                 self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Any	      =    np.array(
                     [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
                 snake_case__       :   List[Any]	      =    retriever.retrieve(__lowerCamelCase     ,							n_docs=1			)
                 self.assertTrue(out is not None			)





     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 			_lowerCAmelCase      (     self     :					Any			):
           import torch

           snake_case__       :   Optional[Any]	      =    1
           snake_case__       :   int	      =    self.get_dummy_canonical_hf_index_retriever()
           snake_case__       :   List[Any]	      =    [[5, 7], [10, 11]]
           snake_case__       :   List[Any]	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__       :   Any	      =    retriever(__lowerCamelCase     ,							__lowerCamelCase     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__lowerCamelCase			)
           snake_case__      ,							snake_case__      ,							snake_case__       :   Any	      =    (
               out['context_input_ids'],
               out['context_attention_mask'],
               out['retrieved_doc_embeds'],
           )
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
           self.assertIsInstance(__lowerCamelCase     ,							__lowerCamelCase			)
           self.assertIsInstance(__lowerCamelCase     ,							np.ndarray			)

           snake_case__       :   Dict	      =    retriever(
               __lowerCamelCase     ,							__lowerCamelCase     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__lowerCamelCase     ,							return_tensors='pt'     ,							)
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Optional[Any]	      =    (  # noqa: F841
               out['context_input_ids'],
               out['context_attention_mask'],
               out['retrieved_doc_embeds'],
               out['doc_ids'],
           )
           self.assertEqual(retrieved_doc_embeds.shape     ,							(2, n_docs, self.retrieval_vector_size)			)
           self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)
           self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)
           self.assertIsInstance(__lowerCamelCase     ,							torch.Tensor			)





     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   List[Any]	      =    self.get_dpr_ctx_encoder_tokenizer()
           snake_case__       :   List[Any]	      =    1
           snake_case__       :   Any	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           retriever.set_ctx_encoder_tokenizer(__lowerCamelCase			)

           snake_case__       :   Optional[int]	      =    [[5, 7], [10, 11]]
           snake_case__       :   Tuple	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__       :   Tuple	      =    retriever(__lowerCamelCase     ,							__lowerCamelCase     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__lowerCamelCase			)

           self.assertEqual(
               len(__lowerCamelCase			)     ,							6			)  # check whether the retriever output consist of 6 attributes including tokenized docs
           self.assertEqual(
               all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')			)     ,							__lowerCamelCase			)  # check for doc token related keys in dictionary.






'''simple docstring'''






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A_			      =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A_			      =		"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   str	      =    tempfile.mkdtemp()
           os.makedirs(os.path.join(self.transformer_dir     ,							'models/bert/'			)			)
           snake_case__       :   Optional[Any]	      =    self.transformer_dir
           shutil.copy(
               os.path.join(__lowerCamelCase     ,							'src/transformers/models/bert/modeling_bert.py'			)     ,							os.path.join(self.transformer_dir     ,							'models/bert/modeling_bert.py'			)     ,							)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Union[str, Any]	      =    'src/transformers'
           shutil.rmtree(self.transformer_dir			)





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]=None			):
           snake_case__       :   Optional[int]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + class_code
           if overwrite_result is not None:
                 snake_case__       :   List[Any]	      =    comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
           snake_case__       :   Optional[int]	      =    black.Mode(target_versions={black.TargetVersion.PYaa}     ,							line_length=119			)
           snake_case__       :   Tuple	      =    black.format_str(__lowerCamelCase     ,							mode=__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    os.path.join(self.transformer_dir     ,							'new_code.py'			)
           with open(__lowerCamelCase     ,							'w'     ,							newline='\n'			) as f:
                 f.write(__lowerCamelCase			)
           if overwrite_result is None:
                 self.assertTrue(len(check_copies.is_copy_consistent(__lowerCamelCase			)			) == 0			)
           else:
                 check_copies.is_copy_consistent(f.name     ,							overwrite=__lowerCamelCase			)
                 with open(__lowerCamelCase     ,							'r'			) as f:
                       self.assertTrue(f.read()     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[str]	      =    check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           # Base copy consistency
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'     ,							'BertLMPredictionHead'     ,							REFERENCE_CODE + '\n'     ,							)

           # With no empty line at the end
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'     ,							'BertLMPredictionHead'     ,							__lowerCamelCase     ,							)

           # Copy consistency with rename
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'     ,							'TestModelLMPredictionHead'     ,							re.sub('Bert'     ,							'TestModel'     ,							__lowerCamelCase			)     ,							)

           # Copy consistency with a really long name
           snake_case__       :   Union[str, Any]	      =    'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
           self.check_copy_consistency(
               F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"     ,							F"{long_class_name}LMPredictionHead"     ,							re.sub('Bert'     ,							__lowerCamelCase     ,							__lowerCamelCase			)     ,							)

           # Copy consistency with overwrite
           self.check_copy_consistency(
               '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'     ,							'TestModelLMPredictionHead'     ,							__lowerCamelCase     ,							overwrite_result=re.sub('Bert'     ,							'TestModel'     ,							__lowerCamelCase			)     ,							)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    check_copies.LOCALIZED_READMES['README_zh-hans.md']

           snake_case__       :   List[str]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
               ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
               ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
               ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
               ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
               ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
               ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
               ' method has been applied to compress GPT2 into'
               ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
               ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
               ' Multilingual BERT into'
               ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
               ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
               ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
               ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
               ' Luong, Quoc V. Le, Christopher D. Manning.'
           )
           snake_case__       :   Union[str, Any]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )
           snake_case__       :   Tuple	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
               ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
               ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
               ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
               ' method has been applied to compress GPT2 into'
               ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
               ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
               ' Multilingual BERT into'
               ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
               ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
               ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
               ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
               ' Christopher D. Manning 发布。\n'
           )

           snake_case__      ,							snake_case__       :   Optional[Any]	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           self.assertFalse(__lowerCamelCase			)
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   Any	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           # Check whether the number of models is equal to README.md after conversion.
           self.assertTrue(__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
               ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
               ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
               ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
           )
           snake_case__       :   int	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
               ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )
           snake_case__       :   List[str]	      =    (
               '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
               ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
               ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
               ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
           )

           snake_case__      ,							snake_case__       :   Any	      =    check_copies.convert_to_localized_md(
               __lowerCamelCase     ,							__lowerCamelCase     ,							localized_readme['format_model_list']			)

           # Check if the model link is synchronized.
           self.assertEqual(__lowerCamelCase     ,							__lowerCamelCase			)
