

'''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 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      int							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir     ,"models/bert/"							)							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.transformer_dir
     shutil.copy(
         os.path.join(SCREAMING_SNAKE_CASE__     ,"src/transformers/models/bert/modeling_bert.py"							)     ,os.path.join(self.transformer_dir     ,"models/bert/modeling_bert.py"							)     ,)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"src/transformers"
     shutil.rmtree(self.transformer_dir							)
   def     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Dict=None							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
     if overwrite_result is not None:
       SCREAMING_SNAKE_CASE:Any						      =					comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
     SCREAMING_SNAKE_CASE:str						      =					black.Mode(target_versions={black.TargetVersion.PYaa}     ,line_length=119							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					black.format_str(SCREAMING_SNAKE_CASE__     ,mode=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					os.path.join(self.transformer_dir     ,"new_code.py"							)
     with open(SCREAMING_SNAKE_CASE__     ,"w"     ,newline="\n"							) as f:
       f.write(SCREAMING_SNAKE_CASE__							)
     if overwrite_result is None:
       self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__							)							) == 0							)
     else:
       check_copies.is_copy_consistent(f.name     ,overwrite=SCREAMING_SNAKE_CASE__							)
       with open(SCREAMING_SNAKE_CASE__     ,"r"							) as f:
         self.assertTrue(f.read()     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
     self.assertEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict							):
     # 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"     ,SCREAMING_SNAKE_CASE__     ,)

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

     # Copy consistency with a really long name
     SCREAMING_SNAKE_CASE:List[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"     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)     ,)

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







   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:List[str]						      =					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

     SCREAMING_SNAKE_CASE:Dict						      =					(
         "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."
     )
     SCREAMING_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。\n"
     )
     SCREAMING_SNAKE_CASE: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。\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"
     )

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					check_copies.convert_to_localized_md(
         SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,localized_readme["format_model_list"]							)

     self.assertFalse(SCREAMING_SNAKE_CASE__							)
     self.assertEqual(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					check_copies.convert_to_localized_md(
         SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,localized_readme["format_model_list"]							)

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

     SCREAMING_SNAKE_CASE:Dict						      =					(
         "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."
     )
     SCREAMING_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"
     )
     SCREAMING_SNAKE_CASE:int						      =					(
         "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"
     )

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					check_copies.convert_to_localized_md(
         SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,localized_readme["format_model_list"]							)

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









'''simple docstring'''

import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  for line in lines:
    SCREAMING_SNAKE_CASE:Any						      =					re.sub(r"#.*"      ,       ""      ,       snake_case )  # remove comments
    if line:
      filtered_lines.append(snake_case )
  SCREAMING_SNAKE_CASE:int						      =					"\n".join(snake_case )

  # Make a hash from all this code
  SCREAMING_SNAKE_CASE:str						      =					full_str.encode("utf-8" )
  return shaaaa(snake_case ).hexdigest()


# get importable module names and hash for caching
A_       						=							{
    "csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    "json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    "pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    "parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    "arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    "text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    "imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    "audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
A_       						=							{
    ".csv": ("csv", {}),
    ".tsv": ("csv", {"sep": "\t"}),
    ".json": ("json", {}),
    ".jsonl": ("json", {}),
    ".parquet": ("parquet", {}),
    ".arrow": ("arrow", {}),
    ".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A_       						=							{"imagefolder", "audiofolder"}

# Used to filter data files based on extensions given a module name
A_       						=							{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
    _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")


_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")






