




'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_			      =		logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class        lowercase_      (						lowerCAmelCase_							):
     A_											=			field(default=lowerCAmelCase_						,       metadata={"help": "Whether to use SortishSampler or not."}							)
     A_											=			field(
         default=lowerCAmelCase_						,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}							)
     A_											=			field(
         default=lowerCAmelCase_						,       metadata={
             "help": (
                 "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                 "to the `max_length` value of the model configuration."
             )
         }						,       )
     A_											=			field(
         default=lowerCAmelCase_						,       metadata={
             "help": (
                 "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                 "to the `num_beams` value of the model configuration."
             )
         }						,       )
     A_											=			field(
         default=lowerCAmelCase_						,       metadata={
             "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
         }						,       )





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   str	      =    super().to_dict()
           for k, v in d.items():
                 if isinstance(__lowerCamelCase     ,							__lowerCamelCase			):
                       snake_case__       :   Optional[Any]	      =    v.to_dict()
           return d






'''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			)
