
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   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		str:
         snake_case				  =      tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir       ,					"models/bert/"     )     )
         snake_case				  =      self.transformer_dir
         shutil.copy(
             os.path.join(_A       ,					"src/transformers/models/bert/modeling_bert.py"     )       ,					os.path.join(self.transformer_dir       ,					"models/bert/modeling_bert.py"     )       ,					)





     def 	UpperCAmelCase(self   :					List[str]     )		->		Any:
         snake_case				  =      "src/transformers"
         shutil.rmtree(self.transformer_dir     )





     def 	UpperCAmelCase(self   :					Dict       ,					_A   :					Tuple       ,					_A   :					Any       ,					_A   :					Any       ,					_A   :					Optional[Any]=None     )		->		Dict:
         snake_case				  =      comment + f'\nclass {class_name}(nn.Module):\n' + class_code
         if overwrite_result is not None:
             snake_case				  =      comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
         snake_case				  =      black.Mode(target_versions={black.TargetVersion.PYaa}       ,					line_length=1_1_9     )
         snake_case				  =      black.format_str(_A       ,					mode=_A     )
         snake_case				  =      os.path.join(self.transformer_dir       ,					"new_code.py"     )
         with open(_A       ,					"w"       ,					newline="\n"     ) as f:
             f.write(_A     )
         if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(_A     )     ) == 0     )
         else:
             check_copies.is_copy_consistent(f.name       ,					overwrite=_A     )
             with open(_A       ,					"r"     ) as f:
                 self.assertTrue(f.read()       ,					_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Union[str, Any]:
         snake_case				  =      check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"     )
         self.assertEqual(_A       ,					_A     )





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         # 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"       ,					_A       ,					)

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

         # Copy consistency with a really long name
         snake_case				  =      "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"       ,					_A       ,					_A     )       ,					)

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





     def 	UpperCAmelCase(self   :					Dict     )		->		Optional[Any]:
         snake_case				  =      check_copies.LOCALIZED_READMES["README_zh-hans.md"]

         snake_case				  =      (
             "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				  =      (
             "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				  =      (
             "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				  =      check_copies.convert_to_localized_md(
             _A       ,					_A       ,					localized_readme["format_model_list"]     )

         self.assertFalse(_A     )
         self.assertEqual(_A       ,					_A     )

         snake_case  ,							snake_case				  =      check_copies.convert_to_localized_md(
             _A       ,					_A       ,					localized_readme["format_model_list"]     )

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

         snake_case				  =      (
             "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				  =      (
             "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				  =      (
             "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				  =      check_copies.convert_to_localized_md(
             _A       ,					_A       ,					localized_readme["format_model_list"]     )

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



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	"canine"





     def __init__(self   :					List[Any]       ,					_A   :					Union[str, Any]=7_6_8       ,					_A   :					Any=1_2       ,					_A   :					List[Any]=1_2       ,					_A   :					List[Any]=3_0_7_2       ,					_A   :					Dict="gelu"       ,					_A   :					Optional[Any]=0.1       ,					_A   :					Tuple=0.1       ,					_A   :					str=1_6_3_8_4       ,					_A   :					Union[str, Any]=1_6       ,					_A   :					Any=0.02       ,					_A   :					List[str]=1E-12       ,					_A   :					Union[str, Any]=0       ,					_A   :					Dict=0Xe0_00       ,					_A   :					List[Any]=0Xe0_01       ,					_A   :					int=4       ,					_A   :					str=4       ,					_A   :					Optional[int]=8       ,					_A   :					Optional[Any]=1_6_3_8_4       ,					_A   :					Optional[Any]=1_2_8       ,					**_A   :					Union[str, Any]       ,					)		->		Union[str, Any]:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      max_position_embeddings
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_act
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      initializer_range
         snake_case				  =      type_vocab_size
         snake_case				  =      layer_norm_eps

         # Character config:
         snake_case				  =      downsampling_rate
         snake_case				  =      upsampling_kernel_size
         snake_case				  =      num_hash_functions
         snake_case				  =      num_hash_buckets
         snake_case				  =      local_transformer_stride

