


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

import black


__snake_case            =   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.
__snake_case            =   "    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    UpperCAmelCase					(       unittest.TestCase    ):




   def   lowerCamelCase_					(						self							:		Any						):







      """simple docstring"""







      UpperCamelCase         =					tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir       ,       """models/bert/"""						)						)
      UpperCamelCase         =					self.transformer_dir
      shutil.copy(
          os.path.join(__magic_name__       ,       """src/transformers/models/bert/modeling_bert.py"""						)       ,       os.path.join(self.transformer_dir       ,       """models/bert/modeling_bert.py"""						)       ,       )




   def   lowerCamelCase_					(						self							:		Optional[Any]						):







      """simple docstring"""







      UpperCamelCase         =					"""src/transformers"""
      shutil.rmtree(self.transformer_dir						)




   def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Tuple=None						):







      """simple docstring"""







      UpperCamelCase         =					comment + F'\nclass {class_name}(nn.Module):\n' + class_code
      if overwrite_result is not None:
         UpperCamelCase         =					comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
      UpperCamelCase         =					black.Mode(target_versions={black.TargetVersion.PYaa}       ,       line_length=1_1_9						)
      UpperCamelCase         =					black.format_str(__magic_name__       ,       mode=__magic_name__						)
      UpperCamelCase         =					os.path.join(self.transformer_dir       ,       """new_code.py"""						)
      with open(__magic_name__       ,       """w"""       ,       newline="""\n"""						) as f:
         f.write(__magic_name__						)
      if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(__magic_name__						)						) == 0						)
      else:
         check_copies.is_copy_consistent(f.name       ,       overwrite=__magic_name__						)
         with open(__magic_name__       ,       """r"""						) as f:
            self.assertTrue(f.read()       ,       __magic_name__						)




   def   lowerCamelCase_					(						self							:		Optional[Any]						):







      """simple docstring"""







      UpperCamelCase         =					check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""						)
      self.assertEqual(__magic_name__       ,       __magic_name__						)




   def   lowerCamelCase_					(						self							:		int						):







      """simple docstring"""







      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"""       ,       __magic_name__       ,       )

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

      # Copy consistency with a really long name
      UpperCamelCase         =					"""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"""       ,       __magic_name__       ,       __magic_name__						)       ,       )

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





   def   lowerCamelCase_					(						self							:		Optional[int]						):







      """simple docstring"""







      UpperCamelCase         =					check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

      UpperCamelCase         =					(
          """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."""
      )
      UpperCamelCase         =					(
          """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"""
      )
      UpperCamelCase         =					(
          """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"""
      )

      UpperCamelCase  ,  UpperCamelCase         =					check_copies.convert_to_localized_md(
          __magic_name__       ,       __magic_name__       ,       localized_readme["""format_model_list"""]						)

      self.assertFalse(__magic_name__						)
      self.assertEqual(__magic_name__       ,       __magic_name__						)

      UpperCamelCase  ,  UpperCamelCase         =					check_copies.convert_to_localized_md(
          __magic_name__       ,       __magic_name__       ,       localized_readme["""format_model_list"""]						)

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

      UpperCamelCase         =					(
          """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."""
      )
      UpperCamelCase         =					(
          """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"""
      )
      UpperCamelCase         =					(
          """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"""
      )

      UpperCamelCase  ,  UpperCamelCase         =					check_copies.convert_to_localized_md(
          __magic_name__       ,       __magic_name__       ,       localized_readme["""format_model_list"""]						)

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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= XLMTokenizer
			lowercase						= False




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCamelCase         =					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """w</w>""",
						    """r</w>""",
						    """t</w>""",
						    """lo""",
						    """low""",
						    """er</w>""",
						    """low</w>""",
						    """lowest</w>""",
						    """newer</w>""",
						    """wider</w>""",
						    """<unk>""",
						]
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""						) as fp:
									fp.write(json.dumps(__magic_name__						)						)
						with open(self.merges_file       ,       """w"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					"""lower newer"""
						return input_text, output_text




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer(self.vocab_file       ,       self.merges_file						)

						UpperCamelCase         =					"""lower"""
						UpperCamelCase         =					["""low""", """er</w>"""]
						UpperCamelCase         =					tokenizer.tokenize(__magic_name__						)
						self.assertListEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					tokens + ["""<unk>"""]
						UpperCamelCase         =					[1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__						)       ,       __magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

						UpperCamelCase         =					tokenizer.encode("""sequence builders"""       ,       add_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.encode("""multi-sequence build"""       ,       add_special_tokens=__magic_name__						)

						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__						)
						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__       ,       __magic_name__						)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]




