






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

import black


lowerCamelCase__							=     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.
lowerCamelCase__							=     """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						tempfile.mkdtemp()
            os.makedirs(os.path.join(self.transformer_dir			,						"""models/bert/"""    )    )
            __snake_case			:Optional[Any]          =						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 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						"""src/transformers"""
            shutil.rmtree(self.transformer_dir    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__=None    ) ->      str:






            '''simple docstring'''





            __snake_case			:Dict          =						comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
            if overwrite_result is not None:
                 __snake_case			:List[str]          =						comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
            __snake_case			:Union[str, Any]          =						black.Mode(target_versions={black.TargetVersion.PYaa}			,						line_length=1_19    )
            __snake_case			:List[str]          =						black.format_str(a__			,						mode=a__    )
            __snake_case			:Optional[int]          =						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 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Tuple          =						check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""    )
            self.assertEqual(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''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"""			,						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			: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"""			,						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 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:List[str]          =						check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

            __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.\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			:Optional[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"""
            )
            __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			:Tuple          =						check_copies.convert_to_localized_md(
                a__			,						a__			,						localized_readme["""format_model_list"""]    )

            self.assertFalse(a__    )
            self.assertEqual(a__			,						a__    )

            __snake_case       , __snake_case			:Tuple          =						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			:Optional[int]          =						(
                """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			:Any          =						(
                """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			:Optional[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       , __snake_case			:List[str]          =						check_copies.convert_to_localized_md(
                a__			,						a__			,						localized_readme["""format_model_list"""]    )

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










import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     1  # (0 is vertical, 1 is horizontal)






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case       , __snake_case			:List[Any]          =						get_dataset(snake_case__	,snake_case__  )
     print("""Processing..."""  )
     __snake_case       , __snake_case       , __snake_case			:Tuple          =						update_image_and_anno(snake_case__	,snake_case__	,snake_case__  )

     for index, image in enumerate(snake_case__  ):
          # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
          __snake_case			:str          =						random_chars(32  )
          __snake_case			:Optional[Any]          =						paths[index].split(os.sep  )[-1].rsplit("""."""	,1  )[0]
          __snake_case			:List[str]          =						f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
          cva.imwrite(f'''/{file_root}.jpg'''	,snake_case__	,[cva.IMWRITE_JPEG_QUALITY, 85]  )
          print(f'''Success {index+1}/{len(snake_case__  )} with {file_name}'''  )
          __snake_case			:Optional[int]          =						[]
          for anno in new_annos[index]:
               __snake_case			:str          =						f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
               annos_list.append(snake_case__  )
          with open(f'''/{file_root}.txt'''	,"""w"""  ) as outfile:
               outfile.write("""\n""".join(line for line in annos_list  )  )






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						[]
     __snake_case			:Dict          =						[]
     for label_file in glob.glob(os.path.join(snake_case__	,"""*.txt"""  )  ):
          __snake_case			:Tuple          =						label_file.split(os.sep  )[-1].rsplit("""."""	,1  )[0]
          with open(snake_case__  ) as in_file:
               __snake_case			:Optional[int]          =						in_file.readlines()
          __snake_case			:Any          =						os.path.join(snake_case__	,f'''{label_name}.jpg'''  )

          __snake_case			:List[str]          =						[]
          for obj_list in obj_lists:
               __snake_case			:int          =						obj_list.rstrip("""\n"""  ).split(""" """  )
               boxes.append(
                   [
                       int(obj[0]  ),
                       float(obj[1]  ),
                       float(obj[2]  ),
                       float(obj[3]  ),
                       float(obj[4]  ),
                   ]  )
          if not boxes:
               continue
          img_paths.append(snake_case__  )
          labels.append(snake_case__  )
     return img_paths, labels






def   UpperCamelCase  (       snake_case__     :   list	,snake_case__     :   list	,snake_case__     :   int = 1  ):




     '''simple docstring'''






     __snake_case			:Dict          =						[]
     __snake_case			:Any          =						[]
     __snake_case			:int          =						[]
     for idx in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						[]
          __snake_case			:List[str]          =						img_list[idx]
          path_list.append(snake_case__  )
          __snake_case			:Union[str, Any]          =						anno_list[idx]
          __snake_case			:List[Any]          =						cva.imread(snake_case__  )
          if flip_type == 1:
               __snake_case			:Optional[Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Union[str, Any]          =						1 - bbox[1]
                    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]  )
          elif flip_type == 0:
               __snake_case			:Union[str, Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Optional[Any]          =						1 - bbox[2]
                    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]  )
          new_annos_lists.append(snake_case__  )
          new_imgs_list.append(snake_case__  )
     return new_imgs_list, new_annos_lists, path_list






def   UpperCamelCase  (       snake_case__     :   int = 32  ):




     '''simple docstring'''






     assert number_char > 1, "The number of character should greater than 1"
     __snake_case			:Optional[Any]          =						ascii_lowercase + digits
     return "".join(random.choice(snake_case__  ) for _ in range(snake_case__  )  )


if __name__ == "__main__":
     main()
     print("""DONE ✅""")


