




'''simple docstring'''



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml







class 		lowercase_   (			yaml.SafeLoader       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[self.constructed_objects[key_node] for key_node, _ in node.value]
      _SCREAMING_SNAKE_CASE	=				[tuple(__lowerCamelCase   ) if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ) else key for key in keys]
      _SCREAMING_SNAKE_CASE	=				Counter(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				[key for key in counter if counter[key] > 1]
      if duplicate_keys:
         raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""   )



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]=False   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().construct_mapping(__lowerCamelCase  ,				deep=__lowerCamelCase   )
      self._check_no_duplicates_on_constructed_node(__lowerCamelCase   )
      return mapping


def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			Tuple[Optional[str], str]:
   _SCREAMING_SNAKE_CASE	=				list(readme_content.splitlines()  )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      _SCREAMING_SNAKE_CASE	=				full_content[1:].index("---"  ) + 1
      _SCREAMING_SNAKE_CASE	=				"\n".join(full_content[1:sep_idx]  )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :]  )

   return None, "\n".join(__A  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata



   @classmethod
   def 							lowerCAmelCase_      (   cls :     List[Any]  ,				__lowerCamelCase :     Path   ):

      """simple docstring"""
      with open(__lowerCamelCase  ,				encoding="utf-8"   ) as readme_file:
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				_split_yaml_from_readme(readme_file.read()   )
      if yaml_string is not None:
         return cls.from_yaml_string(__lowerCamelCase   )
      else:
         return cls()



   def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     Path   ):

      """simple docstring"""
      if path.exists():
         with open(__lowerCamelCase  ,				encoding="utf-8"   ) as readme_file:
            _SCREAMING_SNAKE_CASE	=				readme_file.read()
      else:
         _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				self._to_readme(__lowerCamelCase   )
      with open(__lowerCamelCase  ,				"w"  ,				encoding="utf-8"   ) as readme_file:
         readme_file.write(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Optional[str] = None   ):

      """simple docstring"""
      if readme_content is not None:
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				_split_yaml_from_readme(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				"---\n" + self.to_yaml_string() + "---\n" + content
      else:
         _SCREAMING_SNAKE_CASE	=				"---\n" + self.to_yaml_string() + "---\n"
      return full_content



   @classmethod
   def 							lowerCAmelCase_      (   cls :     Any  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				yaml.load(__lowerCamelCase  ,				Loader=_NoDuplicateSafeLoader   ) or {}

      # Convert the YAML keys to DatasetMetadata fields
      _SCREAMING_SNAKE_CASE	=				{
          (key.replace("-"  ,				"_"   ) if key.replace("-"  ,				"_"   ) in cls._FIELDS_WITH_DASHES else key): value
          for key, value in metadata_dict.items()
      }
      return cls(**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return yaml.safe_dump(
          {
              (key.replace("_"  ,				"-"   ) if key in self._FIELDS_WITH_DASHES else key): value
              for key, value in self.items()
          }  ,				sort_keys=__lowerCamelCase  ,				allow_unicode=__lowerCamelCase  ,				encoding="utf-8"  ,				).decode("utf-8"   )


lowerCamelCase_			=						{
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
 from argparse import ArgumentParser

 lowerCamelCase_			=						ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
 ap.add_argument('readme_filepath')
 lowerCamelCase_			=						ap.parse_args()

 lowerCamelCase_			=						Path(args.readme_filepath)
 lowerCamelCase_			=						DatasetMetadata.from_readme(readme_filepath)
 print(dataset_metadata)
 dataset_metadata.to_readme(readme_filepath)









'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return None







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return None







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						[
       # (model_name, model_kwargs)
       ('''bert-base-cased''', {}),
       ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
   ]



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
      with NamedTemporaryFile(mode="w+t"   ) as vocab_file:
         vocab_file.write("\n".join(__lowerCamelCase   )   )
         vocab_file.flush()
         _SCREAMING_SNAKE_CASE	=				BertTokenizerFast(vocab_file.name   )

      with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig(vocab_size=len(__lowerCamelCase   )   )   )
         model.save_pretrained(__lowerCamelCase   )
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				__lowerCamelCase   )



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(Path(__lowerCamelCase   )   )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(__lowerCamelCase   ).stat().st_size:
            self.fail("Quantized model is bigger than initial ONNX model"   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(__lowerCamelCase   )

         # Ensure the actual quantized model is not bigger than the original one
         if quantized_path.stat().st_size >= Path(__lowerCamelCase   ).stat().st_size:
            self.fail("Quantized model is bigger than initial ONNX model"   )



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[int]=None  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      try:
         # Compute path
         with TemporaryDirectory() as tempdir:
            _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath("model.onnx"   )

         # Remove folder if exists
         if path.parent.exists():
            path.parent.rmdir()

         # Export
         convert(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   )

         return path
      except Exception as e:
         self.fail(__lowerCamelCase   )



   @require_torch
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"pt"   )



   @require_tf
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      from transformers import TFBertModel

      _SCREAMING_SNAKE_CASE	=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"tf"   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FeatureExtractionPipeline(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				infer_shapes(__lowerCamelCase  ,				__lowerCamelCase   )

      # Assert all variables are present
      self.assertEqual(len(__lowerCamelCase   )  ,				len(__lowerCamelCase   )   )
      self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
      self.assertSequenceEqual(variable_names[:3]  ,				__lowerCamelCase   )
      self.assertSequenceEqual(variable_names[3:]  ,				__lowerCamelCase   )

      # Assert inputs are {0: batch, 1: sequence}
      for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
         self.assertDictEqual(shapes[var_name]  ,				{0: "batch", 1: "sequence"}   )

      # Assert outputs are {0: batch, 1: sequence} and {0: batch}
      self.assertDictEqual(shapes["output_0"]  ,				{0: "batch", 1: "sequence"}   )
      self.assertDictEqual(shapes["output_1"]  ,				{0: "batch"}   )



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				["input_ids", "attention_mask", "token_type_ids"]
      _SCREAMING_SNAKE_CASE	=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

      # Should have exactly the same number of args (all are valid)
      self.assertEqual(len(__lowerCamelCase   )  ,				3   )

      # Should have exactly the same input names
      self.assertEqual(set(__lowerCamelCase   )  ,				set(__lowerCamelCase   )   )

      # Parameter should be reordered according to their respective place in the function:
      # (input_ids, token_type_ids, attention_mask)
      self.assertEqual(__lowerCamelCase  ,				(tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])   )

      # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncNonContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

      # Should have exactly the one arg (all before the one not provided "some_other_args")
      self.assertEqual(len(__lowerCamelCase   )  ,				1   )
      self.assertEqual(len(__lowerCamelCase   )  ,				1   )

      # Should have only "input_ids"
      self.assertEqual(inputs_args[0]  ,				tokens["input_ids"]   )
      self.assertEqual(ordered_input_names[0]  ,				"input_ids"   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				generate_identified_filename(Path("/home/something/my_fake_model.onnx"   )  ,				"-test"   )
      self.assertEqual("/home/something/my_fake_model-test.onnx"  ,				generated.as_posix()   )

