

import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


lowerCamelCase									= logging.get_logger(__name__)

if is_vision_available():
       import PIL.Image
       from PIL import Image
       from PIL.Image import Image as ImageType
else:
       lowerCamelCase									= object

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






     a__  =value



   def __str__(    self	)							->   List[str]:






     """simple docstring"""






     return self.to_string()



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






     """simple docstring"""






     logger.error(
         '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''	)
     return self._value



   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     logger.error(
         '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''	)
     return str(self._value	)







class 						__magic_name__      (							__UpperCAmelCase		,	__UpperCAmelCase							):

   '''simple docstring'''




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






     """simple docstring"""






     return self._value



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     return self._value







class 						__magic_name__      (							__UpperCAmelCase		,	__UpperCAmelCase							):

   '''simple docstring'''




   def __init__(    self,   lowercase_	)							->   List[str]:






     """simple docstring"""






     super().__init__(lowerCamelCase__	)

     if not is_vision_available():
       raise ImportError('''PIL must be installed in order to handle images.'''	)

     a__  =None
     a__  =None
     a__  =None

     if isinstance(lowerCamelCase__,   lowerCamelCase__	):
       a__  =value
     elif isinstance(lowerCamelCase__,   (str, pathlib.Path)	):
       a__  =value
     elif isinstance(lowerCamelCase__,   torch.Tensor	):
       a__  =value
     else:
       raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__	)}"""	)



   def 			_UpperCAmelCase			(    self,   lowercase_=None,   lowercase_=None	)							->   Dict:






     """simple docstring"""






     from IPython.display import Image, display

     display(Image(self.to_string()	)	)



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     if self._raw is not None:
       return self._raw

     if self._path is not None:
       a__  =Image.open(self._path	)
       return self._raw



   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     if self._path is not None:
       return self._path

     if self._raw is not None:
       a__  =tempfile.mkdtemp()
       a__  =os.path.join(lowerCamelCase__,   str(uuid.uuida()	) + '''.png'''	)
       self._raw.save(self._path	)

       return self._path

     if self._tensor is not None:
       a__  =self._tensor.cpu().detach().numpy()

       # There is likely simpler than load into image into save
       a__  =Image.fromarray((array * 255).astype(np.uinta	)	)

       a__  =tempfile.mkdtemp()
       a__  =os.path.join(lowerCamelCase__,   str(uuid.uuida()	) + '''.png'''	)

       img.save(self._path	)

       return self._path







class 						__magic_name__      (							__UpperCAmelCase							):

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_=16000	)							->   Tuple:






     """simple docstring"""






     super().__init__(lowerCamelCase__	)

     if not is_soundfile_availble():
       raise ImportError('''soundfile must be installed in order to handle audio.'''	)

     a__  =None
     a__  =None

     a__  =samplerate

     if isinstance(lowerCamelCase__,   (str, pathlib.Path)	):
       a__  =value
     elif isinstance(lowerCamelCase__,   torch.Tensor	):
       a__  =value
     else:
       raise ValueError(F"""Unsupported audio type: {type(lowerCamelCase__	)}"""	)



   def 			_UpperCAmelCase			(    self,   lowercase_=None,   lowercase_=None	)							->   Union[str, Any]:






     """simple docstring"""






     from IPython.display import Audio, display

     display(Audio(self.to_string(),   rate=self.samplerate	)	)



   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     if self._tensor is not None:
       return self._tensor

     if self._path is not None:
       a__,				a__  =sf.read(self._path	)
       a__  =torch.tensor(lowerCamelCase__	)
       return self._tensor



   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     if self._path is not None:
       return self._path

     if self._tensor is not None:
       a__  =tempfile.mkdtemp()
       a__  =os.path.join(lowerCamelCase__,   str(uuid.uuida()	) + '''.wav'''	)
       sf.write(self._path,   self._tensor,   samplerate=self.samplerate	)
       return self._path





lowerCamelCase									= {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
lowerCamelCase									= {str: AgentText}

if is_vision_available():
       lowerCamelCase									= AgentImage




def       UpperCAmelCase__    (      *_A				:			Optional[int]     ,				**_A				:			Dict							):





  '''simple docstring'''







  a__  =[(arg.to_raw() if isinstance(lowercase__     ,				lowercase__							) else arg) for arg in args]
  a__  ={k: (v.to_raw() if isinstance(lowercase__     ,				lowercase__							) else v) for k, v in kwargs.items()}
  return args, kwargs




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			List[str]=None							):





  '''simple docstring'''







  if isinstance(lowercase__     ,				lowercase__							):
    a__  ={}
    for i, (k, v) in enumerate(outputs.items()							):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          a__  =AGENT_TYPE_MAPPING[output_types[i]](lowercase__							)
        else:
          a__  =AgentType(lowercase__							)

      else:
        # If the class does not have defined output, then we map according to the type
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(lowercase__     ,				_k							):
            a__  =_v(lowercase__							)
        if k not in decoded_outputs:
          a__  =AgentType[v]

  elif isinstance(lowercase__     ,				(list, tuple)							):
    a__  =type(lowercase__							)()
    for i, v in enumerate(lowercase__							):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](lowercase__							)							)
        else:
          decoded_outputs.append(AgentType(lowercase__							)							)
      else:
        # If the class does not have defined output, then we map according to the type
        a__  =False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(lowercase__     ,				_k							):
            decoded_outputs.append(_v(lowercase__							)							)
            a__  =True

        if not found:
          decoded_outputs.append(AgentType(lowercase__							)							)

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      a__  =AGENT_TYPE_MAPPING[output_types[0]](lowercase__							)

    else:
      # If the class does not have defined output, then we map according to the type
      for _k, _v in INSTANCE_TYPE_MAPPING.items():
        if isinstance(lowercase__     ,				_k							):
          return _v(lowercase__							)
      return AgentType(lowercase__							)

  return decoded_outputs


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  a__  =min(re.search('''^ +'''     ,				_A							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
  return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A							)]							)


lowerCamelCase									= '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

lowerCamelCase									= '''\
---
---
# Begin of markdown

Some cool dataset card
'''


lowerCamelCase									= '''\
# Begin of markdown

Some cool dataset card
'''







class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =Path(lowercase_	) / '''README.md'''
       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertEqual(lowercase_,   {}	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''	)
     with self.assertRaises(lowercase_	):
       DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)
