

'''simple docstring'''
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict




@pytest.mark.parametrize(
    '''files'''     ,					[
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]     ,					)
def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    int	)		->	int:




  '''simple docstring'''



  lowercase									=tmp_path_factory.mktemp('''dset_infos_dir'''	)
  if "full:README.md" in files:
    with open(dataset_infos_dir / '''README.md'''     ,					'''w'''	) as f:
      f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''	)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / '''README.md'''     ,					'''w'''	) as f:
      f.write(''''''	)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / '''dataset_infos.json'''     ,					'''w'''	) as f:
      f.write('''{"default": {"dataset_size": 42}}'''	)
  lowercase									=DatasetInfosDict.from_directory(lowercase_	)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2




@pytest.mark.parametrize(
    '''dataset_info'''     ,					[
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''     ,					features=Features({'''a''': Value('''int32'''	)}	)     ,					builder_name='''builder'''     ,					config_name='''config'''     ,					version='''1.0.0'''     ,					splits=[{'''name''': '''train'''}]     ,					download_size=4_2     ,					),
    ]     ,					)
def 	UpperCamelCase   (       lowercase_						:    Tuple     ,					lowercase_						:    DatasetInfo	)		->	List[str]:




  '''simple docstring'''



  lowercase									=str(lowercase_	)
  dataset_info.write_to_directory(lowercase_	)
  lowercase									=DatasetInfo.from_directory(lowercase_	)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowercase_     ,					'''dataset_info.json'''	)	)




def 	UpperCamelCase   (       )		->	str:




  '''simple docstring'''



  lowercase									=DatasetInfo(
      description='''foo'''     ,					citation='''bar'''     ,					homepage='''https://foo.bar'''     ,					license='''CC0'''     ,					features=Features({'''a''': Value('''int32'''	)}	)     ,					post_processed={}     ,					supervised_keys=()     ,					task_templates=[]     ,					builder_name='''builder'''     ,					config_name='''config'''     ,					version='''1.0.0'''     ,					splits=[{'''name''': '''train''', '''num_examples''': 4_2}]     ,					download_checksums={}     ,					download_size=1_3_3_7     ,					post_processing_size=4_4_2     ,					dataset_size=1_2_3_4     ,					size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4     ,					)
  lowercase									=dataset_info._to_yaml_dict()
  assert sorted(lowercase_	) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML	)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]     ,					(list, dict, int, str)	)
  lowercase									=yaml.safe_dump(lowercase_	)
  lowercase									=yaml.safe_load(lowercase_	)
  assert dataset_info_yaml_dict == reloaded




def 	UpperCamelCase   (       )		->	List[str]:




  '''simple docstring'''



  lowercase									=DatasetInfo()
  lowercase									=dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}




@pytest.mark.parametrize(
    '''dataset_infos_dict'''     ,					[
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}	),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}	),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''     ,					features=Features({'''a''': Value('''int32'''	)}	)     ,					builder_name='''builder'''     ,					config_name='''config'''     ,					version='''1.0.0'''     ,					splits=[{'''name''': '''train'''}]     ,					download_size=4_2     ,					)
            }	),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=4_2	),
                '''v2''': DatasetInfo(dataset_size=1_3_3_7	),
            }	),
    ]     ,					)
def 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    DatasetInfosDict	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=str(lowercase_	)
  dataset_infos_dict.write_to_directory(lowercase_	)
  lowercase									=DatasetInfosDict.from_directory(lowercase_	)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    lowercase									=config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    lowercase									=DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()	)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(lowercase_     ,					'''README.md'''	)	)







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCAmelCase				:   Optional[int]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   List[str]						       =						{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''umt5'''
     __UpperCamelCase            =  ['''past_key_values''']



     def __init__(				self							:int      ,   snake_case							:Optional[Any]=250_112      ,   snake_case							:Optional[int]=512      ,   snake_case							:Any=64      ,   snake_case							:Union[str, Any]=1_024      ,   snake_case							:Tuple=8      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[str, Any]=6      ,   snake_case							:List[Any]=32      ,   snake_case							:Dict=128      ,   snake_case							:List[str]=0.1      ,   snake_case							:List[Any]=1e-6      ,   snake_case							:Dict=1.0      ,   snake_case							:Union[str, Any]="gated-gelu"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Any=True      ,   snake_case							:List[str]="T5Tokenizer"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=0      ,   snake_case							:List[Any]=1      ,   snake_case							:List[Any]=0      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        super().__init__(
            is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
        A_       :					Union[str, Any]        =      vocab_size
        A_       :					Tuple        =      d_model
        A_       :					List[str]        =      d_kv
        A_       :					Union[str, Any]        =      d_ff
        A_       :					Any        =      num_layers
        A_       :					Optional[Any]        =      (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_       :					List[Any]        =      num_heads
        A_       :					List[str]        =      relative_attention_num_buckets
        A_       :					Dict        =      relative_attention_max_distance
        A_       :					Optional[Any]        =      dropout_rate
        A_       :					Any        =      layer_norm_epsilon
        A_       :					List[Any]        =      initializer_factor
        A_       :					Any        =      feed_forward_proj
        A_       :					Optional[Any]        =      use_cache

        A_       :					int        =      self.feed_forward_proj.split("-"	)
        A_       :					Any        =      act_info[-1]
        A_       :					Tuple        =      act_info[0] == "gated"

        if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 2:
           raise ValueError(
               f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"	)

        if feed_forward_proj == "gated-gelu":
           A_       :					Optional[Any]        =      "gelu_new"



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.d_model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.num_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self.num_layers




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
           A_       :					Any        =      "past_encoder_sequence + sequence"
           A_       :					Union[str, Any]        =      {0: "batch"}
           A_       :					Optional[int]        =      {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
           A_       :					Dict        =      {0: "batch", 1: "decoder_sequence"}
           A_       :					str        =      {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
           self.fill_with_past_key_values_(snake_case      ,   direction="inputs"	)

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 13





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return 5e-4
