



'''simple docstring'''



from typing import Mapping

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


lowerCamelCase	 =       logging.get_logger(__name__)

lowerCamelCase	 =       {
    """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 _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    """umt5"""
				lowerCAmelCase__					    =    ["""past_key_values"""]

				def __init__(							self  :  Tuple  ,       _lowerCAmelCase  :  List[Any]=2_5_0_1_1_2  ,       _lowerCAmelCase  :  List[Any]=5_1_2  ,       _lowerCAmelCase  :  str=6_4  ,       _lowerCAmelCase  :  Optional[Any]=1_0_2_4  ,       _lowerCAmelCase  :  Any=8  ,       _lowerCAmelCase  :  int=None  ,       _lowerCAmelCase  :  Optional[int]=6  ,       _lowerCAmelCase  :  Dict=3_2  ,       _lowerCAmelCase  :  Optional[int]=1_2_8  ,       _lowerCAmelCase  :  Optional[int]=0.1  ,       _lowerCAmelCase  :  Optional[int]=1e-6  ,       _lowerCAmelCase  :  Dict=1.0  ,       _lowerCAmelCase  :  Optional[Any]="gated-gelu"  ,       _lowerCAmelCase  :  Optional[Any]=True  ,       _lowerCAmelCase  :  Dict=True  ,       _lowerCAmelCase  :  Tuple="T5Tokenizer"  ,       _lowerCAmelCase  :  Any=True  ,       _lowerCAmelCase  :  List[Any]=0  ,       _lowerCAmelCase  :  int=1  ,       _lowerCAmelCase  :  Optional[int]=0  ,       **_lowerCAmelCase  :  int  ,       ):







											'''simple docstring'''

											super().__init__(
											    is_encoder_decoder=_lowerCAmelCase  ,       tokenizer_class=_lowerCAmelCase  ,       tie_word_embeddings=_lowerCAmelCase  ,       pad_token_id=_lowerCAmelCase  ,       eos_token_id=_lowerCAmelCase  ,       decoder_start_token_id=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )
											__lowercase       	=vocab_size
											__lowercase       	=d_model
											__lowercase       	=d_kv
											__lowercase       	=d_ff
											__lowercase       	=num_layers
											__lowercase       	=(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											__lowercase       	=num_heads
											__lowercase       	=relative_attention_num_buckets
											__lowercase       	=relative_attention_max_distance
											__lowercase       	=dropout_rate
											__lowercase       	=layer_norm_epsilon
											__lowercase       	=initializer_factor
											__lowercase       	=feed_forward_proj
											__lowercase       	=use_cache

											__lowercase       	=self.feed_forward_proj.split('-')
											__lowercase       	=act_info[-1]
											__lowercase       	=act_info[0] == 'gated'

											if len(_lowerCAmelCase) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase) > 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":
																		__lowercase       	='gelu_new'

				@property
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											return self.d_model

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											return self.num_heads

				@property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											return self.num_layers





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											__lowercase       	={
											    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
											    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
											}
											if self.use_past:
																		__lowercase       	='past_encoder_sequence + sequence'
																		__lowercase       	={0: 'batch'}
																		__lowercase       	={0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																		__lowercase       	={0: 'batch', 1: 'decoder_sequence'}
																		__lowercase       	={0: 'batch', 1: 'decoder_sequence'}

											if self.use_past:
																		self.fill_with_past_key_values_(_lowerCAmelCase  ,       direction='inputs')

											return common_inputs

				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											return 1_3

				@property
				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											return 5e-4








'''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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""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(_lowerCAmelCase					)
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42



@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=42  ,   ),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   ):



							"""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': 42}]  ,   download_checksums={}  ,   download_size=1_337  ,   post_processing_size=442  ,   dataset_size=1_234  ,   size_in_bytes=1_337 + 442 + 1_234  ,   )
							__lowercase       	=dataset_info._to_yaml_dict()
							assert sorted(_lowerCAmelCase					) == 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(_lowerCAmelCase					)
							__lowercase       	=yaml.safe_load(_lowerCAmelCase					)
							assert dataset_info_yaml_dict == reloaded



def   _A       (   ):



							"""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=42  ,   )
            }					),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42					),
                'v2': DatasetInfo(dataset_size=1_337					),
            }					),
    ]  ,   )
def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=str(_lowerCAmelCase					)
							dataset_infos_dict.write_to_directory(_lowerCAmelCase					)
							__lowercase       	=DatasetInfosDict.from_directory(_lowerCAmelCase					)

							# 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(_lowerCAmelCase  ,   'README.md'					)					)



