



"""simple docstring"""

from typing import Mapping

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


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Dict							       =		"""umt5"""
						_A		:							Any							       =		["""past_key_values"""]







						def __init__(self   ,		lowercase__=25_01_12   ,		lowercase__=5_12   ,		lowercase__=64   ,		lowercase__=10_24   ,		lowercase__=8   ,		lowercase__=None   ,		lowercase__=6   ,		lowercase__=32   ,		lowercase__=1_28   ,		lowercase__=0.1   ,		lowercase__=1e-6   ,		lowercase__=1.0   ,		lowercase__="gated-gelu"   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__="T5Tokenizer"   ,		lowercase__=True   ,		lowercase__=0   ,		lowercase__=1   ,		lowercase__=0   ,		**lowercase__   ,		):
											super().__init__(
											    is_encoder_decoder=lowercase__   ,		tokenizer_class=lowercase__   ,		tie_word_embeddings=lowercase__   ,		pad_token_id=lowercase__   ,		eos_token_id=lowercase__   ,		decoder_start_token_id=lowercase__   ,		**lowercase__   ,		)
											snake_case_					:		List[Any] 					=					vocab_size
											snake_case_					:		Dict 					=					d_model
											snake_case_					:		str 					=					d_kv
											snake_case_					:		Optional[Any] 					=					d_ff
											snake_case_					:		List[Any] 					=					num_layers
											snake_case_					:		Dict 					=					(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											snake_case_					:		List[Any] 					=					num_heads
											snake_case_					:		List[str] 					=					relative_attention_num_buckets
											snake_case_					:		Optional[Any] 					=					relative_attention_max_distance
											snake_case_					:		Tuple 					=					dropout_rate
											snake_case_					:		Optional[int] 					=					layer_norm_epsilon
											snake_case_					:		List[str] 					=					initializer_factor
											snake_case_					:		List[str] 					=					feed_forward_proj
											snake_case_					:		List[str] 					=					use_cache

											snake_case_					:		Optional[int] 					=					self.feed_forward_proj.split("""-"""       )
											snake_case_					:		List[Any] 					=					act_info[-1]
											snake_case_					:		int 					=					act_info[0] == """gated"""

											if len(lowercase__       ) > 1 and act_info[0] != "gated" or len(lowercase__       ) > 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":
																snake_case_					:		str 					=					"""gelu_new"""







						@property
						def 						__UpperCamelCase							(self       ):
											return self.d_model







						@property
						def 						__UpperCamelCase							(self       ):
											return self.num_heads







						@property
						def 						__UpperCamelCase							(self       ):
											return self.num_layers







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					{
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
																snake_case_					:		Optional[int] 					=					"""past_encoder_sequence + sequence"""
																snake_case_					:		Dict 					=					{0: """batch"""}
																snake_case_					:		str 					=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																snake_case_					:		Tuple 					=					{0: """batch""", 1: """decoder_sequence"""}
																snake_case_					:		Union[str, Any] 					=					{0: """batch""", 1: """decoder_sequence"""}

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

											return common_inputs







						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 						__UpperCamelCase							(self       ):
											return 13







						@property
						def 						__UpperCamelCase							(self       ):
											return 5e-4









"""simple docstring"""

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""				,    [False, True]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					tmp_path / """cache"""
					snake_case_					:		Optional[int] 					=					{"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_					:		Tuple 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__				,    keep_in_memory=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					tmp_path / """cache"""
					snake_case_					:		int 					=					{"""text""": """string"""}
					snake_case_					:		Any 					=					features.copy() if features else default_expected_features
					snake_case_					:		List[Any] 					=					(
					    Features({feature: Value(SCREAMING_SNAKE_CASE__			) for feature, dtype in features.items()}			) if features is not None else None
					)
					snake_case_					:		Dict 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    features=SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					tmp_path / """cache"""
					snake_case_					:		Optional[Any] 					=					{"""text""": """string"""}
					snake_case_					:		Optional[int] 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__				,    split=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize("""path_type"""				,    [str, list]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					if issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		List[str] 					=					text_path
					elif issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		str 					=					[text_path]
					snake_case_					:		List[str] 					=					tmp_path / """cache"""
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					snake_case_					:		Dict 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]=("train",)			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for split in splits:
										snake_case_					:		Dict 					=					dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""				,    [False, True]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		int 					=					tmp_path / """cache"""
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_					:		Optional[Any] 					=					TextDatasetReader({"""train""": text_path}				,    cache_dir=SCREAMING_SNAKE_CASE__				,    keep_in_memory=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					snake_case_					:		int 					=					features.copy() if features else default_expected_features
					snake_case_					:		Tuple 					=					(
					    Features({feature: Value(SCREAMING_SNAKE_CASE__			) for feature, dtype in features.items()}			) if features is not None else None
					)
					snake_case_					:		str 					=					TextDatasetReader({"""train""": text_path}				,    features=SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					if split:
										snake_case_					:		Union[str, Any] 					=					{split: text_path}
					else:
										snake_case_					:		Union[str, Any] 					=					"""train"""
										snake_case_					:		int 					=					{"""train""": text_path, """test""": text_path}
					snake_case_					:		List[Any] 					=					tmp_path / """cache"""
					snake_case_					:		Tuple 					=					{"""text""": """string"""}
					snake_case_					:		int 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    splits=list(path.keys()			)			)
					assert all(dataset[split].split == split for split in path.keys()			)




