from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset






class 		a__						(					A__					):

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return [
							    {"col_1": 3, "col_2": "a"},
							    {"col_1": 2, "col_2": "b"},
							    {"col_1": 1, "col_2": "c"},
							    {"col_1": 0, "col_2": "d"},
							]

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
							return Dataset.from_dict(_A    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._create_example_records()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    Dataset.from_list(_A    )
							self.assertListEqual(dset.column_names,["col_1", "col_2"]    )
							for i, r in enumerate(_A    ):
									self.assertDictEqual(_A,example_records[i]    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._create_example_records()
							SCREAMING_SNAKE_CASE_  :		Any				      =    Dataset.from_list(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}    )
							self.assertEqual(dset.info,dset_from_dict.info    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):  # checks what happens with missing columns







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [{"col_1": 1}, {"col_2": "x"}]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    Dataset.from_list(_A    )
							self.assertDictEqual(dset[0],{"col_1": 1}    )
							self.assertDictEqual(dset[1],{"col_1": None}    )  # NB: first record is used for columns

					def 				__UpperCamelCase   (    self      :       Any    ):  # checks if the type can be inferred from the second record







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    [{"col_1": []}, {"col_1": [1, 2]}]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    Dataset.from_list(_A    )
							self.assertEqual(dset.info.features["col_1"],Sequence(Value("int64"    )    )    )






					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    Dataset.from_list([]    )
							self.assertEqual(len(_A    ),0    )
							self.assertListEqual(dset.column_names,[]    )




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'camembert'




		def __init__(		self    ,   UpperCamelCase_=30522    ,   UpperCamelCase_=768    ,   UpperCamelCase_=12    ,   UpperCamelCase_=12    ,   UpperCamelCase_=3072    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=2    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=1e-12    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_="absolute"    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_    ,   bos_token_id=UpperCamelCase_    ,   eos_token_id=UpperCamelCase_    ,   **UpperCamelCase_							)

							UpperCamelCase__   :int												= vocab_size
							UpperCamelCase__   :Optional[int]												= hidden_size
							UpperCamelCase__   :Optional[int]												= num_hidden_layers
							UpperCamelCase__   :List[Any]												= num_attention_heads
							UpperCamelCase__   :Union[str, Any]												= hidden_act
							UpperCamelCase__   :List[Any]												= intermediate_size
							UpperCamelCase__   :int												= hidden_dropout_prob
							UpperCamelCase__   :Tuple												= attention_probs_dropout_prob
							UpperCamelCase__   :Union[str, Any]												= max_position_embeddings
							UpperCamelCase__   :Tuple												= type_vocab_size
							UpperCamelCase__   :int												= initializer_range
							UpperCamelCase__   :List[str]												= layer_norm_eps
							UpperCamelCase__   :int												= position_embedding_type
							UpperCamelCase__   :Any												= use_cache
							UpperCamelCase__   :Any												= classifier_dropout



class    lowercase	(							A__					):

		"""simple docstring"""

		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.task == "multiple-choice":
												UpperCamelCase__   :List[str]												= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												UpperCamelCase__   :Tuple												= {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]							)