

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase		:   Dict										=       {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			'''tapas'''




				def __init__(					self      ,   snake_case=3_0522      ,   snake_case=768      ,   snake_case=12      ,   snake_case=12      ,   snake_case=3072      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=1024      ,   snake_case=[3, 256, 256, 2, 256, 256, 10]      ,   snake_case=0.02      ,   snake_case=1e-1_2      ,   snake_case=0      ,   snake_case=10.0      ,   snake_case=0      ,   snake_case=1.0      ,   snake_case=None      ,   snake_case=1.0      ,   snake_case=False      ,   snake_case=None      ,   snake_case=1.0      ,   snake_case=1.0      ,   snake_case=False      ,   snake_case=False      ,   snake_case="ratio"      ,   snake_case=None      ,   snake_case=None      ,   snake_case=64      ,   snake_case=32      ,   snake_case=False      ,   snake_case=True      ,   snake_case=False      ,   snake_case=False      ,   snake_case=True      ,   snake_case=False      ,   snake_case=None      ,   snake_case=None      ,   **snake_case      ,   ):
											super().__init__(pad_token_id=snake_case      ,   **snake_case       )

											# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
											snake_case_							=    vocab_size
											snake_case_							=    hidden_size
											snake_case_							=    num_hidden_layers
											snake_case_							=    num_attention_heads
											snake_case_							=    hidden_act
											snake_case_							=    intermediate_size
											snake_case_							=    hidden_dropout_prob
											snake_case_							=    attention_probs_dropout_prob
											snake_case_							=    max_position_embeddings
											snake_case_							=    type_vocab_sizes
											snake_case_							=    initializer_range
											snake_case_							=    layer_norm_eps

											# Fine-tuning task hyperparameters
											snake_case_							=    positive_label_weight
											snake_case_							=    num_aggregation_labels
											snake_case_							=    aggregation_loss_weight
											snake_case_							=    use_answer_as_supervision
											snake_case_							=    answer_loss_importance
											snake_case_							=    use_normalized_answer_loss
											snake_case_							=    huber_loss_delta
											snake_case_							=    temperature
											snake_case_							=    aggregation_temperature
											snake_case_							=    use_gumbel_for_cells
											snake_case_							=    use_gumbel_for_aggregation
											snake_case_							=    average_approximation_function
											snake_case_							=    cell_selection_preference
											snake_case_							=    answer_loss_cutoff
											snake_case_							=    max_num_rows
											snake_case_							=    max_num_columns
											snake_case_							=    average_logits_per_cell
											snake_case_							=    select_one_column
											snake_case_							=    allow_empty_column_selection
											snake_case_							=    init_cell_selection_weights_to_zero
											snake_case_							=    reset_position_index_per_cell
											snake_case_							=    disable_per_token_loss

											# Aggregation hyperparameters
											snake_case_							=    aggregation_labels
											snake_case_							=    no_aggregation_label_index

											if isinstance(self.aggregation_labels      ,   snake_case       ):
																		snake_case_							=    {int(snake_case       ): v for k, v in aggregation_labels.items()}







from __future__ import annotations

import collections
import pprint
from pathlib import Path





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return "".join(sorted(UpperCamelCase__      )      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return word_by_signature[signature(UpperCamelCase__      )]


_UpperCAmelCase		:   str										=       Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_UpperCAmelCase		:   Dict										=       sorted({word.strip().lower() for word in data.splitlines()})

_UpperCAmelCase		:   List[str]										=       collections.defaultdict(list)
for word in word_list:
					word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
					_UpperCAmelCase		:   Dict										=       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

					with open("""anagrams.txt""", """w""") as file:
										file.write("""all_anagrams = \n """)
										file.write(pprint.pformat(all_anagrams))




