
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A							=logging.get_logger(__name__)

__A							={
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'time_series_transformer'
	lowerCAmelCase__   =					{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	    'num_hidden_layers': 'encoder_layers',
	}



	def __init__(       self		,    lowercase = None		,    lowercase = None		,    lowercase = "student_t"		,    lowercase = "nll"		,    lowercase = 1		,    lowercase = [1, 2, 3, 4, 5, 6, 7]		,    lowercase = "mean"		,    lowercase = 0		,    lowercase = 0		,    lowercase = 0		,    lowercase = 0		,    lowercase = None		,    lowercase = None		,    lowercase = 32		,    lowercase = 32		,    lowercase = 2		,    lowercase = 2		,    lowercase = 2		,    lowercase = 2		,    lowercase = True		,    lowercase = "gelu"		,    lowercase = 64		,    lowercase = 0.1		,    lowercase = 0.1		,    lowercase = 0.1		,    lowercase = 0.1		,    lowercase = 0.1		,    lowercase = 100		,    lowercase = 0.0_2		,    lowercase=True		,    **lowercase		,    )							->			Union[str, Any]:
					# time series specific configuration
					lowerCamelCase_         =     prediction_length
					lowerCamelCase_         =     context_length or prediction_length
					lowerCamelCase_         =     distribution_output
					lowerCamelCase_         =     loss
					lowerCamelCase_         =     input_size
					lowerCamelCase_         =     num_time_features
					lowerCamelCase_         =     lags_sequence
					lowerCamelCase_         =     scaling
					lowerCamelCase_         =     num_dynamic_real_features
					lowerCamelCase_         =     num_static_real_features
					lowerCamelCase_         =     num_static_categorical_features
					if cardinality and num_static_categorical_features > 0:
									if len(lowercase					) != num_static_categorical_features:
													raise ValueError(
													    "The cardinality should be a list of the same length as `num_static_categorical_features`"					)
									lowerCamelCase_         =     cardinality
					else:
									lowerCamelCase_         =     [0]
					if embedding_dimension and num_static_categorical_features > 0:
									if len(lowercase					) != num_static_categorical_features:
													raise ValueError(
													    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"					)
									lowerCamelCase_         =     embedding_dimension
					else:
									lowerCamelCase_         =     [min(50		,    (cat + 1) // 2					) for cat in self.cardinality]
					lowerCamelCase_         =     num_parallel_samples

					# Transformer architecture configuration
					lowerCamelCase_         =     input_size * len(lowercase					) + self._number_of_features
					lowerCamelCase_         =     d_model
					lowerCamelCase_         =     encoder_attention_heads
					lowerCamelCase_         =     decoder_attention_heads
					lowerCamelCase_         =     encoder_ffn_dim
					lowerCamelCase_         =     decoder_ffn_dim
					lowerCamelCase_         =     encoder_layers
					lowerCamelCase_         =     decoder_layers

					lowerCamelCase_         =     dropout
					lowerCamelCase_         =     attention_dropout
					lowerCamelCase_         =     activation_dropout
					lowerCamelCase_         =     encoder_layerdrop
					lowerCamelCase_         =     decoder_layerdrop

					lowerCamelCase_         =     activation_function
					lowerCamelCase_         =     init_std

					lowerCamelCase_         =     use_cache

					super().__init__(is_encoder_decoder=lowercase		,    **lowercase					)



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return (
					    sum(self.embedding_dimension					)
					    + self.num_dynamic_real_features
					    + self.num_time_features
					    + self.num_static_real_features
					    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
					)




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__A							=logging.get_logger(__name__)

__A							={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A							=[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

__A							={
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
__A							={F"""funnel-transformer/{name}""": 5_1_2 for name in _model_names}
__A							={F"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					VOCAB_FILES_NAMES
	lowerCAmelCase__   =					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__   =					PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__   =					FunnelTokenizer
	lowerCAmelCase__   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__   =					2



	def __init__(       self		,    lowercase=None		,    lowercase=None		,    lowercase=True		,    lowercase="<unk>"		,    lowercase="<sep>"		,    lowercase="<pad>"		,    lowercase="<cls>"		,    lowercase="<mask>"		,    lowercase="<s>"		,    lowercase="</s>"		,    lowercase=True		,    lowercase=True		,    lowercase=None		,    lowercase="##"		,    **lowercase		,    )							->			List[str]:
					super().__init__(
					    lowercase		,    tokenizer_file=lowercase		,    do_lower_case=lowercase		,    unk_token=lowercase		,    sep_token=lowercase		,    pad_token=lowercase		,    cls_token=lowercase		,    mask_token=lowercase		,    bos_token=lowercase		,    eos_token=lowercase		,    clean_text=lowercase		,    tokenize_chinese_chars=lowercase		,    strip_accents=lowercase		,    wordpieces_prefix=lowercase		,    **lowercase		,    )

					lowerCamelCase_         =     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
					if (
					    normalizer_state.get("lowercase"		,    lowercase					) != do_lower_case
					    or normalizer_state.get("strip_accents"		,    lowercase					) != strip_accents
					    or normalizer_state.get("handle_chinese_chars"		,    lowercase					) != tokenize_chinese_chars
					):
									lowerCamelCase_         =     getattr(lowercase		,    normalizer_state.pop("type"					)					)
									lowerCamelCase_         =     do_lower_case
									lowerCamelCase_         =     strip_accents
									lowerCamelCase_         =     tokenize_chinese_chars
									lowerCamelCase_         =     normalizer_class(**lowercase					)

					lowerCamelCase_         =     do_lower_case



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=None					)							->			str:
					lowerCamelCase_         =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

					return output



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			List[int]:
					lowerCamelCase_         =     [self.sep_token_id]
					lowerCamelCase_         =     [self.cls_token_id]
					if token_ids_a is None:
									return len(cls					) * [self.cls_token_type_id] + len(token_ids_a + sep					) * [0]
					return len(cls					) * [self.cls_token_type_id] + len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None					)							->			Tuple[str]:
					lowerCamelCase_         =     self._tokenizer.model.save(lowercase		,    name=lowercase					)
					return tuple(lowercase					)


