


"""simple docstring"""
from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE :    Optional[int]									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    List[str]									=				{
    """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 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='time_series_transformer'
		lowerCamelCase__				={
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		    'num_hidden_layers': 'encoder_layers',
		}



		def __init__(self				,       a_ = None				,       a_ = None				,       a_ = "student_t"				,       a_ = "nll"				,       a_ = 1				,       a_ = [1, 2, 3, 4, 5, 6, 7]				,       a_ = "mean"				,       a_ = 0				,       a_ = 0				,       a_ = 0				,       a_ = 0				,       a_ = None				,       a_ = None				,       a_ = 32				,       a_ = 32				,       a_ = 2				,       a_ = 2				,       a_ = 2				,       a_ = 2				,       a_ = True				,       a_ = "gelu"				,       a_ = 64				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 0.1				,       a_ = 1_00				,       a_ = 0.02				,       a_=True				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				prediction_length
			__snake_case	:						List[str]						=				context_length or prediction_length
			__snake_case	:						Tuple						=				distribution_output
			__snake_case	:						Union[str, Any]						=				loss
			__snake_case	:						Tuple						=				input_size
			__snake_case	:						Optional[Any]						=				num_time_features
			__snake_case	:						Tuple						=				lags_sequence
			__snake_case	:						Optional[int]						=				scaling
			__snake_case	:						Optional[Any]						=				num_dynamic_real_features
			__snake_case	:						Optional[Any]						=				num_static_real_features
			__snake_case	:						List[str]						=				num_static_categorical_features
			if cardinality and num_static_categorical_features > 0:
				if len(a_					) != num_static_categorical_features:
					raise ValueError(
					    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''					)
				__snake_case	:						Dict						=				cardinality
			else:
				__snake_case	:						Optional[int]						=				[0]
			if embedding_dimension and num_static_categorical_features > 0:
				if len(a_					) != num_static_categorical_features:
					raise ValueError(
					    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''					)
				__snake_case	:						Dict						=				embedding_dimension
			else:
				__snake_case	:						int						=				[min(50				,       (cat + 1) // 2					) for cat in self.cardinality]
			__snake_case	:						int						=				num_parallel_samples

			# Transformer architecture configuration
			__snake_case	:						Union[str, Any]						=				input_size * len(a_					) + self._number_of_features
			__snake_case	:						Any						=				d_model
			__snake_case	:						Union[str, Any]						=				encoder_attention_heads
			__snake_case	:						Any						=				decoder_attention_heads
			__snake_case	:						Any						=				encoder_ffn_dim
			__snake_case	:						List[Any]						=				decoder_ffn_dim
			__snake_case	:						Tuple						=				encoder_layers
			__snake_case	:						Union[str, Any]						=				decoder_layers

			__snake_case	:						int						=				dropout
			__snake_case	:						List[str]						=				attention_dropout
			__snake_case	:						Optional[int]						=				activation_dropout
			__snake_case	:						Dict						=				encoder_layerdrop
			__snake_case	:						Optional[Any]						=				decoder_layerdrop

			__snake_case	:						int						=				activation_function
			__snake_case	:						Union[str, Any]						=				init_std

			__snake_case	:						int						=				use_cache

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



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			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 unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=None
				_lowercase					=BloomTokenizerFast
				_lowercase					=BloomTokenizerFast
				_lowercase					=True
				_lowercase					=False
				_lowercase					='''tokenizer_file'''
				_lowercase					={'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def      __a						(					self     )				->      Dict:
							super().setUp()
							lowerCAmelCase_						=	BloomTokenizerFast.from_pretrained("bigscience/tokenizer"     )
							tokenizer.save_pretrained(self.tmpdirname     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Tuple:
							kwargs.update(self.special_tokens_map     )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.get_rust_tokenizer()

							lowerCAmelCase_						=	["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase_						=	[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

							lowerCAmelCase_						=	tokenizer.batch_encode_plus(_UpperCamelCase     )["input_ids"]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokenizer.batch_decode(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase=6     )				->      Tuple:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													lowerCAmelCase_						=	"This is a simple input"
													lowerCAmelCase_						=	["This is a simple input 1", "This is a simple input 2"]
													lowerCAmelCase_						=	("This is a simple input", "This is a pair")
													lowerCAmelCase_						=	[
													    ("This is a simple input 1", "This is a simple input 2"),
													    ("This is a simple pair 1", "This is a simple pair 2"),
													]

													# Simple input tests
													try:
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )

																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.encode(_UpperCamelCase				,				max_length=_UpperCamelCase     )
																tokenizer_r.batch_encode_plus(_UpperCamelCase				,				max_length=_UpperCamelCase     )
													except ValueError:
																self.fail("Bloom Tokenizer should be able to deal with padding"     )

													lowerCAmelCase_						=	None  # Hotfixing padding = None
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Simple input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Simple input
													self.assertRaises(
													    _UpperCamelCase				,				tokenizer_r.batch_encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"				,				)

													# Pair input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Pair input
													self.assertRaises(_UpperCamelCase				,				tokenizer_r.encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"     )

													# Pair input
													self.assertRaises(
													    _UpperCamelCase				,				tokenizer_r.batch_encode_plus				,				_UpperCamelCase				,				max_length=_UpperCamelCase				,				padding="max_length"				,				)




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.get_rust_tokenizer()
							lowerCAmelCase_						=	load_dataset("xnli"				,				"all_languages"				,				split="test"				,				streaming=_UpperCamelCase     )

							lowerCAmelCase_						=	next(iter(_UpperCamelCase     )     )["premise"]  # pick up one data
							lowerCAmelCase_						=	list(sample_data.values()     )

							lowerCAmelCase_						=	list(map(tokenizer.encode				,				_UpperCamelCase     )     )
							lowerCAmelCase_						=	[tokenizer.decode(_UpperCamelCase				,				clean_up_tokenization_spaces=_UpperCamelCase     ) for x in output_tokens]
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
							# any sequence length constraints. This test of the parent class will fail since it relies on the
							# maximum sequence length of the positoonal embeddings.
							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )				,				1     )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )				,				1     )



