'''simple docstring'''





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
			lowerCAmelCase_	:		Any			=     """pt"""
elif is_tf_available():
			lowerCAmelCase_	:		Optional[int]			=     """tf"""
else:
			lowerCAmelCase_	:		Union[str, Any]			=     """jax"""




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							ByTaTokenizer
				UpperCAmelCase__							=							False





				def  snake_case__				(						self					:						Optional[Any]					)							->str:


										'''simple docstring'''




										super().setUp()
										_UpperCamelCase			:			Dict   			=						ByTaTokenizer()
										tokenizer.save_pretrained(self.tmpdirname					)





				@cached_property
				def  snake_case__				(						self					:						str					)							->Optional[Any]:


										'''simple docstring'''




										return ByTaTokenizer.from_pretrained("google/byt5-small"					)





				def  snake_case__				(						self					:						Tuple       ,		**lowercase__					:						Dict					)							->ByTaTokenizer:


										'''simple docstring'''




										return self.tokenizer_class.from_pretrained(self.tmpdirname       ,		**lowercase__					)





				def  snake_case__				(						self					:						str       ,		lowercase__					:						Dict       ,		lowercase__					:						Optional[int]=False       ,		lowercase__					:						List[str]=20       ,		lowercase__					:						List[str]=5					)							->Tuple[str, list]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						[]
										for i in range(len(lowercase__					)					):
																try:
																						_UpperCamelCase			:			List[Any]   			=						tokenizer.decode([i]       ,		clean_up_tokenization_spaces=lowercase__					)
																except UnicodeDecodeError:
																						pass
																toks.append((i, tok)					)

										_UpperCamelCase			:			Optional[int]   			=						list(filter(lambda lowercase__					: re.match(r"^[ a-zA-Z]+$"       ,		t[1]					)       ,		lowercase__					)					)
										_UpperCamelCase			:			Any   			=						list(filter(lambda lowercase__					: [t[0]] == tokenizer.encode(t[1]       ,		add_special_tokens=lowercase__					)       ,		lowercase__					)					)
										if max_length is not None and len(lowercase__					) > max_length:
																_UpperCamelCase			:			str   			=						toks[:max_length]
										if min_length is not None and len(lowercase__					) < min_length and len(lowercase__					) > 0:
																while len(lowercase__					) < min_length:
																						_UpperCamelCase			:			Union[str, Any]   			=						toks + toks
        # toks_str = [t[1] for t in toks]
										_UpperCamelCase			:			str   			=						[t[0] for t in toks]

										# Ensure consistency
										_UpperCamelCase			:			Optional[Any]   			=						tokenizer.decode(lowercase__       ,		clean_up_tokenization_spaces=lowercase__					)
										if " " not in output_txt and len(lowercase__					) > 1:
																_UpperCamelCase			:			Any   			=						(
																    tokenizer.decode([toks_ids[0]]       ,		clean_up_tokenization_spaces=lowercase__					)
																    + " "
																    + tokenizer.decode(toks_ids[1:]       ,		clean_up_tokenization_spaces=lowercase__					)
																)
										if with_prefix_space:
																_UpperCamelCase			:			Optional[Any]   			=						" " + output_txt
										_UpperCamelCase			:			int   			=						tokenizer.encode(lowercase__       ,		add_special_tokens=lowercase__					)
										return output_txt, output_ids





				def  snake_case__				(						self					:						Union[str, Any]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						self.ta_base_tokenizer
										_UpperCamelCase			:			Optional[Any]   			=						tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]					)
										_UpperCamelCase			:			Optional[Any]   			=						tokenizer(["hi", "I went to the gym", ""]					)
										self.assertListEqual(batch_with_eos_added["input_ids"]       ,		batch_without_eos_added["input_ids"]					)





				def  snake_case__				(						self					:						Optional[Any]					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						self.ta_base_tokenizer
										_UpperCamelCase			:			int   			=						"Unicode €."
										_UpperCamelCase			:			Dict   			=						tokenizer(lowercase__					)
										_UpperCamelCase			:			Dict   			=						[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
										self.assertEqual(encoded["input_ids"]       ,		lowercase__					)

										# decoding
										_UpperCamelCase			:			int   			=						tokenizer.decode(lowercase__					)
										self.assertEqual(lowercase__       ,		"Unicode €.</s>"					)

										_UpperCamelCase			:			Union[str, Any]   			=						tokenizer("e è é ê ë"					)
										_UpperCamelCase			:			Any   			=						[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
										self.assertEqual(encoded["input_ids"]       ,		lowercase__					)
										# decoding
										_UpperCamelCase			:			int   			=						tokenizer.decode(lowercase__					)
										self.assertEqual(lowercase__       ,		"e è é ê ë</s>"					)

										# encode/decode, but with `encode` instead of `__call__`
										self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"					)					)       ,		"e è é ê ë</s>"					)





				def  snake_case__				(						self					:						Tuple					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						self.ta_base_tokenizer
										_UpperCamelCase			:			Union[str, Any]   			=						["A long paragraph for summarization.", "Another paragraph for summarization."]
										# fmt: off
										_UpperCamelCase			:			Optional[Any]   			=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
										# fmt: on
										_UpperCamelCase			:			Dict   			=						tokenizer(lowercase__       ,		padding=lowercase__       ,		return_tensors=lowercase__					)
										self.assertIsInstance(lowercase__       ,		lowercase__					)

										if FRAMEWORK != "jax":
																_UpperCamelCase			:			Optional[Any]   			=						list(batch.input_ids.numpy()[0]					)
										else:
																_UpperCamelCase			:			int   			=						list(batch.input_ids.tolist()[0]					)

										self.assertListEqual(lowercase__       ,		lowercase__					)

										self.assertEqual((2, 37)       ,		batch.input_ids.shape					)
										self.assertEqual((2, 37)       ,		batch.attention_mask.shape					)





				def  snake_case__				(						self					:						List[str]					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						self.ta_base_tokenizer
										_UpperCamelCase			:			Tuple   			=						["A long paragraph for summarization.", "Another paragraph for summarization."]
										_UpperCamelCase			:			List[str]   			=						tokenizer(lowercase__       ,		padding=lowercase__       ,		return_tensors=lowercase__					)
										# check if input_ids are returned and no decoder_input_ids
										self.assertIn("input_ids"       ,		lowercase__					)
										self.assertIn("attention_mask"       ,		lowercase__					)
										self.assertNotIn("decoder_input_ids"       ,		lowercase__					)
										self.assertNotIn("decoder_attention_mask"       ,		lowercase__					)





				def  snake_case__				(						self					:						List[Any]					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						self.ta_base_tokenizer
										_UpperCamelCase			:			Dict   			=						[
										    "Summary of the text.",
										    "Another summary.",
										]
										_UpperCamelCase			:			Union[str, Any]   			=						tokenizer(
										    text_target=lowercase__       ,		max_length=32       ,		padding="max_length"       ,		truncation=lowercase__       ,		return_tensors=lowercase__					)
										self.assertEqual(32       ,		targets["input_ids"].shape[1]					)





				def  snake_case__				(						self					:						Optional[Any]					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						self.ta_base_tokenizer
										_UpperCamelCase			:			List[str]   			=						["A long paragraph for summarization. </s>"]
										_UpperCamelCase			:			Union[str, Any]   			=						["Summary of the text. </s>"]
										# fmt: off
										_UpperCamelCase			:			Any   			=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
										_UpperCamelCase			:			List[Any]   			=						[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
										# fmt: on

										_UpperCamelCase			:			Optional[int]   			=						tokenizer(lowercase__       ,		text_target=lowercase__					)

										self.assertEqual(lowercase__       ,		batch["input_ids"][0]					)
										self.assertEqual(lowercase__       ,		batch["labels"][0]					)





				def  snake_case__				(						self					:						Optional[int]					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						self.get_tokenizers()
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''					):
																						self.assertNotEqual(tokenizer.model_max_length       ,		42					)

        # Now let's start the test
										_UpperCamelCase			:			List[str]   			=						self.get_tokenizers()
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''					):
																						# Isolate this from the other tests because we save additional tokens/etc
																						_UpperCamelCase			:			Dict   			=						tempfile.mkdtemp()

																						_UpperCamelCase			:			int   			=						" He is very happy, UNwant\u00E9d,running"
																						_UpperCamelCase			:			Dict   			=						tokenizer.encode(lowercase__       ,		add_special_tokens=lowercase__					)
																						tokenizer.save_pretrained(lowercase__					)

																						_UpperCamelCase			:			Optional[Any]   			=						tokenizer.__class__.from_pretrained(lowercase__					)
																						_UpperCamelCase			:			Dict   			=						after_tokenizer.encode(lowercase__       ,		add_special_tokens=lowercase__					)
																						self.assertListEqual(lowercase__       ,		lowercase__					)

																						shutil.rmtree(lowercase__					)

										_UpperCamelCase			:			Union[str, Any]   			=						self.get_tokenizers(model_max_length=42					)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''					):
																						# Isolate this from the other tests because we save additional tokens/etc
																						_UpperCamelCase			:			List[Any]   			=						tempfile.mkdtemp()

																						_UpperCamelCase			:			Optional[Any]   			=						" He is very happy, UNwant\u00E9d,running"
																						tokenizer.add_tokens(["bim", "bambam"]					)
																						_UpperCamelCase			:			List[Any]   			=						tokenizer.additional_special_tokens
																						additional_special_tokens.append("new_additional_special_token"					)
																						tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}					)
																						_UpperCamelCase			:			Any   			=						tokenizer.encode(lowercase__       ,		add_special_tokens=lowercase__					)
																						tokenizer.save_pretrained(lowercase__					)

																						_UpperCamelCase			:			Dict   			=						tokenizer.__class__.from_pretrained(lowercase__					)
																						_UpperCamelCase			:			Dict   			=						after_tokenizer.encode(lowercase__       ,		add_special_tokens=lowercase__					)
																						self.assertListEqual(lowercase__       ,		lowercase__					)
																						self.assertIn("new_additional_special_token"       ,		after_tokenizer.additional_special_tokens					)
																						self.assertEqual(after_tokenizer.model_max_length       ,		42					)

																						_UpperCamelCase			:			Any   			=						tokenizer.__class__.from_pretrained(lowercase__       ,		model_max_length=43					)
																						self.assertEqual(tokenizer.model_max_length       ,		43					)

																						shutil.rmtree(lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						[]
										if self.test_slow_tokenizer:
																tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())					)

										if self.test_rust_tokenizer:
																tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())					)

										for tokenizer_class, tokenizer_utils in tokenizer_list:
																with tempfile.TemporaryDirectory() as tmp_dir:
																						tokenizer_utils.save_pretrained(lowercase__					)

																						with open(os.path.join(lowercase__       ,		"special_tokens_map.json"					)       ,		encoding="utf-8"					) as json_file:
																												_UpperCamelCase			:			Optional[int]   			=						json.load(lowercase__					)

																						with open(os.path.join(lowercase__       ,		"tokenizer_config.json"					)       ,		encoding="utf-8"					) as json_file:
																												_UpperCamelCase			:			int   			=						json.load(lowercase__					)

																						_UpperCamelCase			:			Union[str, Any]   			=						[f'''<extra_id_{i}>''' for i in range(125					)]

																						_UpperCamelCase			:			int   			=						added_tokens_extra_ids + [
																						    "an_additional_special_token"
																						]
																						_UpperCamelCase			:			Tuple   			=						added_tokens_extra_ids + [
																						    "an_additional_special_token"
																						]

																						with open(os.path.join(lowercase__       ,		"special_tokens_map.json"					)       ,		"w"       ,		encoding="utf-8"					) as outfile:
																												json.dump(lowercase__       ,		lowercase__					)
																						with open(os.path.join(lowercase__       ,		"tokenizer_config.json"					)       ,		"w"       ,		encoding="utf-8"					) as outfile:
																												json.dump(lowercase__       ,		lowercase__					)

																						# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																						# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																						# "special_tokens_map.json" files
																						_UpperCamelCase			:			List[Any]   			=						tokenizer_class.from_pretrained(
																						    lowercase__       ,		)
																						self.assertIn(
																						    "an_additional_special_token"       ,		tokenizer_without_change_in_init.additional_special_tokens					)
																						# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																						self.assertEqual(
																						    ["an_additional_special_token"]       ,		tokenizer_without_change_in_init.convert_ids_to_tokens(
																						        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]					)					)       ,		)

																						# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																						_UpperCamelCase			:			List[str]   			=						added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"       ,		lstrip=lowercase__					)]
																						_UpperCamelCase			:			Dict   			=						tokenizer_class.from_pretrained(
																						    lowercase__       ,		additional_special_tokens=lowercase__       ,		)

																						self.assertIn("a_new_additional_special_token"       ,		tokenizer.additional_special_tokens					)
																						self.assertEqual(
																						    ["a_new_additional_special_token"]       ,		tokenizer.convert_ids_to_tokens(
																						        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]					)					)       ,		)





				def  snake_case__				(						self					:						Any					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						[]
										if self.test_slow_tokenizer:
																tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())					)

										if self.test_rust_tokenizer:
																tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())					)

										for tokenizer_class, tokenizer_utils in tokenizer_list:
																with tempfile.TemporaryDirectory() as tmp_dir:
																						tokenizer_utils.save_pretrained(lowercase__					)

																						_UpperCamelCase			:			Optional[int]   			=						tokenizer_class.from_pretrained(lowercase__					)

																						self.assertTrue(tokenizer.decode([255]					) == ""					)





				def  snake_case__				(						self					:						Optional[Any]					)							->Union[str, Any]:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						Optional[int]					)							->Any:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						List[str]					)							->List[Any]:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						Any					)							->Any:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						Optional[Any]					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						self.get_tokenizers(fast=lowercase__       ,		do_lower_case=lowercase__					)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''					):
																						_UpperCamelCase			:			Optional[Any]   			=						["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
																						_UpperCamelCase			:			List[str]   			=						tokenizer.convert_tokens_to_string(lowercase__					)

																						self.assertIsInstance(lowercase__       ,		lowercase__					)







				def  snake_case__				(						self					:						Optional[int]					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						self.get_tokenizers()
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''					):
																						_UpperCamelCase			:			Any   			=						[
																						    "bos_token",
																						    "eos_token",
																						    "unk_token",
																						    "sep_token",
																						    "pad_token",
																						    "cls_token",
																						    "mask_token",
																						]

																						_UpperCamelCase			:			Tuple   			=						0
																						_UpperCamelCase			:			Tuple   			=						tokenizer.convert_ids_to_tokens(
																						    lowercase__       ,		skip_special_tokens=lowercase__					)

																						for attr in attributes_list:
																												setattr(lowercase__       ,		attr + "_id"       ,		lowercase__					)
																												self.assertEqual(getattr(lowercase__       ,		lowercase__					)       ,		lowercase__					)
																												self.assertEqual(getattr(lowercase__       ,		attr + "_id"					)       ,		lowercase__					)

																												setattr(lowercase__       ,		attr + "_id"       ,		lowercase__					)
																												self.assertEqual(getattr(lowercase__       ,		lowercase__					)       ,		lowercase__					)
																												self.assertEqual(getattr(lowercase__       ,		attr + "_id"					)       ,		lowercase__					)

																						setattr(lowercase__       ,		"additional_special_tokens_ids"       ,		[]					)
																						self.assertListEqual(getattr(lowercase__       ,		"additional_special_tokens"					)       ,		[]					)
																						self.assertListEqual(getattr(lowercase__       ,		"additional_special_tokens_ids"					)       ,		[]					)

																						setattr(lowercase__       ,		"additional_special_tokens_ids"       ,		[token_id_to_test_setters]					)
																						self.assertListEqual(getattr(lowercase__       ,		"additional_special_tokens"					)       ,		[token_to_test_setters]					)
																						self.assertListEqual(getattr(lowercase__       ,		"additional_special_tokens_ids"					)       ,		[token_id_to_test_setters]					)



'''simple docstring'''





from typing import List, Optional, Union

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


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

lowerCAmelCase_	:		Tuple			=     {
    """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     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''time_series_transformer'''
				UpperCAmelCase__							=							{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''num_hidden_layers''': '''encoder_layers''',
				}





				def __init__(						self					:						int       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						str = "student_t"       ,		lowercase__					:						str = "nll"       ,		lowercase__					:						int = 1       ,		lowercase__					:						List[int] = [1, 2, 3, 4, 5, 6, 7]       ,		lowercase__					:						Optional[Union[str, bool]] = "mean"       ,		lowercase__					:						int = 0       ,		lowercase__					:						int = 0       ,		lowercase__					:						int = 0       ,		lowercase__					:						int = 0       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						int = 32       ,		lowercase__					:						int = 32       ,		lowercase__					:						int = 2       ,		lowercase__					:						int = 2       ,		lowercase__					:						int = 2       ,		lowercase__					:						int = 2       ,		lowercase__					:						bool = True       ,		lowercase__					:						str = "gelu"       ,		lowercase__					:						int = 64       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						int = 100       ,		lowercase__					:						float = 0.0_2       ,		lowercase__					:						Optional[int]=True       ,		**lowercase__					:						Optional[Any]       ,		)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						prediction_length
										_UpperCamelCase			:			Optional[Any]   			=						context_length or prediction_length
										_UpperCamelCase			:			List[str]   			=						distribution_output
										_UpperCamelCase			:			Optional[Any]   			=						loss
										_UpperCamelCase			:			Tuple   			=						input_size
										_UpperCamelCase			:			Optional[int]   			=						num_time_features
										_UpperCamelCase			:			Union[str, Any]   			=						lags_sequence
										_UpperCamelCase			:			int   			=						scaling
										_UpperCamelCase			:			Dict   			=						num_dynamic_real_features
										_UpperCamelCase			:			str   			=						num_static_real_features
										_UpperCamelCase			:			Any   			=						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`"					)
																_UpperCamelCase			:			Dict   			=						cardinality
										else:
																_UpperCamelCase			:			str   			=						[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`"					)
																_UpperCamelCase			:			int   			=						embedding_dimension
										else:
																_UpperCamelCase			:			Optional[Any]   			=						[min(50       ,		(cat + 1) // 2					) for cat in self.cardinality]
										_UpperCamelCase			:			Union[str, Any]   			=						num_parallel_samples

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

										_UpperCamelCase			:			int   			=						dropout
										_UpperCamelCase			:			Optional[int]   			=						attention_dropout
										_UpperCamelCase			:			int   			=						activation_dropout
										_UpperCamelCase			:			List[Any]   			=						encoder_layerdrop
										_UpperCamelCase			:			int   			=						decoder_layerdrop

										_UpperCamelCase			:			List[Any]   			=						activation_function
										_UpperCamelCase			:			Any   			=						init_std

										_UpperCamelCase			:			Optional[Any]   			=						use_cache

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







				@property
				def  snake_case__				(						self					:						Union[str, Any]					)							->int:


										'''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
										)


