





import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
					from test_module.custom_tokenization_fast import CustomTokenizerFast


class   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   List[str]       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       0






			@slow
			def        a_	(      self       :   Optional[Any]       )     ->     Any:







					"""simple docstring"""






					for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
							self.assertIsNotNone(__lowerCAmelCase       )
							self.assertIsInstance(__lowerCAmelCase							,     (BertTokenizer, BertTokenizerFast)       )
							self.assertGreater(len(__lowerCAmelCase       )							,     0       )

					for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
							self.assertIsNotNone(__lowerCAmelCase       )
							self.assertIsInstance(__lowerCAmelCase							,     (GPTaTokenizer, GPTaTokenizerFast)       )
							self.assertGreater(len(__lowerCAmelCase       )							,     0       )






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







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     (BertTokenizer, BertTokenizerFast)       )
					self.assertEqual(tokenizer.vocab_size							,     12       )






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







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     (RobertaTokenizer, RobertaTokenizerFast)       )
					self.assertEqual(tokenizer.vocab_size							,     20       )






			def        a_	(      self       :   Tuple       )     ->     Any:







					"""simple docstring"""






					A__  				=       AutoConfig.from_pretrained(__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )
					# Check that tokenizer_type ≠ model_type
					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     config=__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     (BertTokenizer, BertTokenizerFast)       )
					self.assertEqual(tokenizer.vocab_size							,     12       )






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







					"""simple docstring"""






					with tempfile.TemporaryDirectory() as tmp_dir:
							shutil.copy("""./tests/fixtures/vocab.txt"""							,     os.path.join(__lowerCAmelCase							,     """vocab.txt"""       )       )

							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     tokenizer_type="""bert"""							,     use_fast=__lowerCAmelCase       )
							self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )

					with tempfile.TemporaryDirectory() as tmp_dir:
							shutil.copy("""./tests/fixtures/vocab.json"""							,     os.path.join(__lowerCAmelCase							,     """vocab.json"""       )       )
							shutil.copy("""./tests/fixtures/merges.txt"""							,     os.path.join(__lowerCAmelCase							,     """merges.txt"""       )       )

							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     tokenizer_type="""gpt2"""							,     use_fast=__lowerCAmelCase       )
							self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )






			@require_tokenizers
			def        a_	(      self       :   Optional[Any]       )     ->     Any:







					"""simple docstring"""






					with tempfile.TemporaryDirectory() as tmp_dir:
							shutil.copy("""./tests/fixtures/vocab.txt"""							,     os.path.join(__lowerCAmelCase							,     """vocab.txt"""       )       )

							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     tokenizer_type="""bert"""       )
							self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )

					with tempfile.TemporaryDirectory() as tmp_dir:
							shutil.copy("""./tests/fixtures/vocab.json"""							,     os.path.join(__lowerCAmelCase							,     """vocab.json"""       )       )
							shutil.copy("""./tests/fixtures/merges.txt"""							,     os.path.join(__lowerCAmelCase							,     """merges.txt"""       )       )

							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     tokenizer_type="""gpt2"""       )
							self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )






			def        a_	(      self       :   List[Any]       )     ->     Dict:







					"""simple docstring"""






					with pytest.raises(__lowerCAmelCase       ):
							AutoTokenizer.from_pretrained("""./"""							,     tokenizer_type="""xxx"""       )






			@require_tokenizers
			def        a_	(      self       :   List[str]       )     ->     List[str]:







					"""simple docstring"""






					for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
							A__  				=       tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased"""       )
							self.assertIsInstance(__lowerCAmelCase							,     (BertTokenizer, BertTokenizerFast)       )

							if isinstance(__lowerCAmelCase							,     __lowerCAmelCase       ):
									self.assertEqual(tokenizer.basic_tokenizer.do_lower_case							,     __lowerCAmelCase       )
							else:
									self.assertEqual(tokenizer.do_lower_case							,     __lowerCAmelCase       )

							self.assertEqual(tokenizer.model_max_length							,     5_12       )






			@require_tokenizers
			def        a_	(      self       :   Tuple       )     ->     List[Any]:







					"""simple docstring"""






					for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
							with self.assertRaisesRegex(
							    __lowerCAmelCase							,     """julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"""							,     ):
									A__  				=       tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists"""       )






			def        a_	(      self       :   str       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       TOKENIZER_MAPPING.values()
					A__  				=       []

					for slow_tok, fast_tok in tokenizers:
							if slow_tok is not None:
									tokenizer_names.append(slow_tok.__name__       )

							if fast_tok is not None:
									tokenizer_names.append(fast_tok.__name__       )

					for tokenizer_name in tokenizer_names:
							# must find the right class
							tokenizer_class_from_name(__lowerCAmelCase       )






			@require_tokenizers
			def        a_	(      self       :   Dict       )     ->     Union[str, Any]:







					"""simple docstring"""






					self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""							,     use_fast=__lowerCAmelCase       )							,     __lowerCAmelCase       )
					self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""       )							,     __lowerCAmelCase       )






			@require_tokenizers
			def        a_	(      self       :   Optional[Any]       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained("""distilbert-base-uncased"""							,     do_lower_case=__lowerCAmelCase       )
					A__  				=       """Hello, world. How are you?"""
					A__  				=       tokenizer.tokenize(__lowerCAmelCase       )
					self.assertEqual("""[UNK]"""							,     tokens[0]       )

					A__  				=       AutoTokenizer.from_pretrained("""microsoft/mpnet-base"""							,     do_lower_case=__lowerCAmelCase       )
					A__  				=       tokenizer.tokenize(__lowerCAmelCase       )
					self.assertEqual("""[UNK]"""							,     tokens[0]       )






			@require_tokenizers
			def        a_	(      self       :   str       )     ->     Dict:







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config"""       )
					self.assertEqual(type(__lowerCAmelCase       )							,     __lowerCAmelCase       )
					self.assertEqual(tokenizer.model_max_length							,     5_12       )
					self.assertEqual(tokenizer.vocab_size							,     3_00_00       )
					self.assertEqual(tokenizer.unk_token							,     """[UNK]"""       )
					self.assertEqual(tokenizer.padding_side							,     """right"""       )
					self.assertEqual(tokenizer.truncation_side							,     """right"""       )






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







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
					self.assertIsInstance(__lowerCAmelCase							,     (BertTokenizer, BertTokenizerFast)       )
					with tempfile.TemporaryDirectory() as tmp_dir:
							tokenizer.save_pretrained(__lowerCAmelCase       )
							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )

					self.assertIsInstance(__lowerCAmelCase							,     tokenizer.__class__       )
					self.assertEqual(tokenizera.vocab_size							,     12       )






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







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained("""ctrl"""       )
					# There is no fast CTRL so this always gives us a slow tokenizer.
					self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )






			def        a_	(      self       :   Any       )     ->     Any:







					"""simple docstring"""






					A__  				=       get_tokenizer_config("""bert-base-cased"""       )
					A__  				=       config.pop("""_commit_hash"""							,     __lowerCAmelCase       )
					# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
					self.assertEqual(__lowerCAmelCase							,     {"""do_lower_case""": False}       )

					# This model does not have a tokenizer_config so we get back an empty dict.
					A__  				=       get_tokenizer_config(__lowerCAmelCase       )
					self.assertDictEqual(__lowerCAmelCase							,     {}       )

					# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
					with tempfile.TemporaryDirectory() as tmp_dir:
							tokenizer.save_pretrained(__lowerCAmelCase       )
							A__  				=       get_tokenizer_config(__lowerCAmelCase       )

					# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
					self.assertEqual(config["""tokenizer_class"""]							,     """BertTokenizer"""       )






			def        a_	(      self       :   Tuple       )     ->     str:







					"""simple docstring"""






					try:
							AutoConfig.register("""custom"""							,     __lowerCAmelCase       )

							AutoTokenizer.register(__lowerCAmelCase							,     slow_tokenizer_class=__lowerCAmelCase       )
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(__lowerCAmelCase       ):
									AutoTokenizer.register(__lowerCAmelCase							,     slow_tokenizer_class=__lowerCAmelCase       )

							A__  				=       CustomTokenizer.from_pretrained(__lowerCAmelCase       )
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(__lowerCAmelCase       )

									A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
									self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]






			@require_tokenizers
			def        a_	(      self       :   List[Any]       )     ->     List[str]:







					"""simple docstring"""






					try:
							AutoConfig.register("""custom"""							,     __lowerCAmelCase       )

							# Can register in two steps
							AutoTokenizer.register(__lowerCAmelCase							,     slow_tokenizer_class=__lowerCAmelCase       )
							self.assertEqual(TOKENIZER_MAPPING[CustomConfig]							,     (CustomTokenizer, None)       )
							AutoTokenizer.register(__lowerCAmelCase							,     fast_tokenizer_class=__lowerCAmelCase       )
							self.assertEqual(TOKENIZER_MAPPING[CustomConfig]							,     (CustomTokenizer, CustomTokenizerFast)       )

							del TOKENIZER_MAPPING._extra_content[CustomConfig]
							# Can register in one step
							AutoTokenizer.register(
							    __lowerCAmelCase							,     slow_tokenizer_class=__lowerCAmelCase							,     fast_tokenizer_class=__lowerCAmelCase       )
							self.assertEqual(TOKENIZER_MAPPING[CustomConfig]							,     (CustomTokenizer, CustomTokenizerFast)       )

							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(__lowerCAmelCase       ):
									AutoTokenizer.register(__lowerCAmelCase							,     fast_tokenizer_class=__lowerCAmelCase       )

							# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
							# and that model does not have a tokenizer.json
							with tempfile.TemporaryDirectory() as tmp_dir:
									A__  				=       BertTokenizerFast.from_pretrained(__lowerCAmelCase       )
									bert_tokenizer.save_pretrained(__lowerCAmelCase       )
									A__  				=       CustomTokenizerFast.from_pretrained(__lowerCAmelCase       )

							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(__lowerCAmelCase       )

									A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
									self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )

									A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     use_fast=__lowerCAmelCase       )
									self.assertIsInstance(__lowerCAmelCase							,     __lowerCAmelCase       )

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]






			def        a_	(      self       :   Dict       )     ->     Any:







					"""simple docstring"""






					with self.assertRaises(__lowerCAmelCase       ):
							A__  				=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""       )
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(__lowerCAmelCase       ):
							A__  				=       AutoTokenizer.from_pretrained(
							    """hf-internal-testing/test_dynamic_tokenizer"""							,     trust_remote_code=__lowerCAmelCase       )

					A__  				=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""							,     trust_remote_code=__lowerCAmelCase       )
					self.assertTrue(tokenizer.special_attribute_present       )
					# Test tokenizer can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
							tokenizer.save_pretrained(__lowerCAmelCase       )
							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     trust_remote_code=__lowerCAmelCase       )
					self.assertTrue(reloaded_tokenizer.special_attribute_present       )

					if is_tokenizers_available():
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizerFast"""       )
							self.assertEqual(reloaded_tokenizer.__class__.__name__							,     """NewTokenizerFast"""       )

							# Test we can also load the slow version
							A__  				=       AutoTokenizer.from_pretrained(
							    """hf-internal-testing/test_dynamic_tokenizer"""							,     trust_remote_code=__lowerCAmelCase							,     use_fast=__lowerCAmelCase       )
							self.assertTrue(tokenizer.special_attribute_present       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizer"""       )
							# Test tokenizer can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(__lowerCAmelCase       )
									A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     trust_remote_code=__lowerCAmelCase							,     use_fast=__lowerCAmelCase       )
							self.assertEqual(reloaded_tokenizer.__class__.__name__							,     """NewTokenizer"""       )
							self.assertTrue(reloaded_tokenizer.special_attribute_present       )
					else:
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizer"""       )
							self.assertEqual(reloaded_tokenizer.__class__.__name__							,     """NewTokenizer"""       )






			@require_tokenizers
			def        a_	(      self       :   Tuple       )     ->     Union[str, Any]:







					"""simple docstring"""






					class   A      (SCREAMING_SNAKE_CASE   ):
								'''simple docstring'''




								__lowerCamelCase     :	Union[str, Any]          =   False


					class   A      (SCREAMING_SNAKE_CASE   ):
								'''simple docstring'''




								__lowerCamelCase     :	Optional[Any]          =   NewTokenizer
								__lowerCamelCase     :	int          =   False

					try:
							AutoConfig.register("""custom"""							,     __lowerCAmelCase       )
							AutoTokenizer.register(__lowerCAmelCase							,     slow_tokenizer_class=__lowerCAmelCase       )
							AutoTokenizer.register(__lowerCAmelCase							,     fast_tokenizer_class=__lowerCAmelCase       )
							# If remote code is not set, the default is to use local
							A__  				=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizerFast"""       )
							self.assertFalse(tokenizer.special_attribute_present       )
							A__  				=       AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""							,     use_fast=__lowerCAmelCase       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizer"""       )
							self.assertFalse(tokenizer.special_attribute_present       )

							# If remote code is disabled, we load the local one.
							A__  				=       AutoTokenizer.from_pretrained(
							    """hf-internal-testing/test_dynamic_tokenizer"""							,     trust_remote_code=__lowerCAmelCase       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizerFast"""       )
							self.assertFalse(tokenizer.special_attribute_present       )
							A__  				=       AutoTokenizer.from_pretrained(
							    """hf-internal-testing/test_dynamic_tokenizer"""							,     trust_remote_code=__lowerCAmelCase							,     use_fast=__lowerCAmelCase       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizer"""       )
							self.assertFalse(tokenizer.special_attribute_present       )

							# If remote is enabled, we load from the Hub
							A__  				=       AutoTokenizer.from_pretrained(
							    """hf-internal-testing/test_dynamic_tokenizer"""							,     trust_remote_code=__lowerCAmelCase       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizerFast"""       )
							self.assertTrue(tokenizer.special_attribute_present       )
							A__  				=       AutoTokenizer.from_pretrained(
							    """hf-internal-testing/test_dynamic_tokenizer"""							,     trust_remote_code=__lowerCAmelCase							,     use_fast=__lowerCAmelCase       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizer"""       )
							self.assertTrue(tokenizer.special_attribute_present       )

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]






			def        a_	(      self       :   Tuple       )     ->     Dict:







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained(
					    """hf-internal-testing/test_dynamic_tokenizer_legacy"""							,     trust_remote_code=__lowerCAmelCase       )
					self.assertTrue(tokenizer.special_attribute_present       )
					if is_tokenizers_available():
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizerFast"""       )

							# Test we can also load the slow version
							A__  				=       AutoTokenizer.from_pretrained(
							    """hf-internal-testing/test_dynamic_tokenizer_legacy"""							,     trust_remote_code=__lowerCAmelCase							,     use_fast=__lowerCAmelCase       )
							self.assertTrue(tokenizer.special_attribute_present       )
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizer"""       )
					else:
							self.assertEqual(tokenizer.__class__.__name__							,     """NewTokenizer"""       )






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







					"""simple docstring"""






					with self.assertRaisesRegex(
					    __lowerCAmelCase							,     """bert-base is not a local folder and is not a valid model identifier"""       ):
							A__  				=       AutoTokenizer.from_pretrained("""bert-base"""       )






			def        a_	(      self       :   List[str]       )     ->     Optional[int]:







					"""simple docstring"""






					with self.assertRaisesRegex(
					    __lowerCAmelCase							,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""       ):
							A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     revision="""aaaaaa"""       )







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







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""       )
					with RequestCounter() as counter:
							A__  				=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""       )
							self.assertEqual(counter.get_request_count							,     0       )
							self.assertEqual(counter.head_request_count							,     1       )
							self.assertEqual(counter.other_request_count							,     0       )










from typing import List, Optional, Union

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


A   : Optional[Any]			=				logging.get_logger(__name__)

A   : 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   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   '''time_series_transformer'''
			__lowerCamelCase     :	Dict          =   {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






			def __init__(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   Optional[int] = None							,     __lowerCAmelCase       :   str = "student_t"							,     __lowerCAmelCase       :   str = "nll"							,     __lowerCAmelCase       :   int = 1							,     __lowerCAmelCase       :   List[int] = [1, 2, 3, 4, 5, 6, 7]							,     __lowerCAmelCase       :   Optional[Union[str, bool]] = "mean"							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   int = 0							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   Optional[List[int]] = None							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 32							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   int = 2							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   str = "gelu"							,     __lowerCAmelCase       :   int = 64							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   float = 0.1							,     __lowerCAmelCase       :   int = 1_00							,     __lowerCAmelCase       :   float = 0.0_2							,     __lowerCAmelCase       :   Optional[Any]=True							,     **__lowerCAmelCase       :   Union[str, Any]							,     )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       prediction_length
					A__  				=       context_length or prediction_length
					A__  				=       distribution_output
					A__  				=       loss
					A__  				=       input_size
					A__  				=       num_time_features
					A__  				=       lags_sequence
					A__  				=       scaling
					A__  				=       num_dynamic_real_features
					A__  				=       num_static_real_features
					A__  				=       num_static_categorical_features
					if cardinality and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The cardinality should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       cardinality
					else:
							A__  				=       [0]
					if embedding_dimension and num_static_categorical_features > 0:
							if len(__lowerCAmelCase       ) != num_static_categorical_features:
									raise ValueError(
									    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""       )
							A__  				=       embedding_dimension
					else:
							A__  				=       [min(50							,     (cat + 1) // 2       ) for cat in self.cardinality]
					A__  				=       num_parallel_samples

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

					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop

					A__  				=       activation_function
					A__  				=       init_std

					A__  				=       use_cache

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







			@property
			def        a_	(      self       :   List[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
					)



