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

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


lowercase__  :      Dict        =					logging.get_logger(__name__)

lowercase__  :      Any        =					{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''informer'''
  lowerCAmelCase__										=	{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }






  def __init__(      self			:       Dict  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       str = "student_t"  ,							UpperCAmelCase__			:       str = "nll"  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       List[int] = None  ,							UpperCAmelCase__			:       Optional[Union[str, bool]] = "mean"  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       int = 64  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "gelu"  ,							UpperCAmelCase__			:       float = 0.05  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       List[Any]=True  ,							UpperCAmelCase__			:       str = "prob"  ,							UpperCAmelCase__			:       int = 5  ,							UpperCAmelCase__			:       bool = True  ,							**UpperCAmelCase__			:       int  ,							)				->int:
        # time series specific configuration
        UpperCAmelCase_            =  prediction_length
        UpperCAmelCase_            =  context_length or prediction_length
        UpperCAmelCase_            =  distribution_output
        UpperCAmelCase_            =  loss
        UpperCAmelCase_            =  input_size
        UpperCAmelCase_            =  num_time_features
        UpperCAmelCase_            =  lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
        UpperCAmelCase_            =  scaling
        UpperCAmelCase_            =  num_dynamic_real_features
        UpperCAmelCase_            =  num_static_real_features
        UpperCAmelCase_            =  num_static_categorical_features

        # set cardinality
        if cardinality and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The cardinality should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  cardinality
        else:
              UpperCAmelCase_            =  [0]

        # set embedding_dimension
        if embedding_dimension and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  embedding_dimension
        else:
              UpperCAmelCase_            =  [min(50  ,							(cat + 1) // 2  ) for cat in self.cardinality]

        UpperCAmelCase_            =  num_parallel_samples

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

        UpperCAmelCase_            =  dropout
        UpperCAmelCase_            =  attention_dropout
        UpperCAmelCase_            =  activation_dropout
        UpperCAmelCase_            =  encoder_layerdrop
        UpperCAmelCase_            =  decoder_layerdrop

        UpperCAmelCase_            =  activation_function
        UpperCAmelCase_            =  init_std

        UpperCAmelCase_            =  use_cache

        # Informer
        UpperCAmelCase_            =  attention_type
        UpperCAmelCase_            =  sampling_factor
        UpperCAmelCase_            =  distil

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






  @property
  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->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
        )






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






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






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['''token_type_ids''']  )  ,							sum(tokens_p['''token_type_ids''']  )  )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )  ,							sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )





