


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)





from typing import List, Optional

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


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='autoformer'
				snake_case	    ={
				    '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_ = True       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = 64       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 32       ,  lowercase_ = 32       ,  lowercase_ = "gelu"       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 100       ,  lowercase_ = 0.02       ,  lowercase_ = True       ,  lowercase_=True       ,  lowercase_ = 10       ,  lowercase_ = 25       ,  lowercase_ = 3       ,  **lowercase_       ,  )  -> Union[str, Any]:
											# time series specific configuration
											a__      =prediction_length
											a__      =context_length if context_length is not None else 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 is not None 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`')
																		a__      =cardinality
											else:
																		a__      =[0]
											if embedding_dimension is not None 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`')
																		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(self.lags_sequence) + 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

											# Autoformer
											a__      =label_length
											a__      =moving_average
											a__      =autocorrelation_factor

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





				@property
				def 						__UpperCamelCase    (				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
											)

