






"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class      UpperCamelCase		(unittest.TestCase     ):






						def      __snake_case      (		self  :List[str]				)      ->int:
										lowercase    :      Union[str, Any]       = tempfile.mkdtemp()

										lowercase    :      List[str]       = BlipImageProcessor()
										lowercase    :      Any       = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""				)

										lowercase    :      List[Any]       = BlipProcessor(lowercase_			, lowercase_				)

										processor.save_pretrained(self.tmpdirname				)






						def      __snake_case      (		self  :Dict			, **__magic_name__  :Union[str, Any]				)      ->str:
										return AutoProcessor.from_pretrained(self.tmpdirname			, **lowercase_				).tokenizer






						def      __snake_case      (		self  :Dict			, **__magic_name__  :Dict				)      ->List[str]:
										return AutoProcessor.from_pretrained(self.tmpdirname			, **lowercase_				).image_processor






						def      __snake_case      (		self  :List[Any]				)      ->Optional[int]:
										shutil.rmtree(self.tmpdirname				)






						def      __snake_case      (		self  :Dict				)      ->str:
										lowercase    :      str       = [np.random.randint(255			, size=(3, 30, 400)			, dtype=np.uinta				)]

										lowercase    :      str       = [Image.fromarray(np.moveaxis(lowercase_			, 0			, -1				)				) for x in image_inputs]

										return image_inputs






						def      __snake_case      (		self  :Optional[Any]				)      ->str:
										lowercase    :      Optional[int]       = BlipProcessor(tokenizer=self.get_tokenizer()			, image_processor=self.get_image_processor()				)
										processor.save_pretrained(self.tmpdirname				)

										lowercase    :      Any       = self.get_tokenizer(bos_token="""(BOS)"""			, eos_token="""(EOS)"""				)
										lowercase    :      Any       = self.get_image_processor(do_normalize=lowercase_			, padding_value=1.0				)

										lowercase    :      Any       = BlipProcessor.from_pretrained(
										    self.tmpdirname			, bos_token="""(BOS)"""			, eos_token="""(EOS)"""			, do_normalize=lowercase_			, padding_value=1.0				)

										self.assertEqual(processor.tokenizer.get_vocab()			, tokenizer_add_kwargs.get_vocab()				)
										self.assertIsInstance(processor.tokenizer			, lowercase_				)

										self.assertEqual(processor.image_processor.to_json_string()			, image_processor_add_kwargs.to_json_string()				)
										self.assertIsInstance(processor.image_processor			, lowercase_				)






						def      __snake_case      (		self  :Dict				)      ->int:
										lowercase    :      Optional[Any]       = self.get_image_processor()
										lowercase    :      str       = self.get_tokenizer()

										lowercase    :      int       = BlipProcessor(tokenizer=lowercase_			, image_processor=lowercase_				)

										lowercase    :      Union[str, Any]       = self.prepare_image_inputs()

										lowercase    :      Optional[Any]       = image_processor(lowercase_			, return_tensors="""np"""				)
										lowercase    :      List[str]       = processor(images=lowercase_			, return_tensors="""np"""				)

										for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()			, input_processor[key].sum()			, delta=1E-2				)






						def      __snake_case      (		self  :Tuple				)      ->List[str]:
										lowercase    :      List[str]       = self.get_image_processor()
										lowercase    :      Dict       = self.get_tokenizer()

										lowercase    :      Any       = BlipProcessor(tokenizer=lowercase_			, image_processor=lowercase_				)

										lowercase    :      Optional[Any]       = """lower newer"""

										lowercase    :      Optional[int]       = processor(text=lowercase_				)

										lowercase    :      int       = tokenizer(lowercase_			, return_token_type_ids=lowercase_				)

										for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			, encoded_processor[key]				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->int:
										lowercase    :      int       = self.get_image_processor()
										lowercase    :      Tuple       = self.get_tokenizer()

										lowercase    :      Any       = BlipProcessor(tokenizer=lowercase_			, image_processor=lowercase_				)

										lowercase    :      Union[str, Any]       = """lower newer"""
										lowercase    :      List[Any]       = self.prepare_image_inputs()

										lowercase    :      Tuple       = processor(text=lowercase_			, images=lowercase_				)

										self.assertListEqual(list(inputs.keys()				)			, ["""pixel_values""", """input_ids""", """attention_mask"""]				)

										# test if it raises when no input is passed
										with pytest.raises(lowercase_				):
														processor()






						def      __snake_case      (		self  :str				)      ->Tuple:
										lowercase    :      Dict       = self.get_image_processor()
										lowercase    :      Optional[int]       = self.get_tokenizer()

										lowercase    :      Tuple       = BlipProcessor(tokenizer=lowercase_			, image_processor=lowercase_				)

										lowercase    :      List[str]       = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowercase    :      Dict       = processor.batch_decode(lowercase_				)
										lowercase    :      str       = tokenizer.batch_decode(lowercase_				)

										self.assertListEqual(lowercase_			, lowercase_				)






						def      __snake_case      (		self  :Tuple				)      ->List[Any]:
										lowercase    :      Union[str, Any]       = self.get_image_processor()
										lowercase    :      Union[str, Any]       = self.get_tokenizer()

										lowercase    :      int       = BlipProcessor(tokenizer=lowercase_			, image_processor=lowercase_				)

										lowercase    :      Optional[Any]       = """lower newer"""
										lowercase    :      Tuple       = self.prepare_image_inputs()

										lowercase    :      Union[str, Any]       = processor(text=lowercase_			, images=lowercase_				)

										# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
										self.assertListEqual(list(inputs.keys()				)			, ["""pixel_values""", """input_ids""", """attention_mask"""]				)




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
											)

