






'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       tempfile.mkdtemp()

														# fmt: off
														lowerCamelCase__					:	int         =       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
														# fmt: on
														lowerCamelCase__					:	Union[str, Any]         =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""vocab_file"""]     )
														with open(self.vocab_file			,							"""w"""			,							encoding="""utf-8"""     ) as vocab_writer:
																					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

														lowerCamelCase__					:	Tuple         =       {
														    """do_resize""": True,
														    """size""": {"""height""": 18, """width""": 18},
														    """do_normalize""": True,
														    """image_mean""": [0.5, 0.5, 0.5],
														    """image_std""": [0.5, 0.5, 0.5],
														}
														lowerCamelCase__					:	Tuple         =       os.path.join(self.tmpdirname			,							UpperCamelCase__     )
														with open(self.image_processor_file			,							"""w"""			,							encoding="""utf-8"""     ) as fp:
																					json.dump(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							**UpperCamelCase__:				List[str]     ):
														return BertTokenizer.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int			,							**UpperCamelCase__:				Tuple     ):
														return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														shutil.rmtree(self.tmpdirname     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[Any]         =       [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta     )]

														lowerCamelCase__					:	Tuple         =       [Image.fromarray(np.moveaxis(UpperCamelCase__			,							0			,							-1     )     ) for x in image_inputs]

														return image_inputs




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_tokenizer()
														lowerCamelCase__					:	Dict         =       self.get_image_processor()

														lowerCamelCase__					:	Optional[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

														self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()     )
														self.assertIsInstance(processor.image_processor			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Dict         =       VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCamelCase__					:	int         =       self.get_tokenizer(bos_token="""(BOS)"""			,							eos_token="""(EOS)"""     )
														lowerCamelCase__					:	List[Any]         =       self.get_image_processor(do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														lowerCamelCase__					:	Tuple         =       VisionTextDualEncoderProcessor.from_pretrained(
														    self.tmpdirname			,							bos_token="""(BOS)"""			,							eos_token="""(EOS)"""			,							do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

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




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	Any         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       self.prepare_image_inputs()

														lowerCamelCase__					:	List[str]         =       image_processor(UpperCamelCase__			,							return_tensors="""np"""     )
														lowerCamelCase__					:	Optional[Any]         =       processor(images=UpperCamelCase__			,							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 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       """lower newer"""

														lowerCamelCase__					:	Union[str, Any]         =       processor(text=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       tokenizer(UpperCamelCase__     )

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




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       """lower newer"""
														lowerCamelCase__					:	Dict         =       self.prepare_image_inputs()

														lowerCamelCase__					:	Tuple         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

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

														# test if it raises when no input is passed
														with self.assertRaises(UpperCamelCase__     ):
																					processor()




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCamelCase__					:	Union[str, Any]         =       processor.batch_decode(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       tokenizer.batch_decode(UpperCamelCase__     )

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       """lower newer"""
														lowerCamelCase__					:	str         =       self.prepare_image_inputs()

														lowerCamelCase__					:	int         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

														self.assertListEqual(list(inputs.keys()     )			,							processor.model_input_names     )








'''simple docstring'''


from typing import List, Optional

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


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

lowerCAmelCase					:     Tuple												={
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class       a_   (				_lowerCAmelCase							):
     __A      =			"autoformer"
     __A      =			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
         "num_hidden_layers": "encoder_layers",
     }





     def __init__( self		:					Any	,      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		:					bool = True	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					int = 64	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 32	,      lowercase		:					int = 32	,      lowercase		:					str = "gelu"	,      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 = 0.02	,      lowercase		:					bool = True	,      lowercase		:					int=True	,      lowercase		:					int = 10	,      lowercase		:					int = 25	,      lowercase		:					int = 3	,      **lowercase		:					Dict	,      ):





        """simple docstring"""




        lowercase_			:str    			= prediction_length
        lowercase_			:Dict    			= context_length if context_length is not None else prediction_length
        lowercase_			:Any    			= distribution_output
        lowercase_			:Tuple    			= loss
        lowercase_			:Dict    			= input_size
        lowercase_			:Tuple    			= num_time_features
        lowercase_			:int    			= lags_sequence
        lowercase_			:Tuple    			= scaling
        lowercase_			:List[Any]    			= num_dynamic_real_features
        lowercase_			:Union[str, Any]    			= num_static_real_features
        lowercase_			:str    			= 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`"						)
           lowercase_			:Optional[int]    			= cardinality
        else:
           lowercase_			:Optional[int]    			= [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`"						)
           lowercase_			:Tuple    			= embedding_dimension
        else:
           lowercase_			:Dict    			= [min(50	,      (cat + 1) // 2						) for cat in self.cardinality]
        lowercase_			:Any    			= num_parallel_samples

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

        lowercase_			:Dict    			= dropout
        lowercase_			:Dict    			= attention_dropout
        lowercase_			:str    			= activation_dropout
        lowercase_			:int    			= encoder_layerdrop
        lowercase_			:Dict    			= decoder_layerdrop

        lowercase_			:List[str]    			= activation_function
        lowercase_			:int    			= init_std

        lowercase_			:Optional[int]    			= use_cache

        # Autoformer
        lowercase_			:List[str]    			= label_length
        lowercase_			:List[str]    			= moving_average
        lowercase_			:Optional[int]    			= autocorrelation_factor

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







     @property
     def 							lowercase__      ( self		:					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
        )


