

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor

class   _a					(			unittest.TestCase				):




      """simple docstring"""

      def __init__(   self							:				Tuple						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Any=7						,			UpperCAmelCase							:				Any=3						,			UpperCAmelCase							:				str=18						,			UpperCAmelCase							:				Union[str, Any]=30						,			UpperCAmelCase							:				Union[str, Any]=400						,			UpperCAmelCase							:				Optional[int]=True						,			UpperCAmelCase							:				Optional[int]=None						,			UpperCAmelCase							:				Union[str, Any]=True						,			UpperCAmelCase							:				Dict=None						,			):
            A_						  =				size if size is not None else {"shortest_edge": 20}
            A_						  =				crop_size if crop_size is not None else {"height": 18, "width": 18}
            A_						  =				parent
            A_						  =				batch_size
            A_						  =				num_channels
            A_						  =				image_size
            A_						  =				min_resolution
            A_						  =				max_resolution
            A_						  =				do_resize
            A_						  =				size
            A_						  =				do_center_crop
            A_						  =				crop_size







      def 		__A	(   self							:				Optional[Any]	):
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
            }




@require_torch
@require_vision
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			Union[str, Any]      		=       MobileNetVaImageProcessor if is_vision_available() else None





      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				MobileNetVaImageProcessingTester(self	)





      @property
      def 		__A	(   self							:				str	):
            return self.image_processor_tester.prepare_image_processor_dict()





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_resize"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"size"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_center_crop"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"crop_size"	)	)





      def 		__A	(   self							:				int	):
            A_						  =				self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size						,			{"shortest_edge": 20}	)
            self.assertEqual(image_processor.crop_size						,			{"height": 18, "width": 18}	)

            A_						  =				self.image_processing_class.from_dict(self.image_processor_dict						,			size=42						,			crop_size=84	)
            self.assertEqual(image_processor.size						,			{"shortest_edge": 42}	)
            self.assertEqual(image_processor.crop_size						,			{"height": 84, "width": 84}	)





      def 		__A	(   self							:				int	):
            pass





      def 		__A	(   self							:				List[str]	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			Image.Image	)

            # Test not batched input
            A_						  =				image_processing(image_inputs[0]						,			return_tensors="pt"	).pixel_values
            self.assertEqual(
                encoded_images.shape						,			(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )						,			)

            # Test batched
            A_						  =				image_processing(UpperCAmelCase						,			return_tensors="pt"	).pixel_values
            self.assertEqual(
                encoded_images.shape						,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )						,			)





      def 		__A	(   self							:				str	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase						,			numpify=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			np.ndarray	)

            # Test not batched input
            A_						  =				image_processing(image_inputs[0]						,			return_tensors="pt"	).pixel_values
            self.assertEqual(
                encoded_images.shape						,			(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )						,			)

            # Test batched
            A_						  =				image_processing(UpperCAmelCase						,			return_tensors="pt"	).pixel_values
            self.assertEqual(
                encoded_images.shape						,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )						,			)







      def 		__A	(   self							:				str	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase						,			torchify=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			torch.Tensor	)

            # Test not batched input
            A_						  =				image_processing(image_inputs[0]						,			return_tensors="pt"	).pixel_values
            self.assertEqual(
                encoded_images.shape						,			(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )						,			)

            # Test batched
            A_						  =				image_processing(UpperCAmelCase						,			return_tensors="pt"	).pixel_values
            self.assertEqual(
                encoded_images.shape						,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )						,			)
'''simple docstring'''


from typing import List, Optional, Union

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


__lowerCAmelCase						:							Union[str, Any]    							=						logging.get_logger(__name__)

__lowerCAmelCase						:							Optional[int]    							=						{
    "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 ( UpperCAmelCase ):
							a_										=   '''time_series_transformer'''
							a_										=   {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''num_hidden_layers''': '''encoder_layers''',
							}





							def __init__(   self					:				str  ,		__a					:				Optional[int] = None  ,		__a					:				Optional[int] = None  ,		__a					:				str = "student_t"  ,		__a					:				str = "nll"  ,		__a					:				int = 1  ,		__a					:				List[int] = [1, 2, 3, 4, 5, 6, 7]  ,		__a					:				Optional[Union[str, bool]] = "mean"  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				int = 0  ,		__a					:				Optional[List[int]] = None  ,		__a					:				Optional[List[int]] = None  ,		__a					:				int = 3_2  ,		__a					:				int = 3_2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				int = 2  ,		__a					:				bool = True  ,		__a					:				str = "gelu"  ,		__a					:				int = 6_4  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				float = 0.1  ,		__a					:				int = 1_0_0  ,		__a					:				float = 0.0_2  ,		__a					:				Optional[int]=True  ,		**__a					:				str  ,		)				->						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
								__UpperCAmelCase      					=      scaling
								__UpperCAmelCase      					=      num_dynamic_real_features
								__UpperCAmelCase      					=      num_static_real_features
								__UpperCAmelCase      					=      num_static_categorical_features
								if cardinality and num_static_categorical_features > 0:
									if len(__a						) != 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]
								if embedding_dimension and num_static_categorical_features > 0:
									if len(__a						) != 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(5_0  ,		(cat + 1) // 2						) for cat in self.cardinality]
								__UpperCAmelCase      					=      num_parallel_samples

								# Transformer architecture configuration
								__UpperCAmelCase      					=      input_size * len(__a						) + 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

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






							@property
							def    snake_case__    (   self					:				List[Any]						)				->						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
								)
