
'''simple docstring'''




from typing import List, Optional, Union

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


lowercase			:				Tuple    		=			logging.get_logger(__name__)

lowercase			:				Union[str, 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  A				(							__snake_case						):
     __magic_name__						=		'''informer'''
     __magic_name__						=		{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }





     def __init__(							self						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = "student_t"						,			SCREAMING_SNAKE_CASE = "nll"						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = "mean"						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 64						,			SCREAMING_SNAKE_CASE = 32						,			SCREAMING_SNAKE_CASE = 32						,			SCREAMING_SNAKE_CASE = 2						,			SCREAMING_SNAKE_CASE = 2						,			SCREAMING_SNAKE_CASE = 2						,			SCREAMING_SNAKE_CASE = 2						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = "gelu"						,			SCREAMING_SNAKE_CASE = 0.05						,			SCREAMING_SNAKE_CASE = 0.1						,			SCREAMING_SNAKE_CASE = 0.1						,			SCREAMING_SNAKE_CASE = 0.1						,			SCREAMING_SNAKE_CASE = 0.1						,			SCREAMING_SNAKE_CASE = 100						,			SCREAMING_SNAKE_CASE = 0.02						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE = "prob"						,			SCREAMING_SNAKE_CASE = 5						,			SCREAMING_SNAKE_CASE = True						,			**SCREAMING_SNAKE_CASE						,			) ->				Any:






         """simple docstring"""






         A       :   Any					=						prediction_length
         A       :   Dict					=						context_length or prediction_length
         A       :   List[Any]					=						distribution_output
         A       :   List[str]					=						loss
         A       :   int					=						input_size
         A       :   List[Any]					=						num_time_features
         A       :   str					=						lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
         A       :   List[str]					=						scaling
         A       :   Any					=						num_dynamic_real_features
         A       :   str					=						num_static_real_features
         A       :   Optional[int]					=						num_static_categorical_features

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

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

         A       :   int					=						num_parallel_samples

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

         A       :   Optional[int]					=						dropout
         A       :   List[Any]					=						attention_dropout
         A       :   List[Any]					=						activation_dropout
         A       :   int					=						encoder_layerdrop
         A       :   str					=						decoder_layerdrop

         A       :   Optional[Any]					=						activation_function
         A       :   List[str]					=						init_std

         A       :   List[str]					=						use_cache

         # Informer
         A       :   List[Any]					=						attention_type
         A       :   Tuple					=						sampling_factor
         A       :   Dict					=						distil

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





     @property
     def 				__lowerCAmelCase							(							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
         )









'''simple docstring'''


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
   from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Any=1_3  ,						UpperCamelCase__      :					Optional[int]=3_2  ,						UpperCamelCase__      :					Any=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					str=[1_0, 2_0, 3_0, 4_0]  ,						UpperCamelCase__      :					str=[2, 2, 3, 2]  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[str]=True  ,						UpperCamelCase__      :					str=3_7  ,						UpperCamelCase__      :					Union[str, Any]="gelu"  ,						UpperCamelCase__      :					Dict=1_0  ,						UpperCamelCase__      :					Union[str, Any]=0.0_2  ,						UpperCamelCase__      :					int=["stage2", "stage3", "stage4"]  ,						UpperCamelCase__      :					List[str]=[2, 3, 4]  ,						UpperCamelCase__      :					Any=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  image_size
       UpperCamelCase					=  num_channels
       UpperCamelCase					=  num_stages
       UpperCamelCase					=  hidden_sizes
       UpperCamelCase					=  depths
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  intermediate_size
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  out_features
       UpperCamelCase					=  out_indices
       UpperCamelCase					=  scope






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       UpperCamelCase					=  None
       if self.use_labels:
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.num_labels )

       UpperCamelCase					=  self.get_config()
       return config, pixel_values, labels






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return ConvNextConfig(
           num_channels=self.num_channels  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						num_stages=self.num_stages  ,						hidden_act=self.hidden_act  ,						is_decoder=UpperCamelCase__  ,						initializer_range=self.initializer_range  ,						out_features=self.out_features  ,						out_indices=self.out_indices  ,						num_labels=self.num_labels  ,						)






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape  ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)  ,						)






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextForImageClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextBackbone(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps )  ,						len(config.out_features ) )
       self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,						[self.batch_size, self.hidden_sizes[1], 4, 4] )

       # verify channels
       self.parent.assertEqual(len(model.channels )  ,						len(config.out_features ) )
       self.parent.assertListEqual(model.channels  ,						config.hidden_sizes[1:] )

       # verify backbone works with out_features=None
       UpperCamelCase					=  None
       UpperCamelCase					=  ConvNextBackbone(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps )  ,						1 )
       self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,						[self.batch_size, self.hidden_sizes[-1], 1, 1] )

       # verify channels
       self.parent.assertEqual(len(model.channels )  ,						1 )
       self.parent.assertListEqual(model.channels  ,						[config.hidden_sizes[-1]] )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  config_and_inputs
       UpperCamelCase					=  {'pixel_values': pixel_values}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						_a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            ConvNextModel,
            ConvNextForImageClassification,
            ConvNextBackbone,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  (
        {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
        if is_torch_available()
        else {}
    )

    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						has_text_modality=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return






    @unittest.skip(reason='ConvNext does not use inputs_embeds' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='ConvNext does not support input and output embeddings' )
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='ConvNext does not use feedforward chunking' )
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       pass






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCamelCase					=  [*signature.parameters.keys()]

          UpperCamelCase					=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       def check_hidden_states_output(UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          model.to(UpperCamelCase__ )
          model.eval()

          with torch.no_grad():
             UpperCamelCase					=  model(**self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )

          UpperCamelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          UpperCamelCase					=  self.model_tester.num_stages
          self.assertEqual(len(UpperCamelCase__ )  ,						expected_num_stages + 1 )

          # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:] )  ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,						)

       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  True
          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCamelCase					=  True

          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )






    @slow
    def  A       (  self      :					Dict ):


       """simple docstring"""







       for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  ConvNextModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )







def 				__lowerCamelCase				(   )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
   return image

@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @cached_property
    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224' ) if is_vision_available() else None






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224' ).to(UpperCamelCase__ )

       UpperCamelCase					=  self.default_image_processor
       UpperCamelCase					=  prepare_img()
       UpperCamelCase					=  image_processor(images=UpperCamelCase__  ,						return_tensors='pt' ).to(UpperCamelCase__ )

       # forward pass
       with torch.no_grad():
          UpperCamelCase					=  model(**UpperCamelCase__ )

       # verify the logits
       UpperCamelCase					=  torch.Size((1, 1_0_0_0) )
       self.assertEqual(outputs.logits.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1] ).to(UpperCamelCase__ )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (ConvNextBackbone,) if is_torch_available() else ()
    _SCREAMING_SNAKE_CASE	  =  ConvNextConfig

    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModelTester(self )

