'''simple docstring'''


import inspect
import unittest

from transformers import BitConfig
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 torch import nn

 from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
 from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=3				,		snake_case_=3_2				,		snake_case_=3				,		snake_case_=1_0				,		snake_case_=[8, 1_6, 3_2, 6_4]				,		snake_case_=[1, 1, 2, 1]				,		snake_case_=True				,		snake_case_=True				,		snake_case_="relu"				,		snake_case_=3				,		snake_case_=None				,		snake_case_=["stage2", "stage3", "stage4"]				,		snake_case_=[2, 3, 4]				,		snake_case_=1				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				parent
       UpperCAmelCase_							:       Union[str, Any]       =				batch_size
       UpperCAmelCase_							:       List[Any]       =				image_size
       UpperCAmelCase_							:       Optional[int]       =				num_channels
       UpperCAmelCase_							:       str       =				embeddings_size
       UpperCAmelCase_							:       Dict       =				hidden_sizes
       UpperCAmelCase_							:       Dict       =				depths
       UpperCAmelCase_							:       int       =				is_training
       UpperCAmelCase_							:       Optional[Any]       =				use_labels
       UpperCAmelCase_							:       Union[str, Any]       =				hidden_act
       UpperCAmelCase_							:       Any       =				num_labels
       UpperCAmelCase_							:       str       =				scope
       UpperCAmelCase_							:       str       =				len(snake_case_						)
       UpperCAmelCase_							:       str       =				out_features
       UpperCAmelCase_							:       Any       =				out_indices
       UpperCAmelCase_							:       int       =				num_groups


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       UpperCAmelCase_							:       Optional[int]       =				None
       if self.use_labels:
            UpperCAmelCase_							:       List[str]       =				ids_tensor([self.batch_size]				,		self.num_labels						)

       UpperCAmelCase_							:       Union[str, Any]       =				self.get_config()

       return config, pixel_values, labels


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return BitConfig(
           num_channels=self.num_channels				,		embeddings_size=self.embeddings_size				,		hidden_sizes=self.hidden_sizes				,		depths=self.depths				,		hidden_act=self.hidden_act				,		num_labels=self.num_labels				,		out_features=self.out_features				,		out_indices=self.out_indices				,		num_groups=self.num_groups				,		)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				BitModel(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_						)
       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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self.num_labels
       UpperCAmelCase_							:       int       =				BitForImageClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Union[str, Any]       =				model(snake_case_				,		labels=snake_case_						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				BitBackbone(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Tuple       =				model(snake_case_						)

       # verify feature maps
       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_							:       Optional[int]       =				None
       UpperCAmelCase_							:       Optional[int]       =				BitBackbone(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       int       =				model(snake_case_						)

       # 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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.prepare_config_and_inputs()
       UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       List[str]       =				config_and_inputs
       UpperCAmelCase_							:       Optional[Any]       =				{'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  lowerCamelCase_		:str						   =		(
      {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCamelCase_		:List[str]						   =		False
  lowerCamelCase_		:List[Any]						   =		False
  lowerCamelCase_		:Optional[Any]						   =		False
  lowerCamelCase_		:List[str]						   =		False
  lowerCamelCase_		:Optional[Any]						   =		False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				BitModelTester(self						)
       UpperCAmelCase_							:       List[Any]       =				ConfigTester(self				,		config_class=snake_case_				,		has_text_modality=snake_case_						)


  def _UpperCamelCase					(			self						):




       '''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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       return


  @unittest.skip(reason='Bit does not output attentions'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @unittest.skip(reason='Bit does not use inputs_embeds'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @unittest.skip(reason='Bit does not support input and output embeddings'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:       Optional[int]       =				model_class(snake_case_						)
            UpperCAmelCase_							:       List[Any]       =				inspect.signature(model.forward						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase_							:       List[str]       =				[*signature.parameters.keys()]

            UpperCAmelCase_							:       Any       =				['pixel_values']
            self.assertListEqual(arg_names[:1]				,		snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_							:       Dict       =				model_class(config=snake_case_						)
            for name, module in model.named_modules():
                 if isinstance(snake_case_				,		(nn.BatchNormad, nn.GroupNorm)						):
                      self.assertTrue(
                          torch.all(module.weight == 1						)				,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,		)
                      self.assertTrue(
                          torch.all(module.bias == 0						)				,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,		)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       def check_hidden_states_output(snake_case_				,		snake_case_				,		snake_case_						):
            UpperCAmelCase_							:       Optional[Any]       =				model_class(snake_case_						)
            model.to(snake_case_						)
            model.eval()

            with torch.no_grad():
                 UpperCAmelCase_							:       Optional[int]       =				model(**self._prepare_for_class(snake_case_				,		snake_case_						)						)

            UpperCAmelCase_							:       Optional[int]       =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase_							:       int       =				self.model_tester.num_stages
            self.assertEqual(len(snake_case_						)				,		expected_num_stages + 1						)

            # Bit'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_							:       List[str]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       Optional[Any]       =				['preactivation', 'bottleneck']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 UpperCAmelCase_							:       Optional[Any]       =				layer_type
                 UpperCAmelCase_							:       List[str]       =				True
                 check_hidden_states_output(snake_case_				,		snake_case_				,		snake_case_						)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 UpperCAmelCase_							:       List[str]       =				True

                 check_hidden_states_output(snake_case_				,		snake_case_				,		snake_case_						)


  @unittest.skip(reason='Bit does not use feedforward chunking'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_						)






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_							:       Optional[int]       =				BitModel.from_pretrained(snake_case_						)
            self.assertIsNotNone(snake_case_						)







def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				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 _UpperCamelCase					(			self						):




       '''simple docstring'''

       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						) if is_vision_available() else None
       )






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case_						)

       UpperCAmelCase_							:       Tuple       =				self.default_image_processor
       UpperCAmelCase_							:       str       =				prepare_img()
       UpperCAmelCase_							:       Dict       =				image_processor(images=snake_case_				,		return_tensors='pt'						).to(snake_case_						)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_							:       List[Any]       =				model(**snake_case_						)

       # verify the logits
       UpperCAmelCase_							:       List[Any]       =				torch.Size((1, 1_0_0_0)						)
       self.assertEqual(outputs.logits.shape				,		snake_case_						)

       UpperCAmelCase_							:       List[str]       =				torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]						).to(snake_case_						)

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







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[Any]						   =		(BitBackbone,) if is_torch_available() else ()
  lowerCamelCase_		:Optional[Any]						   =		BitConfig

  lowerCamelCase_		:Any						   =		False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				BitModelTester(self						)







'''simple docstring'''


from typing import List, Optional

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


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

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






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:List[str]						   =		'''autoformer'''
  lowerCamelCase_		:Optional[Any]						   =		{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }


  def __init__(			self				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = "student_t"				,		snake_case_ = "nll"				,		snake_case_ = 1				,		snake_case_ = [1, 2, 3, 4, 5, 6, 7]				,		snake_case_ = True				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = 6_4				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 3_2				,		snake_case_ = 3_2				,		snake_case_ = "gelu"				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 1_0_0				,		snake_case_ = 0.02				,		snake_case_ = True				,		snake_case_=True				,		snake_case_ = 1_0				,		snake_case_ = 2_5				,		snake_case_ = 3				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				prediction_length
       UpperCAmelCase_							:       List[str]       =				context_length if context_length is not None else prediction_length
       UpperCAmelCase_							:       Optional[int]       =				distribution_output
       UpperCAmelCase_							:       Optional[int]       =				loss
       UpperCAmelCase_							:       Union[str, Any]       =				input_size
       UpperCAmelCase_							:       int       =				num_time_features
       UpperCAmelCase_							:       List[str]       =				lags_sequence
       UpperCAmelCase_							:       Any       =				scaling
       UpperCAmelCase_							:       Any       =				num_dynamic_real_features
       UpperCAmelCase_							:       int       =				num_static_real_features
       UpperCAmelCase_							:       Optional[Any]       =				num_static_categorical_features
       if cardinality is not None and num_static_categorical_features > 0:
            if len(snake_case_						) != num_static_categorical_features:
                 raise ValueError(
                     'The cardinality should be a list of the same length as `num_static_categorical_features`'						)
            UpperCAmelCase_							:       List[Any]       =				cardinality
       else:
            UpperCAmelCase_							:       Optional[int]       =				[0]
       if embedding_dimension is not None and num_static_categorical_features > 0:
            if len(snake_case_						) != num_static_categorical_features:
                 raise ValueError(
                     'The embedding dimension should be a list of the same length as `num_static_categorical_features`'						)
            UpperCAmelCase_							:       List[str]       =				embedding_dimension
       else:
            UpperCAmelCase_							:       List[Any]       =				[min(5_0				,		(cat + 1) // 2						) for cat in self.cardinality]
       UpperCAmelCase_							:       List[str]       =				num_parallel_samples

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

       UpperCAmelCase_							:       str       =				dropout
       UpperCAmelCase_							:       Optional[int]       =				attention_dropout
       UpperCAmelCase_							:       Tuple       =				activation_dropout
       UpperCAmelCase_							:       Any       =				encoder_layerdrop
       UpperCAmelCase_							:       Tuple       =				decoder_layerdrop

       UpperCAmelCase_							:       List[str]       =				activation_function
       UpperCAmelCase_							:       Tuple       =				init_std

       UpperCAmelCase_							:       Union[str, Any]       =				use_cache

       # Autoformer
       UpperCAmelCase_							:       Any       =				label_length
       UpperCAmelCase_							:       Union[str, Any]       =				moving_average
       UpperCAmelCase_							:       Tuple       =				autocorrelation_factor

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






  @property
  def _UpperCamelCase					(			self						):




       '''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
       )






