


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 							lowerCamelCase   :


	def __init__(    self				,     lowercase__				,     lowercase__=3				,     lowercase__=3_2				,     lowercase__=3				,     lowercase__=1_0				,     lowercase__=[8, 1_6, 3_2, 6_4]				,     lowercase__=[1, 1, 2, 1]				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu"				,     lowercase__=3				,     lowercase__=None				,     lowercase__=["stage2", "stage3", "stage4"]				,     lowercase__=[2, 3, 4]				,     lowercase__=1				,     ):
							__UpperCAmelCase			:							Any	    =	parent
							__UpperCAmelCase			:							str	    =	batch_size
							__UpperCAmelCase			:							Optional[int]	    =	image_size
							__UpperCAmelCase			:							Tuple	    =	num_channels
							__UpperCAmelCase			:							int	    =	embeddings_size
							__UpperCAmelCase			:							List[Any]	    =	hidden_sizes
							__UpperCAmelCase			:							Tuple	    =	depths
							__UpperCAmelCase			:							Tuple	    =	is_training
							__UpperCAmelCase			:							List[str]	    =	use_labels
							__UpperCAmelCase			:							Optional[int]	    =	hidden_act
							__UpperCAmelCase			:							str	    =	num_labels
							__UpperCAmelCase			:							Optional[Any]	    =	scope
							__UpperCAmelCase			:							List[Any]	    =	len(lowercase__)
							__UpperCAmelCase			:							Any	    =	out_features
							__UpperCAmelCase			:							Tuple	    =	out_indices
							__UpperCAmelCase			:							int	    =	num_groups


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							__UpperCAmelCase			:							Dict	    =	None
							if self.use_labels:
													__UpperCAmelCase			:							Any	    =	ids_tensor([self.batch_size]				,     self.num_labels)

							__UpperCAmelCase			:							str	    =	self.get_config()

							return config, pixel_values, labels


	def  A(    self):
							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  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[Any]	    =	BitModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							Optional[Any]	    =	model(lowercase__)
							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				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	self.num_labels
							__UpperCAmelCase			:							Union[str, Any]	    =	BitForImageClassification(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							Dict	    =	model(lowercase__				,     labels=lowercase__)
							self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	BitBackbone(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							List[Any]	    =	model(lowercase__)

							# 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			:							Dict	    =	None
							__UpperCAmelCase			:							List[str]	    =	BitBackbone(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							Tuple	    =	model(lowercase__)

							# 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):
							__UpperCAmelCase			:							Dict	    =	self.prepare_config_and_inputs()
							__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Any	    =	config_and_inputs
							__UpperCAmelCase			:							Any	    =	{'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		str										=						(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	_lowerCAmelCase     :		Optional[Any]										=						(
	    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_lowerCAmelCase     :		Tuple										=						False
	_lowerCAmelCase     :		List[Any]										=						False
	_lowerCAmelCase     :		Optional[int]										=						False
	_lowerCAmelCase     :		str										=						False
	_lowerCAmelCase     :		List[str]										=						False


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	BitModelTester(self)
							__UpperCAmelCase			:							List[Any]	    =	ConfigTester(self				,     config_class=lowercase__				,     has_text_modality=lowercase__)


	def  A(    self):
							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):
							return


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


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


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


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

													__UpperCAmelCase			:							Optional[Any]	    =	['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							int	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowercase__)


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__UpperCAmelCase			:							int	    =	model_class(config=lowercase__)
													for name, module in model.named_modules():
																			if isinstance(lowercase__				,     (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  A(    self):
							def check_hidden_states_output(lowercase__				,     lowercase__				,     lowercase__):
													__UpperCAmelCase			:							List[str]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()

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

													__UpperCAmelCase			:							int	    =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													__UpperCAmelCase			:							Dict	    =	self.model_tester.num_stages
													self.assertEqual(len(lowercase__)				,     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			:							Tuple	    =	self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase			:							Union[str, Any]	    =	['''preactivation''', '''bottleneck''']
							for model_class in self.all_model_classes:
													for layer_type in layers_type:
																			__UpperCAmelCase			:							Optional[Any]	    =	layer_type
																			__UpperCAmelCase			:							Optional[int]	    =	True
																			check_hidden_states_output(lowercase__				,     lowercase__				,     lowercase__)

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

																			check_hidden_states_output(lowercase__				,     lowercase__				,     lowercase__)


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


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowercase__)


	@slow
	def  A(    self):
							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__UpperCAmelCase			:							Union[str, Any]	    =	BitModel.from_pretrained(lowercase__)
													self.assertIsNotNone(lowercase__)






def     __SCREAMING_SNAKE_CASE							(       )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
						return image




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


	@cached_property
	def  A(    self):
							return (
							    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
							)


	@slow
	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	self.default_image_processor
							__UpperCAmelCase			:							List[str]	    =	prepare_img()
							__UpperCAmelCase			:							Union[str, Any]	    =	image_processor(images=lowercase__				,     return_tensors='''pt''').to(lowercase__)

							# forward pass
							with torch.no_grad():
													__UpperCAmelCase			:							Optional[Any]	    =	model(**lowercase__)

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

							__UpperCAmelCase			:							List[Any]	    =	torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]).to(lowercase__)

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






@require_torch
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Optional[Any]										=						(BitBackbone,) if is_torch_available() else ()
	_lowerCAmelCase     :		Optional[Any]										=						BitConfig

	_lowerCAmelCase     :		str										=						False


	def  A(    self):
							__UpperCAmelCase			:							int	    =	BitModelTester(self)





from typing import List, Optional

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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[int]										=						'''autoformer'''
	_lowerCAmelCase     :		Any										=						{
	    '''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__ = 6_4				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 3_2				,     lowercase__ = 3_2				,     lowercase__ = "gelu"				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 1_0_0				,     lowercase__ = 0.0_2				,     lowercase__ = True				,     lowercase__=True				,     lowercase__ = 1_0				,     lowercase__ = 2_5				,     lowercase__ = 3				,     **lowercase__				,     ):
							# time series specific configuration
							__UpperCAmelCase			:							Optional[int]	    =	prediction_length
							__UpperCAmelCase			:							Tuple	    =	context_length if context_length is not None else prediction_length
							__UpperCAmelCase			:							Tuple	    =	distribution_output
							__UpperCAmelCase			:							Tuple	    =	loss
							__UpperCAmelCase			:							Dict	    =	input_size
							__UpperCAmelCase			:							str	    =	num_time_features
							__UpperCAmelCase			:							str	    =	lags_sequence
							__UpperCAmelCase			:							int	    =	scaling
							__UpperCAmelCase			:							int	    =	num_dynamic_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_real_features
							__UpperCAmelCase			:							List[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`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	cardinality
							else:
													__UpperCAmelCase			:							List[Any]	    =	[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`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	embedding_dimension
							else:
													__UpperCAmelCase			:							int	    =	[min(5_0				,     (cat + 1) // 2) for cat in self.cardinality]
							__UpperCAmelCase			:							Tuple	    =	num_parallel_samples

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

							__UpperCAmelCase			:							Union[str, Any]	    =	dropout
							__UpperCAmelCase			:							int	    =	attention_dropout
							__UpperCAmelCase			:							int	    =	activation_dropout
							__UpperCAmelCase			:							str	    =	encoder_layerdrop
							__UpperCAmelCase			:							Dict	    =	decoder_layerdrop

							__UpperCAmelCase			:							int	    =	activation_function
							__UpperCAmelCase			:							Optional[int]	    =	init_std

							__UpperCAmelCase			:							Optional[int]	    =	use_cache

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

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


	@property
	def  A(    self):
							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
							)

