


"""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    lowerCamelCase__						:
      def __init__(		self	:	Dict      ,       A_	:	List[str]      ,       A_	:	Union[str, Any]=3      ,       A_	:	List[str]=3_2      ,       A_	:	List[str]=3      ,       A_	:	Any=1_0      ,       A_	:	Dict=[8, 1_6, 3_2, 6_4]      ,       A_	:	Dict=[1, 1, 2, 1]      ,       A_	:	Dict=True      ,       A_	:	List[Any]=True      ,       A_	:	Dict="relu"      ,       A_	:	Optional[Any]=3      ,       A_	:	Optional[int]=None      ,       A_	:	List[Any]=["stage2", "stage3", "stage4"]      ,       A_	:	Tuple=[2, 3, 4]      ,       A_	:	List[str]=1      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	image_size
       __lowercase									=	num_channels
       __lowercase									=	embeddings_size
       __lowercase									=	hidden_sizes
       __lowercase									=	depths
       __lowercase									=	is_training
       __lowercase									=	use_labels
       __lowercase									=	hidden_act
       __lowercase									=	num_labels
       __lowercase									=	scope
       __lowercase									=	len(A_							)
       __lowercase									=	out_features
       __lowercase									=	out_indices
       __lowercase									=	num_groups
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


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

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

       __lowercase									=	self.get_config()

       return config, pixel_values, labels
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	Union[str, Any]      ,       A_	:	Optional[Any]      ,       A_	:	Dict							):






       '''simple docstring'''


       __lowercase									=	BitModel(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_							)
       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 SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	List[Any]      ,       A_	:	str      ,       A_	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	BitForImageClassification(A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_      ,       labels=A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Any      ,       A_	:	str      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	BitBackbone(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_							)

       # 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
       __lowercase									=	None
       __lowercase									=	BitBackbone(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_							)

       # 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 SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	self.prepare_config_and_inputs()
       __lowercase							,		__lowercase							,		__lowercase									=	config_and_inputs
       __lowercase									=	{"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_torch
class    lowerCamelCase__						(		_a			,   _a			,   unittest.TestCase    ):
      a :     int											=		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      a :     str											=		(
          {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
          if is_torch_available()
          else {}
      )

      a :     List[str]											=		False
      a :     Optional[Any]											=		False
      a :     Dict											=		False
      a :     Union[str, Any]											=		False
      a :     int											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	BitModelTester(self							)
       __lowercase									=	ConfigTester(self      ,       config_class=A_      ,       has_text_modality=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''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 SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return
      @unittest.skip(reason="""Bit does not output attentions"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       pass
      @unittest.skip(reason="""Bit does not use inputs_embeds"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


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






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowercase									=	["""pixel_values"""]
        self.assertListEqual(arg_names[:1]      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowercase									=	model_class(config=A_							)
        for name, module in model.named_modules():
         if isinstance(A_      ,       (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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       def check_hidden_states_output(A_	:	Union[str, Any]      ,       A_	:	Tuple      ,       A_	:	Union[str, Any]							):
        __lowercase									=	model_class(A_							)
        model.to(A_							)
        model.eval()

        with torch.no_grad():
         __lowercase									=	model(**self._prepare_for_class(A_      ,       A_							)							)

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

        __lowercase									=	self.model_tester.num_stages
        self.assertEqual(len(A_							)      ,       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]      ,       )

       __lowercase							,		__lowercase									=	self.model_tester.prepare_config_and_inputs_for_common()
       __lowercase									=	["""preactivation""", """bottleneck"""]
       for model_class in self.all_model_classes:
        for layer_type in layers_type:
         __lowercase									=	layer_type
         __lowercase									=	True
         check_hidden_states_output(A_      ,       A_      ,       A_							)

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

         check_hidden_states_output(A_      ,       A_      ,       A_							)
      @unittest.skip(reason="""Bit does not use feedforward chunking"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowercase									=	BitModel.from_pretrained(A_							)
        self.assertIsNotNone(A_							)






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
 return image





@require_torch
@require_vision
class    lowerCamelCase__						(		unittest.TestCase    ):
      @cached_property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


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






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(A_							)

       __lowercase									=	self.default_image_processor
       __lowercase									=	prepare_img()
       __lowercase									=	image_processor(images=A_      ,       return_tensors="""pt"""							).to(A_							)

       # forward pass
       with torch.no_grad():
        __lowercase									=	model(**A_							)

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

       __lowercase									=	torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]							).to(A_							)

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






@require_torch
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     List[Any]											=		(BitBackbone,) if is_torch_available() else ()
      a :     List[Any]											=		BitConfig

      a :     List[Any]											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	BitModelTester(self							)




"""simple docstring"""

from collections.abc import Callable

import numpy as np


def lowerCAmelCase_      (				UpperCamelCase__	: Callable				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float				,   UpperCamelCase__	: float ):






 """simple docstring"""







 __lowercase									=	int(np.ceil((x_end - xa) / step_size ) )
 __lowercase									=	np.zeros((n + 1,) )
 __lowercase									=	ya
 __lowercase									=	xa

 for k in range(UpperCamelCase__ ):
  __lowercase									=	y[k] + step_size * ode_func(UpperCamelCase__				,   y[k] )
  x += step_size

 return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()
