import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
      from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor






class 		_lowerCamelCase		:
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=13			,			lowerCAmelCase=3			,			lowerCAmelCase=True			,			lowerCAmelCase=True			,			lowerCAmelCase=0.1			,			lowerCAmelCase=0.1			,			lowerCAmelCase=224			,			lowerCAmelCase=1000			,			lowerCAmelCase=[3, 3, 6, 4]			,			lowerCAmelCase=[48, 56, 112, 220]			,			)							->       Dict:
     SCREAMING_SNAKE_CASE__: Any=   parent
     SCREAMING_SNAKE_CASE__: Dict=   batch_size
     SCREAMING_SNAKE_CASE__: Tuple=   num_channels
     SCREAMING_SNAKE_CASE__: Any=   is_training
     SCREAMING_SNAKE_CASE__: List[Any]=   use_labels
     SCREAMING_SNAKE_CASE__: str=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__: Any=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__: Dict=   num_labels
     SCREAMING_SNAKE_CASE__: Optional[int]=   image_size
     SCREAMING_SNAKE_CASE__: List[str]=   layer_depths
     SCREAMING_SNAKE_CASE__: int=   embed_dims
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

     SCREAMING_SNAKE_CASE__: int=   self.get_config()

     return config, pixel_values, labels
    def     UpperCamelCase_					(	self )							->       Tuple:
     return SwiftFormerConfig(
         depths=self.layer_depths			,			embed_dims=self.embed_dims			,			mlp_ratio=4			,			downsamples=[True, True, True, True]			,			hidden_act='''gelu'''			,			num_labels=self.num_labels			,			down_patch_size=3			,			down_stride=2			,			down_pad=1			,			drop_rate=0.0			,			drop_path_rate=0.0			,			use_layer_scale=lowerCAmelCase			,			layer_scale_init_value=1e-5			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: int=   SwiftFormerModel(config=lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()
     SCREAMING_SNAKE_CASE__: str=   model(lowerCAmelCase )
     self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.embed_dims[-1], 7, 7) )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.num_labels
     SCREAMING_SNAKE_CASE__: Dict=   SwiftFormerForImageClassification(lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()
     SCREAMING_SNAKE_CASE__: Dict=   model(lowerCAmelCase			,			labels=lowerCAmelCase )
     self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )

     SCREAMING_SNAKE_CASE__: List[Any]=   SwiftFormerForImageClassification(lowerCAmelCase )
     model.to(lowerCAmelCase )
     model.eval()

     SCREAMING_SNAKE_CASE__: Dict=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
     SCREAMING_SNAKE_CASE__: List[str]=   model(lowerCAmelCase )
     self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )



    def     UpperCamelCase_					(	self )							->       int:
     ((SCREAMING_SNAKE_CASE__),						(SCREAMING_SNAKE_CASE__),						(SCREAMING_SNAKE_CASE__)): List[Any]=   self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__: Optional[int]=   {'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    __a						=   (
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: str=   SwiftFormerModelTester(self )
     SCREAMING_SNAKE_CASE__: Dict=   ConfigTester(
         self			,			config_class=lowerCAmelCase			,			has_text_modality=lowerCAmelCase			,			hidden_size=37			,			num_attention_heads=12			,			num_hidden_layers=12			,			)
    def     UpperCamelCase_					(	self )							->       Tuple:
     self.config_tester.run_common_tests()
    @unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     pass
    def     UpperCamelCase_					(	self )							->       Any:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   model_class(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Dict=   model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(lowerCAmelCase			,			nn.Linear ) )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Any=   self.model_tester.prepare_config_and_inputs_for_common()

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

      SCREAMING_SNAKE_CASE__: List[Any]=   ['''pixel_values''']
      self.assertListEqual(arg_names[:1]			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: int=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
    @slow
    def     UpperCamelCase_					(	self )							->       Any:
     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__: List[Any]=   SwiftFormerModel.from_pretrained(lowerCAmelCase )
      self.assertIsNotNone(lowerCAmelCase )
    @unittest.skip(reason='''SwiftFormer does not output attentions''' )
    def     UpperCamelCase_					(	self )							->       List[str]:
     pass
    def     UpperCamelCase_					(	self )							->       Tuple:
     def check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   model_class(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()

      with torch.no_grad():
       SCREAMING_SNAKE_CASE__: Any=   model(**self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase ) )

      SCREAMING_SNAKE_CASE__: Union[str, Any]=   outputs.hidden_states

      SCREAMING_SNAKE_CASE__: str=   8
      self.assertEqual(len(lowerCAmelCase )			,			lowerCAmelCase )  # TODO

      # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
      # with the width and height being successively divided by 2, after every 2 blocks
      for i in range(len(lowerCAmelCase ) ):
       self.assertEqual(
           hidden_states[i].shape			,			torch.Size(
               [
                   self.model_tester.batch_size,
                   self.model_tester.embed_dims[i // 2],
                   (self.model_tester.image_size // 4) // 2 ** (i // 2),
                   (self.model_tester.image_size // 4) // 2 ** (i // 2),
               ] )			,			)

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

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Optional[int]=   True
      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   True

      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     def _config_zero_init(lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: List[str]=   copy.deepcopy(lowerCAmelCase )
      for key in configs_no_init.__dict__.keys():
       if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
        setattr(lowerCAmelCase			,			lowerCAmelCase			,			1e-10 )
       if isinstance(getattr(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )			,			lowerCAmelCase ):
        SCREAMING_SNAKE_CASE__: str=   _config_zero_init(getattr(lowerCAmelCase			,			lowerCAmelCase ) )
        setattr(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
      return configs_no_init

     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Tuple=   self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__: int=   _config_zero_init(lowerCAmelCase )
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: List[str]=   model_class(config=lowerCAmelCase )
      for name, param in model.named_parameters():
       if param.requires_grad:
        self.assertIn(
            ((param.data.mean() * 1e9) / 1e9).round().item()			,			[0.0, 1.0]			,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'			,			)



    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     pass







def       A__   (						):
 SCREAMING_SNAKE_CASE__: List[Any]=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






@require_torch
@require_vision
class 		_lowerCamelCase		( unittest.TestCase     ):
    @cached_property
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None



    @slow
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.default_image_processor
     SCREAMING_SNAKE_CASE__: Dict=   prepare_img()
     SCREAMING_SNAKE_CASE__: List[Any]=   image_processor(images=lowerCAmelCase			,			return_tensors='''pt''' ).to(lowerCAmelCase )

     # forward pass
     with torch.no_grad():
      SCREAMING_SNAKE_CASE__: int=   model(**lowerCAmelCase )

     # verify the logits
     SCREAMING_SNAKE_CASE__: int=   torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape			,			lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]] ).to(lowerCAmelCase )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			lowerCAmelCase			,			atol=1e-4 ) )



'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__						       =      TypeVar("T")






class 	snake_case__       (      Generic[T]					):







		"""simple docstring"""

		def __init__(      self				:			str  ,	UpperCamelCase__				:			list[T]  ,	UpperCamelCase__				:			Callable[[T, T], T]      )						->			None:






				"""simple docstring"""







				snake_case  :       Any | T     			=						None

				snake_case  :       int     			=						len(UpperCamelCase__      )
				snake_case  :       list[T]     			=						[any_type for _ in range(self.N      )] + arr
				snake_case  :       Optional[int]     			=						fnc
				self.build()



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			None:






				"""simple docstring"""







				for p in range(self.N - 1  ,	0  ,	-1      ):
						snake_case  :       List[Any]     			=						self.fn(self.st[p * 2]  ,	self.st[p * 2 + 1]      )



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			T      )						->			None:






				"""simple docstring"""







				p += self.N
				snake_case  :       int     			=						v
				while p > 1:
						snake_case  :       List[Any]     			=						p // 2
						snake_case  :       List[Any]     			=						self.fn(self.st[p * 2]  ,	self.st[p * 2 + 1]      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			int      )						->			T | None:  # noqa: E741






				"""simple docstring"""







				snake_case			,snake_case  :       Union[str, Any]     			=						l + self.N, r + self.N

				snake_case  :       T | None     			=						None
				while l <= r:
						if l % 2 == 1:
								snake_case  :       str     			=						self.st[l] if res is None else self.fn(UpperCamelCase__  ,	self.st[l]      )
						if r % 2 == 0:
								snake_case  :       Tuple     			=						self.st[r] if res is None else self.fn(UpperCamelCase__  ,	self.st[r]      )
						snake_case			,snake_case  :       Dict     			=						(l + 1) // 2, (r - 1) // 2
				return res


if __name__ == "__main__":
						from functools import reduce

						lowercase__						       =      [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

						lowercase__						       =      {
						    0: 7,
						    1: 2,
						    2: 6,
						    3: -1_4,
						    4: 5,
						    5: 4,
						    6: 7,
						    7: -1_0,
						    8: 9,
						    9: 1_0,
						    1_0: 1_2,
						    1_1: 1,
						}

						lowercase__						       =      SegmentTree(test_array, min)
						lowercase__						       =      SegmentTree(test_array, max)
						lowercase__						       =      SegmentTree(test_array, lambda a, b: a + b)




						def    _UpperCamelCase						(       )  ->							None:


								'''simple docstring'''


								for i in range(len(SCREAMING_SNAKE_CASE__			)			):
										for j in range(SCREAMING_SNAKE_CASE__ ,       len(SCREAMING_SNAKE_CASE__			)			):
												snake_case  :       int     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       List[str]     			=						reduce(SCREAMING_SNAKE_CASE__ ,       test_array[i : j + 1]			)
												snake_case  :       Tuple     			=						reduce(lambda SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			: a + b ,       test_array[i : j + 1]			)
												assert min_range == min_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert max_range == max_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
												assert sum_range == sum_segment_tree.query(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

						test_all_segments()

						for index, value in test_updates.items():
												lowercase__						       =      value
												min_segment_tree.update(index, value)
												max_segment_tree.update(index, value)
												sum_segment_tree.update(index, value)
												test_all_segments()


