





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor
class 							snake_case_		(      UpperCAmelCase__):







		def       __lowercase (		self )					-> List[str]:
				lowerCamelCase :			List[str]    		=self.config_class(**self.inputs_dict )
				self.parent.assertTrue(hasattr(_lowerCAmelCase      ,  '''hidden_sizes''' ) )
				self.parent.assertTrue(hasattr(_lowerCAmelCase      ,  '''num_encoder_blocks''' ) )

class 							snake_case_		:







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_3      ,  __lowercase=6_4      ,  __lowercase=3      ,  __lowercase=4      ,  __lowercase=[2, 2, 2, 2]      ,  __lowercase=[8, 4, 2, 1]      ,  __lowercase=[1_6, 3_2, 6_4, 1_2_8]      ,  __lowercase=[1, 4, 8, 1_6]      ,  __lowercase=False      ,  __lowercase=True      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.0_2      ,  __lowercase=3      ,  __lowercase=None      ,  )					-> Any:
				lowerCamelCase :			Dict    		=parent
				lowerCamelCase :			str    		=batch_size
				lowerCamelCase :			List[Any]    		=image_size
				lowerCamelCase :			Any    		=num_channels
				lowerCamelCase :			Tuple    		=num_encoder_blocks
				lowerCamelCase :			List[str]    		=sr_ratios
				lowerCamelCase :			List[Any]    		=depths
				lowerCamelCase :			Dict    		=hidden_sizes
				lowerCamelCase :			Optional[Any]    		=downsampling_rates
				lowerCamelCase :			List[Any]    		=is_training
				lowerCamelCase :			Optional[int]    		=use_labels
				lowerCamelCase :			List[str]    		=hidden_act
				lowerCamelCase :			Tuple    		=hidden_dropout_prob
				lowerCamelCase :			List[Any]    		=initializer_range
				lowerCamelCase :			Tuple    		=num_labels
				lowerCamelCase :			str    		=scope







		def       __lowercase (		self )					-> int:
				lowerCamelCase :			List[str]    		=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

				lowerCamelCase :			Union[str, Any]    		=None
				if self.use_labels:
						lowerCamelCase :			Union[str, Any]    		=ids_tensor([self.batch_size, self.image_size, self.image_size]      ,  self.num_labels )

				lowerCamelCase :			Union[str, Any]    		=PoolFormerConfig(
				    image_size=self.image_size      ,  num_channels=self.num_channels      ,  num_encoder_blocks=self.num_encoder_blocks      ,  depths=self.depths      ,  hidden_sizes=self.hidden_sizes      ,  hidden_act=self.hidden_act      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  initializer_range=self.initializer_range      ,  )

				return config, pixel_values, labels







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> str:
				lowerCamelCase :			Optional[int]    		=PoolFormerModel(config=_lowerCAmelCase )
				model.to(_lowerCAmelCase )
				model.eval()
				lowerCamelCase :			Optional[int]    		=model(_lowerCAmelCase )
				lowerCamelCase :			Union[str, Any]    		=self.image_size // 3_2.0
				self.parent.assertEqual(
				    result.last_hidden_state.shape      ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )







		def       __lowercase (		self )					-> Any:
				lowerCamelCase :			Optional[Any]    		=self.prepare_config_and_inputs()
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Dict    		=config_and_inputs
				lowerCamelCase :			Optional[int]    		={'''pixel_values''': pixel_values}
				return config, inputs_dict

@require_torch
class 							snake_case_		(      UpperCAmelCase__    ,       UpperCAmelCase__    ,       unittest.TestCase):
		lowerCamelCase     :Tuple							=  (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
		lowerCamelCase     :Any							=  (
		    {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase     :Union[str, Any]							=  False
		lowerCamelCase     :Tuple							=  False
		lowerCamelCase     :str							=  False
		lowerCamelCase     :Optional[int]							=  False
		lowerCamelCase     :Optional[int]							=  False







		def       __lowercase (		self )					-> str:
				lowerCamelCase :			Dict    		=PoolFormerModelTester(self )
				lowerCamelCase :			Any    		=PoolFormerConfigTester(self      ,  config_class=_lowerCAmelCase )







		def       __lowercase (		self )					-> Dict:
				self.config_tester.run_common_tests()







		def       __lowercase (		self )					-> Tuple:
				lowerCamelCase :			int    		=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_lowerCAmelCase )







		@unittest.skip('''PoolFormer does not use inputs_embeds''' )
		def       __lowercase (		self )					-> Tuple:
				pass







		@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods''' )
		def       __lowercase (		self )					-> str:
				pass







		def       __lowercase (		self )					-> List[Any]:
				def check_hidden_states_output(__lowercase      ,  __lowercase      ,  __lowercase ):
						lowerCamelCase :			Any    		=model_class(_lowerCAmelCase )
						model.to(_lowerCAmelCase )
						model.eval()

						with torch.no_grad():
								lowerCamelCase :			Union[str, Any]    		=model(**self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase ) )

						lowerCamelCase :			Dict    		=outputs.hidden_states

						lowerCamelCase :			Tuple    		=self.model_tester.num_encoder_blocks
						self.assertEqual(len(_lowerCAmelCase )      ,  _lowerCAmelCase )

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:] )      ,  [
						        self.model_tester.hidden_sizes[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]      ,  )

				lowerCamelCase		, lowerCamelCase :			Optional[Any]    		=self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						lowerCamelCase :			Optional[Any]    		=True
						check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						lowerCamelCase :			Dict    		=True

						check_hidden_states_output(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase )







		def       __lowercase (		self )					-> Optional[int]:
				if not self.model_tester.is_training:
						return

				lowerCamelCase		, lowerCamelCase :			Tuple    		=self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase :			Optional[int]    		=True

				for model_class in self.all_model_classes:
						if model_class in get_values(_lowerCAmelCase ):
								continue
						lowerCamelCase :			Optional[Any]    		=model_class(_lowerCAmelCase )
						model.to(_lowerCAmelCase )
						model.train()
						lowerCamelCase :			List[Any]    		=self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase )
						lowerCamelCase :			str    		=model(**_lowerCAmelCase ).loss
						loss.backward()







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase		, lowerCamelCase :			str    		=self.model_tester.prepare_config_and_inputs_for_common()

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

						lowerCamelCase :			Optional[int]    		=['''pixel_values''']
						self.assertListEqual(arg_names[:1]      ,  _lowerCAmelCase )







		@slow
		def       __lowercase (		self )					-> Tuple:
				for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowerCamelCase :			str    		=PoolFormerModel.from_pretrained(_lowerCAmelCase )
						self.assertIsNotNone(_lowerCAmelCase )


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



@require_torch
class 							snake_case_		(      unittest.TestCase):







		@slow
		def       __lowercase (		self )					-> str:
				lowerCamelCase :			Tuple    		=PoolFormerImageProcessor()
				lowerCamelCase :			str    		=PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12''' ).to(_lowerCAmelCase )

				lowerCamelCase :			List[Any]    		=image_processor(images=prepare_img()      ,  return_tensors='''pt''' ).to(_lowerCAmelCase )

				# forward pass
				with torch.no_grad():
						lowerCamelCase :			List[str]    		=model(**_lowerCAmelCase )

				# verify the logits
				lowerCamelCase :			List[str]    		=torch.Size((1, 1_0_0_0) )
				self.assertEqual(outputs.logits.shape      ,  _lowerCAmelCase )

				lowerCamelCase :			List[Any]    		=torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2] ).to(_lowerCAmelCase )
				self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  _lowerCAmelCase      ,  atol=1e-4 ) )







from __future__ import annotations

from typing import Any


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):  # noqa: A002
            a			   =item
            a			   =next



class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self	):
            a			   =None
            a			   =0
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            a			   =Node(_lowerCAmelCase ,    self.head	)
            self.size += 1
     def        lowerCAmelCase__  (				self	):
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   a			   =self.head.item
                   a			   =self.head.next
                   self.size -= 1
                   return item
     def        lowerCAmelCase__  (				self	):
            return self.head is None
     def __str__(				self	):
            if self.is_empty():
                   return ""
            else:
                   a			   =self.head
                   a			   =""""""
                   a			   =[]
                   while iterate:
                          item_list.append(str(iterate.item	)	)
                          a			   =iterate.next

                   a			   =""" --> """.join(_lowerCAmelCase	)

                   return item_str






     def __len__(				self	):
            return self.size
