





"""simple docstring"""





import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           FocalNetBackbone,
           FocalNetForImageClassification,
           FocalNetForMaskedImageModeling,
           FocalNetModel,
       )
       from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor

class 						lowercase   :





      def __init__(			self ,		lowercase ,		lowercase=13 ,		lowercase=32 ,		lowercase=2 ,		lowercase=3 ,		lowercase=16 ,		lowercase=[32, 64, 128] ,		lowercase=[1, 2, 1] ,		lowercase=[2, 2, 4] ,		lowercase=2 ,		lowercase=2.0 ,		lowercase=True ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.1 ,		lowercase="gelu" ,		lowercase=False ,		lowercase=True ,		lowercase=0.02 ,		lowercase=1e-5 ,		lowercase=True ,		lowercase=None ,		lowercase=True ,		lowercase=10 ,		lowercase=8 ,		lowercase=["stage1", "stage2"] ,		lowercase=[1, 2] ,		)						->	Optional[Any]:
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			batch_size
            lowerCAmelCase    		=			image_size
            lowerCAmelCase    		=			patch_size
            lowerCAmelCase    		=			num_channels
            lowerCAmelCase    		=			embed_dim
            lowerCAmelCase    		=			hidden_sizes
            lowerCAmelCase    		=			depths
            lowerCAmelCase    		=			num_heads
            lowerCAmelCase    		=			window_size
            lowerCAmelCase    		=			mlp_ratio
            lowerCAmelCase    		=			qkv_bias
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			attention_probs_dropout_prob
            lowerCAmelCase    		=			drop_path_rate
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			use_absolute_embeddings
            lowerCAmelCase    		=			patch_norm
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			is_training
            lowerCAmelCase    		=			scope
            lowerCAmelCase    		=			use_labels
            lowerCAmelCase    		=			type_sequence_label_size
            lowerCAmelCase    		=			encoder_stride
            lowerCAmelCase    		=			out_features
            lowerCAmelCase    		=			out_indices





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            lowerCAmelCase    		=			None
            if self.use_labels:
                  lowerCAmelCase    		=			ids_tensor([self.batch_size] ,		self.type_sequence_label_size					)

            lowerCAmelCase    		=			self.get_config()

            return config, pixel_values, labels





      def        _snake_case     (			self					)						->	int:
            return FocalNetConfig(
                image_size=self.image_size ,		patch_size=self.patch_size ,		num_channels=self.num_channels ,		embed_dim=self.embed_dim ,		hidden_sizes=self.hidden_sizes ,		depths=self.depths ,		num_heads=self.num_heads ,		window_size=self.window_size ,		mlp_ratio=self.mlp_ratio ,		qkv_bias=self.qkv_bias ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		drop_path_rate=self.drop_path_rate ,		hidden_act=self.hidden_act ,		use_absolute_embeddings=self.use_absolute_embeddings ,		path_norm=self.patch_norm ,		layer_norm_eps=self.layer_norm_eps ,		initializer_range=self.initializer_range ,		encoder_stride=self.encoder_stride ,		out_features=self.out_features ,		out_indices=self.out_indices ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Union[str, Any]:
            lowerCAmelCase    		=			FocalNetModel(config=__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()
            lowerCAmelCase    		=			model(__lowerCamelCase					)

            lowerCAmelCase    		=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
            lowerCAmelCase    		=			int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

            self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, expected_seq_len, expected_dim)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	int:
            lowerCAmelCase    		=			FocalNetBackbone(config=__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()
            lowerCAmelCase    		=			model(__lowerCamelCase					)

            # 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.image_size, 8, 8]					)

            # 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
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			FocalNetBackbone(config=__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()
            lowerCAmelCase    		=			model(__lowerCamelCase					)

            # verify feature maps
            self.parent.assertEqual(len(result.feature_maps					) ,		1					)
            self.parent.assertListEqual(list(result.feature_maps[0].shape					) ,		[self.batch_size, self.image_size * 2, 4, 4]					)

            # verify channels
            self.parent.assertEqual(len(model.channels					) ,		1					)
            self.parent.assertListEqual(model.channels ,		[config.hidden_sizes[-1]]					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			FocalNetForMaskedImageModeling(config=__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()
            lowerCAmelCase    		=			model(__lowerCamelCase					)
            self.parent.assertEqual(
                result.reconstruction.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

            # test greyscale images
            lowerCAmelCase    		=			1
            lowerCAmelCase    		=			FocalNetForMaskedImageModeling(__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()

            lowerCAmelCase    		=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            lowerCAmelCase    		=			model(__lowerCamelCase					)
            self.parent.assertEqual(result.reconstruction.shape ,		(self.batch_size, 1, self.image_size, self.image_size)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			self.type_sequence_label_size
            lowerCAmelCase    		=			FocalNetForImageClassification(__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()
            lowerCAmelCase    		=			model(__lowerCamelCase ,		labels=__lowerCamelCase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)					)

            # test greyscale images
            lowerCAmelCase    		=			1
            lowerCAmelCase    		=			FocalNetForImageClassification(__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()

            lowerCAmelCase    		=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            lowerCAmelCase    		=			model(__lowerCamelCase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			self.prepare_config_and_inputs()

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			config_and_inputs
            lowerCAmelCase    		=			{"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_torch
class 						lowercase   (   UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = (
          (
              FocalNetModel,
              FocalNetForImageClassification,
              FocalNetForMaskedImageModeling,
              FocalNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE							      = (
          {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE							      = False

      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			FocalNetModelTester(self					)
            lowerCAmelCase    		=			ConfigTester(self ,		config_class=__lowerCamelCase ,		embed_dim=37 ,		has_text_modality=__lowerCamelCase					)





      def        _snake_case     (			self					)						->	Any:
            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        _snake_case     (			self					)						->	List[Any]:
            return





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__lowerCamelCase					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase					)





      @unittest.skip(reason="""FocalNet does not use inputs_embeds"""					)
      def        _snake_case     (			self					)						->	Optional[int]:
            pass





      @unittest.skip(reason="""FocalNet does not use feedforward chunking"""					)
      def        _snake_case     (			self					)						->	Tuple:
            pass





      def        _snake_case     (			self					)						->	Optional[Any]:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes[:-1]:
                  lowerCAmelCase    		=			model_class(__lowerCamelCase					)
                  self.assertIsInstance(model.get_input_embeddings() ,		(nn.Module)					)
                  lowerCAmelCase    		=			model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(__lowerCamelCase ,		nn.Linear					)					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes[:-1]:
                  lowerCAmelCase    		=			model_class(__lowerCamelCase					)
                  lowerCAmelCase    		=			inspect.signature(model.forward					)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowerCAmelCase    		=			[*signature.parameters.keys()]

                  lowerCAmelCase    		=			["""pixel_values"""]
                  self.assertListEqual(arg_names[:1] ,		__lowerCamelCase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	Union[str, Any]:
            lowerCAmelCase    		=			model_class(__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()

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

            lowerCAmelCase    		=			outputs.hidden_states

            lowerCAmelCase    		=			getattr(
                self.model_tester ,		"""expected_num_hidden_layers""" ,		len(self.model_tester.depths					) + 1					)
            self.assertEqual(len(__lowerCamelCase					) ,		__lowerCamelCase					)

            # FocalNet has a different seq_length
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)

            lowerCAmelCase    		=			outputs.reshaped_hidden_states
            self.assertEqual(len(__lowerCamelCase					) ,		__lowerCamelCase					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			reshaped_hidden_states[0].shape
            lowerCAmelCase    		=			(
                reshaped_hidden_states[0].view(__lowerCamelCase ,		__lowerCamelCase ,		height * width					).permute(0 ,		2 ,		1					)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes[:-1]:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase					)

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

                  self.check_hidden_states_output(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase					)





      def        _snake_case     (			self					)						->	str:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase    		=			3

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            lowerCAmelCase    		=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes[:-1]:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		(padded_height, padded_width)					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		(padded_height, padded_width)					)





      @slow
      def        _snake_case     (			self					)						->	Optional[Any]:




            for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase    		=			FocalNetModel.from_pretrained(__lowerCamelCase					)
                  self.assertIsNotNone(__lowerCamelCase					)





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

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







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





      @cached_property
      def        _snake_case     (			self					)						->	Optional[int]:
            return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""					) if is_vision_available() else None





      @slow
      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""					).to(__lowerCamelCase					)
            lowerCAmelCase    		=			self.default_image_processor

            lowerCAmelCase    		=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
            lowerCAmelCase    		=			image_processor(images=__lowerCamelCase ,		return_tensors="""pt"""					).to(__lowerCamelCase					)

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

            # verify the logits
            lowerCAmelCase    		=			torch.Size((1, 1_000)					)
            self.assertEqual(outputs.logits.shape ,		__lowerCamelCase					)
            lowerCAmelCase    		=			torch.tensor([0.2_166, -0.4_368, 0.2_191]					).to(__lowerCamelCase					)
            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,		__lowerCamelCase ,		atol=1e-4					)					)
            self.assertTrue(outputs.logits.argmax(dim=-1					).item() ,		281					)







@require_torch
class 						lowercase   (   UpperCAmelCase_	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = (FocalNetBackbone,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE							      = FocalNetConfig

      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			FocalNetModelTester(self					)













import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[int]:
		return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     _TestCommandArgs(dataset=snake_case						,   all_configs=snake_case						,   save_infos=snake_case							)
		__lowercase											=     TestCommand(*snake_case							)
		test_command.run()
		__lowercase											=     os.path.join(snake_case						,   'README.md'							)
		assert os.path.exists(snake_case							)
		__lowercase											=     DatasetInfosDict.from_directory(snake_case							)
		__lowercase											=     DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'							)							),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']							)							),
		                    'langs': Sequence(Value('string'							)							),
		                    'spans': Sequence(Value('string'							)							),
		                }							)						,   splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_351_563,
		                    'num_examples': 10_000,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 238_418,
		                    'num_examples': 1_000,
		                },
		            ]						,   download_size=3_940_680						,   dataset_size=2_589_981						,   )
		    }							)
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				__lowercase						,      __lowercase											=     getattr(dataset_infos['default']						,   snake_case							), getattr(expected_dataset_infos['default']						,   snake_case							)
				if key == "num_bytes":
						assert is_apercent_close(snake_case						,   snake_case							)
				elif key == "splits":
						assert list(snake_case							) == list(snake_case							)
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,   expected[split].num_bytes							)
				else:
						result == expected




