


'''simple docstring'''





import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar


class 				snake_case (unittest.TestCase   ):



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						logging.get_logger()

           # the current default level is logging.WARNING
           lowercase__		       =						logging.get_verbosity()

           logging.set_verbosity_error()
           self.assertEqual(logger.getEffectiveLevel()     ,logging.get_verbosity()      )

           logging.set_verbosity_warning()
           self.assertEqual(logger.getEffectiveLevel()     ,logging.get_verbosity()      )

           logging.set_verbosity_info()
           self.assertEqual(logger.getEffectiveLevel()     ,logging.get_verbosity()      )

           logging.set_verbosity_debug()
           self.assertEqual(logger.getEffectiveLevel()     ,logging.get_verbosity()      )

           # restore to the original level
           logging.set_verbosity(A_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						logging.get_verbosity()

           lowercase__		       =						logging.get_logger("transformers.models.bart.tokenization_bart"      )
           lowercase__		       =						"Testing 1, 2, 3"

           # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
           if level_origin <= logging.WARNING:
               with CaptureLogger(A_      ) as cl:
                   logger.warning(A_      )
               self.assertEqual(cl.out     ,msg + "\n"      )

           # this is setting the level for all of `transformers.*` loggers
           logging.set_verbosity_error()

           # should not be able to log warnings
           with CaptureLogger(A_      ) as cl:
               logger.warning(A_      )
           self.assertEqual(cl.out     ,""      )

           # should be able to log warnings again
           logging.set_verbosity_warning()
           with CaptureLogger(A_      ) as cl:
               logger.warning(A_      )
           self.assertEqual(cl.out     ,msg + "\n"      )

           # restore to the original level
           logging.set_verbosity(A_      )



       @mockenv(TRANSFORMERS_VERBOSITY="error"      )
       def 							_a      (				self      )  ->							str:
           transformers.utils.logging._reset_library_root_logger()
           # this action activates the env var
           lowercase__		       =						logging.get_logger("transformers.models.bart.tokenization_bart"      )

           lowercase__		       =						os.getenv("TRANSFORMERS_VERBOSITY"     ,A_      )
           lowercase__		       =						logging.log_levels[env_level_str]

           lowercase__		       =						logging.get_verbosity()
           self.assertEqual(
               A_     ,A_     ,F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''     ,)

           # restore to the original level
           lowercase__		       =						""
           transformers.utils.logging._reset_library_root_logger()



       @mockenv(TRANSFORMERS_VERBOSITY="super-error"      )
       def 							_a      (				self      )  ->							Optional[int]:
           transformers.utils.logging._reset_library_root_logger()
           lowercase__		       =						logging.logging.getLogger()
           with CaptureLogger(A_      ) as cl:
               # this action activates the env var
               logging.get_logger("transformers.models.bart.tokenization_bart"      )
           self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error"     ,cl.out      )

           # no need to restore as nothing was changed







       def 							_a      (				self      )  ->							Union[str, Any]:
           transformers.utils.logging._reset_library_root_logger()

           lowercase__		       =						logging.get_logger("transformers.models.bart.tokenization_bart"      )
           lowercase__		       =						"Testing 1, 2, 3"

           with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"      ):
               # nothing should be logged as env var disables this method
               with CaptureLogger(A_      ) as cl:
                   logger.warning_advice(A_      )
               self.assertEqual(cl.out     ,""      )

           with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""      ):
               # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
               with CaptureLogger(A_      ) as cl:
                   logger.warning_advice(A_      )
               self.assertEqual(cl.out     ,msg + "\n"      )





def        lowerCamelCase  (				):





    '''simple docstring'''


    disable_progress_bar()
    assert are_progress_bars_disabled()

    enable_progress_bar()
    assert not are_progress_bars_disabled()









'''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 				snake_case :



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



       def 							_a      (				self      )  ->							int:
           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.type_sequence_label_size      )

           lowercase__		       =						self.get_config()

           return config, pixel_values, labels



       def 							_a      (				self      )  ->							Any:
           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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
           lowercase__		       =						FocalNetModel(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )

           lowercase__		       =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
           lowercase__		       =						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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						FocalNetBackbone(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )

           # 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
           lowercase__		       =						None
           lowercase__		       =						FocalNetBackbone(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )

           # 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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						FocalNetForMaskedImageModeling(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(
               result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						FocalNetForMaskedImageModeling(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						FocalNetForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						FocalNetForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

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







       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						self.prepare_config_and_inputs()

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





@require_torch
class 				snake_case (UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :List[str]						   =					(
           (
               FocalNetModel,
               FocalNetForImageClassification,
               FocalNetForMaskedImageModeling,
               FocalNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__  :str						   =					(
           {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__  :str						   =					False

       lowerCAmelCase__  :List[Any]						   =					False
       lowerCAmelCase__  :Dict						   =					False
       lowerCAmelCase__  :List[Any]						   =					False
       lowerCAmelCase__  :Union[str, Any]						   =					False



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						FocalNetModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,embed_dim=37     ,has_text_modality=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[int]:
           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      )  ->							List[Any]:
           return



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )



       @unittest.skip(reason="FocalNet does not use inputs_embeds"      )
       def 							_a      (				self      )  ->							str:
           pass



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



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

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



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               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]     ,UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						model_class(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

           with torch.no_grad():
               lowercase__		       =						model(**self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__		       =						outputs.hidden_states

           lowercase__		       =						getattr(
               self.model_tester     ,"expected_num_hidden_layers"     ,len(self.model_tester.depths      ) + 1      )
           self.assertEqual(len(UpperCAmelCase_      )     ,UpperCAmelCase_      )

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

           lowercase__		       =						(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]     ,)

           lowercase__		       =						outputs.reshaped_hidden_states
           self.assertEqual(len(UpperCAmelCase_      )     ,UpperCAmelCase_      )

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



       def 							_a      (				self      )  ->							str:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           lowercase__		       =						(
               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]:
               lowercase__		       =						True
               self.check_hidden_states_output(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )

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

               self.check_hidden_states_output(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__		       =						3

           lowercase__		       =						(
               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)
           )
           lowercase__		       =						(
               config.patch_size
               if isinstance(config.patch_size     ,collections.abc.Iterable      )
               else (config.patch_size, config.patch_size)
           )

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

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

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



       @slow
       def 							_a      (				self      )  ->							Dict:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__		       =						FocalNetModel.from_pretrained(UpperCAmelCase_      )
               self.assertIsNotNone(UpperCAmelCase_      )







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

           lowercase__		       =						_config_zero_init(UpperCAmelCase_      )
           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(config=UpperCAmelCase_      )
               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 				snake_case (unittest.TestCase   ):



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







       @slow
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"      ).to(UpperCAmelCase_      )
           lowercase__		       =						self.default_image_processor

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

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )

           # verify the logits
           lowercase__		       =						torch.Size((1, 1_000)      )
           self.assertEqual(outputs.logits.shape     ,UpperCAmelCase_      )
           lowercase__		       =						torch.tensor([0.21_66, -0.43_68, 0.21_91]      ).to(UpperCAmelCase_      )
           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )
           self.assertTrue(outputs.logits.argmax(dim=-1      ).item()     ,281      )





@require_torch
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Tuple						   =					(FocalNetBackbone,) if is_torch_available() else ()
       lowerCAmelCase__  :int						   =					FocalNetConfig

       lowerCAmelCase__  :List[Any]						   =					False



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						FocalNetModelTester(self      )




