
'''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    _a				:





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__=3       ,							A__=32       ,							A__=3       ,							A__=10       ,							A__=[8, 16, 32, 64]       ,							A__=[1, 1, 2, 1]       ,							A__=True       ,							A__=True       ,							A__="relu"       ,							A__=3       ,							A__=None       ,							A__=["stage2", "stage3", "stage4"]       ,							A__=[2, 3, 4]       ,							A__=1       ,							)    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				parent
          _SCREAMING_SNAKE_CASE      =				batch_size
          _SCREAMING_SNAKE_CASE      =				image_size
          _SCREAMING_SNAKE_CASE      =				num_channels
          _SCREAMING_SNAKE_CASE      =				embeddings_size
          _SCREAMING_SNAKE_CASE      =				hidden_sizes
          _SCREAMING_SNAKE_CASE      =				depths
          _SCREAMING_SNAKE_CASE      =				is_training
          _SCREAMING_SNAKE_CASE      =				use_labels
          _SCREAMING_SNAKE_CASE      =				hidden_act
          _SCREAMING_SNAKE_CASE      =				num_labels
          _SCREAMING_SNAKE_CASE      =				scope
          _SCREAMING_SNAKE_CASE      =				len(A__      )
          _SCREAMING_SNAKE_CASE      =				out_features
          _SCREAMING_SNAKE_CASE      =				out_indices
          _SCREAMING_SNAKE_CASE      =				num_groups






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

          _SCREAMING_SNAKE_CASE      =				self.get_config()

          return config, pixel_values, labels






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          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        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				BitModel(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__      )
          self.parent.assertEqual(
              result.last_hidden_state.shape       ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				self.num_labels
          _SCREAMING_SNAKE_CASE      =				BitForImageClassification(A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__       ,							labels=A__      )
          self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				BitBackbone(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				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
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				BitBackbone(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				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        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				self.prepare_config_and_inputs()
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				config_and_inputs
          _SCREAMING_SNAKE_CASE      =				{"""pixel_values""": pixel_values}
          return config, inputs_dict






@require_torch
class    _a				(_lowerCamelCase  ,					_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE      =  (
           {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
           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        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				BitModelTester(self      )
          _SCREAMING_SNAKE_CASE      =				ConfigTester(self       ,							config_class=A__       ,							has_text_modality=A__      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          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        UpperCamelCase				(							self      )    ->			Tuple:
          return






       @unittest.skip(reason="""Bit does not output attentions"""      )
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          pass






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






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






       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_common()

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

             _SCREAMING_SNAKE_CASE      =				["""pixel_values"""]
             self.assertListEqual(arg_names[:1]       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*A__      )






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

          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE      =				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        UpperCamelCase				(							self      )    ->			List[str]:
          def check_hidden_states_output(A__       ,							A__       ,							A__      ):
             _SCREAMING_SNAKE_CASE      =				model_class(A__      )
             model.to(A__      )
             model.eval()

             with torch.no_grad():
                _SCREAMING_SNAKE_CASE      =				model(**self._prepare_for_class(A__       ,							A__      )      )

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

             _SCREAMING_SNAKE_CASE      =				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]       ,							)

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

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

                check_hidden_states_output(A__       ,							A__       ,							A__      )






       @unittest.skip(reason="""Bit does not use feedforward chunking"""      )
       def        UpperCamelCase				(							self      )    ->			List[str]:
          pass






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A__      )






       @slow
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _SCREAMING_SNAKE_CASE      =				BitModel.from_pretrained(A__      )
             self.assertIsNotNone(A__      )






def    lowerCAmelCase_	(				)     ->     str:




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

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





       """simple docstring"""




       @cached_property
       def        UpperCamelCase				(							self      )    ->			int:
          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ) if is_vision_available() else None
          )






       @slow
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(A__      )

          _SCREAMING_SNAKE_CASE      =				self.default_image_processor
          _SCREAMING_SNAKE_CASE      =				prepare_img()
          _SCREAMING_SNAKE_CASE      =				image_processor(images=A__       ,							return_tensors="""pt"""      ).to(A__      )

          # forward pass
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE      =				model(**A__      )

          # verify the logits
          _SCREAMING_SNAKE_CASE      =				torch.Size((1, 10_00)      )
          self.assertEqual(outputs.logits.shape       ,							A__      )

          _SCREAMING_SNAKE_CASE      =				torch.tensor([[-0.6526, -0.5263, -1.4398]]      ).to(A__      )

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






@require_torch
class    _a				(_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  (BitBackbone,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE      =  BitConfig

       SCREAMING_SNAKE_CASE      =  False






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				BitModelTester(self      )








'''simple docstring'''



from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ''
       SCREAMING_SNAKE_CASE      =  'hf-legacy'  # "hf://"" is reserved for hffs






       def __init__(							self       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			Optional[int]:
          super().__init__(self       ,							**A__      )
          _SCREAMING_SNAKE_CASE      =				repo_info
          _SCREAMING_SNAKE_CASE      =				token
          _SCREAMING_SNAKE_CASE      =				None






       def        UpperCamelCase				(							self      )    ->			Tuple:
          if self.dir_cache is None:
             _SCREAMING_SNAKE_CASE      =				{}
             for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _SCREAMING_SNAKE_CASE      =				{
                    """name""": hf_file.rfilename,
                    """size""": None,
                    """type""": """file""",
                }
                self.dir_cache.update(
                    {
                        str(A__      ): {"""name""": str(A__      ), """size""": None, """type""": """directory"""}
                        for d in list(PurePosixPath(hf_file.rfilename      ).parents      )[:-1]
                    }      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = "rb"       ,							**A__       ,							)    ->			Optional[int]:
          if not isinstance(self.repo_info       ,							A__      ):
             raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"      )
          _SCREAMING_SNAKE_CASE      =				hf_hub_url(self.repo_info.id       ,							A__       ,							revision=self.repo_info.sha      )
          return fsspec.open(
              A__       ,							mode=A__       ,							headers=get_authentication_headers_for_url(A__       ,							use_auth_token=self.token      )       ,							client_kwargs={"""trust_env""": True}       ,							).open()






       def        UpperCamelCase				(							self       ,							A__       ,							**A__      )    ->			str:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				self._strip_protocol(A__      )
          if path in self.dir_cache:
             return self.dir_cache[path]
          else:
             raise FileNotFoundError(A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False       ,							**A__      )    ->			List[Any]:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				PurePosixPath(path.strip("""/"""      )      )
          _SCREAMING_SNAKE_CASE      =				{}
          for p, f in self.dir_cache.items():
             _SCREAMING_SNAKE_CASE      =				PurePosixPath(p.strip("""/"""      )      )
             _SCREAMING_SNAKE_CASE      =				p.parent
             if root == path:
                _SCREAMING_SNAKE_CASE      =				f
          _SCREAMING_SNAKE_CASE      =				list(paths.values()      )
          if detail:
             return out
          else:
             return sorted(f["""name"""] for f in out      )






