






import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


UpperCamelCase												=      get_logger(__name__)


if TYPE_CHECKING:
		from .builder import DatasetBuilder

def 			__lowerCamelCase     (						snake_case__			,snake_case__ = None						)				->    Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    importlib.import_module(a_						)

						# TODO(QL): always update the module to add subsequent new authentication
						if hasattr(a_			,"""_patched_for_streaming"""						) and module._patched_for_streaming:
												return

						def wrap_auth(snake_case__						):
												@wraps(a_						)
												def wrapper(*snake_case__			,**snake_case__						):
																		return function(*a_			,use_auth_token=a_			,**a_						)

												_SCREAMING_SNAKE_CASE       =    """wrap_auth"""
												return wrapper

						# open files in a streaming fashion
						patch_submodule(a_			,"""open"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""os.listdir"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""os.walk"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""glob.glob"""			,wrap_auth(a_						)						).start()
						# allow to navigate in remote zip files
						patch_submodule(a_			,"""os.path.join"""			,a_						).start()
						patch_submodule(a_			,"""os.path.dirname"""			,a_						).start()
						patch_submodule(a_			,"""os.path.basename"""			,a_						).start()
						patch_submodule(a_			,"""os.path.relpath"""			,a_						).start()
						patch_submodule(a_			,"""os.path.split"""			,a_						).start()
						patch_submodule(a_			,"""os.path.splitext"""			,a_						).start()
						# allow checks on paths
						patch_submodule(a_			,"""os.path.exists"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""os.path.isdir"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""os.path.isfile"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""os.path.getsize"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""pathlib.Path"""			,a_						).start()
						# file readers
						patch_submodule(a_			,"""gzip.open"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""numpy.load"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""pandas.read_csv"""			,wrap_auth(a_						)			,attrs=["""__version__"""]						).start()
						patch_submodule(a_			,"""pandas.read_excel"""			,wrap_auth(a_						)			,attrs=["""__version__"""]						).start()
						patch_submodule(a_			,"""scipy.io.loadmat"""			,wrap_auth(a_						)			,attrs=["""__version__"""]						).start()
						patch_submodule(a_			,"""xml.etree.ElementTree.parse"""			,wrap_auth(a_						)						).start()
						patch_submodule(a_			,"""xml.dom.minidom.parse"""			,wrap_auth(a_						)						).start()
						_SCREAMING_SNAKE_CASE       =    True




def 			__lowerCamelCase     (						snake_case__						)				->    Any:






						"""simple docstring"""





						extend_module_for_streaming(builder.__module__			,use_auth_token=builder.use_auth_token						)
						# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
						if not builder.__module__.startswith("""datasets."""						):  # check that it's not a packaged builder like csv
												for imports in get_imports(inspect.getfile(builder.__class__						)						):
																		if imports[0] == "internal":
																								_SCREAMING_SNAKE_CASE       =    imports[1]
																								_SCREAMING_SNAKE_CASE       =    """.""".join(builder.__module__.split("""."""						)[:-1] + [internal_import_name]						)
																								extend_module_for_streaming(a_			,use_auth_token=builder.use_auth_token						)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
						from .builder import DatasetBuilder

						_SCREAMING_SNAKE_CASE       =    [
						    cls.__module__
						    for cls in type(a_						).__mro__[1:]  # make sure it's not the same module we've already patched
						    if issubclass(cls			,a_						) and cls.__module__ != DatasetBuilder.__module__
						]  # check it's not a standard builder from datasets.builder
						for module in parent_builder_modules:
												extend_module_for_streaming(a_			,use_auth_token=builder.use_auth_token						)






'''simple docstring'''





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, require_vision, 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, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Tuple						=					self.config_class(**self.inputs_dict  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """hidden_sizes"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_attention_heads"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_encoder_blocks"""  )  )


class 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=13					,    _lowerCamelCase=64					,    _lowerCamelCase=3					,    _lowerCamelCase=4					,    _lowerCamelCase=[2, 2, 2, 2]					,    _lowerCamelCase=[8, 4, 2, 1]					,    _lowerCamelCase=[16, 32, 64, 128]					,    _lowerCamelCase=[1, 4, 8, 16]					,    _lowerCamelCase=[1, 2, 4, 8]					,    _lowerCamelCase=True					,    _lowerCamelCase=True					,    _lowerCamelCase="gelu"					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.02					,    _lowerCamelCase=16					,    _lowerCamelCase=3					,    _lowerCamelCase=None					,    )    ->    Optional[int]:
    A_  :		int						=					parent
    A_  :		int						=					batch_size
    A_  :		Optional[int]						=					image_size
    A_  :		str						=					num_channels
    A_  :		Optional[Any]						=					num_encoder_blocks
    A_  :		List[str]						=					sr_ratios
    A_  :		int						=					depths
    A_  :		str						=					hidden_sizes
    A_  :		Optional[int]						=					downsampling_rates
    A_  :		Any						=					num_attention_heads
    A_  :		List[str]						=					is_training
    A_  :		Optional[Any]						=					use_labels
    A_  :		List[str]						=					hidden_act
    A_  :		Tuple						=					hidden_dropout_prob
    A_  :		Optional[int]						=					attention_probs_dropout_prob
    A_  :		Optional[int]						=					initializer_range
    A_  :		str						=					decoder_hidden_size
    A_  :		Optional[Any]						=					num_labels
    A_  :		List[Any]						=					scope







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		List[Any]						=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

    A_  :		Dict						=					None
    if self.use_labels:
       A_  :		Optional[Any]						=					ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels  )

    A_  :		Any						=					self.get_config()
    return config, pixel_values, labels







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    return GLPNConfig(
        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					,    num_attention_heads=self.num_attention_heads					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    attention_probs_dropout_prob=self.attention_probs_dropout_prob					,    initializer_range=self.initializer_range					,    decoder_hidden_size=self.decoder_hidden_size					,    )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    str:
    A_  :		Tuple						=					GLPNModel(config=_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Optional[Any]						=					model(_lowerCamelCase  )
    A_  :		Optional[int]						=					self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape					,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Optional[int]:
    A_  :		Tuple						=					self.num_labels
    A_  :		Any						=					GLPNForDepthEstimation(_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Dict						=					model(_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )
    A_  :		Optional[int]						=					model(_lowerCamelCase					,    labels=_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		Optional[Any]						=					self.prepare_config_and_inputs()
    A_						,  A_						,  A_  :		Tuple						=					config_and_inputs
    A_  :		Dict						=					{"""pixel_values""": pixel_values}
    return config, inputs_dict


@require_torch
class 							_lowerCAmelCase	(  __A,							__A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 lowerCamelCase									=     (
     {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
 )

 lowerCamelCase									=     False
 lowerCamelCase									=     False
 lowerCamelCase									=     False







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Optional[Any]						=					GLPNModelTester(self  )
    A_  :		Dict						=					GLPNConfigTester(self					,    config_class=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    self.config_tester.run_common_tests()







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		int						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Dict						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase  )







 @unittest.skip("""GLPN does not use inputs_embeds"""  )
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    pass







 @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		Union[str, Any]						=					self.model_tester.prepare_config_and_inputs_for_common()

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

       A_  :		Dict						=					["""pixel_values"""]
       self.assertListEqual(arg_names[:1]					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		List[str]						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		Optional[Any]						=					True

    for model_class in self.all_model_classes:
       A_  :		int						=					True
       A_  :		str						=					False
       A_  :		Any						=					True
       A_  :		Dict						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		List[str]						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		Union[str, Any]						=					outputs.attentions

       A_  :		List[str]						=					sum(self.model_tester.depths  )
       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       A_  :		Dict						=					True
       A_  :		List[str]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Any						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		str						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		str						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )

       # verify the last attentions (last block, last layer)
       A_  :		Optional[Any]						=					(self.model_tester.image_size // 32) ** 2
       A_  :		Optional[Any]						=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]  )					,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,    )
       A_  :		str						=					len(_lowerCamelCase  )

       # Check attention is always last and order is fine
       A_  :		Union[str, Any]						=					True
       A_  :		str						=					True
       A_  :		Tuple						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Dict						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       self.assertEqual(out_len + 1					,    len(_lowerCamelCase  )  )

       A_  :		Any						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )
       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		Optional[int]						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    def check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		List[Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()

       with torch.no_grad():
          A_  :		Tuple						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       A_  :		List[str]						=					outputs.hidden_states

       A_  :		Dict						=					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,
           ]					,    )

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

    for model_class in self.all_model_classes:
       A_  :		Any						=					True
       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       A_  :		Optional[Any]						=					True

       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )







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

    A_						,  A_  :		Any						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		Union[str, Any]						=					True

    for model_class in self.all_model_classes:
       if model_class in get_values(_lowerCamelCase  ):
          continue
       # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       # this can then be incorporated into _prepare_for_class in test_modeling_common.py
       if model_class.__name__ == "GLPNForDepthEstimation":
          A_						,  A_						,  A_						,  A_  :		Any						=					inputs_dict["""pixel_values"""].shape
          A_  :		List[Any]						=					torch.zeros(
              [self.model_tester.batch_size, height, width]					,    device=_lowerCamelCase  ).long()
       A_  :		Union[str, Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.train()
       A_  :		List[str]						=					self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase					,    return_labels=_lowerCamelCase  )
       A_  :		Dict						=					model(**_lowerCamelCase  ).loss
       loss.backward()







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A_  :		Union[str, Any]						=					GLPNModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )



def     UpperCAmelCase     (    )   ->       int:



   """simple docstring"""






   A_  :		int						=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
   return image







@require_torch
@require_vision
@slow
class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 @slow
 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Tuple						=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
    A_  :		List[Any]						=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(_lowerCamelCase  )

    A_  :		str						=					prepare_img()
    A_  :		int						=					image_processor(images=_lowerCamelCase					,    return_tensors="""pt"""  ).to(_lowerCamelCase  )

    # forward pass
    with torch.no_grad():
       A_  :		Optional[int]						=					model(**_lowerCamelCase  )

    # verify the predicted depth
    A_  :		List[Any]						=					torch.Size([1, 480, 640]  )
    self.assertEqual(outputs.predicted_depth.shape					,    _lowerCamelCase  )

    A_  :		Union[str, Any]						=					torch.tensor(
        [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]  ).to(_lowerCamelCase  )

    self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]					,    _lowerCamelCase					,    atol=1e-4  )  )
