
'''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 				UpperCAmelCase_			(		snake_case__   ):





 def     snake_case__						(      self):
     snake_case_	:      Optional[Any]            =  self.config_class(**self.inputs_dict)
     self.parent.assertTrue(hasattr(lowercase_	,    "hidden_sizes"))
     self.parent.assertTrue(hasattr(lowercase_	,    "num_attention_heads"))
     self.parent.assertTrue(hasattr(lowercase_	,    "num_encoder_blocks"))






class 				UpperCAmelCase_			:





 def __init__(      self	,    lowercase_	,    lowercase_=13	,    lowercase_=64	,    lowercase_=3	,    lowercase_=4	,    lowercase_=[2, 2, 2, 2]	,    lowercase_=[8, 4, 2, 1]	,    lowercase_=[16, 32, 64, 1_28]	,    lowercase_=[1, 4, 8, 16]	,    lowercase_=[1, 2, 4, 8]	,    lowercase_=True	,    lowercase_=True	,    lowercase_="gelu"	,    lowercase_=0.1	,    lowercase_=0.1	,    lowercase_=0.02	,    lowercase_=16	,    lowercase_=3	,    lowercase_=None	,    ):
     snake_case_	:      Dict            =  parent
     snake_case_	:      Optional[int]            =  batch_size
     snake_case_	:      str            =  image_size
     snake_case_	:      Dict            =  num_channels
     snake_case_	:      Optional[Any]            =  num_encoder_blocks
     snake_case_	:      Tuple            =  sr_ratios
     snake_case_	:      Union[str, Any]            =  depths
     snake_case_	:      Optional[Any]            =  hidden_sizes
     snake_case_	:      List[Any]            =  downsampling_rates
     snake_case_	:      Union[str, Any]            =  num_attention_heads
     snake_case_	:      List[Any]            =  is_training
     snake_case_	:      Dict            =  use_labels
     snake_case_	:      Dict            =  hidden_act
     snake_case_	:      Optional[int]            =  hidden_dropout_prob
     snake_case_	:      Union[str, Any]            =  attention_probs_dropout_prob
     snake_case_	:      Union[str, Any]            =  initializer_range
     snake_case_	:      Any            =  decoder_hidden_size
     snake_case_	:      Any            =  num_labels
     snake_case_	:      Optional[Any]            =  scope





 def     snake_case__						(      self):
     snake_case_	:      Optional[int]            =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

     snake_case_	:      Dict            =  self.get_config()
     return config, pixel_values, labels





 def     snake_case__						(      self):
     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     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     snake_case_	:      Optional[Any]            =  GLPNModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_	:      str            =  model(lowercase_)
     snake_case_	:      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     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     snake_case_	:      List[str]            =  self.num_labels
     snake_case_	:      List[Any]            =  GLPNForDepthEstimation(lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_	:      Dict            =  model(lowercase_)
     self.parent.assertEqual(result.predicted_depth.shape	,    (self.batch_size, self.image_size, self.image_size))
     snake_case_	:      Optional[Any]            =  model(lowercase_	,    labels=lowercase_)
     self.parent.assertEqual(result.predicted_depth.shape	,    (self.batch_size, self.image_size, self.image_size))





 def     snake_case__						(      self):
     snake_case_	:      Optional[int]            =  self.prepare_config_and_inputs()
     snake_case_     ,	snake_case_     ,	snake_case_	:      List[Any]            =  config_and_inputs
     snake_case_	:      Tuple            =  {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class 				UpperCAmelCase_			(		snake_case__      ,  snake_case__      ,  unittest.TestCase   ):
 UpperCAmelCase_						      =  (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 UpperCAmelCase_						      =  (
     {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
 )

 UpperCAmelCase_						      =  False
 UpperCAmelCase_						      =  False
 UpperCAmelCase_						      =  False





 def     snake_case__						(      self):
     snake_case_	:      Any            =  GLPNModelTester(self)
     snake_case_	:      Dict            =  GLPNConfigTester(self	,    config_class=lowercase_)





 def     snake_case__						(      self):
     self.config_tester.run_common_tests()





 def     snake_case__						(      self):
     snake_case_	:      Any            =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def     snake_case__						(      self):
     snake_case_	:      Dict            =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*lowercase_)





 @unittest.skip("GLPN does not use inputs_embeds")
 def     snake_case__						(      self):
     pass





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





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_	:      int            =  self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_	:      Union[str, Any]            =  ["pixel_values"]
         self.assertListEqual(arg_names[:1]	,    lowercase_)





 def     snake_case__						(      self):
     snake_case_     ,	snake_case_	:      Union[str, Any]            =  self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_	:      List[Any]            =  True

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

         snake_case_	:      int            =  sum(self.model_tester.depths)
         self.assertEqual(len(lowercase_)	,    lowercase_)

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

         self.assertEqual(len(lowercase_)	,    lowercase_)

         # verify the first attentions (first block, first layer)
         snake_case_	:      List[str]            =  (self.model_tester.image_size // 4) ** 2
         snake_case_	:      List[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)
         snake_case_	:      int            =  (self.model_tester.image_size // 32) ** 2
         snake_case_	:      Union[str, 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]	,    )
         snake_case_	:      Any            =  len(lowercase_)

         # Check attention is always last and order is fine
         snake_case_	:      Optional[Any]            =  True
         snake_case_	:      int            =  True
         snake_case_	:      Tuple            =  model_class(lowercase_)
         model.to(lowercase_)
         model.eval()
         with torch.no_grad():
             snake_case_	:      int            =  model(**self._prepare_for_class(lowercase_	,    lowercase_))

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

         snake_case_	:      Dict            =  outputs.attentions

         self.assertEqual(len(lowercase_)	,    lowercase_)
         # verify the first attentions (first block, first layer)
         snake_case_	:      Optional[int]            =  (self.model_tester.image_size // 4) ** 2
         snake_case_	:      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     snake_case__						(      self):
     def check_hidden_states_output(lowercase_	,    lowercase_	,    lowercase_):
         snake_case_	:      int            =  model_class(lowercase_)
         model.to(lowercase_)
         model.eval()

         with torch.no_grad():
             snake_case_	:      Any            =  model(**self._prepare_for_class(lowercase_	,    lowercase_))

         snake_case_	:      Union[str, Any]            =  outputs.hidden_states

         snake_case_	:      str            =  self.model_tester.num_encoder_blocks
         self.assertEqual(len(lowercase_)	,    lowercase_)

         # 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,
             ]	,    )

     snake_case_     ,	snake_case_	:      List[str]            =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         snake_case_	:      Optional[int]            =  True
         check_hidden_states_output(lowercase_	,    lowercase_	,    lowercase_)

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

         check_hidden_states_output(lowercase_	,    lowercase_	,    lowercase_)





 def     snake_case__						(      self):
     if not self.model_tester.is_training:
         return

     snake_case_     ,	snake_case_	:      Dict            =  self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_	:      List[Any]            =  True

     for model_class in self.all_model_classes:
         if model_class in get_values(lowercase_):
             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":
             snake_case_     ,	snake_case_     ,	snake_case_     ,	snake_case_	:      Dict            =  inputs_dict["pixel_values"].shape
             snake_case_	:      Optional[Any]            =  torch.zeros(
                 [self.model_tester.batch_size, height, width]	,    device=lowercase_).long()
         snake_case_	:      Any            =  model_class(lowercase_)
         model.to(lowercase_)
         model.train()
         snake_case_	:      Optional[int]            =  self._prepare_for_class(lowercase_	,    lowercase_	,    return_labels=lowercase_)
         snake_case_	:      Union[str, Any]            =  model(**lowercase_).loss
         loss.backward()





 @slow
 def     snake_case__						(      self):
     for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_	:      Optional[int]            =  GLPNModel.from_pretrained(lowercase_)
         self.assertIsNotNone(lowercase_)


def   UpperCamelCase_       (		):
    """simple docstring"""






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






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





 @slow
 def     snake_case__						(      self):
     snake_case_	:      List[Any]            =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
     snake_case_	:      Optional[int]            =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase_)

     snake_case_	:      Union[str, Any]            =  prepare_img()
     snake_case_	:      List[str]            =  image_processor(images=lowercase_	,    return_tensors="pt").to(lowercase_)

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

     # verify the predicted depth
     snake_case_	:      Optional[Any]            =  torch.Size([1, 4_80, 6_40])
     self.assertEqual(outputs.predicted_depth.shape	,    lowercase_)

     snake_case_	:      int            =  torch.tensor(
         [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]).to(lowercase_)

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






'''simple docstring'''
import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


a_     =  importlib.metadata.version("numpy")
a_     =  ".".join([str(x) for x in sys.version_info[:3]])






class 				UpperCAmelCase_			(		snake_case__   ):





 def     snake_case__						(      self):
     # lt + different version strings
     require_version_core("numpy<1000.4.5")
     require_version_core("numpy<1000.4")
     require_version_core("numpy<1000")

     # le
     require_version_core("numpy<=1000.4.5")
     require_version_core(F'numpy<={numpy_ver}')

     # eq
     require_version_core(F'numpy=={numpy_ver}')

     # ne
     require_version_core("numpy!=1000.4.5")

     # ge
     require_version_core("numpy>=1.0")
     require_version_core("numpy>=1.0.0")
     require_version_core(F'numpy>={numpy_ver}')

     # gt
     require_version_core("numpy>1.0.0")

     # mix
     require_version_core("numpy>1.0.0,<1000")

     # requirement w/o version
     require_version_core("numpy")

     # unmet requirements due to version conflict
     for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'numpy<{numpy_ver}']:
         try:
             require_version_core(lowercase_)
         except ImportError as e:
             self.assertIn(F'{req} is required'	,    str(lowercase_))
             self.assertIn("but found"	,    str(lowercase_))

        # unmet requirements due to missing module
     for req in ["numpipypie>1", "numpipypie2"]:
         try:
             require_version_core(lowercase_)
         except importlib.metadata.PackageNotFoundError as e:
             self.assertIn(F'The \'{req}\' distribution was not found and is required by this application'	,    str(lowercase_))
             self.assertIn("Try: pip install transformers -U"	,    str(lowercase_))

        # bogus requirements formats:
        # 1. whole thing
     for req in ["numpy??1.0.0", "numpy1.0.0"]:
         try:
             require_version_core(lowercase_)
         except ValueError as e:
             self.assertIn("requirement needs to be in the pip package format"	,    str(lowercase_))
        # 2. only operators
     for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
         try:
             require_version_core(lowercase_)
         except ValueError as e:
             self.assertIn("need one of "	,    str(lowercase_))





 def     snake_case__						(      self):
     # matching requirement
     require_version("python>=3.6.0")

     # not matching requirements
     for req in ["python>9.9.9", "python<3.0.0"]:
         try:
             require_version_core(lowercase_)
         except ImportError as e:
             self.assertIn(F'{req} is required'	,    str(lowercase_))
             self.assertIn(F'but found python=={python_ver}'	,    str(lowercase_))




