






'''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 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	List[str]					)  ->       Union[str, Any]:
           __A		   =      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 					a__    :
     '''simple docstring'''







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






     def     _UpperCamelCase   (self    :	List[str]					)  ->       List[str]:
           __A		   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

           __A		   =      self.get_config()
           return config, pixel_values, labels






     def     _UpperCamelCase   (self    :	List[str]					)  ->       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    :	Tuple				,				lowercase__    :	str				,				lowercase__    :	str				,				lowercase__    :	List[Any]					)  ->       Any:
           __A		   =      GLPNModel(config=lowercase__					)
           model.to(lowercase__					)
           model.eval()
           __A		   =      model(lowercase__					)
           __A		   =      __A		   =      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    :	Optional[Any]				,				lowercase__    :	List[str]				,				lowercase__    :	List[Any]				,				lowercase__    :	List[Any]					)  ->       str:
           __A		   =      self.num_labels
           __A		   =      GLPNForDepthEstimation(lowercase__					)
           model.to(lowercase__					)
           model.eval()
           __A		   =      model(lowercase__					)
           self.parent.assertEqual(result.predicted_depth.shape				,				(self.batch_size, self.image_size, self.image_size)					)
           __A		   =      model(lowercase__				,				labels=lowercase__					)
           self.parent.assertEqual(result.predicted_depth.shape				,				(self.batch_size, self.image_size, self.image_size)					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       List[str]:
           __A		   =      self.prepare_config_and_inputs()
           __A   ,				__A   ,				__A		   =      config_and_inputs
           __A		   =      {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class 					a__    (			lowerCamelCase ,						lowerCamelCase ,						unittest.TestCase						):
     '''simple docstring'''







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

     _snake_case             =  False
     _snake_case             =  False
     _snake_case             =  False






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       int:
           __A		   =      GLPNModelTester(self					)
           __A		   =      GLPNConfigTester(self				,				config_class=lowercase__					)






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






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       List[str]:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Union[str, Any]:
           __A		   =      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     _UpperCamelCase   (self    :	str					)  ->       Tuple:
           pass






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






     def     _UpperCamelCase   (self    :	int					)  ->       Union[str, Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

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

                 __A		   =      ["pixel_values"]
                 self.assertListEqual(arg_names[:1]				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Union[str, Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()
           __A		   =      True

           for model_class in self.all_model_classes:
                 __A		   =      True
                 __A		   =      False
                 __A		   =      True
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 with torch.no_grad():
                       __A		   =      model(**self._prepare_for_class(lowercase__				,				lowercase__					)					)
                 __A		   =      outputs.attentions

                 __A		   =      sum(self.model_tester.depths					)
                 self.assertEqual(len(lowercase__					)				,				lowercase__					)

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

                 self.assertEqual(len(lowercase__					)				,				lowercase__					)

                 # verify the first attentions (first block, first layer)
                 __A		   =      (self.model_tester.image_size // 4) ** 2
                 __A		   =      (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		   =      (self.model_tester.image_size // 32) ** 2
                 __A		   =      (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		   =      len(lowercase__					)

                 # Check attention is always last and order is fine
                 __A		   =      True
                 __A		   =      True
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 with torch.no_grad():
                       __A		   =      model(**self._prepare_for_class(lowercase__				,				lowercase__					)					)

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

                 __A		   =      outputs.attentions

                 self.assertEqual(len(lowercase__					)				,				lowercase__					)
                 # verify the first attentions (first block, first layer)
                 __A		   =      (self.model_tester.image_size // 4) ** 2
                 __A		   =      (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    :	Optional[Any]					)  ->       List[str]:
           def check_hidden_states_output(lowercase__    :	Tuple				,				lowercase__    :	List[str]				,				lowercase__    :	Optional[Any]					):
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()

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

                 __A		   =      outputs.hidden_states

                 __A		   =      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,
                     ]				,				)

           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      True
                 check_hidden_states_output(lowercase__				,				lowercase__				,				lowercase__					)

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

                 check_hidden_states_output(lowercase__				,				lowercase__				,				lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       str:
           if not self.model_tester.is_training:
                 return

           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()
           __A		   =      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":
                       __A   ,				__A   ,				__A   ,				__A		   =      inputs_dict["pixel_values"].shape
                       __A		   =      torch.zeros(
                           [self.model_tester.batch_size, height, width]				,				device=lowercase__					).long()
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.train()
                 __A		   =      self._prepare_for_class(lowercase__				,				lowercase__				,				return_labels=lowercase__					)
                 __A		   =      model(**lowercase__					).loss
                 loss.backward()






     @slow
     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       str:
           for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __A		   =      GLPNModel.from_pretrained(lowercase__					)
                 self.assertIsNotNone(lowercase__					)







def 						_UpperCamelCase							(     ):
      __A		   =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
      return image






@require_torch
@require_vision
@slow
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     @slow
     def     _UpperCamelCase   (self    :	int					)  ->       Union[str, Any]:
           __A		   =      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
           __A		   =      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(lowercase__					)

           __A		   =      prepare_img()
           __A		   =      image_processor(images=lowercase__				,				return_tensors="pt"					).to(lowercase__					)

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

           # verify the predicted depth
           __A		   =      torch.Size([1, 480, 640]					)
           self.assertEqual(outputs.predicted_depth.shape				,				lowercase__					)

           __A		   =      torch.tensor(
               [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					).to(lowercase__					)

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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A      :       str      =	{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A      :       str      =	[
           'UMT5EncoderModel',
           'UMT5ForConditionalGeneration',
           'UMT5ForQuestionAnswering',
           'UMT5Model',
           'UMT5PreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

       __A      :       Optional[int]      =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


