





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 	__snake_case					(      _a ):






    '''simple docstring'''





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					self.config_class(**self.inputs_dict				)
      self.parent.assertTrue(hasattr(snake_case      ,    '''hidden_sizes'''				)				)
      self.parent.assertTrue(hasattr(snake_case      ,    '''num_attention_heads'''				)				)
      self.parent.assertTrue(hasattr(snake_case      ,    '''num_encoder_blocks'''				)				)


class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		Optional[Any]      ,    snake_case    :		List[Any]      ,    snake_case    :		Any=13      ,    snake_case    :		Any=64      ,    snake_case    :		List[Any]=3      ,    snake_case    :		Union[str, Any]=4      ,    snake_case    :		Optional[int]=[2, 2, 2, 2]      ,    snake_case    :		Optional[int]=[8, 4, 2, 1]      ,    snake_case    :		int=[16, 32, 64, 128]      ,    snake_case    :		Union[str, Any]=[1, 4, 8, 16]      ,    snake_case    :		List[Any]=[1, 2, 4, 8]      ,    snake_case    :		List[Any]=True      ,    snake_case    :		int=True      ,    snake_case    :		str="gelu"      ,    snake_case    :		int=0.1      ,    snake_case    :		str=0.1      ,    snake_case    :		str=0.02      ,    snake_case    :		List[str]=16      ,    snake_case    :		Union[str, Any]=3      ,    snake_case    :		Tuple=None      ,    ):

      """simple docstring"""





      __lowerCAmelCase								=					parent
      __lowerCAmelCase								=					batch_size
      __lowerCAmelCase								=					image_size
      __lowerCAmelCase								=					num_channels
      __lowerCAmelCase								=					num_encoder_blocks
      __lowerCAmelCase								=					sr_ratios
      __lowerCAmelCase								=					depths
      __lowerCAmelCase								=					hidden_sizes
      __lowerCAmelCase								=					downsampling_rates
      __lowerCAmelCase								=					num_attention_heads
      __lowerCAmelCase								=					is_training
      __lowerCAmelCase								=					use_labels
      __lowerCAmelCase								=					hidden_act
      __lowerCAmelCase								=					hidden_dropout_prob
      __lowerCAmelCase								=					attention_probs_dropout_prob
      __lowerCAmelCase								=					initializer_range
      __lowerCAmelCase								=					decoder_hidden_size
      __lowerCAmelCase								=					num_labels
      __lowerCAmelCase								=					scope





    def 						A		( self    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

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





    def 						A		( self    :		Union[str, Any]				):

      """simple docstring"""





      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 						A		( self    :		Any      ,    snake_case    :		Optional[Any]      ,    snake_case    :		str      ,    snake_case    :		List[str]				):

      """simple docstring"""





      __lowerCAmelCase								=					GLPNModel(config=snake_case				)
      model.to(snake_case				)
      model.eval()
      __lowerCAmelCase								=					model(snake_case				)
      __lowerCAmelCase								=					__lowerCAmelCase								=					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 						A		( self    :		int      ,    snake_case    :		Tuple      ,    snake_case    :		Union[str, Any]      ,    snake_case    :		Optional[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.num_labels
      __lowerCAmelCase								=					GLPNForDepthEstimation(snake_case				)
      model.to(snake_case				)
      model.eval()
      __lowerCAmelCase								=					model(snake_case				)
      self.parent.assertEqual(result.predicted_depth.shape      ,    (self.batch_size, self.image_size, self.image_size)				)
      __lowerCAmelCase								=					model(snake_case      ,    labels=snake_case				)
      self.parent.assertEqual(result.predicted_depth.shape      ,    (self.batch_size, self.image_size, self.image_size)				)





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.prepare_config_and_inputs()
      __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					config_and_inputs
      __lowerCAmelCase								=					{'''pixel_values''': pixel_values}
      return config, inputs_dict


@require_torch
class 	__snake_case					(      _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 						A		( self    :		Tuple				):

      """simple docstring"""





      __lowerCAmelCase								=					GLPNModelTester(self				)
      __lowerCAmelCase								=					GLPNConfigTester(self      ,    config_class=snake_case				)





    def 						A		( self    :		int				):

      """simple docstring"""





      self.config_tester.run_common_tests()





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*snake_case				)





    @unittest.skip('''GLPN does not use inputs_embeds'''				)
    def 						A		( self    :		Any				):

      """simple docstring"""





      pass





    @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''				)
    def 						A		( self    :		int				):

      """simple docstring"""





      pass





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase						,    __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCAmelCase								=					['''pixel_values''']
        self.assertListEqual(arg_names[:1]      ,    snake_case				)





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase						,    __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase								=					True

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

        __lowerCAmelCase								=					sum(self.model_tester.depths				)
        self.assertEqual(len(snake_case				)      ,    snake_case				)

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

        self.assertEqual(len(snake_case				)      ,    snake_case				)

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

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

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

        __lowerCAmelCase								=					outputs.attentions

        self.assertEqual(len(snake_case				)      ,    snake_case				)
        # verify the first attentions (first block, first layer)
        __lowerCAmelCase								=					(self.model_tester.image_size // 4) ** 2
        __lowerCAmelCase								=					(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 						A		( self    :		Optional[int]				):

      """simple docstring"""





      def check_hidden_states_output(snake_case    :		Any      ,    snake_case    :		int      ,    snake_case    :		List[Any]				):
        __lowerCAmelCase								=					model_class(snake_case				)
        model.to(snake_case				)
        model.eval()

        with torch.no_grad():
          __lowerCAmelCase								=					model(**self._prepare_for_class(snake_case      ,    snake_case				)				)

        __lowerCAmelCase								=					outputs.hidden_states

        __lowerCAmelCase								=					self.model_tester.num_encoder_blocks
        self.assertEqual(len(snake_case				)      ,    snake_case				)

        # 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,
            ]      ,    )

      __lowerCAmelCase						,    __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        __lowerCAmelCase								=					True
        check_hidden_states_output(snake_case      ,    snake_case      ,    snake_case				)

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

        check_hidden_states_output(snake_case      ,    snake_case      ,    snake_case				)





    def 						A		( self    :		Tuple				):

      """simple docstring"""





      if not self.model_tester.is_training:
        return

      __lowerCAmelCase						,    __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase								=					True

      for model_class in self.all_model_classes:
        if model_class in get_values(snake_case				):
          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":
          __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					inputs_dict['''pixel_values'''].shape
          __lowerCAmelCase								=					torch.zeros(
              [self.model_tester.batch_size, height, width]      ,    device=snake_case				).long()
        __lowerCAmelCase								=					model_class(snake_case				)
        model.to(snake_case				)
        model.train()
        __lowerCAmelCase								=					self._prepare_for_class(snake_case      ,    snake_case      ,    return_labels=snake_case				)
        __lowerCAmelCase								=					model(**snake_case				).loss
        loss.backward()





    @slow
    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase								=					GLPNModel.from_pretrained(snake_case				)
        self.assertIsNotNone(snake_case				)


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
  return image




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






    '''simple docstring'''





    @slow
    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
      __lowerCAmelCase								=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(snake_case				)

      __lowerCAmelCase								=					prepare_img()
      __lowerCAmelCase								=					image_processor(images=snake_case      ,    return_tensors='''pt'''				).to(snake_case				)

      # forward pass
      with torch.no_grad():
        __lowerCAmelCase								=					model(**snake_case				)

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

      __lowerCAmelCase								=					torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]				).to(snake_case				)

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







# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__lowercase				    =      """Update an existing config file with the latest defaults while maintaining the old configuration."""


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					args.config_file
  if config_file is None and Path(__lowercase).exists():
    __lowerCAmelCase								=					default_config_file
  elif not Path(__lowercase).exists():
    raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.''')
  __lowerCAmelCase								=					load_config_from_file(__lowercase)

  if config_file.endswith('''.json'''):
    config.to_json_file(__lowercase)
  else:
    config.to_yaml_file(__lowercase)
  return config_file


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		int):



  '''simple docstring'''






  __lowerCAmelCase								=					parser.add_parser('''update'''  ,				parents=__lowercase  ,				help=__lowercase  ,				formatter_class=__lowercase)
  parser.add_argument(
      '''--config_file'''  ,				default=__lowercase  ,				help=(
          '''The path to the config file to update. Will default to a file named default_config.yaml in the cache '''
          '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
          '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
          '''with \'huggingface\'.'''
      )  ,				)

  parser.set_defaults(func=__lowercase)
  return parser




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					update_config(__lowercase)
  print(F'''Sucessfully updated the configuration file at {config_file}.''')
