



"""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    __snake_case(					__A       ):




    def   A   (   self			):





      '''simple docstring'''



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




class    __snake_case:




    def __init__(   self    ,							A_    ,							A_=13    ,							A_=64    ,							A_=3    ,							A_=4    ,							A_=[2, 2, 2, 2]    ,							A_=[8, 4, 2, 1]    ,							A_=[16, 32, 64, 128]    ,							A_=[1, 4, 8, 16]    ,							A_=[1, 2, 4, 8]    ,							A_=True    ,							A_=True    ,							A_="gelu"    ,							A_=0.1    ,							A_=0.1    ,							A_=0.02    ,							A_=16    ,							A_=3    ,							A_=None    ,							):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



      _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.image_size, self.image_size]    ,							self.num_labels			)

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




    def   A   (   self			):





      '''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    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			GLPNModel(config=A_			)
      model.to(A_			)
      model.eval()
      _SCREAMING_SNAKE_CASE        =			model(A_			)
      _SCREAMING_SNAKE_CASE        =			_SCREAMING_SNAKE_CASE        =			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    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



      _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    __snake_case(					__A			,			__A			,			unittest.TestCase       ):
    _A       = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
    _A       = (
        {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
    )

    _A       = False
    _A       = False
    _A       = False




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			GLPNModelTester(self			)
      _SCREAMING_SNAKE_CASE        =			GLPNConfigTester(self    ,							config_class=A_			)




    def   A   (   self			):





      '''simple docstring'''



      self.config_tester.run_common_tests()




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*A_			)




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





      '''simple docstring'''



      pass




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





      '''simple docstring'''



      pass




    def   A   (   self			):





      '''simple docstring'''



      _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   A   (   self			):





      '''simple docstring'''



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

      for model_class in self.all_model_classes:
        _SCREAMING_SNAKE_CASE        =			True
        _SCREAMING_SNAKE_CASE        =			False
        _SCREAMING_SNAKE_CASE        =			True
        _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.attentions

        _SCREAMING_SNAKE_CASE        =			sum(self.model_tester.depths			)
        self.assertEqual(len(A_			)    ,							A_			)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        _SCREAMING_SNAKE_CASE        =			True
        _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.attentions

        self.assertEqual(len(A_			)    ,							A_			)

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

        # Check attention is always last and order is fine
        _SCREAMING_SNAKE_CASE        =			True
        _SCREAMING_SNAKE_CASE        =			True
        _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_			)			)

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

        _SCREAMING_SNAKE_CASE        =			outputs.attentions

        self.assertEqual(len(A_			)    ,							A_			)
        # verify the first attentions (first block, first layer)
        _SCREAMING_SNAKE_CASE        =			(self.model_tester.image_size // 4) ** 2
        _SCREAMING_SNAKE_CASE        =			(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			):





      '''simple docstring'''



      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.hidden_states

        _SCREAMING_SNAKE_CASE        =			self.model_tester.num_encoder_blocks
        self.assertEqual(len(A_			)    ,							A_			)

        # 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,
            ]    ,							)

      _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        =			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_			)




    def   A   (   self			):





      '''simple docstring'''



      if not self.model_tester.is_training:
        return

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

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




    @slow
    def   A   (   self			):





      '''simple docstring'''



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



def 							A__ (     ):


  '''simple docstring'''


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







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




    @slow
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
      _SCREAMING_SNAKE_CASE        =			GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(A_			)

      _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 predicted depth
      _SCREAMING_SNAKE_CASE        =			torch.Size([1, 480, 640]			)
      self.assertEqual(outputs.predicted_depth.shape    ,							A_			)

      _SCREAMING_SNAKE_CASE        =			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(A_			)

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





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:     Dict								=  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     int								=  [
        """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CamembertForCausalLM""",
        """CamembertForMaskedLM""",
        """CamembertForMultipleChoice""",
        """CamembertForQuestionAnswering""",
        """CamembertForSequenceClassification""",
        """CamembertForTokenClassification""",
        """CamembertModel""",
        """CamembertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     str								=  [
        """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCamembertForCausalLM""",
        """TFCamembertForMaskedLM""",
        """TFCamembertForMultipleChoice""",
        """TFCamembertForQuestionAnswering""",
        """TFCamembertForSequenceClassification""",
        """TFCamembertForTokenClassification""",
        """TFCamembertModel""",
        """TFCamembertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert import CamembertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert_fast import CamembertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
            CamembertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
            TFCamembertPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase				:     List[Any]								=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
