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 	UpperCAmelCase__       (  self						)	->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =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 			__snake_case   :
 def __init__(  self		,   snake_case__		,   snake_case__=13		,   snake_case__=64		,   snake_case__=3		,   snake_case__=4		,   snake_case__=[2, 2, 2, 2]		,   snake_case__=[8, 4, 2, 1]		,   snake_case__=[16, 32, 64, 128]		,   snake_case__=[1, 4, 8, 16]		,   snake_case__=[1, 2, 4, 8]		,   snake_case__=True		,   snake_case__=True		,   snake_case__="gelu"		,   snake_case__=0.1		,   snake_case__=0.1		,   snake_case__=0.02		,   snake_case__=16		,   snake_case__=3		,   snake_case__=None		,   )	->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase    : Dict      =parent
       UpperCAmelCase    : Dict      =batch_size
       UpperCAmelCase    : List[Any]      =image_size
       UpperCAmelCase    : Dict      =num_channels
       UpperCAmelCase    : Union[str, Any]      =num_encoder_blocks
       UpperCAmelCase    : List[str]      =sr_ratios
       UpperCAmelCase    : Optional[int]      =depths
       UpperCAmelCase    : str      =hidden_sizes
       UpperCAmelCase    : List[str]      =downsampling_rates
       UpperCAmelCase    : Optional[Any]      =num_attention_heads
       UpperCAmelCase    : Optional[Any]      =is_training
       UpperCAmelCase    : int      =use_labels
       UpperCAmelCase    : Optional[Any]      =hidden_act
       UpperCAmelCase    : Dict      =hidden_dropout_prob
       UpperCAmelCase    : str      =attention_probs_dropout_prob
       UpperCAmelCase    : int      =initializer_range
       UpperCAmelCase    : Optional[int]      =decoder_hidden_size
       UpperCAmelCase    : Dict      =num_labels
       UpperCAmelCase    : Any      =scope
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : int      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       UpperCAmelCase    : int      =self.get_config()
       return config, pixel_values, labels
 def 	UpperCAmelCase__       (  self						)	->							str:
       '''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 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__						)	->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =GLPNModel(config=lowerCamelCase__						)
       model.to(lowerCamelCase__						)
       model.eval()
       UpperCAmelCase    : Any      =model(lowerCamelCase__						)
       UpperCAmelCase    : List[str]      =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		,   snake_case__		,   snake_case__		,   snake_case__						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Dict      =self.num_labels
       UpperCAmelCase    : Optional[int]      =GLPNForDepthEstimation(lowerCamelCase__						)
       model.to(lowerCamelCase__						)
       model.eval()
       UpperCAmelCase    : str      =model(lowerCamelCase__						)
       self.parent.assertEqual(result.predicted_depth.shape		,   (self.batch_size, self.image_size, self.image_size)						)
       UpperCAmelCase    : Any      =model(lowerCamelCase__		,   labels=lowerCamelCase__						)
       self.parent.assertEqual(result.predicted_depth.shape		,   (self.batch_size, self.image_size, self.image_size)						)






 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =self.prepare_config_and_inputs()
       UpperCAmelCase    : List[Any]      =config_and_inputs
       UpperCAmelCase    : List[str]      ={"pixel_values": pixel_values}
       return config, inputs_dict







@require_torch
class 			__snake_case   (     _a					,   _a					,   unittest.TestCase   ):
 __lowerCamelCase    :   Optional[Any]     =      (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 __lowerCamelCase    :   Dict     =      (
     {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
 )

 __lowerCamelCase    :   Dict     =      False
 __lowerCamelCase    :   Dict     =      False
 __lowerCamelCase    :   Optional[int]     =      False
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =GLPNModelTester(self						)
       UpperCAmelCase    : List[str]      =GLPNConfigTester(self		,   config_class=lowerCamelCase__						)
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       self.config_tester.run_common_tests()
 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__						)
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : str      =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						)	->							Dict:
       '''simple docstring'''





       pass
 @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       pass
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





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

       for model_class in self.all_model_classes:
             UpperCAmelCase    : Union[str, Any]      =model_class(lowerCamelCase__						)
             UpperCAmelCase    : List[Any]      =inspect.signature(model.forward						)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCAmelCase    : Any      =[*signature.parameters.keys()]

             UpperCAmelCase    : str      =["pixel_values"]
             self.assertListEqual(arg_names[:1]		,   lowerCamelCase__						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Any      =self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase    : Optional[int]      =True

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

             UpperCAmelCase    : Union[str, Any]      =sum(self.model_tester.depths						)
             self.assertEqual(len(lowerCamelCase__						)		,   lowerCamelCase__						)

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

             self.assertEqual(len(lowerCamelCase__						)		,   lowerCamelCase__						)

             # verify the first attentions (first block, first layer)
             UpperCAmelCase    : Optional[Any]      =(self.model_tester.image_size // 4) ** 2
             UpperCAmelCase    : Any      =(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)
             UpperCAmelCase    : int      =(self.model_tester.image_size // 32) ** 2
             UpperCAmelCase    : Tuple      =(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]		,   )
             UpperCAmelCase    : Tuple      =len(lowerCamelCase__						)

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

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

             UpperCAmelCase    : Union[str, Any]      =outputs.attentions

             self.assertEqual(len(lowerCamelCase__						)		,   lowerCamelCase__						)
             # verify the first attentions (first block, first layer)
             UpperCAmelCase    : int      =(self.model_tester.image_size // 4) ** 2
             UpperCAmelCase    : List[str]      =(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						)	->							int:
       '''simple docstring'''





       def check_hidden_states_output(snake_case__		,   snake_case__		,   snake_case__						):
             UpperCAmelCase    : int      =model_class(lowerCamelCase__						)
             model.to(lowerCamelCase__						)
             model.eval()

             with torch.no_grad():
                   UpperCAmelCase    : Dict      =model(**self._prepare_for_class(lowerCamelCase__		,   lowerCamelCase__						)						)

             UpperCAmelCase    : str      =outputs.hidden_states

             UpperCAmelCase    : Union[str, Any]      =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,
                 ]		,   )

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

       for model_class in self.all_model_classes:
             UpperCAmelCase    : Any      =True
             check_hidden_states_output(lowerCamelCase__		,   lowerCamelCase__		,   lowerCamelCase__						)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase    : str      =True

             check_hidden_states_output(lowerCamelCase__		,   lowerCamelCase__		,   lowerCamelCase__						)
 def 	UpperCAmelCase__       (  self						)	->							Union[str, Any]:
       '''simple docstring'''





       if not self.model_tester.is_training:
             return

       UpperCAmelCase    : Union[str, Any]      =self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase    : List[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":
                   UpperCAmelCase    : int      =inputs_dict["pixel_values"].shape
                   UpperCAmelCase    : Optional[int]      =torch.zeros(
                       [self.model_tester.batch_size, height, width]		,   device=lowerCamelCase__						).long()
             UpperCAmelCase    : Optional[Any]      =model_class(lowerCamelCase__						)
             model.to(lowerCamelCase__						)
             model.train()
             UpperCAmelCase    : Any      =self._prepare_for_class(lowerCamelCase__		,   lowerCamelCase__		,   return_labels=lowerCamelCase__						)
             UpperCAmelCase    : Union[str, Any]      =model(**lowerCamelCase__						).loss
             loss.backward()






 @slow
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase    : List[Any]      =GLPNModel.from_pretrained(lowerCamelCase__						)
             self.assertIsNotNone(lowerCamelCase__						)





def   lowerCAmelCase_ (				)->  Any:






      '''simple docstring'''





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







@require_torch
@require_vision
@slow
class 			__snake_case   (     unittest.TestCase   ):
 @slow
 def 	UpperCAmelCase__       (  self						)	->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
       UpperCAmelCase    : List[str]      =GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(lowerCamelCase__						)

       UpperCAmelCase    : str      =prepare_img()
       UpperCAmelCase    : Tuple      =image_processor(images=lowerCamelCase__		,   return_tensors='''pt'''						).to(lowerCamelCase__						)

       # forward pass
       with torch.no_grad():
             UpperCAmelCase    : int      =model(**lowerCamelCase__						)

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

       UpperCAmelCase    : int      =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						)						)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
