




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						str=2							,							_lowerCAmelCase    :						str=True							,							_lowerCAmelCase    :						Optional[Any]=False							,							_lowerCAmelCase    :						Tuple=10							,							_lowerCAmelCase    :						List[str]=3							,							_lowerCAmelCase    :						Union[str, Any]=32 * 8							,							_lowerCAmelCase    :						Dict=32 * 8							,							_lowerCAmelCase    :						Optional[Any]=4							,							_lowerCAmelCase    :						List[str]=64							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     use_auxiliary_loss
       SCREAMING_SNAKE_CASE_      		=     num_queries
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     min_size
       SCREAMING_SNAKE_CASE_      		=     max_size
       SCREAMING_SNAKE_CASE_      		=     num_labels
       SCREAMING_SNAKE_CASE_      		=     hidden_dim
       SCREAMING_SNAKE_CASE_      		=     hidden_dim



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
           _lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.ones([self.batch_size, self.min_size, self.max_size]							,							device=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,							device=_lowerCAmelCase		) > 0.5
       ).float()
       SCREAMING_SNAKE_CASE_      		=     (torch.rand((self.batch_size, self.num_labels)							,							device=_lowerCAmelCase		) > 0.5).long()

       SCREAMING_SNAKE_CASE_      		=     self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerConfig(
           hidden_size=self.hidden_dim							,							)
       SCREAMING_SNAKE_CASE_      		=     self.num_queries
       SCREAMING_SNAKE_CASE_      		=     self.num_labels

       SCREAMING_SNAKE_CASE_      		=     [1, 1, 1, 1]
       SCREAMING_SNAKE_CASE_      		=     self.num_channels

       SCREAMING_SNAKE_CASE_      		=     64
       SCREAMING_SNAKE_CASE_      		=     128
       SCREAMING_SNAKE_CASE_      		=     self.hidden_dim
       SCREAMING_SNAKE_CASE_      		=     self.hidden_dim
       SCREAMING_SNAKE_CASE_      		=     self.hidden_dim
       return config



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_      		=     {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
       return config, inputs_dict



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     output.encoder_hidden_states
       SCREAMING_SNAKE_CASE_      		=     output.pixel_decoder_hidden_states
       SCREAMING_SNAKE_CASE_      		=     output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(_lowerCAmelCase		)							,							len(config.backbone_config.depths		)		)
       self.parent.assertTrue(len(_lowerCAmelCase		)							,							len(config.backbone_config.depths		)		)
       self.parent.assertTrue(len(_lowerCAmelCase		)							,							config.decoder_layers		)



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						Tuple=False		):
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     MaskaFormerModel(config=_lowerCAmelCase		)
             model.to(_lowerCAmelCase		)
             model.eval()

             SCREAMING_SNAKE_CASE_      		=     model(pixel_values=_lowerCAmelCase							,							pixel_mask=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase		)

       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape							,							(self.batch_size, self.num_queries, self.hidden_dim)							,							)
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
       self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

       if output_hidden_states:
             self.check_output_hidden_state(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()

       def comm_check_on_output(_lowerCAmelCase    :						Any		):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None		)
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None		)
             self.parent.assertTrue(result.encoder_last_hidden_state is not None		)
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape							,							(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)							,							)
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape							,							(self.batch_size, self.num_queries, self.num_labels + 1)		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(pixel_values=_lowerCAmelCase							,							pixel_mask=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)

             comm_check_on_output(_lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_      		=     model(
                 pixel_values=_lowerCAmelCase							,							pixel_mask=_lowerCAmelCase							,							mask_labels=_lowerCAmelCase							,							class_labels=_lowerCAmelCase		)

       comm_check_on_output(_lowerCAmelCase		)

       self.parent.assertTrue(result.loss is not None		)
       self.parent.assertEqual(result.loss.shape							,							torch.Size([1]		)		)





@require_torch
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 lowercase_							       =       {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase							,							has_text_modality=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						str		):
       self.config_tester.run_common_tests()



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase							,							**_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCAmelCase		)



 @unittest.skip(reason='Mask2Former does not use inputs_embeds'		)
 def  lowerCAmelCase_    (				self    :						int		):
       pass



 @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 @unittest.skip(reason='Mask2Former is not a generative model'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 @unittest.skip(reason='Mask2Former does not use token embeddings'		)
 def  lowerCAmelCase_    (				self    :						int		):
       pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       pass



 def  lowerCAmelCase_    (				self    :						Any		):
       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(_lowerCAmelCase		)
             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]							,							_lowerCAmelCase		)



 @slow
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             SCREAMING_SNAKE_CASE_      		=     MaskaFormerModel.from_pretrained(_lowerCAmelCase		)
             self.assertIsNotNone(_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     (self.model_tester.min_size,) * 2
       SCREAMING_SNAKE_CASE_      		=     {
           'pixel_values': torch.randn((2, 3, *size)							,							device=_lowerCAmelCase		),
           'mask_labels': torch.randn((2, 10, *size)							,							device=_lowerCAmelCase		),
           'class_labels': torch.zeros(2							,							10							,							device=_lowerCAmelCase		).long(),
       }
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.get_config()

       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation(_lowerCAmelCase		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)
       self.assertTrue(outputs.loss is not None		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase							,							**_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       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(_lowerCAmelCase		).to(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase							,							output_attentions=_lowerCAmelCase		)
             self.assertTrue(outputs.attentions is not None		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE_      		=     self.all_model_classes[1]
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.train()

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							mask_labels=_lowerCAmelCase							,							class_labels=_lowerCAmelCase		).loss
       loss.backward()




 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.all_model_classes[1]
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_      		=     True
       SCREAMING_SNAKE_CASE_      		=     True

       SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		).to(_lowerCAmelCase		)
       model.train()

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							mask_labels=_lowerCAmelCase							,							class_labels=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_      		=     outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_      		=     outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_      		=     outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=_lowerCAmelCase		)

       self.assertIsNotNone(encoder_hidden_states.grad		)
       self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
       self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
       self.assertIsNotNone(attentions.grad		)


lowerCamelCase__					:  List[Any]      							=				1E-4

def    UpperCAmelCase_				( )   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image





@require_vision
@slow
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 @cached_property
 def  lowerCAmelCase_    (				self    :						Any		):
       return "facebook/mask2former-swin-small-coco-instance"



 @cached_property
 def  lowerCAmelCase_    (				self    :						int		):
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
       # check size
       self.assertEqual(_lowerCAmelCase							,							(1, 3, 384, 384)		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]		).to(_lowerCAmelCase		)
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]		).to(_lowerCAmelCase		)
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]		).to(_lowerCAmelCase		)
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_lowerCAmelCase		).eval()
       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
       # check size
       self.assertEqual(_lowerCAmelCase							,							(1, 3, 384, 384)		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)
       # masks_queries_logits
       SCREAMING_SNAKE_CASE_      		=     outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape							,							(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
       SCREAMING_SNAKE_CASE_      		=     [
           [-8.7839, -9.0056, -8.8121],
           [-7.4104, -7.0313, -6.5401],
           [-6.6105, -6.3427, -6.4675],
       ]
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase		).to(_lowerCAmelCase		)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)
       # class_queries_logits
       SCREAMING_SNAKE_CASE_      		=     outputs.class_queries_logits
       self.assertEqual(class_queries_logits.shape							,							(1, model.config.num_queries, model.config.num_labels + 1)		)
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [
               [1.8324, -8.0835, -4.1922],
               [0.8450, -9.0050, -3.6053],
               [0.3045, -7.7293, -3.0275],
           ]		).to(_lowerCAmelCase		)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)




 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_lowerCAmelCase		).eval()
       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor

       SCREAMING_SNAKE_CASE_      		=     image_processor(
           [np.zeros((3, 800, 1_333)		), np.zeros((3, 800, 1_333)		)]							,							segmentation_maps=[np.zeros((384, 384)		).astype(np.floataa		), np.zeros((384, 384)		).astype(np.floataa		)]							,							return_tensors='pt'							,							)

       SCREAMING_SNAKE_CASE_      		=     inputs['pixel_values'].to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     [el.to(_lowerCAmelCase		) for el in inputs['mask_labels']]
       SCREAMING_SNAKE_CASE_      		=     [el.to(_lowerCAmelCase		) for el in inputs['class_labels']]

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       self.assertTrue(outputs.loss is not None		)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__					:  str      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Tuple      							=				{
    'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
    'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
    'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
    'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
    'funnel-transformer/intermediate': (
        'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
    ),
    'funnel-transformer/intermediate-base': (
        'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
    ),
    'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
    'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
    'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
    'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "funnel"
 lowercase_							       =       {
     "hidden_size": "d_model",
     "num_attention_heads": "n_head",
 }



 def __init__(				self    :						int							,							_lowerCAmelCase    :						Optional[int]=30_522							,							_lowerCAmelCase    :						List[str]=[4, 4, 4]							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						int=768							,							_lowerCAmelCase    :						Optional[Any]=12							,							_lowerCAmelCase    :						Optional[Any]=64							,							_lowerCAmelCase    :						Optional[Any]=3_072							,							_lowerCAmelCase    :						List[str]="gelu_new"							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						int=0.1							,							_lowerCAmelCase    :						Tuple=0.0							,							_lowerCAmelCase    :						List[Any]=0.1							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						str=1E-9							,							_lowerCAmelCase    :						Any="mean"							,							_lowerCAmelCase    :						Union[str, Any]="relative_shift"							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Dict=True							,							_lowerCAmelCase    :						Tuple=True							,							**_lowerCAmelCase    :						Optional[Any]							,							):
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     block_sizes
       SCREAMING_SNAKE_CASE_      		=     [1] * len(_lowerCAmelCase		) if block_repeats is None else block_repeats
       assert len(_lowerCAmelCase		) == len(
           self.block_repeats		), "`block_sizes` and `block_repeats` should have the same length."
       SCREAMING_SNAKE_CASE_      		=     num_decoder_layers
       SCREAMING_SNAKE_CASE_      		=     d_model
       SCREAMING_SNAKE_CASE_      		=     n_head
       SCREAMING_SNAKE_CASE_      		=     d_head
       SCREAMING_SNAKE_CASE_      		=     d_inner
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout
       SCREAMING_SNAKE_CASE_      		=     attention_dropout
       SCREAMING_SNAKE_CASE_      		=     activation_dropout
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     initializer_std
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       assert pooling_type in [
           "mean",
           "max",
       ], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
       SCREAMING_SNAKE_CASE_      		=     pooling_type
       assert attention_type in [
           "relative_shift",
           "factorized",
       ], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
       SCREAMING_SNAKE_CASE_      		=     attention_type
       SCREAMING_SNAKE_CASE_      		=     separate_cls
       SCREAMING_SNAKE_CASE_      		=     truncate_seq
       SCREAMING_SNAKE_CASE_      		=     pool_q_only

       super().__init__(**_lowerCAmelCase		)



 @property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       return sum(self.block_sizes		)



 @num_hidden_layers.setter
 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						List[Any]		):
       raise NotImplementedError(
           'This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'		)



 @property
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       return len(self.block_sizes		)




 @num_blocks.setter
 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]		):
       raise NotImplementedError('This model does not support the setting of `num_blocks`. Please set `block_sizes`.'		)