




"""simple docstring"""


import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import BertGenerationDecoder, BertGenerationEncoder






class  lowercase:







 '''simple docstring'''





 def __init__(    self:		str,				a_:		Any,				a_:		Union[str, Any]=13,				a_:		Any=7,				a_:		Optional[Any]=True,				a_:		str=True,				a_:		List[str]=99,				a_:		List[str]=32,				a_:		Tuple=5,				a_:		Any=4,				a_:		Tuple=37,				a_:		Optional[Any]="gelu",				a_:		Union[str, Any]=0.1,				a_:		Dict=0.1,				a_:		Union[str, Any]=50,				a_:		Any=0.02,				a_:		List[Any]=True,				a_:		str=None,				):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  parent
   _snake_case							:		Any								=  batch_size
   _snake_case							:		Tuple								=  seq_length
   _snake_case							:		Union[str, Any]								=  is_training
   _snake_case							:		Tuple								=  use_input_mask
   _snake_case							:		Optional[Any]								=  vocab_size
   _snake_case							:		int								=  hidden_size
   _snake_case							:		Tuple								=  num_hidden_layers
   _snake_case							:		Optional[int]								=  num_attention_heads
   _snake_case							:		str								=  intermediate_size
   _snake_case							:		Union[str, Any]								=  hidden_act
   _snake_case							:		int								=  hidden_dropout_prob
   _snake_case							:		Dict								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  max_position_embeddings
   _snake_case							:		Tuple								=  initializer_range
   _snake_case							:		List[Any]								=  use_labels
   _snake_case							:		str								=  scope


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  ids_tensor([self.batch_size, self.seq_length],				self.vocab_size		)

   _snake_case							:		Optional[Any]								=  None
   if self.use_input_mask:
     _snake_case							:		Optional[int]								=  random_attention_mask([self.batch_size, self.seq_length]		)

   if self.use_labels:
     _snake_case							:		int								=  ids_tensor([self.batch_size, self.seq_length],				self.vocab_size		)

   _snake_case							:		List[Any]								=  self.get_config()

   return config, input_ids, input_mask, token_labels


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   return BertGenerationConfig(
       vocab_size=self.vocab_size,				hidden_size=self.hidden_size,				num_hidden_layers=self.num_hidden_layers,				num_attention_heads=self.num_attention_heads,				intermediate_size=self.intermediate_size,				hidden_act=self.hidden_act,				hidden_dropout_prob=self.hidden_dropout_prob,				attention_probs_dropout_prob=self.attention_probs_dropout_prob,				max_position_embeddings=self.max_position_embeddings,				is_decoder=a_,				initializer_range=self.initializer_range,				)


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   (
       (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, 
   )							:		Tuple								=  self.prepare_config_and_inputs()

   _snake_case							:		Tuple								=  True
   _snake_case							:		Dict								=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
   _snake_case							:		List[str]								=  ids_tensor([self.batch_size, self.seq_length],				vocab_size=2		)

   return (
       config,
       input_ids,
       input_mask,
       token_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		Tuple,				a_:		Tuple,				a_:		str,				a_:		int,				**a_:		List[str],				):




   '''simple docstring'''



   _snake_case							:		Dict								=  BertGenerationEncoder(config=a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		Optional[int]								=  model(a_,				attention_mask=a_		)
   _snake_case							:		Dict								=  model(a_		)
   self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)		)


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		Optional[Any],				a_:		Dict,				a_:		Optional[Any],				a_:		str,				a_:		int,				a_:		List[Any],				**a_:		List[Any],				):




   '''simple docstring'''



   _snake_case							:		Tuple								=  True
   _snake_case							:		Any								=  BertGenerationEncoder(config=a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		str								=  model(
       a_,				attention_mask=a_,				encoder_hidden_states=a_,				encoder_attention_mask=a_,				)
   _snake_case							:		List[str]								=  model(
       a_,				attention_mask=a_,				encoder_hidden_states=a_,				)
   self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)		)


 def 			UpperCamelCase_       (    self:		int,				a_:		Optional[int],				a_:		str,				a_:		str,				a_:		Any,				a_:		Optional[Any],				a_:		str,				**a_:		List[Any],				):




   '''simple docstring'''



   _snake_case							:		Any								=  True
   _snake_case							:		Dict								=  True
   _snake_case							:		List[Any]								=  BertGenerationDecoder(config=a_		).to(a_		).eval()

   # first forward pass
   _snake_case							:		Any								=  model(
       a_,				attention_mask=a_,				encoder_hidden_states=a_,				encoder_attention_mask=a_,				use_cache=a_,				)
   _snake_case							:		Dict								=  outputs.past_key_values

   # create hypothetical multiple next token and extent to next_input_ids
   _snake_case							:		List[str]								=  ids_tensor((self.batch_size, 3),				config.vocab_size		)
   _snake_case							:		Dict								=  ids_tensor((self.batch_size, 3),				vocab_size=2		)

   # append to next input_ids and
   _snake_case							:		int								=  torch.cat([input_ids, next_tokens],				dim=-1		)
   _snake_case							:		List[str]								=  torch.cat([input_mask, next_mask],				dim=-1		)

   _snake_case							:		Any								=  model(
       a_,				attention_mask=a_,				encoder_hidden_states=a_,				encoder_attention_mask=a_,				output_hidden_states=a_,				)["""hidden_states"""][0]
   _snake_case							:		str								=  model(
       a_,				attention_mask=a_,				encoder_hidden_states=a_,				encoder_attention_mask=a_,				past_key_values=a_,				output_hidden_states=a_,				)["""hidden_states"""][0]

   # select random slice
   _snake_case							:		Union[str, Any]								=  ids_tensor((1,),				output_from_past.shape[-1]		).item()
   _snake_case							:		str								=  output_from_no_past[:, -3:, random_slice_idx].detach()
   _snake_case							:		Any								=  output_from_past[:, :, random_slice_idx].detach()

   self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

   # test that outputs are equal for slice
   self.parent.assertTrue(torch.allclose(a_,				a_,				atol=1E-3		)		)


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Union[str, Any],				a_:		int,				a_:		List[str],				a_:		str,				*a_:		Optional[int],				):




   '''simple docstring'''



   _snake_case							:		Dict								=  BertGenerationDecoder(a_		)
   model.to(a_		)
   model.eval()
   _snake_case							:		Optional[int]								=  model(a_,				attention_mask=a_,				labels=a_		)
   self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size)		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case		, _snake_case		, _snake_case		, _snake_case							:		List[Any]								=  self.prepare_config_and_inputs()
   _snake_case							:		List[str]								=  {"""input_ids""": input_ids, """attention_mask""": input_mask}
   return config, inputs_dict







@require_torch
class  lowercase(					__a	,      __a	,      __a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
 lowercase__			 = (BertGenerationDecoder,) if is_torch_available() else ()
 lowercase__			 = (
     {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
     if is_torch_available()
     else {}
 )


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  BertGenerationEncoderTester(self		)
   _snake_case							:		Any								=  ConfigTester(self,				config_class=a_,				hidden_size=37		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_		)


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




   '''simple docstring'''



   _snake_case		, _snake_case		, _snake_case		, _snake_case							:		Tuple								=  self.model_tester.prepare_config_and_inputs()
   _snake_case							:		Optional[int]								=  """bert"""
   self.model_tester.create_and_check_model(a_,				a_,				a_,				a_		)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*a_		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_decoder_model_past_large_inputs(*a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   (
       (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, 
   )							:		Union[str, Any]								=  self.model_tester.prepare_config_and_inputs_for_decoder()

   _snake_case							:		str								=  None

   self.model_tester.create_and_check_model_as_decoder(
       a_,				a_,				a_,				a_,				a_,				a_,				)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_for_causal_lm(*a_		)


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




   '''simple docstring'''



   _snake_case							:		Any								=  BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""		)
   self.assertIsNotNone(a_		)







@require_torch
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 @slow
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""		)
   _snake_case							:		List[Any]								=  torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]		)
   with torch.no_grad():
     _snake_case							:		int								=  model(a_		)[0]
   _snake_case							:		Union[str, Any]								=  torch.Size([1, 8, 1_024]		)
   self.assertEqual(output.shape,				a_		)
   _snake_case							:		Optional[int]								=  torch.tensor(
       [[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]]		)
   self.assertTrue(torch.allclose(output[:, :3, :3],				a_,				atol=1E-4		)		)







@require_torch
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





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




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""		)
   _snake_case							:		Optional[int]								=  torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]		)
   with torch.no_grad():
     _snake_case							:		List[Any]								=  model(a_		)[0]
   _snake_case							:		Tuple								=  torch.Size([1, 8, 50_358]		)
   self.assertEqual(output.shape,				a_		)
   _snake_case							:		Optional[Any]								=  torch.tensor(
       [[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]]		)
   self.assertTrue(torch.allclose(output[:, :3, :3],				a_,				atol=1E-4		)		)








"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

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


