




"""simple docstring"""



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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''swin2sr'''

    lowercase__						 =				{
        '''hidden_size''': '''embed_dim''',
        '''num_attention_heads''': '''num_heads''',
        '''num_hidden_layers''': '''num_layers''',
    }




    def __init__(   self						:	Optional[int]     ,			snake_case_						:	Union[str, Any]=64     ,			snake_case_						:	str=1     ,			snake_case_						:	str=3     ,			snake_case_						:	Tuple=180     ,			snake_case_						:	Optional[Any]=[6, 6, 6, 6, 6, 6]     ,			snake_case_						:	str=[6, 6, 6, 6, 6, 6]     ,			snake_case_						:	Optional[int]=8     ,			snake_case_						:	int=2.0     ,			snake_case_						:	Optional[Any]=True     ,			snake_case_						:	List[Any]=0.0     ,			snake_case_						:	List[str]=0.0     ,			snake_case_						:	str=0.1     ,			snake_case_						:	Union[str, Any]="gelu"     ,			snake_case_						:	Any=False     ,			snake_case_						:	Dict=0.02     ,			snake_case_						:	Union[str, Any]=1e-5     ,			snake_case_						:	List[Any]=2     ,			snake_case_						:	int=1.0     ,			snake_case_						:	str="1conv"     ,			snake_case_						:	Tuple="pixelshuffle"     ,			**snake_case_						:	List[Any]     ,			)				->							List[Any]:




     '''simple docstring'''

     super().__init__(**snake_case_				)

     A__	=    image_size
     A__	=    patch_size
     A__	=    num_channels
     A__	=    embed_dim
     A__	=    depths
     A__	=    len(snake_case_				)
     A__	=    num_heads
     A__	=    window_size
     A__	=    mlp_ratio
     A__	=    qkv_bias
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    drop_path_rate
     A__	=    hidden_act
     A__	=    use_absolute_embeddings
     A__	=    layer_norm_eps
     A__	=    initializer_range
     A__	=    upscale
     A__	=    img_range
     A__	=    resi_connection
     A__	=    upsampler











"""simple docstring"""



import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          AlbertForMaskedLM,
          AlbertForMultipleChoice,
          AlbertForPreTraining,
          AlbertForQuestionAnswering,
          AlbertForSequenceClassification,
          AlbertForTokenClassification,
          AlbertModel,
      )
      from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCAmelCase_  :




    def __init__(   self						:	Tuple     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]=13     ,			snake_case_						:	Optional[int]=7     ,			snake_case_						:	Any=True     ,			snake_case_						:	List[Any]=True     ,			snake_case_						:	Dict=True     ,			snake_case_						:	Dict=True     ,			snake_case_						:	List[str]=99     ,			snake_case_						:	Union[str, Any]=16     ,			snake_case_						:	Any=36     ,			snake_case_						:	List[Any]=6     ,			snake_case_						:	Optional[Any]=6     ,			snake_case_						:	Optional[Any]=6     ,			snake_case_						:	Any=37     ,			snake_case_						:	int="gelu"     ,			snake_case_						:	Any=0.1     ,			snake_case_						:	int=0.1     ,			snake_case_						:	str=512     ,			snake_case_						:	Union[str, Any]=16     ,			snake_case_						:	Tuple=2     ,			snake_case_						:	Any=0.02     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Union[str, Any]=4     ,			snake_case_						:	Any=None     ,			)				->							Tuple:




     '''simple docstring'''

     A__	=    parent
     A__	=    batch_size
     A__	=    seq_length
     A__	=    is_training
     A__	=    use_input_mask
     A__	=    use_token_type_ids
     A__	=    use_labels
     A__	=    vocab_size
     A__	=    embedding_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_hidden_groups
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    type_sequence_label_size
     A__	=    initializer_range
     A__	=    num_labels
     A__	=    num_choices
     A__	=    scope




    def 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




     '''simple docstring'''

     A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size				)

     A__	=    None
     if self.use_input_mask:
      A__	=    random_attention_mask([self.batch_size, self.seq_length]				)

     A__	=    None
     if self.use_token_type_ids:
      A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size				)

     A__	=    None
     A__	=    None
     A__	=    None
     if self.use_labels:
      A__	=    ids_tensor([self.batch_size]     ,			self.type_sequence_label_size				)
      A__	=    ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels				)
      A__	=    ids_tensor([self.batch_size]     ,			self.num_choices				)

     A__	=    self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




    def 							__magic_name__							(   self						:	int				)				->							Dict:




     '''simple docstring'''

     return AlbertConfig(
         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     ,			type_vocab_size=self.type_vocab_size     ,			initializer_range=self.initializer_range     ,			num_hidden_groups=self.num_hidden_groups     ,			)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]     ,			snake_case_						:	List[str]     ,			snake_case_						:	Any     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    AlbertModel(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    model(snake_case_     ,			token_type_ids=snake_case_				)
     A__	=    model(snake_case_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)
     self.parent.assertEqual(result.pooler_output.shape     ,			(self.batch_size, self.hidden_size)				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]				)				->							int:




     '''simple docstring'''

     A__	=    AlbertForPreTraining(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_     ,			sentence_order_label=snake_case_     ,			)
     self.parent.assertEqual(result.prediction_logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)				)
     self.parent.assertEqual(result.sop_logits.shape     ,			(self.batch_size, config.num_labels)				)




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[str]				)				->							Dict:




     '''simple docstring'''

     A__	=    AlbertForMaskedLM(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	List[str]     ,			snake_case_						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    AlbertForQuestionAnswering(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			start_positions=snake_case_     ,			end_positions=snake_case_     ,			)
     self.parent.assertEqual(result.start_logits.shape     ,			(self.batch_size, self.seq_length)				)
     self.parent.assertEqual(result.end_logits.shape     ,			(self.batch_size, self.seq_length)				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	str     ,			snake_case_						:	Any				)				->							Any:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    AlbertForSequenceClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)				)




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Dict     ,			snake_case_						:	int     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	int     ,			snake_case_						:	str     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.num_labels
     A__	=    AlbertForTokenClassification(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.num_choices
     A__	=    AlbertForMultipleChoice(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    input_ids.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    token_type_ids.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    input_mask.unsqueeze(1				).expand(-1     ,			self.num_choices     ,			-1				).contiguous()
     A__	=    model(
         snake_case_     ,			attention_mask=snake_case_     ,			token_type_ids=snake_case_     ,			labels=snake_case_     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)				)




    def 							__magic_name__							(   self						:	Any				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     (
         (
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				(
         A__
     ),				
     )	=    config_and_inputs
     A__	=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            AlbertModel,
            AlbertForPreTraining,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				(
        {
            '''feature-extraction''': AlbertModel,
            '''fill-mask''': AlbertForMaskedLM,
            '''question-answering''': AlbertForQuestionAnswering,
            '''text-classification''': AlbertForSequenceClassification,
            '''token-classification''': AlbertForTokenClassification,
            '''zero-shot''': AlbertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase__						 =				True




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Dict     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]=False				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    super()._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)

     if return_labels:
      if model_class in get_values(snake_case_				):
       A__	=    torch.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)     ,			dtype=torch.long     ,			device=snake_case_				)
       A__	=    torch.zeros(
           self.model_tester.batch_size     ,			dtype=torch.long     ,			device=snake_case_				)
     return inputs_dict




    def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''simple docstring'''

     A__	=    AlbertModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			hidden_size=37				)




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Optional[int]:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    def 							__magic_name__							(   self						:	Any				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*snake_case_				)




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case_				)




    def 							__magic_name__							(   self						:	Any				)				->							str:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case_				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      A__	=    type
      self.model_tester.create_and_check_model(*snake_case_				)




    @slow
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Dict:




     '''simple docstring'''

     for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    AlbertModel.from_pretrained(snake_case_				)
      self.assertIsNotNone(snake_case_				)






@require_torch
class      UpperCAmelCase_  (						unittest.TestCase							):




    @slow
    def 							__magic_name__							(   self						:	int				)				->							Tuple:




     '''simple docstring'''

     A__	=    AlbertModel.from_pretrained("albert-base-v2"				)
     A__	=    torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]				)
     A__	=    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
     with torch.no_grad():
      A__	=    model(snake_case_     ,			attention_mask=snake_case_				)[0]
     A__	=    torch.Size((1, 11, 768)				)
     self.assertEqual(output.shape     ,			snake_case_				)
     A__	=    torch.tensor(
         [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]				)

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,			snake_case_     ,			atol=1e-4				)				)





