import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
     import torch

     from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=99   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=7   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=False   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=30   ,				SCREAMING_SNAKE_CASE_=0   ,				SCREAMING_SNAKE_CASE_=1   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      decoder_seq_length
       # For common tests
       UpperCamelCase__										=      self.decoder_seq_length
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_attention_mask
       UpperCamelCase__										=      use_labels

       UpperCamelCase__										=      vocab_size
       UpperCamelCase__										=      d_model
       UpperCamelCase__										=      d_model
       UpperCamelCase__										=      decoder_layers
       UpperCamelCase__										=      decoder_layers
       UpperCamelCase__										=      decoder_ffn_dim
       UpperCamelCase__										=      decoder_attention_heads
       UpperCamelCase__										=      decoder_attention_heads
       UpperCamelCase__										=      eos_token_id
       UpperCamelCase__										=      bos_token_id
       UpperCamelCase__										=      pad_token_id
       UpperCamelCase__										=      decoder_start_token_id
       UpperCamelCase__										=      use_cache
       UpperCamelCase__										=      max_position_embeddings

       UpperCamelCase__										=      None
       UpperCamelCase__										=      decoder_seq_length
       UpperCamelCase__										=      2
       UpperCamelCase__										=      1






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size	)

       UpperCamelCase__										=      None
       if self.use_attention_mask:
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				vocab_size=2	)

       UpperCamelCase__										=      None
       if self.use_labels:
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size	)

       UpperCamelCase__										=      TrOCRConfig(
           vocab_size=self.vocab_size   ,				d_model=self.d_model   ,				decoder_layers=self.decoder_layers   ,				decoder_ffn_dim=self.decoder_ffn_dim   ,				decoder_attention_heads=self.decoder_attention_heads   ,				eos_token_id=self.eos_token_id   ,				bos_token_id=self.bos_token_id   ,				use_cache=self.use_cache   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.decoder_start_token_id   ,				max_position_embeddings=self.max_position_embeddings   ,				)

       return (config, input_ids, attention_mask, lm_labels)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      True
       UpperCamelCase__										=      TrOCRDecoder(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()
       UpperCamelCase__										=      input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				use_cache=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				use_cache=SCREAMING_SNAKE_CASE_	)

       self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	)	)
       self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	) + 1	)

       UpperCamelCase__										=      outputs['''past_key_values''']

       # create hypothetical next token and extent to next_input_ids
       UpperCamelCase__										=      ids_tensor((2, 1)   ,				config.vocab_size - 1	) + 1

       # append to next input_ids and
       UpperCamelCase__										=      torch.cat([input_ids, next_tokens]   ,				dim=-1	)

       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)['''last_hidden_state''']
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				past_key_values=SCREAMING_SNAKE_CASE_	)['''last_hidden_state''']

       # select random slice
       UpperCamelCase__										=      ids_tensor((1,)   ,				output_from_past.shape[-1]	).item()
       UpperCamelCase__										=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       UpperCamelCase__										=      output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-3	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      config_and_inputs

       UpperCamelCase__										=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_torch
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE_	:	Any						=(TrOCRForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE_	:	Optional[int]						={"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=True
  SCREAMING_SNAKE_CASE_	:	Dict						=False






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      TrOCRStandaloneDecoderModelTester(self   ,				is_training=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       return






  @unittest.skip('''The model doesn\'t support left padding'''	)  # and it's not used enough to be worth fixing :)
  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       pass




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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      qkv_bias
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



