


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 lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=99		,				_UpperCAmelCase=13		,				_UpperCAmelCase=16		,				_UpperCAmelCase=7		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=2		,				_UpperCAmelCase=32		,				_UpperCAmelCase=4		,				_UpperCAmelCase=4		,				_UpperCAmelCase=30		,				_UpperCAmelCase=0		,				_UpperCAmelCase=1		,				_UpperCAmelCase=2		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				decoder_seq_length
       # For common tests
       snake_case_	=				self.decoder_seq_length
       snake_case_	=				is_training
       snake_case_	=				use_attention_mask
       snake_case_	=				use_labels

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

       snake_case_	=				None
       snake_case_	=				decoder_seq_length
       snake_case_	=				2
       snake_case_	=				1



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

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

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

       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       snake_case_	=				True
       snake_case_	=				TrOCRDecoder(config=_UpperCAmelCase ).to(_UpperCAmelCase ).eval()
       snake_case_	=				input_ids[:2]

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

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

       snake_case_	=				outputs['''past_key_values''']

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

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

       snake_case_	=				model(_UpperCAmelCase )['''last_hidden_state''']
       snake_case_	=				model(_UpperCAmelCase		,				past_key_values=_UpperCAmelCase )['''last_hidden_state''']

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

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





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				config_and_inputs

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







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 __snake_case												=  (TrOCRForCausalLM,) if is_torch_available() else ()
 __snake_case												=  {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
 __snake_case												=  True
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				TrOCRStandaloneDecoderModelTester(self		,				is_training=_UpperCAmelCase )
       snake_case_	=				ConfigTester(self		,				config_class=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       return





 @unittest.skip('''The model doesn\'t support left padding''' )  # and it's not used enough to be worth fixing :)
 def 						UpperCamelCase__					(     self ):
       pass



import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''a''', '''b''', '''c''']

       # Defaults to last layer if both are None
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[2] )

       # Out indices set to match out features
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(['''a''', '''c''']		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features set to match out indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[0, 2]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features selected from negative indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[-3, -1]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[-3, -1] )



 def 						UpperCamelCase__					(     self ):
       # Stage names must be set
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				_UpperCAmelCase )

       # Out features must be a list
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(('''a''', '''b''')		,				(0, 1)		,				['''a''', '''b'''] )

       # Out features must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				['''a'''] )

       # Out indices must be a list or tuple
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				0		,				['''a''', '''b'''] )

       # Out indices must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				(0, 1)		,				['''a'''] )

       # Out features and out indices must be the same length
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0,)		,				['''a''', '''b''', '''c'''] )

       # Out features should match out indices
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 2)		,				['''a''', '''b''', '''c'''] )

       # Out features and out indices should be in order
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''b''', '''a''']		,				(0, 1)		,				['''a''', '''b'''] )

       # Check passes with valid inputs
       verify_out_features_out_indices(['''a''', '''b''', '''d''']		,				(0, 1, -1)		,				['''a''', '''b''', '''c''', '''d'''] )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				BackboneMixin()

       snake_case_	=				['''a''', '''b''', '''c''']
       snake_case_	=				['''a''', '''c''']
       snake_case_	=				[0, 2]

       # Check that the output features and indices are set correctly
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[0, 2] )

       # Check out features and indices are updated correctly
       snake_case_	=				['''a''', '''b''']
       self.assertEqual(backbone.out_features		,				['''a''', '''b'''] )
       self.assertEqual(backbone.out_indices		,				[0, 1] )

       snake_case_	=				[-3, -1]
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[-3, -1] )