'''simple docstring'''

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   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






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

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

								_lowerCAmelCase										=None
								_lowerCAmelCase										=decoder_seq_length
								_lowerCAmelCase										=2
								_lowerCAmelCase										=1






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=ids_tensor([self.batch_size, self.decoder_seq_length]		,						self.vocab_size				)

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

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

								_lowerCAmelCase										=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		,						__A		,						__A		,						__A		,						__A		,						) ->     Union[str, Any]:
								_lowerCAmelCase										=True
								_lowerCAmelCase										=TrOCRDecoder(config=__A				).to(__A				).eval()
								_lowerCAmelCase										=input_ids[:2]

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

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

								_lowerCAmelCase										=outputs['past_key_values']

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

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

								_lowerCAmelCase										=model(__A				)['last_hidden_state']
								_lowerCAmelCase										=model(__A		,						past_key_values=__A				)['last_hidden_state']

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

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






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.prepare_config_and_inputs()
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=config_and_inputs

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

@require_torch
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      Optional[int]       =							(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
		lowercase   :      Optional[int]       =							(TrOCRForCausalLM,) if is_torch_available() else ()
		lowercase   :      Optional[int]       =							{'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
		lowercase   :      Union[str, Any]       =							True
		lowercase   :      Union[str, Any]       =							False






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=TrOCRStandaloneDecoderModelTester(self		,						is_training=__A				)
								_lowerCAmelCase										=ConfigTester(self		,						config_class=__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								pass






		def 		UpperCamelCase__       (   self				) ->     Any:
								pass






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								pass






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								self.config_tester.run_common_tests()






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past(*__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								return






		@unittest.skip('The model doesn\'t support left padding'				)  # and it's not used enough to be worth fixing :)
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								pass






'''simple docstring'''

lowercase_     		= '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_     		= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_     		= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





