



"""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    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=99   ,		lowercase__=13   ,		lowercase__=16   ,		lowercase__=7   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=2   ,		lowercase__=32   ,		lowercase__=4   ,		lowercase__=4   ,		lowercase__=30   ,		lowercase__=0   ,		lowercase__=1   ,		lowercase__=2   ,		lowercase__=None   ,		):
											snake_case_					:		Tuple 					=					parent
											snake_case_					:		Tuple 					=					batch_size
											snake_case_					:		List[str] 					=					decoder_seq_length
											# For common tests
											snake_case_					:		List[str] 					=					self.decoder_seq_length
											snake_case_					:		List[Any] 					=					is_training
											snake_case_					:		List[Any] 					=					use_attention_mask
											snake_case_					:		Dict 					=					use_labels

											snake_case_					:		Union[str, Any] 					=					vocab_size
											snake_case_					:		List[str] 					=					d_model
											snake_case_					:		str 					=					d_model
											snake_case_					:		List[str] 					=					decoder_layers
											snake_case_					:		Any 					=					decoder_layers
											snake_case_					:		int 					=					decoder_ffn_dim
											snake_case_					:		Tuple 					=					decoder_attention_heads
											snake_case_					:		Union[str, Any] 					=					decoder_attention_heads
											snake_case_					:		Optional[int] 					=					eos_token_id
											snake_case_					:		Dict 					=					bos_token_id
											snake_case_					:		int 					=					pad_token_id
											snake_case_					:		List[Any] 					=					decoder_start_token_id
											snake_case_					:		Union[str, Any] 					=					use_cache
											snake_case_					:		List[str] 					=					max_position_embeddings

											snake_case_					:		Union[str, Any] 					=					None
											snake_case_					:		Tuple 					=					decoder_seq_length
											snake_case_					:		int 					=					2
											snake_case_					:		Optional[Any] 					=					1







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

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

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

											snake_case_					:		int 					=					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   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		):
											snake_case_					:		Optional[int] 					=					True
											snake_case_					:		Optional[int] 					=					TrOCRDecoder(config=lowercase__       ).to(lowercase__       ).eval()
											snake_case_					:		List[str] 					=					input_ids[:2]

											input_ids[input_ids == 0] += 1
											# first forward pass
											snake_case_					:		Tuple 					=					model(lowercase__   ,		use_cache=lowercase__       )
											snake_case_					:		Optional[Any] 					=					model(lowercase__       )
											snake_case_					:		Optional[Any] 					=					model(lowercase__   ,		use_cache=lowercase__       )

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

											snake_case_					:		List[str] 					=					outputs["""past_key_values"""]

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

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

											snake_case_					:		Optional[Any] 					=					model(lowercase__       )["""last_hidden_state"""]
											snake_case_					:		List[Any] 					=					model(lowercase__   ,		past_key_values=lowercase__       )["""last_hidden_state"""]

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

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					self.prepare_config_and_inputs()
											snake_case_					:		List[str] 					=					config_and_inputs

											snake_case_					:		Union[str, Any] 					=					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict







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







						"""simple docstring"""







						_A		:							Optional[int]							       =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						_A		:							Any							       =		(TrOCRForCausalLM,) if is_torch_available() else ()
						_A		:							Optional[Any]							       =		{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
						_A		:							Optional[int]							       =		True
						_A		:							int							       =		False







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					TrOCRStandaloneDecoderModelTester(self   ,		is_training=lowercase__       )
											snake_case_					:		Tuple 					=					ConfigTester(self   ,		config_class=lowercase__       )







						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_					:		Optional[Any] 					=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_decoder_model_past(*lowercase__       )







						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





"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		Any 					=					None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																    """ instead."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("""You need to specify an `image_processor`."""       )
											if tokenizer is None:
																raise ValueError("""You need to specify a `tokenizer`."""       )

											super().__init__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):

											if text is None and images is None:
																raise ValueError("""You have to specify either text or images. Both cannot be none."""       )

											if text is not None:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if images is not None:
																snake_case_					:		Tuple 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

											if text is not None and images is not None:
																snake_case_					:		List[Any] 					=					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**lowercase__       )   ,		tensor_type=lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







						@property
						def 						__UpperCamelCase							(self       ):
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,		lowercase__   ,		)
											return self.image_processor_class




