

"""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			:				int   ,lowercase_			:				Any   ,lowercase_			:				int=9_9   ,lowercase_			:				Optional[Any]=1_3   ,lowercase_			:				str=1_6   ,lowercase_			:				List[str]=7   ,lowercase_			:				int=True   ,lowercase_			:				str=True   ,lowercase_			:				Optional[Any]=True   ,lowercase_			:				Any=False   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				List[Any]=2   ,lowercase_			:				Optional[int]=3_2   ,lowercase_			:				List[Any]=4   ,lowercase_			:				str=4   ,lowercase_			:				int=3_0   ,lowercase_			:				Dict=0   ,lowercase_			:				Dict=1   ,lowercase_			:				Tuple=2   ,lowercase_			:				Any=None   ,):
						lowerCAmelCase__	:						int  				=	parent
						lowerCAmelCase__	:						Optional[int]  				=	batch_size
						lowerCAmelCase__	:						Dict  				=	decoder_seq_length
						# For common tests
						lowerCAmelCase__	:						Optional[int]  				=	self.decoder_seq_length
						lowerCAmelCase__	:						str  				=	is_training
						lowerCAmelCase__	:						int  				=	use_attention_mask
						lowerCAmelCase__	:						int  				=	use_labels

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

						lowerCAmelCase__	:						Any  				=	None
						lowerCAmelCase__	:						List[str]  				=	decoder_seq_length
						lowerCAmelCase__	:						Optional[int]  				=	2
						lowerCAmelCase__	:						int  				=	1







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Tuple  				=	ids_tensor([self.batch_size, self.decoder_seq_length]   ,self.vocab_size		)

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

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

						lowerCAmelCase__	:						Any  				=	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        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				str   ,lowercase_			:				int   ,lowercase_			:				List[str]   ,lowercase_			:				int   ,):
						lowerCAmelCase__	:						Optional[int]  				=	True
						lowerCAmelCase__	:						List[Any]  				=	TrOCRDecoder(config=lowercase_		).to(lowercase_		).eval()
						lowerCAmelCase__	:						int  				=	input_ids[:2]

						input_ids[input_ids == 0] += 1
						# first forward pass
						lowerCAmelCase__	:						List[Any]  				=	model(lowercase_   ,use_cache=lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_		)
						lowerCAmelCase__	:						Any  				=	model(lowercase_   ,use_cache=lowercase_		)

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

						lowerCAmelCase__	:						str  				=	outputs['''past_key_values''']

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

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

						lowerCAmelCase__	:						Union[str, Any]  				=	model(lowercase_		)['''last_hidden_state''']
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_   ,past_key_values=lowercase_		)['''last_hidden_state''']

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

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







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	self.prepare_config_and_inputs()
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	config_and_inputs

						lowerCAmelCase__	:						List[Any]  				=	{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
						return config, inputs_dict







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






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[str]  				=	TrOCRStandaloneDecoderModelTester(self   ,is_training=lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	ConfigTester(self   ,config_class=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict		):
						pass







					def        __lowerCAmelCase (       self			:				Any		):
						pass







					def        __lowerCAmelCase (       self			:				str		):
						pass







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						self.config_tester.run_common_tests()







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Any  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past(*lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						return







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








from __future__ import annotations







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			2
		__lowercase=			[]
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.append(lowercase__	)
		if n > 1:
				factors.append(lowercase__	)
		return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()






